{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "342dcaf3", "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow import keras\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "id": "5fe9ea24", "metadata": {}, "outputs": [], "source": [ "x=[-1,1,3,5,7]\n", "y=[2*i-1 for i in x ]" ] }, { "cell_type": "code", "execution_count": null, "id": "f150e796", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "51901155", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "47a69f94", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "c9bc3a1e", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 5, "id": "51d885cc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n", "1/1 [==============================] - 0s 0s/step - loss: 107.9462\n", "Epoch 2/10\n", "1/1 [==============================] - 0s 1ms/step - loss: 46.0317\n", "Epoch 3/10\n", "1/1 [==============================] - 0s 3ms/step - loss: 19.9337\n", "Epoch 4/10\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.9273\n", "Epoch 5/10\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.2801\n", "Epoch 6/10\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.3126\n", "Epoch 7/10\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.4743\n", "Epoch 8/10\n", "1/1 [==============================] - 0s 5ms/step - loss: 1.1120\n", "Epoch 9/10\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.9504\n", "Epoch 10/10\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.8736\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC32858B0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/20\n", "1/1 [==============================] - 0s 0s/step - loss: 27.7395\n", "Epoch 2/20\n", "1/1 [==============================] - 0s 1ms/step - loss: 12.0821\n", "Epoch 3/20\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.4776\n", "Epoch 4/20\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.6877\n", "Epoch 5/20\n", "1/1 [==============================] - 0s 1ms/step - loss: 1.5053\n", "Epoch 6/20\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.0003\n", "Epoch 7/20\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.7809\n", "Epoch 8/20\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6819\n", "Epoch 9/20\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6338\n", "Epoch 10/20\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.6072\n", "Epoch 11/20\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.5899\n", "Epoch 12/20\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5765\n", "Epoch 13/20\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5649\n", "Epoch 14/20\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5541\n", "Epoch 15/20\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5439\n", "Epoch 16/20\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5339\n", "Epoch 17/20\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.5242\n", "Epoch 18/20\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5147\n", "Epoch 19/20\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5053\n", "Epoch 20/20\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4961\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC1E318B0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/30\n", "1/1 [==============================] - 0s 2ms/step - loss: 134.5536\n", "Epoch 2/30\n", "1/1 [==============================] - 0s 2ms/step - loss: 57.2829\n", "Epoch 3/30\n", "1/1 [==============================] - 0s 2ms/step - loss: 24.7136\n", "Epoch 4/30\n", "1/1 [==============================] - 0s 2ms/step - loss: 10.9799\n", "Epoch 5/30\n", "1/1 [==============================] - 0s 999us/step - loss: 5.1828\n", "Epoch 6/30\n", "1/1 [==============================] - 0s 999us/step - loss: 2.7301\n", "Epoch 7/30\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.6867\n", "Epoch 8/30\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.2373\n", "Epoch 9/30\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.0384\n", "Epoch 10/30\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.9452\n", "Epoch 11/30\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8967\n", "Epoch 12/30\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8672\n", "Epoch 13/30\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8459\n", "Epoch 14/30\n", "1/1 [==============================] - ETA: 0s - loss: 0.828 - 0s 2ms/step - loss: 0.8282\n", "Epoch 15/30\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8122\n", "Epoch 16/30\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.7970\n", "Epoch 17/30\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7824\n", "Epoch 18/30\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.7681\n", "Epoch 19/30\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7541\n", "Epoch 20/30\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7404\n", "Epoch 21/30\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7270\n", "Epoch 22/30\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7138\n", "Epoch 23/30\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7008\n", "Epoch 24/30\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6881\n", "Epoch 25/30\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6756\n", "Epoch 26/30\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6633\n", "Epoch 27/30\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6513\n", "Epoch 28/30\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.6394\n", "Epoch 29/30\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.6278\n", "Epoch 30/30\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6164\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC4937280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/40\n", "1/1 [==============================] - 0s 0s/step - loss: 81.8326\n", "Epoch 2/40\n", "1/1 [==============================] - 0s 2ms/step - loss: 34.9858\n", "Epoch 3/40\n", "1/1 [==============================] - 0s 2ms/step - loss: 15.2374\n", "Epoch 4/40\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.9073\n", "Epoch 5/40\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.3885\n", "Epoch 6/40\n", "1/1 [==============================] - 0s 1ms/step - loss: 1.8972\n", "Epoch 7/40\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.2602\n", "Epoch 8/40\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.9835\n", "Epoch 9/40\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8587\n", "Epoch 10/40\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7980\n", "Epoch 11/40\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.7645\n", "Epoch 12/40\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.7427\n", "Epoch 13/40\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.7258\n", "Epoch 14/40\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7112\n", "Epoch 15/40\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6977\n", "Epoch 16/40\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 2ms/step - loss: 0.6848\n", "Epoch 17/40\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6722\n", "Epoch 18/40\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6600\n", "Epoch 19/40\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6480\n", "Epoch 20/40\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6362\n", "Epoch 21/40\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6247\n", "Epoch 22/40\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.6133\n", "Epoch 23/40\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.6022\n", "Epoch 24/40\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5912\n", "Epoch 25/40\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.5805\n", "Epoch 26/40\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5700\n", "Epoch 27/40\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5596\n", "Epoch 28/40\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.5495\n", "Epoch 29/40\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.5395\n", "Epoch 30/40\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5297\n", "Epoch 31/40\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.5201\n", "Epoch 32/40\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.5106\n", "Epoch 33/40\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5014\n", "Epoch 34/40\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.4923\n", "Epoch 35/40\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4833\n", "Epoch 36/40\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4745\n", "Epoch 37/40\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4659\n", "Epoch 38/40\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4575\n", "Epoch 39/40\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4492\n", "Epoch 40/40\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.4410\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC36E6040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/50\n", "1/1 [==============================] - 0s 0s/step - loss: 63.7546\n", "Epoch 2/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 27.3359\n", "Epoch 3/50\n", "1/1 [==============================] - 0s 1000us/step - loss: 11.9821\n", "Epoch 4/50\n", "1/1 [==============================] - 0s 1ms/step - loss: 5.5042\n", "Epoch 5/50\n", "1/1 [==============================] - 0s 998us/step - loss: 2.7665\n", "Epoch 6/50\n", "1/1 [==============================] - 0s 1ms/step - loss: 1.6048\n", "Epoch 7/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.1073\n", "Epoch 8/50\n", "1/1 [==============================] - 0s 998us/step - loss: 0.8899\n", "Epoch 9/50\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.7906\n", "Epoch 10/50\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.7412\n", "Epoch 11/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7130\n", "Epoch 12/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6938\n", "Epoch 13/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6786\n", "Epoch 14/50\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.6652\n", "Epoch 15/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6526\n", "Epoch 16/50\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6406\n", "Epoch 17/50\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6289\n", "Epoch 18/50\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.6174\n", "Epoch 19/50\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6062\n", "Epoch 20/50\n", "1/1 [==============================] - 0s 999us/step - loss: 0.5952\n", "Epoch 21/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5844\n", "Epoch 22/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5738\n", "Epoch 23/50\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.5633\n", "Epoch 24/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5531\n", "Epoch 25/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5431\n", "Epoch 26/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5332\n", "Epoch 27/50\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5235\n", "Epoch 28/50\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5140\n", "Epoch 29/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5047\n", "Epoch 30/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4955\n", "Epoch 31/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4865\n", "Epoch 32/50\n", "1/1 [==============================] - 0s 998us/step - loss: 0.4777\n", "Epoch 33/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4690\n", "Epoch 34/50\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4605\n", "Epoch 35/50\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4521\n", "Epoch 36/50\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4439\n", "Epoch 37/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4359\n", "Epoch 38/50\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.4280\n", "Epoch 39/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4202\n", "Epoch 40/50\n", "1/1 [==============================] - 0s 999us/step - loss: 0.4126\n", "Epoch 41/50\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4051\n", "Epoch 42/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3977\n", "Epoch 43/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3905\n", "Epoch 44/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3834\n", "Epoch 45/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3764\n", "Epoch 46/50\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3696\n", "Epoch 47/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3629\n", "Epoch 48/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3563\n", "Epoch 49/50\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3498\n", "Epoch 50/50\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3435\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC1A90F70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/60\n", "1/1 [==============================] - 0s 0s/step - loss: 32.4981\n", "Epoch 2/60\n", "1/1 [==============================] - 0s 1ms/step - loss: 14.0993\n", "Epoch 3/60\n", "1/1 [==============================] - 0s 1ms/step - loss: 6.3394\n", "Epoch 4/60\n", "1/1 [==============================] - 0s 1ms/step - loss: 3.0625\n", "Epoch 5/60\n", "1/1 [==============================] - 0s 1ms/step - loss: 1.6747\n", "Epoch 6/60\n", "1/1 [==============================] - 0s 1ms/step - loss: 1.0829\n", "Epoch 7/60\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.8267\n", "Epoch 8/60\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.7121\n", "Epoch 9/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6571\n", "Epoch 10/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6275\n", "Epoch 11/60\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 3ms/step - loss: 0.6087\n", "Epoch 12/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5945\n", "Epoch 13/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5824\n", "Epoch 14/60\n", "1/1 [==============================] - 0s 999us/step - loss: 0.5712\n", "Epoch 15/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5606\n", "Epoch 16/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5503\n", "Epoch 17/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5403\n", "Epoch 18/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5305\n", "Epoch 19/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5208\n", "Epoch 20/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5114\n", "Epoch 21/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5021\n", "Epoch 22/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4930\n", "Epoch 23/60\n", "1/1 [==============================] - 0s 999us/step - loss: 0.4840\n", "Epoch 24/60\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.4752\n", "Epoch 25/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4666\n", "Epoch 26/60\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.4581\n", "Epoch 27/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4498\n", "Epoch 28/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4416\n", "Epoch 29/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4336\n", "Epoch 30/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4258\n", "Epoch 31/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4180\n", "Epoch 32/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4104\n", "Epoch 33/60\n", "1/1 [==============================] - 0s 998us/step - loss: 0.4030\n", "Epoch 34/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3957\n", "Epoch 35/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3885\n", "Epoch 36/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3814\n", "Epoch 37/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3745\n", "Epoch 38/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3677\n", "Epoch 39/60\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.3610\n", "Epoch 40/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3545\n", "Epoch 41/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3480\n", "Epoch 42/60\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.3417\n", "Epoch 43/60\n", "1/1 [==============================] - 0s 999us/step - loss: 0.3355\n", "Epoch 44/60\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.3294\n", "Epoch 45/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3234\n", "Epoch 46/60\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.3176\n", "Epoch 47/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3118\n", "Epoch 48/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3061\n", "Epoch 49/60\n", "1/1 [==============================] - 0s 999us/step - loss: 0.3006\n", "Epoch 50/60\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.2951\n", "Epoch 51/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2898\n", "Epoch 52/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2845\n", "Epoch 53/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2793\n", "Epoch 54/60\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.2743\n", "Epoch 55/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2693\n", "Epoch 56/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2644\n", "Epoch 57/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2596\n", "Epoch 58/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2549\n", "Epoch 59/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2503\n", "Epoch 60/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2457\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC1E319D0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/70\n", "1/1 [==============================] - 0s 0s/step - loss: 113.6994\n", "Epoch 2/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 48.4647\n", "Epoch 3/70\n", "1/1 [==============================] - 0s 1ms/step - loss: 20.9676\n", "Epoch 4/70\n", "1/1 [==============================] - 0s 1ms/step - loss: 9.3715\n", "Epoch 5/70\n", "1/1 [==============================] - 0s 998us/step - loss: 4.4757\n", "Epoch 6/70\n", "1/1 [==============================] - 0s 1ms/step - loss: 2.4033\n", "Epoch 7/70\n", "1/1 [==============================] - 0s 5ms/step - loss: 1.5206\n", "Epoch 8/70\n", "1/1 [==============================] - 0s 1ms/step - loss: 1.1395\n", "Epoch 9/70\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.9699\n", "Epoch 10/70\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.8895\n", "Epoch 11/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8469\n", "Epoch 12/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8204\n", "Epoch 13/70\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.8008\n", "Epoch 14/70\n", "1/1 [==============================] - 0s 6ms/step - loss: 0.7843\n", "Epoch 15/70\n", "1/1 [==============================] - 0s 14ms/step - loss: 0.7692\n", "Epoch 16/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7549\n", "Epoch 17/70\n", "1/1 [==============================] - 0s 999us/step - loss: 0.7410\n", "Epoch 18/70\n", "1/1 [==============================] - 0s 999us/step - loss: 0.7275\n", "Epoch 19/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7143\n", "Epoch 20/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7013\n", "Epoch 21/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6885\n", "Epoch 22/70\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.6760\n", "Epoch 23/70\n", "1/1 [==============================] - 0s 999us/step - loss: 0.6638\n", "Epoch 24/70\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6517\n", "Epoch 25/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6399\n", "Epoch 26/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6283\n", "Epoch 27/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6169\n", "Epoch 28/70\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.6057\n", "Epoch 29/70\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5947\n", "Epoch 30/70\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5839\n", "Epoch 31/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5733\n", "Epoch 32/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5629\n", "Epoch 33/70\n", "1/1 [==============================] - ETA: 0s - loss: 0.552 - 0s 2ms/step - loss: 0.5526\n", "Epoch 34/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5426\n", "Epoch 35/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5327\n", "Epoch 36/70\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.5231\n", "Epoch 37/70\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5136\n", "Epoch 38/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5043\n", "Epoch 39/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4951\n", "Epoch 40/70\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4861\n", "Epoch 41/70\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4773\n", "Epoch 42/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4686\n", "Epoch 43/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4601\n", "Epoch 44/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4518\n", "Epoch 45/70\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 2ms/step - loss: 0.4435\n", "Epoch 46/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4355\n", "Epoch 47/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4276\n", "Epoch 48/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4198\n", "Epoch 49/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4122\n", "Epoch 50/70\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4047\n", "Epoch 51/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3974\n", "Epoch 52/70\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.3902\n", "Epoch 53/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3831\n", "Epoch 54/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3761\n", "Epoch 55/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3693\n", "Epoch 56/70\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.3626\n", "Epoch 57/70\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.3560\n", "Epoch 58/70\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.3495\n", "Epoch 59/70\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3432\n", "Epoch 60/70\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.3370\n", "Epoch 61/70\n", "1/1 [==============================] - 0s 999us/step - loss: 0.3308\n", "Epoch 62/70\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3248\n", "Epoch 63/70\n", "1/1 [==============================] - 0s 999us/step - loss: 0.3189\n", "Epoch 64/70\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3131\n", "Epoch 65/70\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3075\n", "Epoch 66/70\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3019\n", "Epoch 67/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2964\n", "Epoch 68/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2910\n", "Epoch 69/70\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2857\n", "Epoch 70/70\n", "1/1 [==============================] - 0s 999us/step - loss: 0.2805\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC20E2CA0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/80\n", "1/1 [==============================] - 0s 0s/step - loss: 102.3132\n", "Epoch 2/80\n", "1/1 [==============================] - 0s 999us/step - loss: 43.6493\n", "Epoch 3/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 18.9211\n", "Epoch 4/80\n", "1/1 [==============================] - 0s 999us/step - loss: 8.4921\n", "Epoch 5/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.0883\n", "Epoch 6/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.2235\n", "Epoch 7/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.4287\n", "Epoch 8/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.0848\n", "Epoch 9/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.9312\n", "Epoch 10/80\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.8578\n", "Epoch 11/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8185\n", "Epoch 12/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7936\n", "Epoch 13/80\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.7750\n", "Epoch 14/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7591\n", "Epoch 15/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7446\n", "Epoch 16/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7307\n", "Epoch 17/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7173\n", "Epoch 18/80\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.7043\n", "Epoch 19/80\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6914\n", "Epoch 20/80\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.6789\n", "Epoch 21/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6665\n", "Epoch 22/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6544\n", "Epoch 23/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6426\n", "Epoch 24/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6309\n", "Epoch 25/80\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6194\n", "Epoch 26/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6082\n", "Epoch 27/80\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5971\n", "Epoch 28/80\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5863\n", "Epoch 29/80\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.5757\n", "Epoch 30/80\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5652\n", "Epoch 31/80\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5549\n", "Epoch 32/80\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5449\n", "Epoch 33/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5350\n", "Epoch 34/80\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.5253\n", "Epoch 35/80\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.5157\n", "Epoch 36/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5064\n", "Epoch 37/80\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4972\n", "Epoch 38/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4881\n", "Epoch 39/80\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4793\n", "Epoch 40/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4706\n", "Epoch 41/80\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.4620\n", "Epoch 42/80\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.4536\n", "Epoch 43/80\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.4454\n", "Epoch 44/80\n", "1/1 [==============================] - 0s 998us/step - loss: 0.4373\n", "Epoch 45/80\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4294\n", "Epoch 46/80\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4216\n", "Epoch 47/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4139\n", "Epoch 48/80\n", "1/1 [==============================] - 0s 998us/step - loss: 0.4064\n", "Epoch 49/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3990\n", "Epoch 50/80\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3918\n", "Epoch 51/80\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3847\n", "Epoch 52/80\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3777\n", "Epoch 53/80\n", "1/1 [==============================] - 0s 999us/step - loss: 0.3708\n", "Epoch 54/80\n", "1/1 [==============================] - 0s 999us/step - loss: 0.3641\n", "Epoch 55/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3575\n", "Epoch 56/80\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.3510\n", "Epoch 57/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3446\n", "Epoch 58/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3384\n", "Epoch 59/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3322\n", "Epoch 60/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3262\n", "Epoch 61/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3203\n", "Epoch 62/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3145\n", "Epoch 63/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3087\n", "Epoch 64/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3031\n", "Epoch 65/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2976\n", "Epoch 66/80\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2922\n", "Epoch 67/80\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.2869\n", "Epoch 68/80\n", "1/1 [==============================] - 0s 999us/step - loss: 0.2817\n", "Epoch 69/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2766\n", "Epoch 70/80\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 3ms/step - loss: 0.2716\n", "Epoch 71/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2666\n", "Epoch 72/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2618\n", "Epoch 73/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2571\n", "Epoch 74/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2524\n", "Epoch 75/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2478\n", "Epoch 76/80\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2433\n", "Epoch 77/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2389\n", "Epoch 78/80\n", "1/1 [==============================] - 0s 998us/step - loss: 0.2345\n", "Epoch 79/80\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.2303\n", "Epoch 80/80\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2261\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC493C790> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/90\n", "1/1 [==============================] - 0s 0s/step - loss: 10.1980\n", "Epoch 2/90\n", "1/1 [==============================] - 0s 1ms/step - loss: 4.6352\n", "Epoch 3/90\n", "1/1 [==============================] - 0s 1ms/step - loss: 2.2852\n", "Epoch 4/90\n", "1/1 [==============================] - 0s 1ms/step - loss: 1.2890\n", "Epoch 5/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8633\n", "Epoch 6/90\n", "1/1 [==============================] - 0s 998us/step - loss: 0.6781\n", "Epoch 7/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5943\n", "Epoch 8/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5535\n", "Epoch 9/90\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5307\n", "Epoch 10/90\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5158\n", "Epoch 11/90\n", "1/1 [==============================] - 0s 999us/step - loss: 0.5041\n", "Epoch 12/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4940\n", "Epoch 13/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4847\n", "Epoch 14/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4757\n", "Epoch 15/90\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.4670\n", "Epoch 16/90\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4585\n", "Epoch 17/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4501\n", "Epoch 18/90\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.4420\n", "Epoch 19/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4339\n", "Epoch 20/90\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4261\n", "Epoch 21/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4183\n", "Epoch 22/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4107\n", "Epoch 23/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4033\n", "Epoch 24/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3959\n", "Epoch 25/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3888\n", "Epoch 26/90\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.3817\n", "Epoch 27/90\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3748\n", "Epoch 28/90\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.3680\n", "Epoch 29/90\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.3613\n", "Epoch 30/90\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.3547\n", "Epoch 31/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3483\n", "Epoch 32/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3420\n", "Epoch 33/90\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3357\n", "Epoch 34/90\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3297\n", "Epoch 35/90\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.3237\n", "Epoch 36/90\n", "1/1 [==============================] - 0s 999us/step - loss: 0.3178\n", "Epoch 37/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3120\n", "Epoch 38/90\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3064\n", "Epoch 39/90\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.3008\n", "Epoch 40/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2953\n", "Epoch 41/90\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2900\n", "Epoch 42/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2847\n", "Epoch 43/90\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2795\n", "Epoch 44/90\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2745\n", "Epoch 45/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2695\n", "Epoch 46/90\n", "1/1 [==============================] - 0s 999us/step - loss: 0.2646\n", "Epoch 47/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2598\n", "Epoch 48/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2551\n", "Epoch 49/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2504\n", "Epoch 50/90\n", "1/1 [==============================] - 0s 998us/step - loss: 0.2459\n", "Epoch 51/90\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.2414\n", "Epoch 52/90\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2370\n", "Epoch 53/90\n", "1/1 [==============================] - 0s 999us/step - loss: 0.2327\n", "Epoch 54/90\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.2285\n", "Epoch 55/90\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.2244\n", "Epoch 56/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2203\n", "Epoch 57/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2163\n", "Epoch 58/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2124\n", "Epoch 59/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2085\n", "Epoch 60/90\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2047\n", "Epoch 61/90\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2010\n", "Epoch 62/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1974\n", "Epoch 63/90\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.1938\n", "Epoch 64/90\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1902\n", "Epoch 65/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1868\n", "Epoch 66/90\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1834\n", "Epoch 67/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1801\n", "Epoch 68/90\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.1768\n", "Epoch 69/90\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1736\n", "Epoch 70/90\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.1704\n", "Epoch 71/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1673\n", "Epoch 72/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1643\n", "Epoch 73/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1613\n", "Epoch 74/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1584\n", "Epoch 75/90\n", "1/1 [==============================] - 0s 999us/step - loss: 0.1555\n", "Epoch 76/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1527\n", "Epoch 77/90\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1499\n", "Epoch 78/90\n", "1/1 [==============================] - 0s 999us/step - loss: 0.1472\n", "Epoch 79/90\n", "1/1 [==============================] - 0s 998us/step - loss: 0.1445\n", "Epoch 80/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1419\n", "Epoch 81/90\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.1393\n", "Epoch 82/90\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.1368\n", "Epoch 83/90\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.1343\n", "Epoch 84/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1319\n", "Epoch 85/90\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 6ms/step - loss: 0.1295\n", "Epoch 86/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1271\n", "Epoch 87/90\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.1248\n", "Epoch 88/90\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.1226\n", "Epoch 89/90\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1203\n", "Epoch 90/90\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1181\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC1E4F280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/100\n", "1/1 [==============================] - 0s 0s/step - loss: 98.9353\n", "Epoch 2/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 42.2206\n", "Epoch 3/100\n", "1/1 [==============================] - 0s 999us/step - loss: 18.3138\n", "Epoch 4/100\n", "1/1 [==============================] - 0s 1ms/step - loss: 8.2310\n", "Epoch 5/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.9732\n", "Epoch 6/100\n", "1/1 [==============================] - 0s 1ms/step - loss: 2.1700\n", "Epoch 7/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.4012\n", "Epoch 8/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.0684\n", "Epoch 9/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.9195\n", "Epoch 10/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8482\n", "Epoch 11/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8098\n", "Epoch 12/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7854\n", "Epoch 13/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.7671\n", "Epoch 14/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7515\n", "Epoch 15/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.7371\n", "Epoch 16/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7234\n", "Epoch 17/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7101\n", "Epoch 18/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6972\n", "Epoch 19/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6845\n", "Epoch 20/100\n", "1/1 [==============================] - 0s 998us/step - loss: 0.6721\n", "Epoch 21/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6599\n", "Epoch 22/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6479\n", "Epoch 23/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6361\n", "Epoch 24/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6246\n", "Epoch 25/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6132\n", "Epoch 26/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6021\n", "Epoch 27/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5912\n", "Epoch 28/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5804\n", "Epoch 29/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5699\n", "Epoch 30/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5595\n", "Epoch 31/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5494\n", "Epoch 32/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5394\n", "Epoch 33/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5296\n", "Epoch 34/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5200\n", "Epoch 35/100\n", "1/1 [==============================] - 0s 998us/step - loss: 0.5106\n", "Epoch 36/100\n", "1/1 [==============================] - 0s 999us/step - loss: 0.5013\n", "Epoch 37/100\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.4922\n", "Epoch 38/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4832\n", "Epoch 39/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4745\n", "Epoch 40/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4659\n", "Epoch 41/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4574\n", "Epoch 42/100\n", "1/1 [==============================] - 0s 999us/step - loss: 0.4491\n", "Epoch 43/100\n", "1/1 [==============================] - 0s 999us/step - loss: 0.4409\n", "Epoch 44/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4329\n", "Epoch 45/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4251\n", "Epoch 46/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4174\n", "Epoch 47/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4098\n", "Epoch 48/100\n", "1/1 [==============================] - 0s 998us/step - loss: 0.4023\n", "Epoch 49/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3950\n", "Epoch 50/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3879\n", "Epoch 51/100\n", "1/1 [==============================] - 0s 999us/step - loss: 0.3808\n", "Epoch 52/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3739\n", "Epoch 53/100\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.3671\n", "Epoch 54/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3604\n", "Epoch 55/100\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.3539\n", "Epoch 56/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3475\n", "Epoch 57/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3412\n", "Epoch 58/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3350\n", "Epoch 59/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3289\n", "Epoch 60/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3229\n", "Epoch 61/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3171\n", "Epoch 62/100\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.3113\n", "Epoch 63/100\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.3056\n", "Epoch 64/100\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.3001\n", "Epoch 65/100\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.2947\n", "Epoch 66/100\n", "1/1 [==============================] - 0s 998us/step - loss: 0.2893\n", "Epoch 67/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2840\n", "Epoch 68/100\n", "1/1 [==============================] - 0s 995us/step - loss: 0.2789\n", "Epoch 69/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2738\n", "Epoch 70/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2689\n", "Epoch 71/100\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.2640\n", "Epoch 72/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2592\n", "Epoch 73/100\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.2545\n", "Epoch 74/100\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.2499\n", "Epoch 75/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2453\n", "Epoch 76/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2409\n", "Epoch 77/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2365\n", "Epoch 78/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2322\n", "Epoch 79/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2280\n", "Epoch 80/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2238\n", "Epoch 81/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2198\n", "Epoch 82/100\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.2158\n", "Epoch 83/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2119\n", "Epoch 84/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2080\n", "Epoch 85/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2042\n", "Epoch 86/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2005\n", "Epoch 87/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1969\n", "Epoch 88/100\n", "1/1 [==============================] - 0s 999us/step - loss: 0.1933\n", "Epoch 89/100\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 3ms/step - loss: 0.1898\n", "Epoch 90/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1864\n", "Epoch 91/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1830\n", "Epoch 92/100\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.1797\n", "Epoch 93/100\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.1764\n", "Epoch 94/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1732\n", "Epoch 95/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1700\n", "Epoch 96/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1670\n", "Epoch 97/100\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.1639\n", "Epoch 98/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1610\n", "Epoch 99/100\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1580\n", "Epoch 100/100\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1552\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC1E4FC10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.5490\n", "Epoch 2/110\n", "1/1 [==============================] - 0s 4ms/step - loss: 3.9334\n", "Epoch 3/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.9826\n", "Epoch 4/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.1547\n", "Epoch 5/110\n", "1/1 [==============================] - 0s 999us/step - loss: 0.8001\n", "Epoch 6/110\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.6449\n", "Epoch 7/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5740\n", "Epoch 8/110\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.5386\n", "Epoch 9/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5183\n", "Epoch 10/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5045\n", "Epoch 11/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4934\n", "Epoch 12/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4837\n", "Epoch 13/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4746\n", "Epoch 14/110\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.4658\n", "Epoch 15/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4573\n", "Epoch 16/110\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.4490\n", "Epoch 17/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4408\n", "Epoch 18/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4328\n", "Epoch 19/110\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.4250\n", "Epoch 20/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4172\n", "Epoch 21/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4097\n", "Epoch 22/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4022\n", "Epoch 23/110\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.3949\n", "Epoch 24/110\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.3877\n", "Epoch 25/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3807\n", "Epoch 26/110\n", "1/1 [==============================] - 0s 6ms/step - loss: 0.3738\n", "Epoch 27/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3670\n", "Epoch 28/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3603\n", "Epoch 29/110\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.3538\n", "Epoch 30/110\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.3474\n", "Epoch 31/110\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.3411\n", "Epoch 32/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3349\n", "Epoch 33/110\n", "1/1 [==============================] - 0s 998us/step - loss: 0.3288\n", "Epoch 34/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3228\n", "Epoch 35/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3170\n", "Epoch 36/110\n", "1/1 [==============================] - 0s 999us/step - loss: 0.3112\n", "Epoch 37/110\n", "1/1 [==============================] - 0s 999us/step - loss: 0.3056\n", "Epoch 38/110\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.3000\n", "Epoch 39/110\n", "1/1 [==============================] - 0s 998us/step - loss: 0.2946\n", "Epoch 40/110\n", "1/1 [==============================] - 0s 999us/step - loss: 0.2892\n", "Epoch 41/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2840\n", "Epoch 42/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2788\n", "Epoch 43/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2737\n", "Epoch 44/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2688\n", "Epoch 45/110\n", "1/1 [==============================] - 0s 2ms/step - 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0s 2ms/step - loss: 0.2118\n", "Epoch 58/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2080\n", "Epoch 59/110\n", "1/1 [==============================] - 0s 999us/step - loss: 0.2042\n", "Epoch 60/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2005\n", "Epoch 61/110\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.1968\n", "Epoch 62/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1933\n", "Epoch 63/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1898\n", "Epoch 64/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1863\n", "Epoch 65/110\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.1829\n", "Epoch 66/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1796\n", "Epoch 67/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1763\n", "Epoch 68/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1731\n", "Epoch 69/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1700\n", "Epoch 70/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1669\n", "Epoch 71/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1639\n", "Epoch 72/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1609\n", "Epoch 73/110\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.1580\n", "Epoch 74/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1551\n", "Epoch 75/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1523\n", "Epoch 76/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1495\n", "Epoch 77/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1468\n", "Epoch 78/110\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.1442\n", "Epoch 79/110\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.1415\n", "Epoch 80/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1390\n", "Epoch 81/110\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.1364\n", "Epoch 82/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1340\n", "Epoch 83/110\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 2ms/step - loss: 0.1315\n", "Epoch 84/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1291\n", "Epoch 85/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1268\n", "Epoch 86/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1245\n", "Epoch 87/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1222\n", "Epoch 88/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1200\n", "Epoch 89/110\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.1178\n", "Epoch 90/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1157\n", "Epoch 91/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1136\n", "Epoch 92/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1115\n", "Epoch 93/110\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.1095\n", "Epoch 94/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1075\n", "Epoch 95/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1056\n", "Epoch 96/110\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.1037\n", "Epoch 97/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1018\n", "Epoch 98/110\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0999\n", "Epoch 99/110\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0981\n", "Epoch 100/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0963\n", "Epoch 101/110\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0946\n", "Epoch 102/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0929\n", "Epoch 103/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0912\n", "Epoch 104/110\n", "1/1 [==============================] - 0s 998us/step - loss: 0.0895\n", "Epoch 105/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0879\n", "Epoch 106/110\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0863\n", "Epoch 107/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0847\n", "Epoch 108/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0832\n", "Epoch 109/110\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0817\n", "Epoch 110/110\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0802\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC1A90940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 182.8413\n", "Epoch 2/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 77.6956\n", "Epoch 3/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 33.3798\n", "Epoch 4/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 14.6954\n", "Epoch 5/120\n", "1/1 [==============================] - 0s 1000us/step - loss: 6.8112\n", "Epoch 6/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.4779\n", "Epoch 7/120\n", "1/1 [==============================] - 0s 1ms/step - loss: 2.0623\n", "Epoch 8/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 1.4550\n", "Epoch 9/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.1885\n", "Epoch 10/120\n", "1/1 [==============================] - 0s 5ms/step - loss: 1.0657\n", "Epoch 11/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.0038\n", "Epoch 12/120\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.9676\n", "Epoch 13/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.9424\n", "Epoch 14/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.9221\n", "Epoch 15/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.9040\n", "Epoch 16/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8871\n", "Epoch 17/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.8707\n", "Epoch 18/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.8548\n", "Epoch 19/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.8392\n", "Epoch 20/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.8240\n", "Epoch 21/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.8090\n", "Epoch 22/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.7943\n", "Epoch 23/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.7799\n", "Epoch 24/120\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.7657\n", "Epoch 25/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.7518\n", "Epoch 26/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7382\n", "Epoch 27/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.7248\n", "Epoch 28/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.7116\n", "Epoch 29/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6987\n", "Epoch 30/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.6860\n", "Epoch 31/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.6736\n", "Epoch 32/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6613\n", "Epoch 33/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.6493\n", "Epoch 34/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6375\n", "Epoch 35/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6260\n", "Epoch 36/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6146\n", "Epoch 37/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6034\n", "Epoch 38/120\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.5925\n", "Epoch 39/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.5817\n", "Epoch 40/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5712\n", "Epoch 41/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5608\n", "Epoch 42/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5506\n", "Epoch 43/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5406\n", "Epoch 44/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.5308\n", "Epoch 45/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.5212\n", "Epoch 46/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5117\n", "Epoch 47/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.5024\n", "Epoch 48/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.4933\n", "Epoch 49/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4843\n", "Epoch 50/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4755\n", "Epoch 51/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4669\n", "Epoch 52/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4584\n", "Epoch 53/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4501\n", "Epoch 54/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.4419\n", "Epoch 55/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.4339\n", "Epoch 56/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.4260\n", "Epoch 57/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4183\n", "Epoch 58/120\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.4107\n", "Epoch 59/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4032\n", "Epoch 60/120\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.3959\n", "Epoch 61/120\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.3887\n", "Epoch 62/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3817\n", "Epoch 63/120\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.3747\n", "Epoch 64/120\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.3679\n", "Epoch 65/120\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.3613\n", "Epoch 66/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3547\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 67/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3483\n", "Epoch 68/120\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.3419\n", "Epoch 69/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3357\n", "Epoch 70/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.3296\n", "Epoch 71/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3236\n", "Epoch 72/120\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.3178\n", "Epoch 73/120\n", "1/1 [==============================] - 0s 999us/step - loss: 0.3120\n", "Epoch 74/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3063\n", "Epoch 75/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3008\n", "Epoch 76/120\n", "1/1 [==============================] - 0s 999us/step - loss: 0.2953\n", "Epoch 77/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2899\n", "Epoch 78/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2847\n", "Epoch 79/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2795\n", "Epoch 80/120\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.2744\n", "Epoch 81/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.2695\n", "Epoch 82/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.2646\n", "Epoch 83/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.2598\n", "Epoch 84/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2550\n", "Epoch 85/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2504\n", "Epoch 86/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.2459\n", "Epoch 87/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2414\n", "Epoch 88/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2370\n", "Epoch 89/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2327\n", "Epoch 90/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.2285\n", "Epoch 91/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2243\n", "Epoch 92/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.2203\n", "Epoch 93/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2163\n", "Epoch 94/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2123\n", "Epoch 95/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2085\n", "Epoch 96/120\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.2047\n", "Epoch 97/120\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.2010\n", "Epoch 98/120\n", "1/1 [==============================] - 0s 999us/step - loss: 0.1973\n", "Epoch 99/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.1938\n", "Epoch 100/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1902\n", "Epoch 101/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1868\n", "Epoch 102/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1834\n", "Epoch 103/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1801\n", "Epoch 104/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1768\n", "Epoch 105/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1736\n", "Epoch 106/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1704\n", "Epoch 107/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1673\n", "Epoch 108/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1643\n", "Epoch 109/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1613\n", "Epoch 110/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.1584\n", "Epoch 111/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1555\n", "Epoch 112/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1527\n", "Epoch 113/120\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.1499\n", "Epoch 114/120\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.1472\n", "Epoch 115/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1445\n", "Epoch 116/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1419\n", "Epoch 117/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1393\n", "Epoch 118/120\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1368\n", "Epoch 119/120\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1343\n", "Epoch 120/120\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.1319\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC36E60D0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/130\n", "1/1 [==============================] - 0s 0s/step - loss: 183.6131\n", "Epoch 2/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 78.0218\n", "Epoch 3/130\n", "1/1 [==============================] - 0s 1ms/step - loss: 33.5183\n", "Epoch 4/130\n", "1/1 [==============================] - 0s 1ms/step - loss: 14.7547\n", "Epoch 5/130\n", "1/1 [==============================] - 0s 999us/step - loss: 6.8371\n", "Epoch 6/130\n", "1/1 [==============================] - 0s 997us/step - loss: 3.4897\n", "Epoch 7/130\n", "1/1 [==============================] - 0s 4ms/step - loss: 2.0682\n", "Epoch 8/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.4584\n", "Epoch 9/130\n", "1/1 [==============================] - 0s 1ms/step - loss: 1.1908\n", "Epoch 10/130\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.0676\n", "Epoch 11/130\n", "1/1 [==============================] - 0s 4ms/step - loss: 1.0054\n", "Epoch 12/130\n", "1/1 [==============================] - 0s 999us/step - loss: 0.9691\n", "Epoch 13/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.9439\n", "Epoch 14/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.9236\n", "Epoch 15/130\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.9054\n", "Epoch 16/130\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.8884\n", "Epoch 17/130\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.8721\n", "Epoch 18/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8561\n", "Epoch 19/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8405\n", "Epoch 20/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8253\n", "Epoch 21/130\n", "1/1 [==============================] - 0s 999us/step - loss: 0.8103\n", "Epoch 22/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7956\n", "Epoch 23/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7811\n", "Epoch 24/130\n", "1/1 [==============================] - 0s 999us/step - loss: 0.7669\n", "Epoch 25/130\n", "1/1 [==============================] - 0s 999us/step - loss: 0.7530\n", "Epoch 26/130\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.7393\n", "Epoch 27/130\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.7259\n", "Epoch 28/130\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.7127\n", "Epoch 29/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6998\n", "Epoch 30/130\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.6871\n", "Epoch 31/130\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6746\n", "Epoch 32/130\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6624\n", "Epoch 33/130\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.6503\n", "Epoch 34/130\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.6385\n", "Epoch 35/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6269\n", "Epoch 36/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6155\n", "Epoch 37/130\n", "1/1 [==============================] - 0s 6ms/step - loss: 0.6044\n", "Epoch 38/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5934\n", "Epoch 39/130\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5826\n", "Epoch 40/130\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 2ms/step - loss: 0.5720\n", "Epoch 41/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5617\n", "Epoch 42/130\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5515\n", "Epoch 43/130\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.5414\n", "Epoch 44/130\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.5316\n", "Epoch 45/130\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5220\n", "Epoch 46/130\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5125\n", "Epoch 47/130\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.5032\n", "Epoch 48/130\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4940\n", "Epoch 49/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4851\n", "Epoch 50/130\n", "1/1 [==============================] - 0s 996us/step - loss: 0.4763\n", "Epoch 51/130\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4676\n", "Epoch 52/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4591\n", "Epoch 53/130\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4508\n", "Epoch 54/130\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4426\n", "Epoch 55/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4346\n", "Epoch 56/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4267\n", "Epoch 57/130\n", "1/1 [==============================] - 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0s 16ms/step - loss: 0.2166\n", "Epoch 94/130\n", "1/1 [==============================] - 0s 0s/step - loss: 0.2127\n", "Epoch 95/130\n", "1/1 [==============================] - 0s 0s/step - loss: 0.2088\n", "Epoch 96/130\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.2050\n", "Epoch 97/130\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.2013\n", "Epoch 98/130\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1976\n", "Epoch 99/130\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1941\n", "Epoch 100/130\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1905\n", "Epoch 101/130\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.1871\n", "Epoch 102/130\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1837\n", "Epoch 103/130\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1803\n", "Epoch 104/130\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.1771\n", "Epoch 105/130\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.1738\n", "Epoch 106/130\n", "1/1 [==============================] - ETA: 0s - loss: 0.170 - 0s 0s/step - loss: 0.1707\n", "Epoch 107/130\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1676\n", "Epoch 108/130\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1646\n", "Epoch 109/130\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1616\n", "Epoch 110/130\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1586\n", "Epoch 111/130\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1557\n", "Epoch 112/130\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.1529\n", "Epoch 113/130\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1501\n", "Epoch 114/130\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1474\n", "Epoch 115/130\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1447\n", "Epoch 116/130\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1421\n", "Epoch 117/130\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1395\n", "Epoch 118/130\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1370\n", "Epoch 119/130\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.1345\n", "Epoch 120/130\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.1321\n", "Epoch 121/130\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1297\n", "Epoch 122/130\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1273\n", "Epoch 123/130\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1250\n", "Epoch 124/130\n", "1/1 [==============================] - 0s 999us/step - loss: 0.1227\n", "Epoch 125/130\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1205\n", "Epoch 126/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1183\n", "Epoch 127/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1162\n", "Epoch 128/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1141\n", "Epoch 129/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1120\n", "Epoch 130/130\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1100\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC3626F70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/140\n", "1/1 [==============================] - 0s 0s/step - loss: 0.8272\n", "Epoch 2/140\n", "1/1 [==============================] - 0s 0s/step - loss: 0.6256\n", "Epoch 3/140\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 0s/step - loss: 0.5357\n", "Epoch 4/140\n", "1/1 [==============================] - 0s 0s/step - loss: 0.4929\n", "Epoch 5/140\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.4700\n", "Epoch 6/140\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.4556\n", "Epoch 7/140\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.4448\n", "Epoch 8/140\n", "1/1 [==============================] - 0s 0s/step - loss: 0.4357\n", "Epoch 9/140\n", "1/1 [==============================] - 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loss: 0.0763\n", "Epoch 104/140\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0749\n", "Epoch 105/140\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0735\n", "Epoch 106/140\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 2ms/step - loss: 0.0722\n", "Epoch 107/140\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0709\n", "Epoch 108/140\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0696\n", "Epoch 109/140\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0683\n", "Epoch 110/140\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0671\n", "Epoch 111/140\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0659\n", "Epoch 112/140\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0647\n", "Epoch 113/140\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0635\n", "Epoch 114/140\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0624\n", "Epoch 115/140\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0612\n", "Epoch 116/140\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0601\n", "Epoch 117/140\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0590\n", "Epoch 118/140\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0579\n", "Epoch 119/140\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0569\n", "Epoch 120/140\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0559\n", "Epoch 121/140\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0548\n", "Epoch 122/140\n", "1/1 [==============================] - 0s 998us/step - loss: 0.0539\n", "Epoch 123/140\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0529\n", "Epoch 124/140\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0519\n", "Epoch 125/140\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0510\n", "Epoch 126/140\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0500\n", "Epoch 127/140\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0491\n", "Epoch 128/140\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0482\n", "Epoch 129/140\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0474\n", "Epoch 130/140\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0465\n", "Epoch 131/140\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0457\n", "Epoch 132/140\n", "1/1 [==============================] - 0s 998us/step - loss: 0.0448\n", "Epoch 133/140\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0440\n", "Epoch 134/140\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0432\n", "Epoch 135/140\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0424\n", "Epoch 136/140\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0417\n", "Epoch 137/140\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0409\n", "Epoch 138/140\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0402\n", "Epoch 139/140\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0394\n", "Epoch 140/140\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0387\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC330E940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.0953\n", "Epoch 2/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.6054\n", "Epoch 3/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.9722\n", "Epoch 4/150\n", "1/1 [==============================] - 0s 0s/step - loss: 0.7000\n", "Epoch 5/150\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5800\n", "Epoch 6/150\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.5243\n", "Epoch 7/150\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.4957\n", "Epoch 8/150\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.4787\n", "Epoch 9/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4666\n", "Epoch 10/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4567\n", "Epoch 11/150\n", "1/1 [==============================] - 0s 999us/step - loss: 0.4478\n", "Epoch 12/150\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.4395\n", "Epoch 13/150\n", "1/1 [==============================] - 0s 999us/step - loss: 0.4314\n", "Epoch 14/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4235\n", "Epoch 15/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4158\n", "Epoch 16/150\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4082\n", "Epoch 17/150\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.4008\n", "Epoch 18/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3935\n", "Epoch 19/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3864\n", "Epoch 20/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3794\n", "Epoch 21/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3725\n", "Epoch 22/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3657\n", "Epoch 23/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3591\n", "Epoch 24/150\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3526\n", "Epoch 25/150\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3462\n", "Epoch 26/150\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.3399\n", "Epoch 27/150\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.3337\n", "Epoch 28/150\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.3276\n", "Epoch 29/150\n", "1/1 [==============================] - 0s 998us/step - loss: 0.3217\n", "Epoch 30/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3159\n", "Epoch 31/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3101\n", "Epoch 32/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3045\n", "Epoch 33/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2990\n", "Epoch 34/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2935\n", "Epoch 35/150\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.2882\n", "Epoch 36/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2830\n", "Epoch 37/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2778\n", "Epoch 38/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2728\n", "Epoch 39/150\n", "1/1 [==============================] - 0s 0s/step - loss: 0.2678\n", "Epoch 40/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2630\n", "Epoch 41/150\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.2582\n", "Epoch 42/150\n", "1/1 [==============================] - 0s 999us/step - loss: 0.2535\n", "Epoch 43/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2489\n", "Epoch 44/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2444\n", "Epoch 45/150\n", "1/1 [==============================] - 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0s 2ms/step - loss: 0.1264\n", "Epoch 81/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1241\n", "Epoch 82/150\n", "1/1 [==============================] - 0s 999us/step - loss: 0.1218\n", "Epoch 83/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1196\n", "Epoch 84/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1174\n", "Epoch 85/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1153\n", "Epoch 86/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1132\n", "Epoch 87/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1111\n", "Epoch 88/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1091\n", "Epoch 89/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1071\n", "Epoch 90/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1052\n", "Epoch 91/150\n", "1/1 [==============================] - 0s 998us/step - loss: 0.1033\n", "Epoch 92/150\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.1014\n", "Epoch 93/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0996\n", "Epoch 94/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0978\n", "Epoch 95/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0960\n", "Epoch 96/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0942\n", "Epoch 97/150\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0925\n", "Epoch 98/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0909\n", "Epoch 99/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0892\n", "Epoch 100/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0876\n", "Epoch 101/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0860\n", "Epoch 102/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0844\n", "Epoch 103/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0829\n", "Epoch 104/150\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0814\n", "Epoch 105/150\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0799\n", "Epoch 106/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0785\n", "Epoch 107/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0770\n", "Epoch 108/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0756\n", "Epoch 109/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0743\n", "Epoch 110/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0729\n", "Epoch 111/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0716\n", "Epoch 112/150\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0703\n", "Epoch 113/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0690\n", "Epoch 114/150\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0678\n", "Epoch 115/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0665\n", "Epoch 116/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0653\n", "Epoch 117/150\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0641\n", "Epoch 118/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0630\n", "Epoch 119/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0618\n", "Epoch 120/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0607\n", "Epoch 121/150\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0596\n", "Epoch 122/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0585\n", "Epoch 123/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0575\n", "Epoch 124/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0564\n", "Epoch 125/150\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0554\n", "Epoch 126/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0544\n", "Epoch 127/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0534\n", "Epoch 128/150\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0524\n", "Epoch 129/150\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0515\n", "Epoch 130/150\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0505\n", "Epoch 131/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0496\n", "Epoch 132/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0487\n", "Epoch 133/150\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0478\n", "Epoch 134/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0470\n", "Epoch 135/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0461\n", "Epoch 136/150\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0453\n", "Epoch 137/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0445\n", "Epoch 138/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0437\n", "Epoch 139/150\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0429\n", "Epoch 140/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0421\n", "Epoch 141/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0413\n", "Epoch 142/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0406\n", "Epoch 143/150\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0398\n", "Epoch 144/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0391\n", "Epoch 145/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0384\n", "Epoch 146/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0377\n", "Epoch 147/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0370\n", "Epoch 148/150\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0363\n", "Epoch 149/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0357\n", "Epoch 150/150\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0350\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC37B3940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/160\n", "1/1 [==============================] - 0s 999us/step - loss: 96.4616\n", "Epoch 2/160\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 1ms/step - loss: 41.1743\n", "Epoch 3/160\n", "1/1 [==============================] - 0s 1ms/step - loss: 17.8690\n", "Epoch 4/160\n", "1/1 [==============================] - 0s 1ms/step - loss: 8.0397\n", "Epoch 5/160\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.8888\n", "Epoch 6/160\n", "1/1 [==============================] - 0s 1ms/step - loss: 2.1307\n", "Epoch 7/160\n", "1/1 [==============================] - 0s 999us/step - loss: 1.3810\n", "Epoch 8/160\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.0563\n", "Epoch 9/160\n", "1/1 [==============================] - 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loss: 0.1458\n", "Epoch 104/160\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1431\n", "Epoch 105/160\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 3ms/step - loss: 0.1405\n", "Epoch 106/160\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1380\n", "Epoch 107/160\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1355\n", "Epoch 108/160\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.1330\n", "Epoch 109/160\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1306\n", "Epoch 110/160\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.1282\n", "Epoch 111/160\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1259\n", "Epoch 112/160\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1236\n", "Epoch 113/160\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1214\n", "Epoch 114/160\n", "1/1 [==============================] - 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loss: 0.0974\n", "Epoch 126/160\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0956\n", "Epoch 127/160\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0939\n", "Epoch 128/160\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0922\n", "Epoch 129/160\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0905\n", "Epoch 130/160\n", "1/1 [==============================] - 0s 998us/step - loss: 0.0889\n", "Epoch 131/160\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0873\n", "Epoch 132/160\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0857\n", "Epoch 133/160\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0841\n", "Epoch 134/160\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0826\n", "Epoch 135/160\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0811\n", "Epoch 136/160\n", "1/1 [==============================] - 0s 59us/step - loss: 0.0796\n", "Epoch 137/160\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0782\n", "Epoch 138/160\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0768\n", "Epoch 139/160\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0754\n", "Epoch 140/160\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0740\n", "Epoch 141/160\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0727\n", "Epoch 142/160\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0713\n", "Epoch 143/160\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0700\n", "Epoch 144/160\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0688\n", "Epoch 145/160\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0675\n", "Epoch 146/160\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0663\n", "Epoch 147/160\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0651\n", "Epoch 148/160\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0639\n", "Epoch 149/160\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0627\n", "Epoch 150/160\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0616\n", "Epoch 151/160\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0605\n", "Epoch 152/160\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0594\n", "Epoch 153/160\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0583\n", "Epoch 154/160\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0573\n", "Epoch 155/160\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0562\n", "Epoch 156/160\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0552\n", "Epoch 157/160\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0542\n", "Epoch 158/160\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0532\n", "Epoch 159/160\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0522\n", "Epoch 160/160\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0513\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC4C0F040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/170\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.3240\n", "Epoch 2/170\n", "1/1 [==============================] - 0s 1ms/step - loss: 2.1314\n", "Epoch 3/170\n", "1/1 [==============================] - 0s 1ms/step - loss: 1.2019\n", "Epoch 4/170\n", "1/1 [==============================] - 0s 999us/step - loss: 0.8048\n", "Epoch 5/170\n", "1/1 [==============================] - 0s 999us/step - loss: 0.6320\n", "Epoch 6/170\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.5539\n", "Epoch 7/170\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.5158\n", "Epoch 8/170\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4946\n", "Epoch 9/170\n", "1/1 [==============================] - 0s 999us/step - loss: 0.4806\n", "Epoch 10/170\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4698\n", "Epoch 11/170\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4604\n", "Epoch 12/170\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4516\n", "Epoch 13/170\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4433\n", "Epoch 14/170\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4352\n", "Epoch 15/170\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4272\n", "Epoch 16/170\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4195\n", "Epoch 17/170\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4118\n", "Epoch 18/170\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.4044\n", "Epoch 19/170\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3970\n", "Epoch 20/170\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3898\n", "Epoch 21/170\n", "1/1 [==============================] - 0s 999us/step - loss: 0.3827\n", "Epoch 22/170\n", "1/1 [==============================] - 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loss: 0.0465\n", "Epoch 137/170\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0457\n", "Epoch 138/170\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0449\n", "Epoch 139/170\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0440\n", "Epoch 140/170\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 0s/step - loss: 0.0432\n", "Epoch 141/170\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0425\n", "Epoch 142/170\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0417\n", "Epoch 143/170\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0409\n", "Epoch 144/170\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0402\n", "Epoch 145/170\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0395\n", "Epoch 146/170\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0387\n", "Epoch 147/170\n", "1/1 [==============================] - 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loss: 0.0311\n", "Epoch 159/170\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0305\n", "Epoch 160/170\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0300\n", "Epoch 161/170\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0294\n", "Epoch 162/170\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0289\n", "Epoch 163/170\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0284\n", "Epoch 164/170\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0279\n", "Epoch 165/170\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0274\n", "Epoch 166/170\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0269\n", "Epoch 167/170\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0264\n", "Epoch 168/170\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0259\n", "Epoch 169/170\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0254\n", "Epoch 170/170\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0250\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC3626700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/180\n", "1/1 [==============================] - 0s 0s/step - loss: 27.0516\n", "Epoch 2/180\n", "1/1 [==============================] - 0s 999us/step - loss: 11.7904\n", "Epoch 3/180\n", "1/1 [==============================] - 0s 0s/step - loss: 5.3529\n", "Epoch 4/180\n", "1/1 [==============================] - 0s 0s/step - loss: 2.6334\n", "Epoch 5/180\n", "1/1 [==============================] - 0s 0s/step - loss: 1.4807\n", "Epoch 6/180\n", "1/1 [==============================] - 0s 0s/step - loss: 0.9883\n", "Epoch 7/180\n", "1/1 [==============================] - 0s 0s/step - loss: 0.7741\n", "Epoch 8/180\n", "1/1 [==============================] - 0s 0s/step - loss: 0.6774\n", "Epoch 9/180\n", "1/1 [==============================] - 0s 0s/step - loss: 0.6303\n", "Epoch 10/180\n", "1/1 [==============================] - 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loss: 0.0600\n", "Epoch 136/180\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0589\n", "Epoch 137/180\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0579\n", "Epoch 138/180\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0568\n", "Epoch 139/180\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0558\n", "Epoch 140/180\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0548\n", "Epoch 141/180\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0538\n", "Epoch 142/180\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0528\n", "Epoch 143/180\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0519\n", "Epoch 144/180\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0509\n", "Epoch 145/180\n", "1/1 [==============================] - 0s 998us/step - loss: 0.0500\n", "Epoch 146/180\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0491\n", "Epoch 147/180\n", "1/1 [==============================] - 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loss: 0.0394\n", "Epoch 159/180\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0387\n", "Epoch 160/180\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0380\n", "Epoch 161/180\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0373\n", "Epoch 162/180\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0366\n", "Epoch 163/180\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0359\n", "Epoch 164/180\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0353\n", "Epoch 165/180\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 2ms/step - loss: 0.0347\n", "Epoch 166/180\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0340\n", "Epoch 167/180\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0334\n", "Epoch 168/180\n", "1/1 [==============================] - 0s 995us/step - loss: 0.0328\n", "Epoch 169/180\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0322\n", "Epoch 170/180\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0316\n", "Epoch 171/180\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0310\n", "Epoch 172/180\n", "1/1 [==============================] - 0s 997us/step - loss: 0.0305\n", "Epoch 173/180\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0299\n", "Epoch 174/180\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0294\n", "Epoch 175/180\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0288\n", "Epoch 176/180\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0283\n", "Epoch 177/180\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0278\n", "Epoch 178/180\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0273\n", "Epoch 179/180\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0268\n", "Epoch 180/180\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0263\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC1AB7550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/190\n", "1/1 [==============================] - 0s 1ms/step - loss: 92.2239\n", "Epoch 2/190\n", "1/1 [==============================] - 0s 2ms/step - loss: 39.3818\n", "Epoch 3/190\n", "1/1 [==============================] - 0s 2ms/step - loss: 17.1069\n", "Epoch 4/190\n", "1/1 [==============================] - 0s 1ms/step - loss: 7.7119\n", "Epoch 5/190\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.7441\n", "Epoch 6/190\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.0633\n", "Epoch 7/190\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.3462\n", "Epoch 8/190\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.0354\n", "Epoch 9/190\n", "1/1 [==============================] - 0s 0s/step - loss: 0.8960\n", "Epoch 10/190\n", "1/1 [==============================] - 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loss: 0.0406\n", "Epoch 173/190\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0399\n", "Epoch 174/190\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0392\n", "Epoch 175/190\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0385\n", "Epoch 176/190\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0378\n", "Epoch 177/190\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0371\n", "Epoch 178/190\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0364\n", "Epoch 179/190\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 2ms/step - loss: 0.0357\n", "Epoch 180/190\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0351\n", "Epoch 181/190\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0345\n", "Epoch 182/190\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0338\n", "Epoch 183/190\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0332\n", "Epoch 184/190\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0326\n", "Epoch 185/190\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0320\n", "Epoch 186/190\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0314\n", "Epoch 187/190\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0309\n", "Epoch 188/190\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0303\n", "Epoch 189/190\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0298\n", "Epoch 190/190\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0292\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC20E2310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/200\n", "1/1 [==============================] - 0s 1ms/step - loss: 61.0112\n", "Epoch 2/200\n", "1/1 [==============================] - 0s 2ms/step - loss: 26.1747\n", "Epoch 3/200\n", "1/1 [==============================] - 0s 2ms/step - loss: 11.4877\n", "Epoch 4/200\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.2909\n", "Epoch 5/200\n", "1/1 [==============================] - 0s 1ms/step - loss: 2.6716\n", "Epoch 6/200\n", "1/1 [==============================] - 0s 1ms/step - loss: 1.5600\n", "Epoch 7/200\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.0837\n", "Epoch 8/200\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8752\n", "Epoch 9/200\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7798\n", "Epoch 10/200\n", "1/1 [==============================] - 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0s 3ms/step - loss: 0.0297\n", "Epoch 184/200\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 2ms/step - loss: 0.0292\n", "Epoch 185/200\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0286\n", "Epoch 186/200\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0281\n", "Epoch 187/200\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0276\n", "Epoch 188/200\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0271\n", "Epoch 189/200\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0266\n", "Epoch 190/200\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0261\n", "Epoch 191/200\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0256\n", "Epoch 192/200\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0252\n", "Epoch 193/200\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0247\n", "Epoch 194/200\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0243\n", "Epoch 195/200\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0238\n", "Epoch 196/200\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0234\n", "Epoch 197/200\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0230\n", "Epoch 198/200\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0226\n", "Epoch 199/200\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0221\n", "Epoch 200/200\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0217\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC1E31160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/210\n", "1/1 [==============================] - 0s 2ms/step - loss: 124.3655\n", "Epoch 2/210\n", "1/1 [==============================] - 0s 1ms/step - loss: 52.9751\n", "Epoch 3/210\n", "1/1 [==============================] - 0s 2ms/step - loss: 22.8838\n", "Epoch 4/210\n", "1/1 [==============================] - 0s 1ms/step - loss: 10.1945\n", "Epoch 5/210\n", "1/1 [==============================] - 0s 999us/step - loss: 4.8377\n", "Epoch 6/210\n", "1/1 [==============================] - 0s 0s/step - loss: 2.5708\n", "Epoch 7/210\n", "1/1 [==============================] - 0s 0s/step - loss: 1.6059\n", "Epoch 8/210\n", "1/1 [==============================] - 0s 0s/step - loss: 1.1899\n", "Epoch 9/210\n", "1/1 [==============================] - 0s 0s/step - loss: 1.0052\n", "Epoch 10/210\n", "1/1 [==============================] - 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0s 0s/step - loss: 0.0303\n", "Epoch 194/210\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.0298\n", "Epoch 195/210\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0292\n", "Epoch 196/210\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0287\n", "Epoch 197/210\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0282\n", "Epoch 198/210\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0277\n", "Epoch 199/210\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0272\n", "Epoch 200/210\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0267\n", "Epoch 201/210\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0262\n", "Epoch 202/210\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0257\n", "Epoch 203/210\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0252\n", "Epoch 204/210\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0248\n", "Epoch 205/210\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0243\n", "Epoch 206/210\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0239\n", "Epoch 207/210\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0235\n", "Epoch 208/210\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0230\n", "Epoch 209/210\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0226\n", "Epoch 210/210\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0222\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC4C399D0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/220\n", "1/1 [==============================] - 0s 0s/step - loss: 24.0982\n", "Epoch 2/220\n", "1/1 [==============================] - 0s 0s/step - loss: 10.5379\n", "Epoch 3/220\n", "1/1 [==============================] - 0s 0s/step - loss: 4.8173\n", "Epoch 4/220\n", "1/1 [==============================] - 0s 0s/step - loss: 2.4000\n", "Epoch 5/220\n", "1/1 [==============================] - 0s 0s/step - loss: 1.3747\n", "Epoch 6/220\n", "1/1 [==============================] - 0s 0s/step - loss: 0.9361\n", "Epoch 7/220\n", "1/1 [==============================] - 0s 0s/step - loss: 0.7449\n", "Epoch 8/220\n", "1/1 [==============================] - 0s 0s/step - loss: 0.6579\n", "Epoch 9/220\n", "1/1 [==============================] - 0s 0s/step - loss: 0.6150\n", "Epoch 10/220\n", "1/1 [==============================] - 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loss: 0.0244\n", "Epoch 184/220\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0240\n", "Epoch 185/220\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0235\n", "Epoch 186/220\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0231\n", "Epoch 187/220\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0227\n", "Epoch 188/220\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0223\n", "Epoch 189/220\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0219\n", "Epoch 190/220\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0215\n", "Epoch 191/220\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0211\n", "Epoch 192/220\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0207\n", "Epoch 193/220\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0203\n", "Epoch 194/220\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0199\n", "Epoch 195/220\n", "1/1 [==============================] - 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loss: 0.0160\n", "Epoch 207/220\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0157\n", "Epoch 208/220\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0154\n", "Epoch 209/220\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0152\n", "Epoch 210/220\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0149\n", "Epoch 211/220\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0146\n", "Epoch 212/220\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0143\n", "Epoch 213/220\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0141\n", "Epoch 214/220\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0138\n", "Epoch 215/220\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0136\n", "Epoch 216/220\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0133\n", "Epoch 217/220\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0131\n", "Epoch 218/220\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0129\n", "Epoch 219/220\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0126\n", "Epoch 220/220\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0124\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC3680310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/230\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.3635\n", "Epoch 2/230\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.0022\n", "Epoch 3/230\n", "1/1 [==============================] - 0s 0s/step - loss: 1.5800\n", "Epoch 4/230\n", "1/1 [==============================] - 0s 0s/step - loss: 0.9751\n", "Epoch 5/230\n", "1/1 [==============================] - 0s 0s/step - loss: 0.7146\n", "Epoch 6/230\n", "1/1 [==============================] - 0s 0s/step - loss: 0.5993\n", "Epoch 7/230\n", "1/1 [==============================] - 0s 0s/step - loss: 0.5453\n", "Epoch 8/230\n", "1/1 [==============================] - 0s 0s/step - loss: 0.5172\n", "Epoch 9/230\n", "1/1 [==============================] - 0s 0s/step - loss: 0.5002\n", "Epoch 10/230\n", "1/1 [==============================] - 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loss: 0.0141\n", "Epoch 204/230\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0139\n", "Epoch 205/230\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0136\n", "Epoch 206/230\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0134\n", "Epoch 207/230\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0131\n", "Epoch 208/230\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0129\n", "Epoch 209/230\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0127\n", "Epoch 210/230\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0124\n", "Epoch 211/230\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0122\n", "Epoch 212/230\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0120\n", "Epoch 213/230\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0118\n", "Epoch 214/230\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0116\n", "Epoch 215/230\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0113\n", "Epoch 216/230\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0111\n", "Epoch 217/230\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0109\n", "Epoch 218/230\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0107\n", "Epoch 219/230\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0105\n", "Epoch 220/230\n", "1/1 [==============================] - ETA: 0s - loss: 0.010 - 0s 0s/step - loss: 0.0104\n", "Epoch 221/230\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0102\n", "Epoch 222/230\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0100\n", "Epoch 223/230\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0098\n", "Epoch 224/230\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0096\n", "Epoch 225/230\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0094\n", "Epoch 226/230\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0093\n", "Epoch 227/230\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0091\n", "Epoch 228/230\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0089\n", "Epoch 229/230\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0088\n", "Epoch 230/230\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0086\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC1C5FEE0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/240\n", "1/1 [==============================] - 0s 16ms/step - loss: 159.9739\n", "Epoch 2/240\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 0s/step - loss: 68.0296\n", "Epoch 3/240\n", "1/1 [==============================] - 0s 0s/step - loss: 29.2770\n", "Epoch 4/240\n", "1/1 [==============================] - 0s 0s/step - loss: 12.9372\n", "Epoch 5/240\n", "1/1 [==============================] - 0s 16ms/step - loss: 6.0414\n", "Epoch 6/240\n", "1/1 [==============================] - 0s 16ms/step - loss: 3.1251\n", "Epoch 7/240\n", "1/1 [==============================] - 0s 16ms/step - loss: 1.8857\n", "Epoch 8/240\n", "1/1 [==============================] - 0s 0s/step - loss: 1.3532\n", "Epoch 9/240\n", "1/1 [==============================] - 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loss: 0.0270\n", "Epoch 205/240\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0265\n", "Epoch 206/240\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0260\n", "Epoch 207/240\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 16ms/step - loss: 0.0255\n", "Epoch 208/240\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0251\n", "Epoch 209/240\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0246\n", "Epoch 210/240\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0242\n", "Epoch 211/240\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0237\n", "Epoch 212/240\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0233\n", "Epoch 213/240\n", "1/1 [==============================] - 0s 998us/step - loss: 0.0229\n", "Epoch 214/240\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0225\n", "Epoch 215/240\n", "1/1 [==============================] - 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loss: 0.0180\n", "Epoch 227/240\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0177\n", "Epoch 228/240\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0174\n", "Epoch 229/240\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0171\n", "Epoch 230/240\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0167\n", "Epoch 231/240\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0164\n", "Epoch 232/240\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0161\n", "Epoch 233/240\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0159\n", "Epoch 234/240\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0156\n", "Epoch 235/240\n", "1/1 [==============================] - 0s 7ms/step - loss: 0.0153\n", "Epoch 236/240\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0150\n", "Epoch 237/240\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0147\n", "Epoch 238/240\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0145\n", "Epoch 239/240\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0142\n", "Epoch 240/240\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0139\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC30EAD30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/250\n", "1/1 [==============================] - 0s 2ms/step - loss: 54.9779\n", "Epoch 2/250\n", "1/1 [==============================] - 0s 1ms/step - loss: 23.6207\n", "Epoch 3/250\n", "1/1 [==============================] - 0s 0s/step - loss: 10.3999\n", "Epoch 4/250\n", "1/1 [==============================] - 0s 0s/step - loss: 4.8212\n", "Epoch 5/250\n", "1/1 [==============================] - 0s 0s/step - loss: 2.4625\n", "Epoch 6/250\n", "1/1 [==============================] - 0s 0s/step - loss: 1.4609\n", "Epoch 7/250\n", "1/1 [==============================] - 0s 0s/step - loss: 1.0311\n", "Epoch 8/250\n", "1/1 [==============================] - 0s 0s/step - loss: 0.8425\n", "Epoch 9/250\n", "1/1 [==============================] - 0s 0s/step - loss: 0.7556\n", "Epoch 10/250\n", "1/1 [==============================] - 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loss: 0.0106\n", "Epoch 239/250\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0104\n", "Epoch 240/250\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0102\n", "Epoch 241/250\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0100\n", "Epoch 242/250\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0098\n", "Epoch 243/250\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0096\n", "Epoch 244/250\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0095\n", "Epoch 245/250\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0093\n", "Epoch 246/250\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0091\n", "Epoch 247/250\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0090\n", "Epoch 248/250\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0088\n", "Epoch 249/250\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0086\n", "Epoch 250/250\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0085\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC1AB7670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/260\n", "1/1 [==============================] - 0s 0s/step - loss: 4.9174\n", "Epoch 2/260\n", "1/1 [==============================] - 0s 0s/step - loss: 2.3850\n", "Epoch 3/260\n", "1/1 [==============================] - 0s 0s/step - loss: 1.3123\n", "Epoch 4/260\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 0s/step - loss: 0.8547\n", "Epoch 5/260\n", "1/1 [==============================] - 0s 0s/step - loss: 0.6564\n", "Epoch 6/260\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5675\n", "Epoch 7/260\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.5247\n", "Epoch 8/260\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.5015\n", "Epoch 9/260\n", "1/1 [==============================] - 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loss: 0.0128\n", "Epoch 208/260\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0126\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 209/260\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0124\n", "Epoch 210/260\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0121\n", "Epoch 211/260\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0119\n", "Epoch 212/260\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0117\n", "Epoch 213/260\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0115\n", "Epoch 214/260\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0113\n", "Epoch 215/260\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0111\n", "Epoch 216/260\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0109\n", "Epoch 217/260\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0107\n", "Epoch 218/260\n", "1/1 [==============================] - 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loss: 0.0086\n", "Epoch 230/260\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0084\n", "Epoch 231/260\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0083\n", "Epoch 232/260\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0081\n", "Epoch 233/260\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0080\n", "Epoch 234/260\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0078\n", "Epoch 235/260\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0077\n", "Epoch 236/260\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0075\n", "Epoch 237/260\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0074\n", "Epoch 238/260\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0073\n", "Epoch 239/260\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0071\n", "Epoch 240/260\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0070\n", "Epoch 241/260\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0069\n", "Epoch 242/260\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0067\n", "Epoch 243/260\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0066\n", "Epoch 244/260\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0065\n", "Epoch 245/260\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0064\n", "Epoch 246/260\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0063\n", "Epoch 247/260\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0062\n", "Epoch 248/260\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0060\n", "Epoch 249/260\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0059\n", "Epoch 250/260\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0058\n", "Epoch 251/260\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0057\n", "Epoch 252/260\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0056\n", "Epoch 253/260\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0055\n", "Epoch 254/260\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0054\n", "Epoch 255/260\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0053\n", "Epoch 256/260\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0052\n", "Epoch 257/260\n", "1/1 [==============================] - 0s 996us/step - loss: 0.0051\n", "Epoch 258/260\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0050\n", "Epoch 259/260\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0049\n", "Epoch 260/260\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0049\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC499B9D0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/270\n", "1/1 [==============================] - 0s 1ms/step - loss: 1.2712\n", "Epoch 2/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.8196\n", "Epoch 3/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.6242\n", "Epoch 4/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.5368\n", "Epoch 5/270\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4950\n", "Epoch 6/270\n", "1/1 [==============================] - 0s 999us/step - loss: 0.4725\n", "Epoch 7/270\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4582\n", "Epoch 8/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.4475\n", "Epoch 9/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.4384\n", "Epoch 10/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.4300\n", "Epoch 11/270\n", "1/1 [==============================] - ETA: 0s - loss: 0.422 - 0s 2ms/step - loss: 0.4220\n", "Epoch 12/270\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4143\n", "Epoch 13/270\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4067\n", "Epoch 14/270\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3993\n", "Epoch 15/270\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3921\n", "Epoch 16/270\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3850\n", "Epoch 17/270\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.3780\n", "Epoch 18/270\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3711\n", "Epoch 19/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.3644\n", "Epoch 20/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.3577\n", "Epoch 21/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.3513\n", "Epoch 22/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.3449\n", "Epoch 23/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.3386\n", "Epoch 24/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.3325\n", "Epoch 25/270\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3264\n", "Epoch 26/270\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.3205\n", "Epoch 27/270\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.3147\n", "Epoch 28/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.3090\n", "Epoch 29/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.3034\n", "Epoch 30/270\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2979\n", "Epoch 31/270\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2924\n", "Epoch 32/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.2871\n", "Epoch 33/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.2819\n", "Epoch 34/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.2768\n", "Epoch 35/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.2718\n", "Epoch 36/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.2668\n", "Epoch 37/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.2620\n", "Epoch 38/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.2572\n", "Epoch 39/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.2526\n", "Epoch 40/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.2480\n", "Epoch 41/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.2435\n", "Epoch 42/270\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 0s/step - loss: 0.2391\n", "Epoch 43/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.2347\n", "Epoch 44/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.2305\n", "Epoch 45/270\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2263\n", "Epoch 46/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.2222\n", "Epoch 47/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.2181\n", "Epoch 48/270\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2142\n", "Epoch 49/270\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2103\n", "Epoch 50/270\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2065\n", "Epoch 51/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.2027\n", "Epoch 52/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1990\n", "Epoch 53/270\n", "1/1 [==============================] - ETA: 0s - loss: 0.195 - 0s 0s/step - loss: 0.1954\n", "Epoch 54/270\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1919\n", "Epoch 55/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1884\n", "Epoch 56/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1850\n", "Epoch 57/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1816\n", "Epoch 58/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.1783\n", "Epoch 59/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1751\n", "Epoch 60/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.1719\n", "Epoch 61/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.1688\n", "Epoch 62/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1657\n", "Epoch 63/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1627\n", "Epoch 64/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.1597\n", "Epoch 65/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1568\n", "Epoch 66/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.1540\n", "Epoch 67/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.1512\n", "Epoch 68/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1485\n", "Epoch 69/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.1458\n", "Epoch 70/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.1431\n", "Epoch 71/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1405\n", "Epoch 72/270\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.1380\n", "Epoch 73/270\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1355\n", "Epoch 74/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1330\n", "Epoch 75/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1306\n", "Epoch 76/270\n", "1/1 [==============================] - ETA: 0s - loss: 0.128 - 0s 0s/step - loss: 0.1282\n", "Epoch 77/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1259\n", "Epoch 78/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1236\n", "Epoch 79/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1214\n", "Epoch 80/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1192\n", "Epoch 81/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.1170\n", "Epoch 82/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.1149\n", "Epoch 83/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1128\n", "Epoch 84/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1107\n", "Epoch 85/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1087\n", "Epoch 86/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1067\n", "Epoch 87/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.1048\n", "Epoch 88/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.1029\n", "Epoch 89/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.1010\n", "Epoch 90/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0992\n", "Epoch 91/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0974\n", "Epoch 92/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0956\n", "Epoch 93/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0939\n", "Epoch 94/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0922\n", "Epoch 95/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0905\n", "Epoch 96/270\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0889\n", "Epoch 97/270\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0873\n", "Epoch 98/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0857\n", "Epoch 99/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0841\n", "Epoch 100/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0826\n", "Epoch 101/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0811\n", "Epoch 102/270\n", "1/1 [==============================] - 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loss: 0.0532\n", "Epoch 125/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0522\n", "Epoch 126/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0513\n", "Epoch 127/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0504\n", "Epoch 128/270\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0494\n", "Epoch 129/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0485\n", "Epoch 130/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0477\n", "Epoch 131/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0468\n", "Epoch 132/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0460\n", "Epoch 133/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0451\n", "Epoch 134/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0443\n", "Epoch 135/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0435\n", "Epoch 136/270\n", "1/1 [==============================] - 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0s 16ms/step - loss: 0.0057\n", "Epoch 247/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0056\n", "Epoch 248/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0055\n", "Epoch 249/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0054\n", "Epoch 250/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0053\n", "Epoch 251/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0052\n", "Epoch 252/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0051\n", "Epoch 253/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0050\n", "Epoch 254/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0049\n", "Epoch 255/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0048\n", "Epoch 256/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0047\n", "Epoch 257/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0047\n", "Epoch 258/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0046\n", "Epoch 259/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0045\n", "Epoch 260/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0044\n", "Epoch 261/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0043\n", "Epoch 262/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0042\n", "Epoch 263/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0042\n", "Epoch 264/270\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0041\n", "Epoch 265/270\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0040\n", "Epoch 266/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0039\n", "Epoch 267/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0039\n", "Epoch 268/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0038\n", "Epoch 269/270\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0037\n", "Epoch 270/270\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0037\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC374C3A0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/280\n", "1/1 [==============================] - 0s 0s/step - loss: 9.9170\n", "Epoch 2/280\n", "1/1 [==============================] - 0s 0s/step - loss: 4.5157\n", "Epoch 3/280\n", "1/1 [==============================] - 0s 0s/step - loss: 2.2337\n", "Epoch 4/280\n", "1/1 [==============================] - 0s 0s/step - loss: 1.2661\n", "Epoch 5/280\n", "1/1 [==============================] - 0s 0s/step - loss: 0.8526\n", "Epoch 6/280\n", "1/1 [==============================] - 0s 644us/step - loss: 0.6725\n", "Epoch 7/280\n", "1/1 [==============================] - 0s 0s/step - loss: 0.5909\n", "Epoch 8/280\n", "1/1 [==============================] - 0s 0s/step - loss: 0.5510\n", "Epoch 9/280\n", "1/1 [==============================] - 0s 0s/step - loss: 0.5287\n", "Epoch 10/280\n", "1/1 [==============================] - 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loss: 0.0049\n", "Epoch 264/280\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0049\n", "Epoch 265/280\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0048\n", "Epoch 266/280\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0047\n", "Epoch 267/280\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0046\n", "Epoch 268/280\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0045\n", "Epoch 269/280\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0044\n", "Epoch 270/280\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0044\n", "Epoch 271/280\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0043\n", "Epoch 272/280\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 2ms/step - loss: 0.0042\n", "Epoch 273/280\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0041\n", "Epoch 274/280\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0040\n", "Epoch 275/280\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0040\n", "Epoch 276/280\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0039\n", "Epoch 277/280\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0038\n", "Epoch 278/280\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0038\n", "Epoch 279/280\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0037\n", "Epoch 280/280\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0036\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC4BD0AF0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/290\n", "1/1 [==============================] - 0s 2ms/step - loss: 25.6631\n", "Epoch 2/290\n", "1/1 [==============================] - 0s 2ms/step - loss: 11.2017\n", "Epoch 3/290\n", "1/1 [==============================] - 0s 1ms/step - loss: 5.1012\n", "Epoch 4/290\n", "1/1 [==============================] - 0s 1ms/step - loss: 2.5238\n", "Epoch 5/290\n", "1/1 [==============================] - 0s 999us/step - loss: 1.4310\n", "Epoch 6/290\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.9638\n", "Epoch 7/290\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.7605\n", "Epoch 8/290\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6683\n", "Epoch 9/290\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6232\n", "Epoch 10/290\n", "1/1 [==============================] - 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Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 64.4164\n", "Epoch 2/300\n", "1/1 [==============================] - 0s 1ms/step - loss: 27.6160\n", "Epoch 3/300\n", "1/1 [==============================] - 0s 0s/step - loss: 12.1014\n", "Epoch 4/300\n", "1/1 [==============================] - 0s 0s/step - loss: 5.5557\n", "Epoch 5/300\n", "1/1 [==============================] - 0s 0s/step - loss: 2.7893\n", "Epoch 6/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.6156\n", "Epoch 7/300\n", "1/1 [==============================] - 0s 1ms/step - loss: 1.1130\n", "Epoch 8/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.8934\n", "Epoch 9/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.7932\n", "Epoch 10/300\n", "1/1 [==============================] - 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0s 2ms/step - loss: 0.5753\n", "Epoch 23/300\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5648\n", "Epoch 24/300\n", "1/1 [==============================] - 0s 999us/step - loss: 0.5546\n", "Epoch 25/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.5445\n", "Epoch 26/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.5346\n", "Epoch 27/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5249\n", "Epoch 28/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5154\n", "Epoch 29/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.5060\n", "Epoch 30/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.4968\n", "Epoch 31/300\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4878\n", "Epoch 32/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4790\n", "Epoch 33/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.4703\n", "Epoch 34/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.4617\n", "Epoch 35/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.4533\n", "Epoch 36/300\n", "1/1 [==============================] - 0s 998us/step - loss: 0.4451\n", "Epoch 37/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4370\n", "Epoch 38/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.4291\n", "Epoch 39/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.4213\n", "Epoch 40/300\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.4137\n", "Epoch 41/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4061\n", "Epoch 42/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.3988\n", "Epoch 43/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.3915\n", "Epoch 44/300\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.3844\n", "Epoch 45/300\n", "1/1 [==============================] - 0s 999us/step - loss: 0.3774\n", "Epoch 46/300\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.3706\n", "Epoch 47/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3639\n", "Epoch 48/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3573\n", "Epoch 49/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.3508\n", "Epoch 50/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.3444\n", "Epoch 51/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.3381\n", "Epoch 52/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3320\n", "Epoch 53/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3260\n", "Epoch 54/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.3201\n", "Epoch 55/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.3142\n", "Epoch 56/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3085\n", "Epoch 57/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3029\n", "Epoch 58/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.2974\n", "Epoch 59/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.2920\n", "Epoch 60/300\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.2867\n", "Epoch 61/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2815\n", "Epoch 62/300\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.2764\n", "Epoch 63/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.2714\n", "Epoch 64/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.2665\n", "Epoch 65/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2616\n", "Epoch 66/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2569\n", "Epoch 67/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.2522\n", "Epoch 68/300\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2476\n", "Epoch 69/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2431\n", "Epoch 70/300\n", "1/1 [==============================] - 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loss: 0.0080\n", "Epoch 256/300\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0079\n", "Epoch 257/300\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0078\n", "Epoch 258/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0076\n", "Epoch 259/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0075\n", "Epoch 260/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0073\n", "Epoch 261/300\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0072\n", "Epoch 262/300\n", "1/1 [==============================] - ETA: 0s - loss: 0.007 - 0s 0s/step - loss: 0.0071\n", "Epoch 263/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0070\n", "Epoch 264/300\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0068\n", "Epoch 265/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0067\n", "Epoch 266/300\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0066\n", "Epoch 267/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0065\n", "Epoch 268/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0063\n", "Epoch 269/300\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0062\n", "Epoch 270/300\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0061\n", "Epoch 271/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0060\n", "Epoch 272/300\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0059\n", "Epoch 273/300\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0058\n", "Epoch 274/300\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0057\n", "Epoch 275/300\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0056\n", "Epoch 276/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0055\n", "Epoch 277/300\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0054\n", "Epoch 278/300\n", "1/1 [==============================] - 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loss: 0.0043\n", "Epoch 290/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0042\n", "Epoch 291/300\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0042\n", "Epoch 292/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0041\n", "Epoch 293/300\n", "1/1 [==============================] - 0s 998us/step - loss: 0.0040\n", "Epoch 294/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0039\n", "Epoch 295/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0039\n", "Epoch 296/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0038\n", "Epoch 297/300\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 2ms/step - loss: 0.0037\n", "Epoch 298/300\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0037\n", "Epoch 299/300\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0036\n", "Epoch 300/300\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0035\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC09FD430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/310\n", "1/1 [==============================] - 0s 0s/step - loss: 1.5924\n", "Epoch 2/310\n", "1/1 [==============================] - 0s 0s/step - loss: 0.9588\n", "Epoch 3/310\n", "1/1 [==============================] - 0s 0s/step - loss: 0.6866\n", "Epoch 4/310\n", "1/1 [==============================] - 0s 0s/step - loss: 0.5668\n", "Epoch 5/310\n", "1/1 [==============================] - 0s 0s/step - loss: 0.5113\n", "Epoch 6/310\n", "1/1 [==============================] - 0s 0s/step - loss: 0.4830\n", "Epoch 7/310\n", "1/1 [==============================] - 0s 0s/step - loss: 0.4662\n", "Epoch 8/310\n", "1/1 [==============================] - 0s 0s/step - loss: 0.4543\n", "Epoch 9/310\n", "1/1 [==============================] - 0s 0s/step - loss: 0.4446\n", "Epoch 10/310\n", "1/1 [==============================] - 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loss: 0.0064\n", "Epoch 241/310\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0063\n", "Epoch 242/310\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0062\n", "Epoch 243/310\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0061\n", "Epoch 244/310\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0060\n", "Epoch 245/310\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0059\n", "Epoch 246/310\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0058\n", "Epoch 247/310\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0057\n", "Epoch 248/310\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0056\n", "Epoch 249/310\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0055\n", "Epoch 250/310\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0054\n", "Epoch 251/310\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0053\n", "Epoch 252/310\n", "1/1 [==============================] - 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loss: 0.0028\n", "Epoch 287/310\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0027\n", "Epoch 288/310\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0027\n", "Epoch 289/310\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0026\n", "Epoch 290/310\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.0026\n", "Epoch 291/310\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0025\n", "Epoch 292/310\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0025\n", "Epoch 293/310\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0024\n", "Epoch 294/310\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0024\n", "Epoch 295/310\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 2ms/step - loss: 0.0023\n", "Epoch 296/310\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0023\n", "Epoch 297/310\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0023\n", "Epoch 298/310\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0022\n", "Epoch 299/310\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0022\n", "Epoch 300/310\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0021\n", "Epoch 301/310\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0021\n", "Epoch 302/310\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0021\n", "Epoch 303/310\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0020\n", "Epoch 304/310\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0020\n", "Epoch 305/310\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0020\n", "Epoch 306/310\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0019\n", "Epoch 307/310\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0019\n", "Epoch 308/310\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0019\n", "Epoch 309/310\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0018\n", "Epoch 310/310\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0018\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC4BD0D30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/320\n", "1/1 [==============================] - 0s 16ms/step - loss: 13.5787\n", "Epoch 2/320\n", "1/1 [==============================] - 0s 0s/step - loss: 6.0726\n", "Epoch 3/320\n", "1/1 [==============================] - 0s 0s/step - loss: 2.9035\n", "Epoch 4/320\n", "1/1 [==============================] - 0s 0s/step - loss: 1.5619\n", "Epoch 5/320\n", "1/1 [==============================] - 0s 0s/step - loss: 0.9905\n", "Epoch 6/320\n", "1/1 [==============================] - 0s 0s/step - loss: 0.7437\n", "Epoch 7/320\n", "1/1 [==============================] - 0s 0s/step - loss: 0.6337\n", "Epoch 8/320\n", "1/1 [==============================] - 0s 0s/step - loss: 0.5816\n", "Epoch 9/320\n", "1/1 [==============================] - 0s 0s/step - loss: 0.5539\n", "Epoch 10/320\n", "1/1 [==============================] - 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loss: 0.0043\n", "Epoch 274/320\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0042\n", "Epoch 275/320\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0041\n", "Epoch 276/320\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0041\n", "Epoch 277/320\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0040\n", "Epoch 278/320\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0039\n", "Epoch 279/320\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0038\n", "Epoch 280/320\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0038\n", "Epoch 281/320\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0037\n", "Epoch 282/320\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 16ms/step - loss: 0.0036\n", "Epoch 283/320\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0036\n", "Epoch 284/320\n", "1/1 [==============================] - 0s 998us/step - loss: 0.0035\n", "Epoch 285/320\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0034\n", "Epoch 286/320\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0034\n", "Epoch 287/320\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0033\n", "Epoch 288/320\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0033\n", "Epoch 289/320\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0032\n", "Epoch 290/320\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0031\n", "Epoch 291/320\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0031\n", "Epoch 292/320\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0030\n", "Epoch 293/320\n", "1/1 [==============================] - 0s 998us/step - loss: 0.0030\n", "Epoch 294/320\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0029\n", "Epoch 295/320\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0029\n", "Epoch 296/320\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0028\n", "Epoch 297/320\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0028\n", "Epoch 298/320\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0027\n", "Epoch 299/320\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0027\n", "Epoch 300/320\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0026\n", "Epoch 301/320\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0026\n", "Epoch 302/320\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0025\n", "Epoch 303/320\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0025\n", "Epoch 304/320\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0024\n", "Epoch 305/320\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0024\n", "Epoch 306/320\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0023\n", "Epoch 307/320\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0023\n", "Epoch 308/320\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0023\n", "Epoch 309/320\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0022\n", "Epoch 310/320\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0022\n", "Epoch 311/320\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0021\n", "Epoch 312/320\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0021\n", "Epoch 313/320\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0021\n", "Epoch 314/320\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0020\n", "Epoch 315/320\n", "1/1 [==============================] - 0s 998us/step - loss: 0.0020\n", "Epoch 316/320\n", "1/1 [==============================] - ETA: 0s - loss: 0.002 - 0s 2ms/step - loss: 0.0020\n", "Epoch 317/320\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0019\n", "Epoch 318/320\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0019\n", "Epoch 319/320\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0018\n", "Epoch 320/320\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0018\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC30EA280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/330\n", "1/1 [==============================] - 0s 2ms/step - loss: 51.4476\n", "Epoch 2/330\n", "1/1 [==============================] - 0s 1000us/step - loss: 22.1261\n", "Epoch 3/330\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.7631\n", "Epoch 4/330\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.5459\n", "Epoch 5/330\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.3398\n", "Epoch 6/330\n", "1/1 [==============================] - 0s 1ms/step - loss: 1.4025\n", "Epoch 7/330\n", "1/1 [==============================] - 0s 1000us/step - loss: 1.0000\n", "Epoch 8/330\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.8230\n", "Epoch 9/330\n", "1/1 [==============================] - 0s 999us/step - loss: 0.7411\n", "Epoch 10/330\n", "1/1 [==============================] - 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loss: 0.0028\n", "Epoch 310/330\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0028\n", "Epoch 311/330\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0027\n", "Epoch 312/330\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0027\n", "Epoch 313/330\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0026\n", "Epoch 314/330\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0026\n", "Epoch 315/330\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.0025\n", "Epoch 316/330\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0025\n", "Epoch 317/330\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0024\n", "Epoch 318/330\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0024\n", "Epoch 319/330\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0024\n", "Epoch 320/330\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0023\n", "Epoch 321/330\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0023\n", "Epoch 322/330\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0022\n", "Epoch 323/330\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0022\n", "Epoch 324/330\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0022\n", "Epoch 325/330\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0021\n", "Epoch 326/330\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0021\n", "Epoch 327/330\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0020\n", "Epoch 328/330\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0020\n", "Epoch 329/330\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0020\n", "Epoch 330/330\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0019\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC360B040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/340\n", "1/1 [==============================] - 0s 998us/step - loss: 89.5875\n", "Epoch 2/340\n", "1/1 [==============================] - 0s 2ms/step - loss: 38.2665\n", "Epoch 3/340\n", "1/1 [==============================] - 0s 999us/step - loss: 16.6327\n", "Epoch 4/340\n", "1/1 [==============================] - 0s 999us/step - loss: 7.5079\n", "Epoch 5/340\n", "1/1 [==============================] - 0s 1ms/step - loss: 3.6540\n", "Epoch 6/340\n", "1/1 [==============================] - 0s 3ms/step - loss: 2.0212\n", "Epoch 7/340\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.3245\n", "Epoch 8/340\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.0224\n", "Epoch 9/340\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.8866\n", "Epoch 10/340\n", "1/1 [==============================] - 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0s 2ms/step - loss: 0.0026\n", "Epoch 323/340\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0025\n", "Epoch 324/340\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0025\n", "Epoch 325/340\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0024\n", "Epoch 326/340\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0024\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 327/340\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0024\n", "Epoch 328/340\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0023\n", "Epoch 329/340\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0023\n", "Epoch 330/340\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0022\n", "Epoch 331/340\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0022\n", "Epoch 332/340\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0021\n", "Epoch 333/340\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0021\n", "Epoch 334/340\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0021\n", "Epoch 335/340\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0020\n", "Epoch 336/340\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0020\n", "Epoch 337/340\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0020\n", "Epoch 338/340\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0019\n", "Epoch 339/340\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0019\n", "Epoch 340/340\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0019\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC360B670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/350\n", "1/1 [==============================] - 0s 2ms/step - loss: 106.8347\n", "Epoch 2/350\n", "1/1 [==============================] - 0s 0s/step - loss: 45.5616\n", "Epoch 3/350\n", "1/1 [==============================] - 0s 0s/step - loss: 19.7339\n", "Epoch 4/350\n", "1/1 [==============================] - 0s 0s/step - loss: 8.8414\n", "Epoch 5/350\n", "1/1 [==============================] - 0s 0s/step - loss: 4.2423\n", "Epoch 6/350\n", "1/1 [==============================] - 0s 0s/step - loss: 2.2950\n", "Epoch 7/350\n", "1/1 [==============================] - 0s 0s/step - loss: 1.4653\n", "Epoch 8/350\n", "1/1 [==============================] - 0s 0s/step - loss: 1.1067\n", "Epoch 9/350\n", "1/1 [==============================] - 0s 0s/step - loss: 0.9467\n", "Epoch 10/350\n", "1/1 [==============================] - 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0s 16ms/step - loss: 0.0023\n", "Epoch 331/350\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0023\n", "Epoch 332/350\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0023\n", "Epoch 333/350\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0022\n", "Epoch 334/350\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0022\n", "Epoch 335/350\n", "1/1 [==============================] - 0s 17ms/step - loss: 0.0021\n", "Epoch 336/350\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0021\n", "Epoch 337/350\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0021\n", "Epoch 338/350\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0020\n", "Epoch 339/350\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0020\n", "Epoch 340/350\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0020\n", "Epoch 341/350\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0019\n", "Epoch 342/350\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0019\n", "Epoch 343/350\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0018\n", "Epoch 344/350\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0018\n", "Epoch 345/350\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0018\n", "Epoch 346/350\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0018\n", "Epoch 347/350\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0017\n", "Epoch 348/350\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0017\n", "Epoch 349/350\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0017\n", "Epoch 350/350\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0016\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC09FD430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/360\n", "1/1 [==============================] - 0s 0s/step - loss: 173.4894\n", "Epoch 2/360\n", "1/1 [==============================] - 0s 16ms/step - loss: 73.7427\n", "Epoch 3/360\n", "1/1 [==============================] - 0s 0s/step - loss: 31.7021\n", "Epoch 4/360\n", "1/1 [==============================] - 0s 0s/step - loss: 13.9766\n", "Epoch 5/360\n", "1/1 [==============================] - 0s 0s/step - loss: 6.4966\n", "Epoch 6/360\n", "1/1 [==============================] - 0s 0s/step - loss: 3.3338\n", "Epoch 7/360\n", "1/1 [==============================] - 0s 0s/step - loss: 1.9903\n", "Epoch 8/360\n", "1/1 [==============================] - 0s 0s/step - loss: 1.4136\n", "Epoch 9/360\n", "1/1 [==============================] - 0s 0s/step - loss: 1.1602\n", "Epoch 10/360\n", "1/1 [==============================] - 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0s 16ms/step - loss: 0.0023\n", "Epoch 342/360\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0022\n", "Epoch 343/360\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0022\n", "Epoch 344/360\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0021\n", "Epoch 345/360\n", "1/1 [==============================] - 0s 998us/step - loss: 0.0021\n", "Epoch 346/360\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0021\n", "Epoch 347/360\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0020\n", "Epoch 348/360\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0020\n", "Epoch 349/360\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0019\n", "Epoch 350/360\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0019\n", "Epoch 351/360\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.0019\n", "Epoch 352/360\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0018\n", "Epoch 353/360\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0018\n", "Epoch 354/360\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0018\n", "Epoch 355/360\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0017\n", "Epoch 356/360\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.0017\n", "Epoch 357/360\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0017\n", "Epoch 358/360\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0017\n", "Epoch 359/360\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0016\n", "Epoch 360/360\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0016\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC3749670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/370\n", "1/1 [==============================] - 0s 0s/step - loss: 79.5487\n", "Epoch 2/370\n", "1/1 [==============================] - 0s 0s/step - loss: 34.0195\n", "Epoch 3/370\n", "1/1 [==============================] - 0s 0s/step - loss: 14.8264\n", "Epoch 4/370\n", "1/1 [==============================] - 0s 0s/step - loss: 6.7303\n", "Epoch 5/370\n", "1/1 [==============================] - 0s 0s/step - loss: 3.3102\n", "Epoch 6/370\n", "1/1 [==============================] - 0s 0s/step - loss: 1.8605\n", "Epoch 7/370\n", "1/1 [==============================] - 0s 0s/step - loss: 1.2412\n", "Epoch 8/370\n", "1/1 [==============================] - 0s 0s/step - loss: 0.9719\n", "Epoch 9/370\n", "1/1 [==============================] - 0s 0s/step - loss: 0.8503\n", "Epoch 10/370\n", "1/1 [==============================] - 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loss: 0.0017\n", "Epoch 345/370\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0016\n", "Epoch 346/370\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0016\n", "Epoch 347/370\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0016\n", "Epoch 348/370\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0015\n", "Epoch 349/370\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0015\n", "Epoch 350/370\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0015\n", "Epoch 351/370\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0015\n", "Epoch 352/370\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0014\n", "Epoch 353/370\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0014\n", "Epoch 354/370\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0014\n", "Epoch 355/370\n", "1/1 [==============================] - 0s 15ms/step - loss: 0.0014\n", "Epoch 356/370\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0013\n", "Epoch 357/370\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0013\n", "Epoch 358/370\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0013\n", "Epoch 359/370\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0013\n", "Epoch 360/370\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0012\n", "Epoch 361/370\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0012\n", "Epoch 362/370\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0012\n", "Epoch 363/370\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0012\n", "Epoch 364/370\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0012\n", "Epoch 365/370\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0011\n", "Epoch 366/370\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0011\n", "Epoch 367/370\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0011\n", "Epoch 368/370\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0011\n", "Epoch 369/370\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0011\n", "Epoch 370/370\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0010\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC36E6790> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/380\n", "1/1 [==============================] - 0s 0s/step - loss: 5.9033\n", "Epoch 2/380\n", "1/1 [==============================] - 0s 16ms/step - loss: 2.8059\n", "Epoch 3/380\n", "1/1 [==============================] - 0s 16ms/step - loss: 1.4950\n", "Epoch 4/380\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.9370\n", "Epoch 5/380\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.6962\n", "Epoch 6/380\n", "1/1 [==============================] - 0s 0s/step - loss: 0.5893\n", "Epoch 7/380\n", "1/1 [==============================] - 0s 0s/step - loss: 0.5389\n", "Epoch 8/380\n", "1/1 [==============================] - 0s 0s/step - loss: 0.5124\n", "Epoch 9/380\n", "1/1 [==============================] - 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loss: 0.0013\n", "Epoch 332/380\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0013\n", "Epoch 333/380\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0013\n", "Epoch 334/380\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0013\n", "Epoch 335/380\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0012\n", "Epoch 336/380\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0012\n", "Epoch 337/380\n", "1/1 [==============================] - 0s 16ms/step - loss: 0.0012\n", "Epoch 338/380\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0012\n", "Epoch 339/380\n", "1/1 [==============================] - 0s 0s/step - loss: 0.0012\n", "Epoch 340/380\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0011\n", "Epoch 341/380\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0011\n", "Epoch 342/380\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0011\n", "Epoch 343/380\n", "1/1 [==============================] - 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0s 2ms/step - loss: 8.8190e-04\n", "Epoch 355/380\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.6589e-04\n", "Epoch 356/380\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.5018e-04\n", "Epoch 357/380\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.3473e-04\n", "Epoch 358/380\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.1958e-04\n", "Epoch 359/380\n", "1/1 [==============================] - 0s 999us/step - loss: 8.0470e-04\n", "Epoch 360/380\n", "1/1 [==============================] - 0s 1ms/step - loss: 7.9009e-04\n", "Epoch 361/380\n", "1/1 [==============================] - 0s 2ms/step - loss: 7.7574e-04\n", "Epoch 362/380\n", "1/1 [==============================] - 0s 3ms/step - loss: 7.6166e-04\n", "Epoch 363/380\n", "1/1 [==============================] - 0s 998us/step - loss: 7.4783e-04\n", "Epoch 364/380\n", "1/1 [==============================] - 0s 2ms/step - loss: 7.3425e-04\n", "Epoch 365/380\n", "1/1 [==============================] - 0s 1000us/step - loss: 7.2092e-04\n", "Epoch 366/380\n", "1/1 [==============================] - 0s 2ms/step - loss: 7.0783e-04\n", "Epoch 367/380\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.9498e-04\n", "Epoch 368/380\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.8236e-04\n", "Epoch 369/380\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.6997e-04\n", "Epoch 370/380\n", "1/1 [==============================] - 0s 999us/step - loss: 6.5780e-04\n", "Epoch 371/380\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.4586e-04\n", "Epoch 372/380\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.3413e-04\n", "Epoch 373/380\n", "1/1 [==============================] - 0s 999us/step - loss: 6.2261e-04\n", "Epoch 374/380\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.1131e-04\n", "Epoch 375/380\n", "1/1 [==============================] - 0s 999us/step - loss: 6.0022e-04\n", "Epoch 376/380\n", "1/1 [==============================] - 0s 1ms/step - loss: 5.8932e-04\n", "Epoch 377/380\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.7862e-04\n", "Epoch 378/380\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.6811e-04\n", "Epoch 379/380\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.5780e-04\n", "Epoch 380/380\n", "1/1 [==============================] - 0s 999us/step - loss: 5.4767e-04\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC4B26C10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/390\n", "1/1 [==============================] - 0s 1ms/step - loss: 71.8625\n", "Epoch 2/390\n", "1/1 [==============================] - 0s 1000us/step - loss: 30.7672\n", "Epoch 3/390\n", "1/1 [==============================] - 0s 2ms/step - loss: 13.4426\n", "Epoch 4/390\n", "1/1 [==============================] - 0s 998us/step - loss: 6.1341\n", "Epoch 5/390\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.0461\n", "Epoch 6/390\n", "1/1 [==============================] - 0s 999us/step - loss: 1.7365\n", "Epoch 7/390\n", "1/1 [==============================] - 0s 1000us/step - loss: 1.1765\n", "Epoch 8/390\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.9325\n", "Epoch 9/390\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.8217\n", "Epoch 10/390\n", "1/1 [==============================] - 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0s 2ms/step - loss: 9.1871e-04\n", "Epoch 376/390\n", "1/1 [==============================] - 0s 3ms/step - loss: 9.0203e-04\n", "Epoch 377/390\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.8565e-04\n", "Epoch 378/390\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.6958e-04\n", "Epoch 379/390\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.5378e-04\n", "Epoch 380/390\n", "1/1 [==============================] - 0s 998us/step - loss: 8.3828e-04\n", "Epoch 381/390\n", "1/1 [==============================] - 0s 0s/step - loss: 8.2307e-04\n", "Epoch 382/390\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.0812e-04\n", "Epoch 383/390\n", "1/1 [==============================] - 0s 2ms/step - loss: 7.9344e-04\n", "Epoch 384/390\n", "1/1 [==============================] - 0s 1ms/step - loss: 7.7904e-04\n", "Epoch 385/390\n", "1/1 [==============================] - 0s 2ms/step - loss: 7.6489e-04\n", "Epoch 386/390\n", "1/1 [==============================] - 0s 1ms/step - loss: 7.5101e-04\n", "Epoch 387/390\n", "1/1 [==============================] - 0s 3ms/step - loss: 7.3737e-04\n", "Epoch 388/390\n", "1/1 [==============================] - 0s 2ms/step - loss: 7.2399e-04\n", "Epoch 389/390\n", "1/1 [==============================] - 0s 2ms/step - loss: 7.1084e-04\n", "Epoch 390/390\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.9793e-04\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC1E853A0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/400\n", "1/1 [==============================] - 0s 4ms/step - loss: 23.1272\n", "Epoch 2/400\n", "1/1 [==============================] - 0s 0s/step - loss: 10.1261\n", "Epoch 3/400\n", "1/1 [==============================] - 0s 0s/step - loss: 4.6411\n", "Epoch 4/400\n", "1/1 [==============================] - 0s 0s/step - loss: 2.3231\n", "Epoch 5/400\n", "1/1 [==============================] - 0s 0s/step - loss: 1.3398\n", "Epoch 6/400\n", "1/1 [==============================] - 0s 0s/step - loss: 0.9189\n", "Epoch 7/400\n", "1/1 [==============================] - 0s 0s/step - loss: 0.7351\n", "Epoch 8/400\n", "1/1 [==============================] - 0s 0s/step - loss: 0.6513\n", "Epoch 9/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6098\n", "Epoch 10/400\n", "1/1 [==============================] - 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loss: 0.0019\n", "Epoch 322/400\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0019\n", "Epoch 323/400\n", "1/1 [==============================] - 0s 6ms/step - loss: 0.0019\n", "Epoch 324/400\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0018\n", "Epoch 325/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0018\n", "Epoch 326/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0018\n", "Epoch 327/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0017\n", "Epoch 328/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0017\n", "Epoch 329/400\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0017\n", "Epoch 330/400\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0016\n", "Epoch 331/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0016\n", "Epoch 332/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0016\n", "Epoch 333/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0016\n", "Epoch 334/400\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 1ms/step - loss: 0.0015\n", "Epoch 335/400\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0015\n", "Epoch 336/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0015\n", "Epoch 337/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0014\n", "Epoch 338/400\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0014\n", "Epoch 339/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0014\n", "Epoch 340/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0014\n", "Epoch 341/400\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.0013\n", "Epoch 342/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0013\n", "Epoch 343/400\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0013\n", "Epoch 344/400\n", "1/1 [==============================] - 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loss: 0.0010\n", "Epoch 356/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0010\n", "Epoch 357/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.9934e-04\n", "Epoch 358/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.8119e-04\n", "Epoch 359/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.6337e-04\n", "Epoch 360/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.4588e-04\n", "Epoch 361/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.2871e-04\n", "Epoch 362/400\n", "1/1 [==============================] - 0s 1ms/step - loss: 9.1185e-04\n", "Epoch 363/400\n", "1/1 [==============================] - 0s 1ms/step - loss: 8.9529e-04\n", "Epoch 364/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.7904e-04\n", "Epoch 365/400\n", "1/1 [==============================] - 0s 3ms/step - loss: 8.6308e-04\n", "Epoch 366/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.4741e-04\n", "Epoch 367/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.3202e-04\n", "Epoch 368/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.1692e-04\n", "Epoch 369/400\n", "1/1 [==============================] - 0s 3ms/step - loss: 8.0208e-04\n", "Epoch 370/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 7.8751e-04\n", "Epoch 371/400\n", "1/1 [==============================] - 0s 3ms/step - loss: 7.7322e-04\n", "Epoch 372/400\n", "1/1 [==============================] - 0s 3ms/step - loss: 7.5918e-04\n", "Epoch 373/400\n", "1/1 [==============================] - 0s 4ms/step - loss: 7.4540e-04\n", "Epoch 374/400\n", "1/1 [==============================] - 0s 4ms/step - loss: 7.3187e-04\n", "Epoch 375/400\n", "1/1 [==============================] - 0s 4ms/step - loss: 7.1858e-04\n", "Epoch 376/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 7.0553e-04\n", "Epoch 377/400\n", "1/1 [==============================] - 0s 999us/step - loss: 6.9272e-04\n", "Epoch 378/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.8014e-04\n", "Epoch 379/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.6780e-04\n", "Epoch 380/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.5567e-04\n", "Epoch 381/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.4376e-04\n", "Epoch 382/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.3207e-04\n", "Epoch 383/400\n", "1/1 [==============================] - 0s 999us/step - loss: 6.2059e-04\n", "Epoch 384/400\n", "1/1 [==============================] - 0s 3ms/step - loss: 6.0933e-04\n", "Epoch 385/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.9827e-04\n", "Epoch 386/400\n", "1/1 [==============================] - 0s 3ms/step - loss: 5.8741e-04\n", "Epoch 387/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.7674e-04\n", "Epoch 388/400\n", "1/1 [==============================] - 0s 3ms/step - loss: 5.6627e-04\n", "Epoch 389/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.5599e-04\n", "Epoch 390/400\n", "1/1 [==============================] - 0s 3ms/step - loss: 5.4590e-04\n", "Epoch 391/400\n", "1/1 [==============================] - 0s 4ms/step - loss: 5.3598e-04\n", "Epoch 392/400\n", "1/1 [==============================] - 0s 6ms/step - loss: 5.2625e-04\n", "Epoch 393/400\n", "1/1 [==============================] - 0s 3ms/step - loss: 5.1670e-04\n", "Epoch 394/400\n", "1/1 [==============================] - 0s 4ms/step - loss: 5.0731e-04\n", "Epoch 395/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.9810e-04\n", "Epoch 396/400\n", "1/1 [==============================] - 0s 4ms/step - loss: 4.8906e-04\n", "Epoch 397/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.8018e-04\n", "Epoch 398/400\n", "1/1 [==============================] - 0s 3ms/step - loss: 4.7146e-04\n", "Epoch 399/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.6290e-04\n", "Epoch 400/400\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.5449e-04\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC1E85F70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/410\n", "1/1 [==============================] - 0s 1000us/step - loss: 40.2312\n", "Epoch 2/410\n", "1/1 [==============================] - 0s 3ms/step - loss: 17.3759\n", "Epoch 3/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 7.7379\n", "Epoch 4/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.6694\n", "Epoch 5/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.9476\n", "Epoch 6/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.2149\n", "Epoch 7/410\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.8990\n", "Epoch 8/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7588\n", "Epoch 9/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6929\n", "Epoch 10/410\n", "1/1 [==============================] - 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loss: 0.0026\n", "Epoch 311/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0026\n", "Epoch 312/410\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0025\n", "Epoch 313/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0025\n", "Epoch 314/410\n", "1/1 [==============================] - 0s 998us/step - loss: 0.0024\n", "Epoch 315/410\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0024\n", "Epoch 316/410\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.0024\n", "Epoch 317/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0023\n", "Epoch 318/410\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0023\n", "Epoch 319/410\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0022\n", "Epoch 320/410\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0022\n", "Epoch 321/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0022\n", "Epoch 322/410\n", "1/1 [==============================] - 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loss: 0.0014\n", "Epoch 345/410\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0014\n", "Epoch 346/410\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0014\n", "Epoch 347/410\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.0013\n", "Epoch 348/410\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0013\n", "Epoch 349/410\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0013\n", "Epoch 350/410\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0013\n", "Epoch 351/410\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0012\n", "Epoch 352/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0012\n", "Epoch 353/410\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0012\n", "Epoch 354/410\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.0012\n", "Epoch 355/410\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0012\n", "Epoch 356/410\n", "1/1 [==============================] - 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0s 3ms/step - loss: 9.2565e-04\n", "Epoch 368/410\n", "1/1 [==============================] - 0s 3ms/step - loss: 9.0884e-04\n", "Epoch 369/410\n", "1/1 [==============================] - 0s 4ms/step - loss: 8.9234e-04\n", "Epoch 370/410\n", "1/1 [==============================] - 0s 999us/step - loss: 8.7614e-04\n", "Epoch 371/410\n", "1/1 [==============================] - 0s 3ms/step - loss: 8.6023e-04\n", "Epoch 372/410\n", "1/1 [==============================] - 0s 3ms/step - loss: 8.4461e-04\n", "Epoch 373/410\n", "1/1 [==============================] - 0s 3ms/step - loss: 8.2928e-04\n", "Epoch 374/410\n", "1/1 [==============================] - 0s 4ms/step - loss: 8.1422e-04\n", "Epoch 375/410\n", "1/1 [==============================] - 0s 4ms/step - loss: 7.9944e-04\n", "Epoch 376/410\n", "1/1 [==============================] - 0s 4ms/step - loss: 7.8492e-04\n", "Epoch 377/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 7.7067e-04\n", "Epoch 378/410\n", "1/1 [==============================] - 0s 3ms/step - loss: 7.5668e-04\n", "Epoch 379/410\n", "1/1 [==============================] - 0s 5ms/step - loss: 7.4294e-04\n", "Epoch 380/410\n", "1/1 [==============================] - 0s 3ms/step - loss: 7.2945e-04\n", "Epoch 381/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 7.1621e-04\n", "Epoch 382/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 7.0320e-04\n", "Epoch 383/410\n", "1/1 [==============================] - 0s 999us/step - loss: 6.9043e-04\n", "Epoch 384/410\n", "1/1 [==============================] - 0s 3ms/step - loss: 6.7790e-04\n", "Epoch 385/410\n", "1/1 [==============================] - 0s 3ms/step - loss: 6.6559e-04\n", "Epoch 386/410\n", "1/1 [==============================] - 0s 4ms/step - loss: 6.5351e-04\n", "Epoch 387/410\n", "1/1 [==============================] - 0s 3ms/step - loss: 6.4164e-04\n", "Epoch 388/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.2999e-04\n", "Epoch 389/410\n", "1/1 [==============================] - 0s 3ms/step - loss: 6.1855e-04\n", "Epoch 390/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.0733e-04\n", "Epoch 391/410\n", "1/1 [==============================] - 0s 3ms/step - loss: 5.9629e-04\n", "Epoch 392/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.8547e-04\n", "Epoch 393/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.7484e-04\n", "Epoch 394/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.6440e-04\n", "Epoch 395/410\n", "1/1 [==============================] - 0s 3ms/step - loss: 5.5415e-04\n", "Epoch 396/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.4409e-04\n", "Epoch 397/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.3421e-04\n", "Epoch 398/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.2452e-04\n", "Epoch 399/410\n", "1/1 [==============================] - 0s 3ms/step - loss: 5.1499e-04\n", "Epoch 400/410\n", "1/1 [==============================] - 0s 1000us/step - loss: 5.0564e-04\n", "Epoch 401/410\n", "1/1 [==============================] - 0s 1000us/step - loss: 4.9646e-04\n", "Epoch 402/410\n", "1/1 [==============================] - 0s 999us/step - loss: 4.8745e-04\n", "Epoch 403/410\n", "1/1 [==============================] - 0s 3ms/step - loss: 4.7859e-04\n", "Epoch 404/410\n", "1/1 [==============================] - 0s 5ms/step - loss: 4.6991e-04\n", "Epoch 405/410\n", "1/1 [==============================] - 0s 6ms/step - loss: 4.6138e-04\n", "Epoch 406/410\n", "1/1 [==============================] - 0s 5ms/step - loss: 4.5300e-04\n", "Epoch 407/410\n", "1/1 [==============================] - 0s 4ms/step - loss: 4.4478e-04\n", "Epoch 408/410\n", "1/1 [==============================] - 0s 5ms/step - loss: 4.3670e-04\n", "Epoch 409/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.2877e-04\n", "Epoch 410/410\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.2098e-04\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC30EAEE0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/420\n", "1/1 [==============================] - 0s 1ms/step - loss: 169.4147\n", "Epoch 2/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 72.0204\n", "Epoch 3/420\n", "1/1 [==============================] - 0s 1000us/step - loss: 30.9711\n", "Epoch 4/420\n", "1/1 [==============================] - 0s 1ms/step - loss: 13.6633\n", "Epoch 5/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 6.3594\n", "Epoch 6/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.2710\n", "Epoch 7/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.9589\n", "Epoch 8/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.3955\n", "Epoch 9/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.1477\n", "Epoch 10/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.0332\n", "Epoch 11/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.9749\n", "Epoch 12/420\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 2ms/step - loss: 0.9406\n", "Epoch 13/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.9165\n", "Epoch 14/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8969\n", "Epoch 15/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8794\n", "Epoch 16/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8629\n", "Epoch 17/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8470\n", "Epoch 18/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8315\n", "Epoch 19/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.8164\n", "Epoch 20/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8016\n", "Epoch 21/420\n", "1/1 [==============================] - 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loss: 0.0049\n", "Epoch 299/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0048\n", "Epoch 300/420\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0047\n", "Epoch 301/420\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0047\n", "Epoch 302/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0046\n", "Epoch 303/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0045\n", "Epoch 304/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0044\n", "Epoch 305/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0043\n", "Epoch 306/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0042\n", "Epoch 307/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0042\n", "Epoch 308/420\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0041\n", "Epoch 309/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0040\n", "Epoch 310/420\n", "1/1 [==============================] - 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loss: 0.0026\n", "Epoch 333/420\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0026\n", "Epoch 334/420\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0025\n", "Epoch 335/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0025\n", "Epoch 336/420\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0025\n", "Epoch 337/420\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.0024\n", "Epoch 338/420\n", "1/1 [==============================] - 0s 998us/step - loss: 0.0024\n", "Epoch 339/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0023\n", "Epoch 340/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0023\n", "Epoch 341/420\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0022\n", "Epoch 342/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0022\n", "Epoch 343/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0022\n", "Epoch 344/420\n", "1/1 [==============================] - 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loss: 0.0017\n", "Epoch 356/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0017\n", "Epoch 357/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0017\n", "Epoch 358/420\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0016\n", "Epoch 359/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0016\n", "Epoch 360/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0016\n", "Epoch 361/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0015\n", "Epoch 362/420\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.0015\n", "Epoch 363/420\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0015\n", "Epoch 364/420\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0015\n", "Epoch 365/420\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0014\n", "Epoch 366/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0014\n", "Epoch 367/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0014\n", "Epoch 368/420\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0014\n", "Epoch 369/420\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0013\n", "Epoch 370/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0013\n", "Epoch 371/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0013\n", "Epoch 372/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0013\n", "Epoch 373/420\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0012\n", "Epoch 374/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0012\n", "Epoch 375/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0012\n", "Epoch 376/420\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0012\n", "Epoch 377/420\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0012\n", "Epoch 378/420\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0011\n", "Epoch 379/420\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0011\n", "Epoch 380/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0011\n", "Epoch 381/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0011\n", "Epoch 382/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0011\n", "Epoch 383/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0010\n", "Epoch 384/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0010\n", "Epoch 385/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.9845e-04\n", "Epoch 386/420\n", "1/1 [==============================] - 0s 998us/step - loss: 9.8033e-04\n", "Epoch 387/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.6253e-04\n", "Epoch 388/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.4505e-04\n", "Epoch 389/420\n", "1/1 [==============================] - 0s 1000us/step - loss: 9.2788e-04\n", "Epoch 390/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 9.1104e-04\n", "Epoch 391/420\n", "1/1 [==============================] - 0s 1ms/step - loss: 8.9450e-04\n", "Epoch 392/420\n", "1/1 [==============================] - 0s 998us/step - loss: 8.7826e-04\n", "Epoch 393/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 8.6232e-04\n", "Epoch 394/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.4666e-04\n", "Epoch 395/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 8.3129e-04\n", "Epoch 396/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.1619e-04\n", "Epoch 397/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 8.0137e-04\n", "Epoch 398/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 7.8683e-04\n", "Epoch 399/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 7.7254e-04\n", "Epoch 400/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 7.5851e-04\n", "Epoch 401/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 7.4474e-04\n", "Epoch 402/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 7.3122e-04\n", "Epoch 403/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 7.1794e-04\n", "Epoch 404/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 7.0491e-04\n", "Epoch 405/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 6.9211e-04\n", "Epoch 406/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.7954e-04\n", "Epoch 407/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.6721e-04\n", "Epoch 408/420\n", "1/1 [==============================] - 0s 4ms/step - loss: 6.5509e-04\n", "Epoch 409/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.4319e-04\n", "Epoch 410/420\n", "1/1 [==============================] - 0s 4ms/step - loss: 6.3152e-04\n", "Epoch 411/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 6.2005e-04\n", "Epoch 412/420\n", "1/1 [==============================] - 0s 999us/step - loss: 6.0879e-04\n", "Epoch 413/420\n", "1/1 [==============================] - 0s 3ms/step - loss: 5.9774e-04\n", "Epoch 414/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.8689e-04\n", "Epoch 415/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.7623e-04\n", "Epoch 416/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.6577e-04\n", "Epoch 417/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.5550e-04\n", "Epoch 418/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.4541e-04\n", "Epoch 419/420\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 3ms/step - loss: 5.3550e-04\n", "Epoch 420/420\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.2578e-04\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC1AB7670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 31.2964\n", "Epoch 2/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 13.5900\n", "Epoch 3/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.1219\n", "Epoch 4/430\n", "1/1 [==============================] - 0s 1000us/step - loss: 2.9680\n", "Epoch 5/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.6320\n", "Epoch 6/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.0622\n", "Epoch 7/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8153\n", "Epoch 8/430\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.7046\n", "Epoch 9/430\n", "1/1 [==============================] - 0s 999us/step - loss: 0.6514\n", "Epoch 10/430\n", "1/1 [==============================] - 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loss: 0.0011\n", "Epoch 356/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0011\n", "Epoch 357/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0011\n", "Epoch 358/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0010\n", "Epoch 359/430\n", "1/1 [==============================] - 0s 999us/step - loss: 0.0010\n", "Epoch 360/430\n", "1/1 [==============================] - 0s 999us/step - loss: 9.9972e-04\n", "Epoch 361/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.8158e-04\n", "Epoch 362/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.6375e-04\n", "Epoch 363/430\n", "1/1 [==============================] - 0s 999us/step - loss: 9.4625e-04\n", "Epoch 364/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 9.2907e-04\n", "Epoch 365/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.1220e-04\n", "Epoch 366/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.9564e-04\n", "Epoch 367/430\n", "1/1 [==============================] - 0s 999us/step - loss: 8.7937e-04\n", "Epoch 368/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.6341e-04\n", "Epoch 369/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 8.4774e-04\n", "Epoch 370/430\n", "1/1 [==============================] - 0s 4ms/step - loss: 8.3234e-04\n", "Epoch 371/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 8.1724e-04\n", "Epoch 372/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.0240e-04\n", "Epoch 373/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 7.8783e-04\n", "Epoch 374/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 7.7352e-04\n", "Epoch 375/430\n", "1/1 [==============================] - 0s 4ms/step - loss: 7.5947e-04\n", "Epoch 376/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 7.4569e-04\n", "Epoch 377/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 7.3215e-04\n", "Epoch 378/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 7.1886e-04\n", "Epoch 379/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 7.0579e-04\n", "Epoch 380/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.9298e-04\n", "Epoch 381/430\n", "1/1 [==============================] - 0s 4ms/step - loss: 6.8040e-04\n", "Epoch 382/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.6805e-04\n", "Epoch 383/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.5592e-04\n", "Epoch 384/430\n", "1/1 [==============================] - 0s 4ms/step - loss: 6.4401e-04\n", "Epoch 385/430\n", "1/1 [==============================] - 0s 998us/step - loss: 6.3232e-04\n", "Epoch 386/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 6.2084e-04\n", "Epoch 387/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 6.0957e-04\n", "Epoch 388/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.9850e-04\n", "Epoch 389/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.8763e-04\n", "Epoch 390/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 5.7696e-04\n", "Epoch 391/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.6648e-04\n", "Epoch 392/430\n", "1/1 [==============================] - 0s 4ms/step - loss: 5.5620e-04\n", "Epoch 393/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 5.4610e-04\n", "Epoch 394/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.3618e-04\n", "Epoch 395/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.2645e-04\n", "Epoch 396/430\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 3ms/step - loss: 5.1689e-04\n", "Epoch 397/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.0750e-04\n", "Epoch 398/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.9829e-04\n", "Epoch 399/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.8924e-04\n", "Epoch 400/430\n", "1/1 [==============================] - 0s 5ms/step - loss: 4.8036e-04\n", "Epoch 401/430\n", "1/1 [==============================] - 0s 4ms/step - loss: 4.7164e-04\n", "Epoch 402/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.6308e-04\n", "Epoch 403/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 4.5467e-04\n", "Epoch 404/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 4.4641e-04\n", "Epoch 405/430\n", "1/1 [==============================] - 0s 4ms/step - loss: 4.3831e-04\n", "Epoch 406/430\n", "1/1 [==============================] - 0s 4ms/step - loss: 4.3035e-04\n", "Epoch 407/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 4.2253e-04\n", "Epoch 408/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 4.1486e-04\n", "Epoch 409/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.0733e-04\n", "Epoch 410/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.9993e-04\n", "Epoch 411/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.9267e-04\n", "Epoch 412/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.8554e-04\n", "Epoch 413/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.7854e-04\n", "Epoch 414/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.7167e-04\n", "Epoch 415/430\n", "1/1 [==============================] - 0s 4ms/step - loss: 3.6492e-04\n", "Epoch 416/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.5830e-04\n", "Epoch 417/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.5179e-04\n", "Epoch 418/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.4540e-04\n", "Epoch 419/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.3913e-04\n", "Epoch 420/430\n", "1/1 [==============================] - 0s 998us/step - loss: 3.3297e-04\n", "Epoch 421/430\n", "1/1 [==============================] - 0s 999us/step - loss: 3.2692e-04\n", "Epoch 422/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.2099e-04\n", "Epoch 423/430\n", "1/1 [==============================] - 0s 1ms/step - loss: 3.1516e-04\n", "Epoch 424/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.0944e-04\n", "Epoch 425/430\n", "1/1 [==============================] - 0s 999us/step - loss: 3.0382e-04\n", "Epoch 426/430\n", "1/1 [==============================] - 0s 999us/step - loss: 2.9830e-04\n", "Epoch 427/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.9289e-04\n", "Epoch 428/430\n", "1/1 [==============================] - 0s 4ms/step - loss: 2.8757e-04\n", "Epoch 429/430\n", "1/1 [==============================] - 0s 3ms/step - loss: 2.8234e-04\n", "Epoch 430/430\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.7722e-04\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC36E6DC0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/440\n", "1/1 [==============================] - 0s 1ms/step - loss: 19.4247\n", "Epoch 2/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.5551\n", "Epoch 3/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.9684\n", "Epoch 4/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.0292\n", "Epoch 5/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.2056\n", "Epoch 6/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.8522\n", "Epoch 7/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.6971\n", "Epoch 8/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6255\n", "Epoch 9/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5894\n", "Epoch 10/440\n", "1/1 [==============================] - 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loss: 0.0012\n", "Epoch 346/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0012\n", "Epoch 347/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0012\n", "Epoch 348/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0011\n", "Epoch 349/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0011\n", "Epoch 350/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0011\n", "Epoch 351/440\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.0011\n", "Epoch 352/440\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0011\n", "Epoch 353/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0010\n", "Epoch 354/440\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0010\n", "Epoch 355/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0010\n", "Epoch 356/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.8873e-04\n", "Epoch 357/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.7078e-04\n", "Epoch 358/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 9.5314e-04\n", "Epoch 359/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 9.3584e-04\n", "Epoch 360/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.1885e-04\n", "Epoch 361/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.0217e-04\n", "Epoch 362/440\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 3ms/step - loss: 8.8579e-04\n", "Epoch 363/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.6970e-04\n", "Epoch 364/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.5391e-04\n", "Epoch 365/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.3841e-04\n", "Epoch 366/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 8.2318e-04\n", "Epoch 367/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.0824e-04\n", "Epoch 368/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 7.9356e-04\n", "Epoch 369/440\n", "1/1 [==============================] - 0s 998us/step - loss: 7.7915e-04\n", "Epoch 370/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 7.6501e-04\n", "Epoch 371/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 7.5112e-04\n", "Epoch 372/440\n", "1/1 [==============================] - 0s 1ms/step - loss: 7.3748e-04\n", "Epoch 373/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 7.2409e-04\n", "Epoch 374/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 7.1095e-04\n", "Epoch 375/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.9803e-04\n", "Epoch 376/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.8536e-04\n", "Epoch 377/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.7291e-04\n", "Epoch 378/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.6070e-04\n", "Epoch 379/440\n", "1/1 [==============================] - 0s 999us/step - loss: 6.4870e-04\n", "Epoch 380/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 6.3693e-04\n", "Epoch 381/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 6.2537e-04\n", "Epoch 382/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.1401e-04\n", "Epoch 383/440\n", "1/1 [==============================] - 0s 4ms/step - loss: 6.0285e-04\n", "Epoch 384/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.9191e-04\n", "Epoch 385/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.8116e-04\n", "Epoch 386/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.7061e-04\n", "Epoch 387/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 5.6025e-04\n", "Epoch 388/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.5008e-04\n", "Epoch 389/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.4009e-04\n", "Epoch 390/440\n", "1/1 [==============================] - 0s 4ms/step - loss: 5.3029e-04\n", "Epoch 391/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.2066e-04\n", "Epoch 392/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.1120e-04\n", "Epoch 393/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.0192e-04\n", "Epoch 394/440\n", "1/1 [==============================] - 0s 999us/step - loss: 4.9281e-04\n", "Epoch 395/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.8386e-04\n", "Epoch 396/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 4.7508e-04\n", "Epoch 397/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.6645e-04\n", "Epoch 398/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 4.5798e-04\n", "Epoch 399/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 4.4967e-04\n", "Epoch 400/440\n", "1/1 [==============================] - 0s 4ms/step - loss: 4.4150e-04\n", "Epoch 401/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.3349e-04\n", "Epoch 402/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.2561e-04\n", "Epoch 403/440\n", "1/1 [==============================] - 0s 999us/step - loss: 4.1789e-04\n", "Epoch 404/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 4.1030e-04\n", "Epoch 405/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 4.0285e-04\n", "Epoch 406/440\n", "1/1 [==============================] - 0s 4ms/step - loss: 3.9553e-04\n", "Epoch 407/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.8835e-04\n", "Epoch 408/440\n", "1/1 [==============================] - 0s 999us/step - loss: 3.8130e-04\n", "Epoch 409/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.7438e-04\n", "Epoch 410/440\n", "1/1 [==============================] - 0s 1ms/step - loss: 3.6758e-04\n", "Epoch 411/440\n", "1/1 [==============================] - 0s 1000us/step - loss: 3.6091e-04\n", "Epoch 412/440\n", "1/1 [==============================] - 0s 998us/step - loss: 3.5435e-04\n", "Epoch 413/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.4792e-04\n", "Epoch 414/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.4161e-04\n", "Epoch 415/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.3540e-04\n", "Epoch 416/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.2931e-04\n", "Epoch 417/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.2333e-04\n", "Epoch 418/440\n", "1/1 [==============================] - 0s 997us/step - loss: 3.1747e-04\n", "Epoch 419/440\n", "1/1 [==============================] - 0s 1ms/step - loss: 3.1170e-04\n", "Epoch 420/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.0604e-04\n", "Epoch 421/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.0048e-04\n", "Epoch 422/440\n", "1/1 [==============================] - 0s 4ms/step - loss: 2.9503e-04\n", "Epoch 423/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.8967e-04\n", "Epoch 424/440\n", "1/1 [==============================] - 0s 998us/step - loss: 2.8441e-04\n", "Epoch 425/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.7925e-04\n", "Epoch 426/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 2.7418e-04\n", "Epoch 427/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 2.6920e-04\n", "Epoch 428/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.6431e-04\n", "Epoch 429/440\n", "1/1 [==============================] - 0s 4ms/step - loss: 2.5951e-04\n", "Epoch 430/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 2.5480e-04\n", "Epoch 431/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.5017e-04\n", "Epoch 432/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.4563e-04\n", "Epoch 433/440\n", "1/1 [==============================] - 0s 1000us/step - loss: 2.4117e-04\n", "Epoch 434/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 2.3679e-04\n", "Epoch 435/440\n", "1/1 [==============================] - 0s 4ms/step - loss: 2.3250e-04\n", "Epoch 436/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.2827e-04\n", "Epoch 437/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 2.2413e-04\n", "Epoch 438/440\n", "1/1 [==============================] - 0s 1000us/step - loss: 2.2006e-04\n", "Epoch 439/440\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.1606e-04\n", "Epoch 440/440\n", "1/1 [==============================] - 0s 3ms/step - loss: 2.1214e-04\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC3749E50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/450\n", "1/1 [==============================] - 0s 0s/step - loss: 0.7203\n", "Epoch 2/450\n", "1/1 [==============================] - 0s 999us/step - loss: 0.5784\n", "Epoch 3/450\n", "1/1 [==============================] - 0s 999us/step - loss: 0.5136\n", "Epoch 4/450\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.4814\n", "Epoch 5/450\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.4630\n", "Epoch 6/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4505\n", "Epoch 7/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4407\n", "Epoch 8/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4319\n", "Epoch 9/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4238\n", "Epoch 10/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4160\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 11/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4084\n", "Epoch 12/450\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.4009\n", "Epoch 13/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3936\n", "Epoch 14/450\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.3865\n", "Epoch 15/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3795\n", "Epoch 16/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3726\n", "Epoch 17/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3658\n", "Epoch 18/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3592\n", "Epoch 19/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3526\n", "Epoch 20/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3462\n", "Epoch 21/450\n", "1/1 [==============================] - 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loss: 9.6587e-04\n", "Epoch 342/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 9.4832e-04\n", "Epoch 343/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 9.3110e-04\n", "Epoch 344/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.1420e-04\n", "Epoch 345/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.9759e-04\n", "Epoch 346/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.8130e-04\n", "Epoch 347/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.6530e-04\n", "Epoch 348/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.4958e-04\n", "Epoch 349/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.3416e-04\n", "Epoch 350/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 8.1901e-04\n", "Epoch 351/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.0414e-04\n", "Epoch 352/450\n", "1/1 [==============================] - 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0s 3ms/step - loss: 6.4542e-04\n", "Epoch 364/450\n", "1/1 [==============================] - 0s 1ms/step - loss: 6.3371e-04\n", "Epoch 365/450\n", "1/1 [==============================] - 0s 4ms/step - loss: 6.2219e-04\n", "Epoch 366/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 6.1090e-04\n", "Epoch 367/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.9980e-04\n", "Epoch 368/450\n", "1/1 [==============================] - 0s 1ms/step - loss: 5.8892e-04\n", "Epoch 369/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.7822e-04\n", "Epoch 370/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.6772e-04\n", "Epoch 371/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.5742e-04\n", "Epoch 372/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 5.4729e-04\n", "Epoch 373/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 5.3736e-04\n", "Epoch 374/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 5.2760e-04\n", "Epoch 375/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 5.1802e-04\n", "Epoch 376/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.0862e-04\n", "Epoch 377/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 4.9938e-04\n", "Epoch 378/450\n", "1/1 [==============================] - 0s 999us/step - loss: 4.9032e-04\n", "Epoch 379/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.8141e-04\n", "Epoch 380/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.7267e-04\n", "Epoch 381/450\n", "1/1 [==============================] - 0s 5ms/step - loss: 4.6409e-04\n", "Epoch 382/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.5567e-04\n", "Epoch 383/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 4.4739e-04\n", "Epoch 384/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.3927e-04\n", "Epoch 385/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.3129e-04\n", "Epoch 386/450\n", "1/1 [==============================] - 0s 1000us/step - loss: 4.2347e-04\n", "Epoch 387/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 4.1577e-04\n", "Epoch 388/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.0822e-04\n", "Epoch 389/450\n", "1/1 [==============================] - 0s 999us/step - loss: 4.0081e-04\n", "Epoch 390/450\n", "1/1 [==============================] - 0s 999us/step - loss: 3.9353e-04\n", "Epoch 391/450\n", "1/1 [==============================] - 0s 4ms/step - loss: 3.8639e-04\n", "Epoch 392/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.7937e-04\n", "Epoch 393/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.7248e-04\n", "Epoch 394/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.6572e-04\n", "Epoch 395/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.5908e-04\n", "Epoch 396/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.5256e-04\n", "Epoch 397/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.4616e-04\n", "Epoch 398/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.3987e-04\n", "Epoch 399/450\n", "1/1 [==============================] - 0s 1ms/step - loss: 3.3370e-04\n", "Epoch 400/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.2765e-04\n", "Epoch 401/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.2170e-04\n", "Epoch 402/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.1585e-04\n", "Epoch 403/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.1012e-04\n", "Epoch 404/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.0449e-04\n", "Epoch 405/450\n", "1/1 [==============================] - 0s 999us/step - loss: 2.9896e-04\n", "Epoch 406/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 2.9353e-04\n", "Epoch 407/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 2.8820e-04\n", "Epoch 408/450\n", "1/1 [==============================] - 0s 999us/step - loss: 2.8297e-04\n", "Epoch 409/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 2.7783e-04\n", "Epoch 410/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.7279e-04\n", "Epoch 411/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.6783e-04\n", "Epoch 412/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.6297e-04\n", "Epoch 413/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.5820e-04\n", "Epoch 414/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.5351e-04\n", "Epoch 415/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 2.4891e-04\n", "Epoch 416/450\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 2ms/step - loss: 2.4439e-04\n", "Epoch 417/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 2.3995e-04\n", "Epoch 418/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.3559e-04\n", "Epoch 419/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 2.3131e-04\n", "Epoch 420/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.2712e-04\n", "Epoch 421/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.2299e-04\n", "Epoch 422/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.1894e-04\n", "Epoch 423/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.1497e-04\n", "Epoch 424/450\n", "1/1 [==============================] - 0s 1ms/step - loss: 2.1107e-04\n", "Epoch 425/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.0723e-04\n", "Epoch 426/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.0347e-04\n", "Epoch 427/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.9977e-04\n", "Epoch 428/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.9615e-04\n", "Epoch 429/450\n", "1/1 [==============================] - 0s 4ms/step - loss: 1.9259e-04\n", "Epoch 430/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.8909e-04\n", "Epoch 431/450\n", "1/1 [==============================] - 0s 999us/step - loss: 1.8566e-04\n", "Epoch 432/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.8228e-04\n", "Epoch 433/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.7898e-04\n", "Epoch 434/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.7573e-04\n", "Epoch 435/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.7253e-04\n", "Epoch 436/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.6940e-04\n", "Epoch 437/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.6633e-04\n", "Epoch 438/450\n", "1/1 [==============================] - 0s 998us/step - loss: 1.6331e-04\n", "Epoch 439/450\n", "1/1 [==============================] - 0s 999us/step - loss: 1.6034e-04\n", "Epoch 440/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.5743e-04\n", "Epoch 441/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.5457e-04\n", "Epoch 442/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.5177e-04\n", "Epoch 443/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.4901e-04\n", "Epoch 444/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.4631e-04\n", "Epoch 445/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.4365e-04\n", "Epoch 446/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.4104e-04\n", "Epoch 447/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.3848e-04\n", "Epoch 448/450\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.3596e-04\n", "Epoch 449/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.3350e-04\n", "Epoch 450/450\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.3107e-04\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC36B83A0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/460\n", "1/1 [==============================] - 0s 0s/step - loss: 19.6699\n", "Epoch 2/460\n", "1/1 [==============================] - 0s 1ms/step - loss: 8.6592\n", "Epoch 3/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.0130\n", "Epoch 4/460\n", "1/1 [==============================] - 0s 1ms/step - loss: 2.0487\n", "Epoch 5/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.2145\n", "Epoch 6/460\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.8567\n", "Epoch 7/460\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.6996\n", "Epoch 8/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6273\n", "Epoch 9/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5908\n", "Epoch 10/460\n", "1/1 [==============================] - 0s 999us/step - loss: 0.5695\n", "Epoch 11/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5547\n", "Epoch 12/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5427\n", "Epoch 13/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5321\n", "Epoch 14/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5221\n", "Epoch 15/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5125\n", "Epoch 16/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5031\n", "Epoch 17/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4939\n", "Epoch 18/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4850\n", "Epoch 19/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4761\n", "Epoch 20/460\n", "1/1 [==============================] - 0s 999us/step - loss: 0.4675\n", "Epoch 21/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4590\n", "Epoch 22/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4507\n", "Epoch 23/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4425\n", "Epoch 24/460\n", "1/1 [==============================] - 0s 999us/step - loss: 0.4345\n", "Epoch 25/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4266\n", "Epoch 26/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4188\n", "Epoch 27/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.4112\n", "Epoch 28/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4038\n", "Epoch 29/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3964\n", "Epoch 30/460\n", "1/1 [==============================] - 0s 999us/step - loss: 0.3892\n", "Epoch 31/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3822\n", "Epoch 32/460\n", "1/1 [==============================] - 0s 999us/step - loss: 0.3752\n", "Epoch 33/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3684\n", "Epoch 34/460\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.3617\n", "Epoch 35/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3551\n", "Epoch 36/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3487\n", "Epoch 37/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3424\n", "Epoch 38/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3362\n", "Epoch 39/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3300\n", "Epoch 40/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3241\n", "Epoch 41/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3182\n", "Epoch 42/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3124\n", "Epoch 43/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3067\n", "Epoch 44/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3012\n", "Epoch 45/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2957\n", "Epoch 46/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2903\n", "Epoch 47/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2850\n", "Epoch 48/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2799\n", "Epoch 49/460\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.2748\n", "Epoch 50/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2698\n", "Epoch 51/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2649\n", "Epoch 52/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2601\n", "Epoch 53/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2554\n", "Epoch 54/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2507\n", "Epoch 55/460\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.2462\n", "Epoch 56/460\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.2417\n", "Epoch 57/460\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 999us/step - loss: 0.2373\n", "Epoch 58/460\n", "1/1 [==============================] - 0s 999us/step - loss: 0.2330\n", "Epoch 59/460\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.2288\n", "Epoch 60/460\n", "1/1 [==============================] - 0s 998us/step - loss: 0.2246\n", "Epoch 61/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2205\n", "Epoch 62/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2165\n", "Epoch 63/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2126\n", "Epoch 64/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.2088\n", "Epoch 65/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2050\n", "Epoch 66/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2012\n", "Epoch 67/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1976\n", "Epoch 68/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1940\n", "Epoch 69/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1905\n", "Epoch 70/460\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.1870\n", "Epoch 71/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1836\n", "Epoch 72/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1803\n", "Epoch 73/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1770\n", "Epoch 74/460\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.1738\n", "Epoch 75/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1706\n", "Epoch 76/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1675\n", "Epoch 77/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1645\n", "Epoch 78/460\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.1615\n", "Epoch 79/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1586\n", "Epoch 80/460\n", "1/1 [==============================] - 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0s 2ms/step - loss: 0.1250\n", "Epoch 93/460\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.1227\n", "Epoch 94/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1205\n", "Epoch 95/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1183\n", "Epoch 96/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1161\n", "Epoch 97/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1140\n", "Epoch 98/460\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.1120\n", "Epoch 99/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1099\n", "Epoch 100/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1079\n", "Epoch 101/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1060\n", "Epoch 102/460\n", "1/1 [==============================] - 0s 999us/step - loss: 0.1040\n", "Epoch 103/460\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.1022\n", "Epoch 104/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.1003\n", "Epoch 105/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0985\n", "Epoch 106/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0967\n", "Epoch 107/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0949\n", "Epoch 108/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0932\n", "Epoch 109/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0915\n", "Epoch 110/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0899\n", "Epoch 111/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0882\n", "Epoch 112/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0866\n", "Epoch 113/460\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0851\n", "Epoch 114/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0835\n", "Epoch 115/460\n", "1/1 [==============================] - 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loss: 0.0670\n", "Epoch 127/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0658\n", "Epoch 128/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0646\n", "Epoch 129/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0634\n", "Epoch 130/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0623\n", "Epoch 131/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0612\n", "Epoch 132/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0600\n", "Epoch 133/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0590\n", "Epoch 134/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0579\n", "Epoch 135/460\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.0568\n", "Epoch 136/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0558\n", "Epoch 137/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0548\n", "Epoch 138/460\n", "1/1 [==============================] - 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loss: 0.0083\n", "Epoch 241/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0081\n", "Epoch 242/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0080\n", "Epoch 243/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0079\n", "Epoch 244/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0077\n", "Epoch 245/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0076\n", "Epoch 246/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0074\n", "Epoch 247/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0073\n", "Epoch 248/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0072\n", "Epoch 249/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0070\n", "Epoch 250/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0069\n", "Epoch 251/460\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0068\n", "Epoch 252/460\n", "1/1 [==============================] - 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loss: 0.0013\n", "Epoch 344/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0012\n", "Epoch 345/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0012\n", "Epoch 346/460\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.0012\n", "Epoch 347/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0012\n", "Epoch 348/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0011\n", "Epoch 349/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0011\n", "Epoch 350/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0011\n", "Epoch 351/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0011\n", "Epoch 352/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.0011\n", "Epoch 353/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0010\n", "Epoch 354/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.0010\n", "Epoch 355/460\n", "1/1 [==============================] - 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0s 999us/step - loss: 1.6752e-04\n", "Epoch 454/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.6448e-04\n", "Epoch 455/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.6149e-04\n", "Epoch 456/460\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.5856e-04\n", "Epoch 457/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.5568e-04\n", "Epoch 458/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.5285e-04\n", "Epoch 459/460\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 3ms/step - loss: 1.5008e-04\n", "Epoch 460/460\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.4735e-04\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC4B279D0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/470\n", "1/1 [==============================] - 0s 1ms/step - loss: 6.3119\n", "Epoch 2/470\n", "1/1 [==============================] - 0s 999us/step - loss: 2.9802\n", "Epoch 3/470\n", "1/1 [==============================] - 0s 1ms/step - loss: 1.5705\n", "Epoch 4/470\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.9708\n", "Epoch 5/470\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7125\n", "Epoch 6/470\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.5982\n", "Epoch 7/470\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.5446\n", "Epoch 8/470\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.5167\n", "Epoch 9/470\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4997\n", "Epoch 10/470\n", "1/1 [==============================] - 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loss: 3.9644e-04\n", "Epoch 399/470\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.8924e-04\n", "Epoch 400/470\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.8217e-04\n", "Epoch 401/470\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.7523e-04\n", "Epoch 402/470\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.6841e-04\n", "Epoch 403/470\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.6172e-04\n", "Epoch 404/470\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.5516e-04\n", "Epoch 405/470\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.4871e-04\n", "Epoch 406/470\n", "1/1 [==============================] - 0s 999us/step - loss: 3.4238e-04\n", "Epoch 407/470\n", "1/1 [==============================] - 0s 4ms/step - loss: 3.3616e-04\n", "Epoch 408/470\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.3006e-04\n", "Epoch 409/470\n", "1/1 [==============================] - 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0s 6ms/step - loss: 1.7702e-04\n", "Epoch 443/470\n", "1/1 [==============================] - 0s 5ms/step - loss: 1.7381e-04\n", "Epoch 444/470\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.7065e-04\n", "Epoch 445/470\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.6756e-04\n", "Epoch 446/470\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.6451e-04\n", "Epoch 447/470\n", "1/1 [==============================] - 0s 1000us/step - loss: 1.6152e-04\n", "Epoch 448/470\n", "1/1 [==============================] - 0s 999us/step - loss: 1.5859e-04\n", "Epoch 449/470\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.5571e-04\n", "Epoch 450/470\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.5289e-04\n", "Epoch 451/470\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.5011e-04\n", "Epoch 452/470\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.4738e-04\n", "Epoch 453/470\n", "1/1 [==============================] - 0s 1ms/step - loss: 1.4471e-04\n", "Epoch 454/470\n", "1/1 [==============================] - 0s 4ms/step - loss: 1.4208e-04\n", "Epoch 455/470\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.3950e-04\n", "Epoch 456/470\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.3697e-04\n", "Epoch 457/470\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.3448e-04\n", "Epoch 458/470\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.3204e-04\n", "Epoch 459/470\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.2964e-04\n", "Epoch 460/470\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.2729e-04\n", "Epoch 461/470\n", "1/1 [==============================] - 0s 998us/step - loss: 1.2498e-04\n", "Epoch 462/470\n", "1/1 [==============================] - 0s 1000us/step - loss: 1.2271e-04\n", "Epoch 463/470\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.2048e-04\n", "Epoch 464/470\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.1829e-04\n", "Epoch 465/470\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.1614e-04\n", "Epoch 466/470\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.1403e-04\n", "Epoch 467/470\n", "1/1 [==============================] - 0s 1ms/step - loss: 1.1196e-04\n", "Epoch 468/470\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.0993e-04\n", "Epoch 469/470\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.0794e-04\n", "Epoch 470/470\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.0598e-04\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC3626EE0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.5653\n", "Epoch 2/480\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.9471\n", "Epoch 3/480\n", "1/1 [==============================] - 0s 1000us/step - loss: 0.6814\n", "Epoch 4/480\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5643\n", "Epoch 5/480\n", "1/1 [==============================] - 0s 1ms/step - loss: 0.5100\n", "Epoch 6/480\n", "1/1 [==============================] - 0s 999us/step - loss: 0.4821\n", "Epoch 7/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4655\n", "Epoch 8/480\n", "1/1 [==============================] - 0s 998us/step - loss: 0.4538\n", "Epoch 9/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4441\n", "Epoch 10/480\n", "1/1 [==============================] - 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0s 2ms/step - loss: 4.2705e-04\n", "Epoch 389/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.1931e-04\n", "Epoch 390/480\n", "1/1 [==============================] - 0s 998us/step - loss: 4.1169e-04\n", "Epoch 391/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 4.0422e-04\n", "Epoch 392/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.9688e-04\n", "Epoch 393/480\n", "1/1 [==============================] - 0s 5ms/step - loss: 3.8967e-04\n", "Epoch 394/480\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.8260e-04\n", "Epoch 395/480\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.7565e-04\n", "Epoch 396/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.6883e-04\n", "Epoch 397/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.6213e-04\n", "Epoch 398/480\n", "1/1 [==============================] - 0s 4ms/step - loss: 3.5556e-04\n", "Epoch 399/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.4910e-04\n", "Epoch 400/480\n", "1/1 [==============================] - 0s 999us/step - loss: 3.4276e-04\n", "Epoch 401/480\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.3654e-04\n", "Epoch 402/480\n", "1/1 [==============================] - 0s 999us/step - loss: 3.3043e-04\n", "Epoch 403/480\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.2443e-04\n", "Epoch 404/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.1854e-04\n", "Epoch 405/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.1275e-04\n", "Epoch 406/480\n", "1/1 [==============================] - 0s 4ms/step - loss: 3.0707e-04\n", "Epoch 407/480\n", "1/1 [==============================] - 0s 999us/step - loss: 3.0150e-04\n", "Epoch 408/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.9603e-04\n", "Epoch 409/480\n", "1/1 [==============================] - 0s 998us/step - loss: 2.9065e-04\n", "Epoch 410/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.8537e-04\n", "Epoch 411/480\n", "1/1 [==============================] - 0s 3ms/step - loss: 2.8019e-04\n", "Epoch 412/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.7511e-04\n", "Epoch 413/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.7011e-04\n", "Epoch 414/480\n", "1/1 [==============================] - 0s 5ms/step - loss: 2.6521e-04\n", "Epoch 415/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.6039e-04\n", "Epoch 416/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.5566e-04\n", "Epoch 417/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.5102e-04\n", "Epoch 418/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.4646e-04\n", "Epoch 419/480\n", "1/1 [==============================] - 0s 1ms/step - loss: 2.4199e-04\n", "Epoch 420/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.3759e-04\n", "Epoch 421/480\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 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0s 7ms/step - loss: 1.9069e-04\n", "Epoch 433/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.8723e-04\n", "Epoch 434/480\n", "1/1 [==============================] - 0s 4ms/step - loss: 1.8383e-04\n", "Epoch 435/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.8049e-04\n", "Epoch 436/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.7722e-04\n", "Epoch 437/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.7400e-04\n", "Epoch 438/480\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.7084e-04\n", "Epoch 439/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.6774e-04\n", "Epoch 440/480\n", "1/1 [==============================] - 0s 4ms/step - loss: 1.6469e-04\n", "Epoch 441/480\n", "1/1 [==============================] - 0s 4ms/step - loss: 1.6170e-04\n", "Epoch 442/480\n", "1/1 [==============================] - 0s 4ms/step - loss: 1.5877e-04\n", "Epoch 443/480\n", "1/1 [==============================] - 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0s 3ms/step - loss: 1.0417e-04\n", "Epoch 466/480\n", "1/1 [==============================] - 0s 5ms/step - loss: 1.0228e-04\n", "Epoch 467/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.0042e-04\n", "Epoch 468/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.8593e-05\n", "Epoch 469/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.6804e-05\n", "Epoch 470/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.5044e-05\n", "Epoch 471/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.3320e-05\n", "Epoch 472/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 9.1624e-05\n", "Epoch 473/480\n", "1/1 [==============================] - 0s 3ms/step - loss: 8.9963e-05\n", "Epoch 474/480\n", "1/1 [==============================] - 0s 1000us/step - loss: 8.8328e-05\n", "Epoch 475/480\n", "1/1 [==============================] - 0s 3ms/step - loss: 8.6723e-05\n", "Epoch 476/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.5149e-05\n", "Epoch 477/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.3606e-05\n", "Epoch 478/480\n", "1/1 [==============================] - 0s 3ms/step - loss: 8.2087e-05\n", "Epoch 479/480\n", "1/1 [==============================] - 0s 2ms/step - loss: 8.0597e-05\n", "Epoch 480/480\n", "1/1 [==============================] - 0s 3ms/step - loss: 7.9131e-05\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC483CCA0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/490\n", "1/1 [==============================] - 0s 3ms/step - loss: 62.4045\n", "Epoch 2/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 26.7645\n", "Epoch 3/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 11.7388\n", "Epoch 4/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 5.3993\n", "Epoch 5/490\n", "1/1 [==============================] - 0s 1ms/step - loss: 2.7198\n", "Epoch 6/490\n", "1/1 [==============================] - 0s 999us/step - loss: 1.5827\n", "Epoch 7/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.0957\n", "Epoch 8/490\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.8827\n", "Epoch 9/490\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.7853\n", "Epoch 10/490\n", "1/1 [==============================] - 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0s 2ms/step - loss: 3.9547e-04\n", "Epoch 420/490\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.8829e-04\n", "Epoch 421/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.8124e-04\n", "Epoch 422/490\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.7432e-04\n", "Epoch 423/490\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.6752e-04\n", "Epoch 424/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.6085e-04\n", "Epoch 425/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.5430e-04\n", "Epoch 426/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 3.4786e-04\n", "Epoch 427/490\n", "1/1 [==============================] - 0s 3ms/step - loss: 3.4155e-04\n", "Epoch 428/490\n", "1/1 [==============================] - 0s 7ms/step - loss: 3.3535e-04\n", "Epoch 429/490\n", "1/1 [==============================] - 0s 999us/step - loss: 3.2927e-04\n", "Epoch 430/490\n", "1/1 [==============================] - 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loss: 2.6916e-04\n", "Epoch 441/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.6427e-04\n", "Epoch 442/490\n", "1/1 [==============================] - 0s 3ms/step - loss: 2.5947e-04\n", "Epoch 443/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.5476e-04\n", "Epoch 444/490\n", "1/1 [==============================] - 0s 4ms/step - loss: 2.5013e-04\n", "Epoch 445/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.4559e-04\n", "Epoch 446/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.4113e-04\n", "Epoch 447/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.3676e-04\n", "Epoch 448/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.3246e-04\n", "Epoch 449/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.2824e-04\n", "Epoch 450/490\n", "1/1 [==============================] - 0s 3ms/step - loss: 2.2409e-04\n", "Epoch 451/490\n", "1/1 [==============================] - 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0s 2ms/step - loss: 1.4703e-04\n", "Epoch 474/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.4436e-04\n", "Epoch 475/490\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.4174e-04\n", "Epoch 476/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.3917e-04\n", "Epoch 477/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.3664e-04\n", "Epoch 478/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.3416e-04\n", "Epoch 479/490\n", "1/1 [==============================] - 0s 5ms/step - loss: 1.3172e-04\n", "Epoch 480/490\n", "1/1 [==============================] - 0s 1ms/step - loss: 1.2933e-04\n", "Epoch 481/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.2698e-04\n", "Epoch 482/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.2468e-04\n", "Epoch 483/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.2241e-04\n", "Epoch 484/490\n", "1/1 [==============================] - 0s 1ms/step - loss: 1.2019e-04\n", "Epoch 485/490\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.1801e-04\n", "Epoch 486/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.1586e-04\n", "Epoch 487/490\n", "1/1 [==============================] - 0s 4ms/step - loss: 1.1376e-04\n", "Epoch 488/490\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.1170e-04\n", "Epoch 489/490\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.0967e-04\n", "Epoch 490/490\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.0768e-04\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EC37495E0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "Epoch 1/500\n", "1/1 [==============================] - 0s 1000us/step - loss: 149.8395\n", "Epoch 2/500\n", "1/1 [==============================] - 0s 2ms/step - loss: 63.7454\n", "Epoch 3/500\n", "1/1 [==============================] - 0s 1ms/step - loss: 27.4580\n", "Epoch 4/500\n", "1/1 [==============================] - 0s 1ms/step - loss: 12.1572\n", "Epoch 5/500\n", "1/1 [==============================] - 0s 1ms/step - loss: 5.6995\n", "Epoch 6/500\n", "1/1 [==============================] - 0s 1ms/step - loss: 2.9680\n", "Epoch 7/500\n", "1/1 [==============================] - 0s 1ms/step - loss: 1.8068\n", "Epoch 8/500\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.3074\n", "Epoch 9/500\n", "1/1 [==============================] - 0s 2ms/step - loss: 1.0870\n", "Epoch 10/500\n", "1/1 [==============================] - 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loss: 3.4277e-04\n", "Epoch 442/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 3.3655e-04\n", "Epoch 443/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 3.3044e-04\n", "Epoch 444/500\n", "1/1 [==============================] - 0s 0s/step - loss: 3.2444e-04\n", "Epoch 445/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 3.1854e-04\n", "Epoch 446/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 3.1276e-04\n", "Epoch 447/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 3.0708e-04\n", "Epoch 448/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 3.0151e-04\n", "Epoch 449/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 2.9603e-04\n", "Epoch 450/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 2.9065e-04\n", "Epoch 451/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 2.8538e-04\n", "Epoch 452/500\n", "1/1 [==============================] - 0s 0s/step - loss: 2.8020e-04\n", "Epoch 453/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 2.7511e-04\n", "Epoch 454/500\n", "1/1 [==============================] - 0s 0s/step - loss: 2.7011e-04\n", "Epoch 455/500\n", "1/1 [==============================] - 0s 0s/step - loss: 2.6521e-04\n", "Epoch 456/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 2.6040e-04\n", "Epoch 457/500\n", "1/1 [==============================] - 0s 0s/step - loss: 2.5567e-04\n", "Epoch 458/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 2.5102e-04\n", "Epoch 459/500\n", "1/1 [==============================] - 0s 0s/step - loss: 2.4647e-04\n", "Epoch 460/500\n", "1/1 [==============================] - 0s 0s/step - loss: 2.4199e-04\n", "Epoch 461/500\n", "1/1 [==============================] - 0s 0s/step - loss: 2.3760e-04\n", "Epoch 462/500\n", "1/1 [==============================] - 0s 3ms/step - loss: 2.3328e-04\n", "Epoch 463/500\n", "1/1 [==============================] - 0s 2ms/step - loss: 2.2905e-04\n", "Epoch 464/500\n", "1/1 [==============================] - 0s 0s/step - loss: 2.2489e-04\n", "Epoch 465/500\n", "1/1 [==============================] - 0s 0s/step - loss: 2.2081e-04\n", "Epoch 466/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 2.1680e-04\n", "Epoch 467/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 2.1286e-04\n", "Epoch 468/500\n", "1/1 [==============================] - 0s 0s/step - loss: 2.0900e-04\n", "Epoch 469/500\n", "1/1 [==============================] - 0s 0s/step - loss: 2.0520e-04\n", "Epoch 470/500\n", "1/1 [==============================] - 0s 0s/step - loss: 2.0147e-04\n", "Epoch 471/500\n", "1/1 [==============================] - 0s 0s/step - loss: 1.9782e-04\n", "Epoch 472/500\n", "1/1 [==============================] - 0s 0s/step - loss: 1.9423e-04\n", "Epoch 473/500\n", "1/1 [==============================] - 0s 0s/step - loss: 1.9070e-04\n", "Epoch 474/500\n", "1/1 [==============================] - 0s 0s/step - loss: 1.8724e-04\n", "Epoch 475/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 1.8384e-04\n", "Epoch 476/500\n", "1/1 [==============================] - 0s 0s/step - loss: 1.8050e-04\n", "Epoch 477/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 1.7722e-04\n", "Epoch 478/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 1.7400e-04\n", "Epoch 479/500\n", "1/1 [==============================] - 0s 0s/step - loss: 1.7084e-04\n", "Epoch 480/500\n", "1/1 [==============================] - 0s 0s/step - loss: 1.6774e-04\n", "Epoch 481/500\n", "1/1 [==============================] - 0s 0s/step - loss: 1.6470e-04\n", "Epoch 482/500\n", "1/1 [==============================] - ETA: 0s - loss: 1.6171e-0 - 0s 16ms/step - loss: 1.6171e-04\n", "Epoch 483/500\n", "1/1 [==============================] - 0s 0s/step - loss: 1.5877e-04\n", "Epoch 484/500\n", "1/1 [==============================] - 0s 0s/step - loss: 1.5589e-04\n", "Epoch 485/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 1.5306e-04\n", "Epoch 486/500\n", "1/1 [==============================] - 0s 0s/step - loss: 1.5028e-04\n", "Epoch 487/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 1.4755e-04\n", "Epoch 488/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 1.4487e-04\n", "Epoch 489/500\n", "1/1 [==============================] - 0s 0s/step - loss: 1.4224e-04\n", "Epoch 490/500\n", "1/1 [==============================] - 0s 0s/step - loss: 1.3966e-04\n", "Epoch 491/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 1.3712e-04\n", "Epoch 492/500\n", "1/1 [==============================] - 0s 0s/step - loss: 1.3463e-04\n", "Epoch 493/500\n", "1/1 [==============================] - 0s 0s/step - loss: 1.3219e-04\n", "Epoch 494/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 1.2979e-04\n", "Epoch 495/500\n", "1/1 [==============================] - 0s 0s/step - loss: 1.2743e-04\n", "Epoch 496/500\n", "1/1 [==============================] - 0s 0s/step - loss: 1.2512e-04\n", "Epoch 497/500\n", "1/1 [==============================] - 0s 0s/step - loss: 1.2285e-04\n", "Epoch 498/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 1.2062e-04\n", "Epoch 499/500\n", "1/1 [==============================] - 0s 0s/step - loss: 1.1843e-04\n", "Epoch 500/500\n", "1/1 [==============================] - 0s 16ms/step - loss: 1.1628e-04\n", "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x0000021EB408A3A0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] } ], "source": [ "ep=[10*x for x in range(1,51)]\n", "predict=[]\n", "for e in ep:\n", " model= tf.keras.Sequential([keras.layers.Dense(units=1 , input_shape=[1])])\n", " model.compile(optimizer='sgd', loss='mean_squared_error')\n", " model.fit(x,y, epochs= e)\n", " predict.append(model.predict([10])[0][0])\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "5245f06b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "plt.figure(figsize=(15,5))\n", "plt.plot(ep, predict)\n", "plt.axhline(19, color='k', linestyle='--')" ] }, { "cell_type": "code", "execution_count": null, "id": "8b3029b6", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 7, "id": "d65c6817", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500]\n" ] } ], "source": [ "print(ep)\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "51fb1a7c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[17.602348, 18.170645, 18.077929, 18.22015, 18.31175, 18.41789, 18.378002, 18.441595, 18.596355, 18.537426, 18.667433, 18.573563, 18.610594, 18.768915, 18.780182, 18.734041, 18.814484, 18.809471, 18.799286, 18.82682, 18.82504, 18.869291, 18.89098, 18.861328, 18.89186, 18.918198, 18.928904, 18.929325, 18.930784, 18.930243, 18.950384, 18.95, 18.948448, 18.949429, 18.95263, 18.953144, 18.96222, 18.972517, 18.968975, 18.974964, 18.975906, 18.973072, 18.980448, 18.982895, 18.986555, 18.985746, 18.987911, 18.989553, 18.987814, 18.987337]\n" ] } ], "source": [ "print(predict)" ] }, { "cell_type": "code", "execution_count": null, "id": "8f406f76", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 }