{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "zX4Kg8DUTKWO"
   },
   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "56XEQOGknrAk",
    "outputId": "0f446cf8-5d23-4dab-9783-08a1d284e3a3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "sLl52leVp5wU"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "tP7oqUdkk0gY"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--2021-07-10 01:47:21--  https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.111.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 67921 (66K) [text/plain]\n",
      "Saving to: '/tmp/daily-min-temperatures.csv'\n",
      "\n",
      "     0K .......... .......... .......... .......... .......... 75% 1.09M 0s\n",
      "    50K .......... ......                                     100% 3.02M=0.05s\n",
      "\n",
      "2021-07-10 01:47:22 (1.29 MB/s) - '/tmp/daily-min-temperatures.csv' saved [67921/67921]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget --no-check-certificate \\\n",
    "    https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv \\\n",
    "    -O /tmp/daily-min-temperatures.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "id": "NcG9r1eClbTh",
    "outputId": "9c9c679a-f57c-4b0d-8b2a-f98a8e6fb9e3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import csv\n",
    "time_step = []\n",
    "temps = []\n",
    "\n",
    "with open('/tmp/daily-min-temperatures.csv') as csvfile:\n",
    "  reader = csv.reader(csvfile, delimiter=',')\n",
    "  next(reader)\n",
    "  step=0\n",
    "  for row in reader:\n",
    "    temps.append(float(row[1]))\n",
    "    time_step.append(step)\n",
    "    step = step + 1\n",
    "\n",
    "series = np.array(temps)\n",
    "time = np.array(time_step)\n",
    "plt.figure(figsize=(10, 6))\n",
    "plot_series(time, series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "L92YRw_IpCFG"
   },
   "outputs": [],
   "source": [
    "split_time = 2500\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 = 30\n",
    "batch_size = 32\n",
    "shuffle_buffer_size = 1000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "lJwUUZscnG38"
   },
   "outputs": [],
   "source": [
    "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
    "    series = tf.expand_dims(series, axis=-1)\n",
    "    ds = tf.data.Dataset.from_tensor_slices(series)\n",
    "    ds = ds.window(window_size + 1, shift=1, drop_remainder=True)\n",
    "    ds = ds.flat_map(lambda w: w.batch(window_size + 1))\n",
    "    ds = ds.shuffle(shuffle_buffer)\n",
    "    ds = ds.map(lambda w: (w[:-1], w[1:]))\n",
    "    return ds.batch(batch_size).prefetch(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "4XwGrf-A_wF0"
   },
   "outputs": [],
   "source": [
    "def model_forecast(model, series, window_size):\n",
    "    ds = tf.data.Dataset.from_tensor_slices(series)\n",
    "    ds = ds.window(window_size, shift=1, drop_remainder=True)\n",
    "    ds = ds.flat_map(lambda w: w.batch(window_size))\n",
    "    ds = ds.batch(32).prefetch(1)\n",
    "    forecast = model.predict(ds)\n",
    "    return forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "AclfYY3Mn6Ph",
    "outputId": "dd1fef93-d819-4d56-df20-330169907e16"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:AutoGraph could not transform <function windowed_dataset.<locals>.<lambda> at 0x0000019A74553820> 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 <function windowed_dataset.<locals>.<lambda> at 0x0000019A74553820> 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",
      "<PrefetchDataset shapes: ((None, None, 1), (None, None, 1)), types: (tf.float64, tf.float64)>\n",
      "(2500,)\n",
      "10/10 [==============================] - 3s 342ms/step - loss: 31.1571 - mae: 31.6550\n"
     ]
    }
   ],
   "source": [
    "tf.keras.backend.clear_session()\n",
    "tf.random.set_seed(51)\n",
    "np.random.seed(51)\n",
    "window_size = 64\n",
    "batch_size = 256\n",
    "train_set = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n",
    "print(train_set)\n",
    "print(x_train.shape)\n",
    "\n",
    "model = tf.keras.models.Sequential([\n",
    "  tf.keras.layers.Conv1D(filters=32, kernel_size=5,\n",
    "                      strides=1, padding=\"causal\",\n",
    "                      activation=\"relu\",\n",
    "                      input_shape=[None, 1]),\n",
    "  tf.keras.layers.LSTM(64, return_sequences=True),\n",
    "  tf.keras.layers.LSTM(64, return_sequences=True),\n",
    "  tf.keras.layers.Dense(30, activation=\"relu\"),\n",
    "  tf.keras.layers.Dense(10, activation=\"relu\"),\n",
    "  tf.keras.layers.Dense(1),\n",
    "  tf.keras.layers.Lambda(lambda x: x * 400)\n",
    "])\n",
    "\n",
    "lr_schedule = tf.keras.callbacks.LearningRateScheduler(\n",
    "    lambda epoch: 1e-8 * 10**(epoch / 20))\n",
    "optimizer = tf.keras.optimizers.SGD(lr=1e-8, momentum=0.9)\n",
    "model.compile(loss=tf.keras.losses.Huber(),\n",
    "              optimizer=optimizer,\n",
    "              metrics=[\"mae\"])\n",
    "history = model.fit(train_set, epochs=1, callbacks=[lr_schedule])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 290
    },
    "id": "vVcKmg7Q_7rD",
    "outputId": "5e9b8029-e996-4a2b-e016-666c69865b11"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1e-08, 0.0001, 0.0, 60.0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogx(history.history[\"lr\"], history.history[\"loss\"])\n",
    "plt.axis([1e-8, 1e-4, 0, 60])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "QsksvkcXAAgq",
    "outputId": "70263fd4-3c3a-4e93-a451-ee942131e0d4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:AutoGraph could not transform <function windowed_dataset.<locals>.<lambda> at 0x0000019A7B171DC0> 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 <function windowed_dataset.<locals>.<lambda> at 0x0000019A7B171DC0> 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",
      "Epoch 1/50\n",
      "25/25 [==============================] - 4s 172ms/step - loss: 7.2518 - mae: 7.7378\n",
      "Epoch 2/50\n",
      "25/25 [==============================] - 4s 172ms/step - loss: 2.4926 - mae: 2.9558\n",
      "Epoch 3/50\n",
      "25/25 [==============================] - 4s 171ms/step - loss: 1.9949 - mae: 2.4488\n",
      "Epoch 4/50\n",
      "25/25 [==============================] - 4s 173ms/step - loss: 1.8903 - mae: 2.3429\n",
      "Epoch 5/50\n",
      "25/25 [==============================] - 4s 174ms/step - loss: 1.7945 - mae: 2.2447\n",
      "Epoch 6/50\n",
      "25/25 [==============================] - 4s 174ms/step - loss: 1.7615 - mae: 2.2114\n",
      "Epoch 7/50\n",
      "25/25 [==============================] - 4s 172ms/step - loss: 1.7285 - mae: 2.1773\n",
      "Epoch 8/50\n",
      "25/25 [==============================] - 4s 172ms/step - loss: 1.7102 - mae: 2.1587\n",
      "Epoch 9/50\n",
      "25/25 [==============================] - 4s 173ms/step - loss: 1.6683 - mae: 2.1153\n",
      "Epoch 10/50\n",
      "25/25 [==============================] - 4s 176ms/step - loss: 1.6474 - mae: 2.0932\n",
      "Epoch 11/50\n",
      "25/25 [==============================] - 4s 175ms/step - loss: 1.6300 - mae: 2.0751 0s - loss: 1.6340 - mae: 2.\n",
      "Epoch 12/50\n",
      "25/25 [==============================] - 4s 180ms/step - loss: 1.6053 - mae: 2.0493\n",
      "Epoch 13/50\n",
      "25/25 [==============================] - 4s 175ms/step - loss: 1.6135 - mae: 2.0586\n",
      "Epoch 14/50\n",
      "25/25 [==============================] - 4s 177ms/step - loss: 1.5885 - mae: 2.0316\n",
      "Epoch 15/50\n",
      "25/25 [==============================] - 4s 176ms/step - loss: 1.5843 - mae: 2.0275\n",
      "Epoch 16/50\n",
      "25/25 [==============================] - 4s 173ms/step - loss: 1.5801 - mae: 2.0236\n",
      "Epoch 17/50\n",
      "25/25 [==============================] - 4s 173ms/step - loss: 1.6958 - mae: 2.1450\n",
      "Epoch 18/50\n",
      "25/25 [==============================] - 4s 176ms/step - loss: 1.6250 - mae: 2.0709\n",
      "Epoch 19/50\n",
      "25/25 [==============================] - 4s 174ms/step - loss: 1.5505 - mae: 1.9926\n",
      "Epoch 20/50\n",
      "25/25 [==============================] - 4s 175ms/step - loss: 1.5543 - mae: 1.9970 2s - loss: \n",
      "Epoch 21/50\n",
      "25/25 [==============================] - 4s 176ms/step - loss: 1.5533 - mae: 1.9963\n",
      "Epoch 22/50\n",
      "25/25 [==============================] - 4s 178ms/step - loss: 1.5508 - mae: 1.9937\n",
      "Epoch 23/50\n",
      "25/25 [==============================] - 4s 172ms/step - loss: 1.5773 - mae: 2.0220\n",
      "Epoch 24/50\n",
      "25/25 [==============================] - 4s 173ms/step - loss: 1.5503 - mae: 1.9931 2s - loss: 1.\n",
      "Epoch 25/50\n",
      "25/25 [==============================] - 4s 173ms/step - loss: 1.5476 - mae: 1.9903\n",
      "Epoch 26/50\n",
      "25/25 [==============================] - 4s 176ms/step - loss: 1.5697 - mae: 2.0134\n",
      "Epoch 27/50\n",
      "25/25 [==============================] - 4s 179ms/step - loss: 1.5573 - mae: 2.0013\n",
      "Epoch 28/50\n",
      "25/25 [==============================] - 4s 176ms/step - loss: 1.5344 - mae: 1.9764\n",
      "Epoch 29/50\n",
      "25/25 [==============================] - 5s 185ms/step - loss: 1.5416 - mae: 1.9840\n",
      "Epoch 30/50\n",
      "25/25 [==============================] - 5s 182ms/step - loss: 1.5397 - mae: 1.9821\n",
      "Epoch 31/50\n",
      "25/25 [==============================] - 4s 179ms/step - loss: 1.5171 - mae: 1.9582\n",
      "Epoch 32/50\n",
      "25/25 [==============================] - 5s 181ms/step - loss: 1.5247 - mae: 1.9662 0s - loss: 1.5257 - mae: 1.9\n",
      "Epoch 33/50\n",
      "25/25 [==============================] - 4s 178ms/step - loss: 1.5425 - mae: 1.9857\n",
      "Epoch 34/50\n",
      "25/25 [==============================] - 4s 175ms/step - loss: 1.5472 - mae: 1.9904\n",
      "Epoch 35/50\n",
      "25/25 [==============================] - 4s 176ms/step - loss: 1.5365 - mae: 1.9791\n",
      "Epoch 36/50\n",
      "25/25 [==============================] - 4s 178ms/step - loss: 1.5099 - mae: 1.9503\n",
      "Epoch 37/50\n",
      "25/25 [==============================] - 4s 173ms/step - loss: 1.5068 - mae: 1.9472\n",
      "Epoch 38/50\n",
      "25/25 [==============================] - 4s 176ms/step - loss: 1.5312 - mae: 1.9733\n",
      "Epoch 39/50\n",
      "25/25 [==============================] - 4s 176ms/step - loss: 1.5107 - mae: 1.9518\n",
      "Epoch 40/50\n",
      "25/25 [==============================] - 4s 174ms/step - loss: 1.5126 - mae: 1.9535\n",
      "Epoch 41/50\n",
      "25/25 [==============================] - 4s 178ms/step - loss: 1.5055 - mae: 1.9462\n",
      "Epoch 42/50\n",
      "25/25 [==============================] - 4s 180ms/step - loss: 1.5002 - mae: 1.9403\n",
      "Epoch 43/50\n",
      "25/25 [==============================] - 4s 177ms/step - loss: 1.5119 - mae: 1.9526\n",
      "Epoch 44/50\n",
      "25/25 [==============================] - 4s 172ms/step - loss: 1.5193 - mae: 1.9607\n",
      "Epoch 45/50\n",
      "25/25 [==============================] - 4s 174ms/step - loss: 1.5294 - mae: 1.9717\n",
      "Epoch 46/50\n",
      "25/25 [==============================] - 4s 174ms/step - loss: 1.5128 - mae: 1.9541\n",
      "Epoch 47/50\n",
      "25/25 [==============================] - 4s 173ms/step - loss: 1.5025 - mae: 1.9434\n",
      "Epoch 48/50\n",
      "25/25 [==============================] - 4s 173ms/step - loss: 1.5018 - mae: 1.9419\n",
      "Epoch 49/50\n",
      "25/25 [==============================] - 5s 182ms/step - loss: 1.5225 - mae: 1.9642\n",
      "Epoch 50/50\n",
      "25/25 [==============================] - 4s 177ms/step - loss: 1.5074 - mae: 1.9483\n"
     ]
    }
   ],
   "source": [
    "tf.keras.backend.clear_session()\n",
    "tf.random.set_seed(51)\n",
    "np.random.seed(51)\n",
    "train_set = windowed_dataset(x_train, window_size=60, batch_size=100, shuffle_buffer=shuffle_buffer_size)\n",
    "model = tf.keras.models.Sequential([\n",
    "  tf.keras.layers.Conv1D(filters=60, kernel_size=5,\n",
    "                      strides=1, padding=\"causal\",\n",
    "                      activation=\"relu\",\n",
    "                      input_shape=[None, 1]),\n",
    "  tf.keras.layers.LSTM(80, return_sequences=True),\n",
    "  tf.keras.layers.LSTM(50, return_sequences=True),\n",
    "  #tf.keras.layers.LSTM(60, return_sequences=True),\n",
    "  tf.keras.layers.Dense(30, activation=\"relu\"),\n",
    "  tf.keras.layers.Dense(10, activation=\"relu\"),\n",
    "  tf.keras.layers.Dense(1),\n",
    "  tf.keras.layers.Lambda(lambda x: x * 400)\n",
    "])\n",
    "\n",
    "\n",
    "optimizer = tf.keras.optimizers.SGD(lr=1e-5, momentum=0.9)\n",
    "model.compile(loss=tf.keras.losses.Huber(),\n",
    "              optimizer=optimizer,\n",
    "              metrics=[\"mae\"])\n",
    "history = model.fit(train_set,epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "GaC6NNMRp0lb"
   },
   "outputs": [],
   "source": [
    "rnn_forecast = model_forecast(model, series[..., np.newaxis], window_size)\n",
    "rnn_forecast = rnn_forecast[split_time - window_size:-1, -1, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "id": "PrktQX3hKYex",
    "outputId": "6f51b039-76a2-4c2d-9f37-c89c6aab20e2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plot_series(time_valid, x_valid)\n",
    "plot_series(time_valid, rnn_forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "13XrorC5wQoE",
    "outputId": "7f5bda4a-160c-4c0f-e511-a5a1b6945d35"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.806378"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.metrics.mean_absolute_error(x_valid, rnn_forecast).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "AOVzQXxCwkzP",
    "outputId": "3b3fd11c-f9b3-4cbd-e32d-2d7d22b6ff7f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12.992437 12.209662 12.796669 ... 13.718358 13.840805 14.998388]\n"
     ]
    }
   ],
   "source": [
    "print(rnn_forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7893597"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1.7893597"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "S+P Week 4 Exercise Answer.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
}