{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "id": "BOjujz601HcS" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.3.0\n" ] } ], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "print(tf.__version__)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "Zswl7jRtGzkk" }, "outputs": [], "source": [ "def plot_series(time, series, format=\"-\", start=0, end=None):\n", " plt.plot(time[start:end], series[start:end], format)\n", " plt.xlabel(\"Time\")\n", " plt.ylabel(\"Value\")\n", " plt.grid(True)\n", "\n", "def trend(time, slope=0):\n", " return slope * time\n", "\n", "def seasonal_pattern(season_time):\n", " \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n", " return np.where(season_time < 0.4,\n", " np.cos(season_time * 2 * np.pi),\n", " 1 / np.exp(3 * season_time))\n", "\n", "def seasonality(time, period, amplitude=1, phase=0):\n", " \"\"\"Repeats the same pattern at each period\"\"\"\n", " season_time = ((time + phase) % period) / period\n", " return amplitude * seasonal_pattern(season_time)\n", "\n", "def noise(time, noise_level=1, seed=None):\n", " rnd = np.random.RandomState(seed)\n", " return rnd.randn(len(time)) * noise_level\n", "\n", "time = np.arange(4 * 365 + 1, dtype=\"float32\")\n", "baseline = 10\n", "series = trend(time, 0.1) \n", "baseline = 10\n", "amplitude = 40\n", "slope = 0.05\n", "noise_level = 5\n", "\n", "# Create the series\n", "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n", "# Update with noise\n", "series += noise(time, noise_level, seed=42)\n", "\n", "split_time = 1000\n", "time_train = time[:split_time]\n", "x_train = series[:split_time]\n", "time_valid = time[split_time:]\n", "x_valid = series[split_time:]\n", "\n", "window_size = 20\n", "batch_size = 32\n", "shuffle_buffer_size = 1000" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "4sTTIOCbyShY" }, "outputs": [], "source": [ "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n", " dataset = tf.data.Dataset.from_tensor_slices(series)\n", " dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n", " dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n", " dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1]))\n", " dataset = dataset.batch(batch_size).prefetch(1)\n", " return dataset" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "ou-WmE2AXu6B" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:AutoGraph could not transform . at 0x000002B6E662B310> and will run it as-is.\n", "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n", "Cause: module 'gast' has no attribute 'Index'\n", "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n", "WARNING: AutoGraph could not transform . at 0x000002B6E662B310> and will run it as-is.\n", "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n", "Cause: module 'gast' has no attribute 'Index'\n", "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n", "\n", "Layer weights [array([[-0.00067667],\n", " [-0.07594975],\n", " [ 0.12638146],\n", " [-0.1085402 ],\n", " [ 0.10240081],\n", " [-0.03875897],\n", " [-0.03040683],\n", " [-0.01419524],\n", " [ 0.01719459],\n", " [ 0.00295796],\n", " [ 0.04138799],\n", " [-0.02478334],\n", " [ 0.0437271 ],\n", " [-0.03452239],\n", " [-0.00909923],\n", " [ 0.05353767],\n", " [ 0.01224581],\n", " [ 0.21521768],\n", " [ 0.26557449],\n", " [ 0.4449922 ]], dtype=float32), array([0.01107508], dtype=float32)]\n" ] } ], "source": [ "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n", "print(dataset)\n", "l0 = tf.keras.layers.Dense(1, input_shape=[window_size])\n", "model = tf.keras.models.Sequential([l0])\n", "\n", "\n", "model.compile(loss=\"mse\", optimizer=tf.keras.optimizers.SGD(lr=1e-6, momentum=0.9))\n", "model.fit(dataset,epochs=100,verbose=0)\n", "\n", "print(\"Layer weights {}\".format(l0.get_weights()))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-gtVJuLVxR-M" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8\n", "9\n", "10\n", "11\n", "12\n", "13\n", "14\n", "15\n", "16\n", "17\n", "18\n", "19\n", "20\n", "21\n", "22\n", "23\n", "24\n", "25\n", "26\n", "27\n", "28\n", "29\n", "30\n", "31\n", "32\n", "33\n", "34\n", "35\n", "36\n", "37\n", "38\n", "39\n", "40\n", "41\n", "42\n", "43\n", "44\n", "45\n", "46\n", "47\n", "48\n", "49\n", "50\n", "51\n", "52\n", "53\n", "54\n", "55\n", "56\n", "57\n", "58\n", "59\n", "60\n", "61\n", "62\n", "63\n", "64\n", "65\n", "66\n", "67\n", "68\n", "69\n", "70\n", "71\n", "72\n", "73\n", "74\n", 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[ "tf.keras.metrics.mean_absolute_error(x_valid, results).numpy()" ] } ], "metadata": { "colab": { "name": "S+P Week 2 Lesson 2.ipynb", "provenance": [], "toc_visible": true }, "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": 1 }