8. forecasting_with_lstm.ipynb

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      "name": "l08c08_forecasting_with_lstm.ipynb",
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    {
      "cell_type": "markdown",
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      "source": [
        "# Forecasting with an LSTM"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vidayERjaO5q"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gqWabzlJ63nL"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "\n",
        "keras = tf.keras"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "source": [
        "def plot_series(time, series, format=\"-\", start=0, end=None, label=None):\n",
        "    plt.plot(time[start:end], series[start:end], format, label=label)\n",
        "    plt.xlabel(\"Time\")\n",
        "    plt.ylabel(\"Value\")\n",
        "    if label:\n",
        "        plt.legend(fontsize=14)\n",
        "    plt.grid(True)\n",
        "    \n",
        "def trend(time, slope=0):\n",
        "    return slope * time\n",
        "  \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",
        "  \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",
        "  \n",
        "def white_noise(time, noise_level=1, seed=None):\n",
        "    rnd = np.random.RandomState(seed)\n",
        "    return rnd.randn(len(time)) * noise_level\n",
        "  \n",
        "\n",
        "def sequential_window_dataset(series, window_size):\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=window_size, drop_remainder=True)\n",
        "    ds = ds.flat_map(lambda window: window.batch(window_size + 1))\n",
        "    ds = ds.map(lambda window: (window[:-1], window[1:]))\n",
        "    return ds.batch(1).prefetch(1)"
      ],
      "execution_count": 2,
      "outputs": []
    },
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      "source": [
        "time = np.arange(4 * 365 + 1)\n",
        "\n",
        "slope = 0.05\n",
        "baseline = 10\n",
        "amplitude = 40\n",
        "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n",
        "\n",
        "noise_level = 5\n",
        "noise = white_noise(time, noise_level, seed=42)\n",
        "\n",
        "series += noise\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(time, series)\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
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157.386299 208.458661 \nL 157.733746 225.396217 \nL 158.428641 212.010429 \nL 158.776088 208.28316 \nL 159.123535 229.555713 \nL 159.470982 220.624257 \nL 160.165876 223.882092 \nL 160.513323 198.94987 \nL 161.208217 230.09553 \nL 161.555664 207.633966 \nL 161.903111 195.210287 \nL 162.250558 206.418053 \nL 162.598006 232.687822 \nL 162.945453 222.004689 \nL 163.2929 203.944696 \nL 163.640347 224.265657 \nL 163.987794 212.381843 \nL 164.335241 208.951773 \nL 164.682688 226.458176 \nL 165.030135 217.498946 \nL 165.377582 250.252513 \nL 165.725029 227.389985 \nL 166.072476 219.413518 \nL 166.419923 229.640028 \nL 166.76737 199.942266 \nL 167.114817 231.464791 \nL 167.462265 221.237165 \nL 167.809712 215.328585 \nL 168.157159 201.798672 \nL 168.504606 231.408618 \nL 168.852053 204.603717 \nL 169.546947 226.635314 \nL 169.894394 211.731468 \nL 170.241841 214.408647 \nL 170.589288 222.609406 \nL 170.936735 215.673224 \nL 171.284182 220.327372 \nL 171.979077 209.466431 \nL 172.326524 199.912724 \nL 172.673971 228.966665 \nL 173.021418 194.203779 \nL 173.368865 236.250165 \nL 173.716312 217.665636 \nL 174.063759 210.002534 \nL 174.411206 213.129282 \nL 174.758653 222.399144 \nL 175.1061 218.087966 \nL 175.453547 220.98192 \nL 175.800994 221.933326 \nL 176.148441 207.067577 \nL 176.495888 212.099778 \nL 176.843336 222.873065 \nL 177.190783 206.424063 \nL 177.53823 212.482016 \nL 177.885677 207.229535 \nL 178.233124 209.0724 \nL 178.580571 224.053591 \nL 178.928018 221.238613 \nL 179.275465 207.723105 \nL 179.622912 209.087012 \nL 179.970359 215.54313 \nL 180.317806 214.071558 \nL 180.665253 202.069987 \nL 181.0127 221.277989 \nL 181.360147 209.498748 \nL 181.707595 217.168414 \nL 182.055042 217.278174 \nL 182.402489 203.666068 \nL 182.749936 206.431502 \nL 183.097383 206.503067 \nL 183.44483 201.383384 \nL 183.792277 214.563828 \nL 184.139724 207.702511 \nL 184.487171 217.871372 \nL 184.834618 211.28239 \nL 185.182065 215.908994 \nL 185.529512 213.515058 \nL 185.876959 208.328708 \nL 186.224407 222.834226 \nL 186.571854 192.794571 \nL 186.919301 224.65809 \nL 187.266748 226.746653 \nL 187.614195 202.251372 \nL 187.961642 205.969703 \nL 188.309089 207.638576 \nL 188.656536 207.537549 \nL 189.003983 214.078928 \nL 189.35143 223.137851 \nL 189.698877 213.055002 \nL 190.046324 220.752933 \nL 190.393771 203.672155 \nL 190.741218 215.1729 \nL 191.088666 222.102003 \nL 191.436113 216.848364 \nL 191.78356 209.222843 \nL 192.131007 219.223059 \nL 192.478454 221.824598 \nL 192.825901 210.782076 \nL 193.173348 210.706789 \nL 193.520795 218.39036 \nL 193.868242 217.957701 \nL 194.215689 210.651532 \nL 194.563136 227.896486 \nL 194.910583 227.414267 \nL 195.605478 214.985292 \nL 195.952925 209.51879 \nL 196.300372 197.457864 \nL 196.647819 203.755907 \nL 196.995266 214.173342 \nL 197.342713 212.655641 \nL 197.69016 222.72133 \nL 198.037607 212.51765 \nL 198.385054 215.233779 \nL 198.732501 208.868353 \nL 199.079948 220.647445 \nL 199.774842 196.199783 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151.194924 \nL 214.020172 145.697132 \nL 214.367619 146.017661 \nL 214.715067 151.907997 \nL 215.062514 141.644046 \nL 215.409961 159.866763 \nL 215.757408 151.161567 \nL 216.104855 149.300295 \nL 216.452302 146.102697 \nL 216.799749 144.951957 \nL 217.147196 164.766663 \nL 217.494643 169.900093 \nL 217.84209 141.865612 \nL 218.536984 164.652028 \nL 218.884431 141.942337 \nL 219.231879 157.726613 \nL 219.579326 147.780967 \nL 219.926773 160.260784 \nL 220.27422 140.768078 \nL 220.621667 144.969705 \nL 220.969114 166.68694 \nL 221.316561 155.19614 \nL 221.664008 159.652651 \nL 222.011455 153.310817 \nL 222.358902 178.472107 \nL 222.706349 162.597769 \nL 223.053796 159.907099 \nL 223.401243 190.085772 \nL 223.74869 185.32518 \nL 224.096138 195.321792 \nL 224.443585 178.278267 \nL 225.138479 162.432927 \nL 225.485926 178.363961 \nL 225.833373 163.575735 \nL 226.18082 195.805354 \nL 226.528267 200.378128 \nL 226.875714 184.652436 \nL 227.223161 181.38073 \nL 227.570608 186.937942 \nL 227.918055 209.169651 \nL 228.265502 190.065505 \nL 228.612949 203.867404 \nL 228.960397 184.816953 \nL 229.307844 189.2306 \nL 229.655291 203.985384 \nL 230.002738 200.896288 \nL 230.350185 207.817388 \nL 231.045079 189.680736 \nL 231.392526 210.986069 \nL 231.739973 196.951456 \nL 232.08742 208.920709 \nL 232.434867 212.941132 \nL 232.782314 207.191272 \nL 233.129761 218.069162 \nL 233.477209 214.423226 \nL 233.824656 222.374529 \nL 234.172103 191.107509 \nL 234.51955 212.296277 \nL 234.866997 221.177849 \nL 235.214444 213.072509 \nL 235.561891 217.738691 \nL 235.909338 220.159272 \nL 236.256785 212.85359 \nL 236.604232 212.670556 \nL 236.951679 227.22849 \nL 237.299126 228.979572 \nL 237.646573 227.160057 \nL 237.99402 249.313626 \nL 238.341468 242.475957 \nL 238.688915 214.046052 \nL 239.036362 212.434765 \nL 239.383809 233.192614 \nL 239.731256 225.926011 \nL 240.078703 229.889481 \nL 240.42615 202.599738 \nL 240.773597 223.996956 \nL 241.468491 247.771733 \nL 241.815938 255.66097 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194.05518 \nL 257.451057 176.869825 \nL 257.798504 174.764711 \nL 258.145951 186.830065 \nL 258.493398 171.007571 \nL 258.840845 194.773631 \nL 259.188292 178.407114 \nL 259.535739 190.269962 \nL 260.230633 177.437768 \nL 260.57808 186.853648 \nL 260.925527 182.01389 \nL 261.272974 167.74674 \nL 261.620421 184.116101 \nL 261.967869 169.622361 \nL 262.315316 189.926559 \nL 262.662763 172.886396 \nL 263.01021 163.54806 \nL 263.357657 203.914325 \nL 264.052551 172.609627 \nL 264.399998 180.695604 \nL 264.747445 174.828534 \nL 265.094892 184.921015 \nL 265.442339 177.852521 \nL 265.789786 180.392749 \nL 266.137233 166.801991 \nL 266.48468 176.253284 \nL 266.832128 175.437216 \nL 267.179575 183.19787 \nL 267.527022 184.016525 \nL 267.874469 183.48678 \nL 268.221916 175.003296 \nL 268.569363 183.438811 \nL 268.91681 176.15065 \nL 269.264257 157.788322 \nL 269.959151 182.589575 \nL 270.306598 166.885802 \nL 271.001492 200.312532 \nL 271.34894 189.727571 \nL 271.696387 198.640196 \nL 272.043834 183.013079 \nL 272.391281 179.225244 \nL 272.738728 162.166778 \nL 273.086175 176.09021 \nL 273.433622 181.735325 \nL 273.781069 170.952883 \nL 274.128516 202.293932 \nL 274.475963 177.105091 \nL 274.82341 171.607125 \nL 275.170857 194.795591 \nL 275.518304 167.794931 \nL 276.213199 183.88124 \nL 276.560646 173.095525 \nL 276.908093 156.232457 \nL 277.25554 177.760516 \nL 277.602987 177.085441 \nL 277.950434 184.382375 \nL 278.297881 188.411727 \nL 278.645328 171.108646 \nL 278.992775 188.486668 \nL 279.340222 178.934219 \nL 279.687669 184.595331 \nL 280.035116 174.742516 \nL 280.382563 176.240945 \nL 280.730011 168.990288 \nL 281.077458 184.932577 \nL 281.424905 182.457545 \nL 281.772352 189.758385 \nL 282.119799 184.249578 \nL 282.467246 175.782066 \nL 282.814693 171.866159 \nL 283.16214 189.159197 \nL 283.509587 170.694703 \nL 283.857034 165.680864 \nL 284.204481 175.379608 \nL 284.551928 160.295966 \nL 284.899375 187.592543 \nL 285.59427 197.924461 \nL 285.941717 164.177065 \nL 286.636611 180.136434 \nL 286.984058 176.665936 \nL 287.331505 191.13023 \nL 287.678952 154.324911 \nL 288.026399 178.173442 \nL 288.373846 178.361101 \nL 288.721293 171.994073 \nL 289.416187 177.12767 \nL 289.763634 187.557991 \nL 290.458529 159.985661 \nL 290.805976 165.492225 \nL 291.153423 162.917698 \nL 291.50087 184.57405 \nL 291.848317 189.478905 \nL 292.195764 180.555977 \nL 292.543211 178.661375 \nL 292.890658 167.937966 \nL 293.238105 196.61952 \nL 293.585552 163.400396 \nL 293.932999 180.758022 \nL 294.280446 183.500037 \nL 294.975341 196.092821 \nL 295.322788 170.535023 \nL 295.670235 178.229555 \nL 296.017682 192.242877 \nL 296.365129 192.264125 \nL 296.712576 182.349621 \nL 297.060023 161.663893 \nL 297.40747 181.498833 \nL 297.754917 194.275847 \nL 298.102364 181.288025 \nL 298.449811 181.531054 \nL 298.797258 206.468789 \nL 299.144705 179.209344 \nL 299.492152 180.992471 \nL 300.187047 159.491101 \nL 300.534494 166.894824 \nL 300.881941 181.231765 \nL 301.229388 189.821673 \nL 301.576835 151.872502 \nL 301.924282 177.732358 \nL 302.619176 178.508826 \nL 302.966623 176.177848 \nL 303.661517 184.043081 \nL 304.008964 183.723652 \nL 304.356412 178.383646 \nL 304.703859 183.600191 \nL 305.051306 185.300766 \nL 305.398753 176.815519 \nL 305.7462 180.492947 \nL 306.093647 162.326228 \nL 306.441094 205.08312 \nL 306.788541 166.481815 \nL 307.135988 164.841814 \nL 307.483435 198.990321 \nL 307.830882 181.11252 \nL 308.178329 181.359812 \nL 308.525776 191.983832 \nL 308.873223 185.447049 \nL 309.220671 188.824827 \nL 309.568118 159.281728 \nL 309.915565 167.642989 \nL 310.263012 164.131338 \nL 310.610459 169.744173 \nL 310.957906 188.757574 \nL 311.305353 182.477044 \nL 311.6528 171.978929 \nL 312.000247 189.556867 \nL 312.347694 169.567611 \nL 312.695141 179.334149 \nL 313.042588 180.664894 \nL 313.390035 169.424219 \nL 313.737482 172.116087 \nL 314.08493 180.355416 \nL 314.432377 164.637294 \nL 314.779824 187.660606 \nL 315.127271 170.121367 \nL 315.474718 170.299107 \nL 315.822165 179.540124 \nL 316.169612 172.933253 \nL 316.517059 189.123108 \nL 316.864506 166.656307 \nL 317.211953 178.020932 \nL 317.5594 181.441406 \nL 317.906847 165.189716 \nL 318.254294 183.191992 \nL 318.601742 190.384922 \nL 318.949189 191.850229 \nL 319.296636 169.509753 \nL 319.644083 188.881678 \nL 319.99153 157.551345 \nL 320.338977 197.014011 \nL 320.686424 158.027112 \nL 321.033871 173.266683 \nL 321.381318 176.373427 \nL 321.728765 180.926995 \nL 322.076212 171.137463 \nL 322.423659 175.572588 \nL 322.771106 163.754192 \nL 323.118553 173.878399 \nL 323.466001 173.441436 \nL 323.813448 178.670024 \nL 324.508342 171.621057 \nL 324.855789 192.343201 \nL 325.203236 188.547277 \nL 325.550683 166.934461 \nL 325.89813 172.763748 \nL 326.245577 176.35159 \nL 326.593024 174.198308 \nL 326.940471 170.739189 \nL 327.287918 179.811798 \nL 327.635365 182.200363 \nL 327.982812 93.784597 \nL 328.33026 105.790333 \nL 328.677707 91.439223 \nL 329.025154 113.142626 \nL 329.372601 84.983236 \nL 329.720048 90.723549 \nL 330.067495 92.985683 \nL 330.762389 78.600909 \nL 331.109836 85.412272 \nL 331.457283 114.954342 \nL 332.152177 102.754808 \nL 332.847072 91.408859 \nL 333.194519 104.465557 \nL 333.541966 95.336107 \nL 333.889413 105.336471 \nL 334.23686 104.172326 \nL 334.584307 112.704903 \nL 334.931754 108.089039 \nL 335.279201 112.890149 \nL 335.626648 109.428758 \nL 335.974095 88.953956 \nL 336.321542 110.044214 \nL 336.668989 73.623198 \nL 337.016436 96.159302 \nL 337.363883 99.859152 \nL 337.711331 111.149681 \nL 338.058778 95.658025 \nL 338.406225 109.389413 \nL 338.753672 102.81048 \nL 339.101119 78.323063 \nL 339.448566 106.336014 \nL 339.796013 94.17736 \nL 340.14346 113.951762 \nL 340.490907 107.779214 \nL 340.838354 89.90722 \nL 341.185801 115.359005 \nL 341.533248 90.998539 \nL 342.228143 117.061257 \nL 342.57559 105.578353 \nL 342.923037 102.918985 \nL 343.270484 107.395061 \nL 343.617931 130.85746 \nL 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zmp1JXKxk9Vb"
      },
      "source": [
        "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:]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9fPenJpTtuDE"
      },
      "source": [
        "class ResetStatesCallback(keras.callbacks.Callback):\n",
        "    def on_epoch_begin(self, epoch, logs):\n",
        "        self.model.reset_states()"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EPjK0l9P8OJM"
      },
      "source": [
        "## LSTM RNN Forecasting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cSoUmW-x8OJN"
      },
      "source": [
        "keras.backend.clear_session()\n",
        "tf.random.set_seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "window_size = 30\n",
        "train_set = sequential_window_dataset(x_train, window_size)\n",
        "\n",
        "model = keras.models.Sequential([\n",
        "  keras.layers.LSTM(100, return_sequences=True, stateful=True,\n",
        "                    batch_input_shape=[1, None, 1]),\n",
        "  keras.layers.LSTM(100, return_sequences=True, stateful=True),\n",
        "  keras.layers.Dense(1),\n",
        "  keras.layers.Lambda(lambda x: x * 200.0)\n",
        "])\n",
        "lr_schedule = keras.callbacks.LearningRateScheduler(\n",
        "    lambda epoch: 1e-8 * 10**(epoch / 20))\n",
        "reset_states = ResetStatesCallback()\n",
        "optimizer = keras.optimizers.SGD(lr=1e-8, momentum=0.9)\n",
        "model.compile(loss=keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])\n",
        "history = model.fit(train_set, epochs=100,\n",
        "                    callbacks=[lr_schedule, reset_states])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/home/ritesh/.local/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  warnings.warn(\n",
            "Epoch 1/100\n",
            "33/33 [==============================] - 3s 17ms/step - loss: 90.9191 - mae: 91.4191\n",
            "Epoch 2/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 83.8978 - mae: 84.3978\n",
            "Epoch 3/100\n",
            "33/33 [==============================] - 1s 15ms/step - loss: 75.4974 - mae: 75.9974\n",
            "Epoch 4/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 66.0676 - mae: 66.5676\n",
            "Epoch 5/100\n",
            "33/33 [==============================] - 1s 21ms/step - loss: 55.5131 - mae: 56.0131\n",
            "Epoch 6/100\n",
            "33/33 [==============================] - 1s 15ms/step - loss: 43.7599 - mae: 44.2597\n",
            "Epoch 7/100\n",
            "33/33 [==============================] - 1s 15ms/step - loss: 30.7909 - mae: 31.2900\n",
            "Epoch 8/100\n",
            "33/33 [==============================] - 1s 15ms/step - loss: 17.1478 - mae: 17.6436\n",
            "Epoch 9/100\n",
            "33/33 [==============================] - 0s 15ms/step - loss: 10.0311 - mae: 10.5178\n",
            "Epoch 10/100\n",
            "33/33 [==============================] - 1s 15ms/step - loss: 10.0607 - mae: 10.5524\n",
            "Epoch 11/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 9.8063 - mae: 10.2981\n",
            "Epoch 12/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 9.3912 - mae: 9.8815\n",
            "Epoch 13/100\n",
            "33/33 [==============================] - 0s 15ms/step - loss: 9.0001 - mae: 9.4885\n",
            "Epoch 14/100\n",
            "33/33 [==============================] - 0s 14ms/step - loss: 8.6473 - mae: 9.1320\n",
            "Epoch 15/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 8.3308 - mae: 8.8157\n",
            "Epoch 16/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 8.0550 - mae: 8.5387\n",
            "Epoch 17/100\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 7.8428 - mae: 8.3241\n",
            "Epoch 18/100\n",
            "33/33 [==============================] - 1s 22ms/step - loss: 7.6701 - mae: 8.1520\n",
            "Epoch 19/100\n",
            "33/33 [==============================] - 1s 19ms/step - loss: 7.5326 - mae: 8.0152\n",
            "Epoch 20/100\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 7.4123 - mae: 7.8949\n",
            "Epoch 21/100\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 7.3345 - mae: 7.8176\n",
            "Epoch 22/100\n",
            "33/33 [==============================] - 1s 15ms/step - loss: 7.2263 - mae: 7.7074\n",
            "Epoch 23/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 7.0942 - mae: 7.5725\n",
            "Epoch 24/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 6.9832 - mae: 7.4620\n",
            "Epoch 25/100\n",
            "33/33 [==============================] - 0s 15ms/step - loss: 6.8444 - mae: 7.3253\n",
            "Epoch 26/100\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 6.7290 - mae: 7.2108\n",
            "Epoch 27/100\n",
            "33/33 [==============================] - 1s 15ms/step - loss: 6.5833 - mae: 7.0666\n",
            "Epoch 28/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 6.4450 - mae: 6.9279\n",
            "Epoch 29/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 6.2907 - mae: 6.7729\n",
            "Epoch 30/100\n",
            "33/33 [==============================] - 1s 15ms/step - loss: 6.1173 - mae: 6.5962\n",
            "Epoch 31/100\n",
            "33/33 [==============================] - 1s 15ms/step - loss: 5.9434 - mae: 6.4210\n",
            "Epoch 32/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 5.7794 - mae: 6.2585\n",
            "Epoch 33/100\n",
            "33/33 [==============================] - 1s 15ms/step - loss: 5.6534 - mae: 6.1340\n",
            "Epoch 34/100\n",
            "33/33 [==============================] - 0s 15ms/step - loss: 5.5627 - mae: 6.0405\n",
            "Epoch 35/100\n",
            "33/33 [==============================] - 0s 15ms/step - loss: 5.4656 - mae: 5.9413\n",
            "Epoch 36/100\n",
            "33/33 [==============================] - 0s 15ms/step - loss: 5.3491 - mae: 5.8258\n",
            "Epoch 37/100\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 5.2397 - mae: 5.7178\n",
            "Epoch 38/100\n",
            "33/33 [==============================] - 1s 15ms/step - loss: 5.1747 - mae: 5.6543\n",
            "Epoch 39/100\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 5.1366 - mae: 5.6164\n",
            "Epoch 40/100\n",
            "33/33 [==============================] - 1s 21ms/step - loss: 5.0991 - mae: 5.5778\n",
            "Epoch 41/100\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 5.1034 - mae: 5.5833\n",
            "Epoch 42/100\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 5.1371 - mae: 5.6149\n",
            "Epoch 43/100\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 5.1539 - mae: 5.6325\n",
            "Epoch 44/100\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 5.1025 - mae: 5.5813\n",
            "Epoch 45/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 5.5236 - mae: 6.0050\n",
            "Epoch 46/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 6.0236 - mae: 6.5054\n",
            "Epoch 47/100\n",
            "33/33 [==============================] - 1s 20ms/step - loss: 6.3322 - mae: 6.8183\n",
            "Epoch 48/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 7.4925 - mae: 7.9806\n",
            "Epoch 49/100\n",
            "33/33 [==============================] - 0s 13ms/step - loss: 8.1304 - mae: 8.6211\n",
            "Epoch 50/100\n",
            "33/33 [==============================] - 0s 12ms/step - loss: 11.0320 - mae: 11.5217\n",
            "Epoch 51/100\n",
            "33/33 [==============================] - 0s 12ms/step - loss: 5.9611 - mae: 6.4466\n",
            "Epoch 52/100\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 8.0885 - mae: 8.5763\n",
            "Epoch 53/100\n",
            "33/33 [==============================] - 0s 14ms/step - loss: 8.5858 - mae: 9.0743\n",
            "Epoch 54/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 11.8131 - mae: 12.3059\n",
            "Epoch 55/100\n",
            "33/33 [==============================] - 1s 15ms/step - loss: 7.7890 - mae: 8.2772\n",
            "Epoch 56/100\n",
            "33/33 [==============================] - 1s 19ms/step - loss: 22.9060 - mae: 23.4027\n",
            "Epoch 57/100\n",
            "33/33 [==============================] - 0s 15ms/step - loss: 8.7350 - mae: 9.2233\n",
            "Epoch 58/100\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 9.8931 - mae: 10.3808\n",
            "Epoch 59/100\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 17.9399 - mae: 18.4328\n",
            "Epoch 60/100\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 23.5103 - mae: 24.0073\n",
            "Epoch 61/100\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 29.4360 - mae: 29.9315\n",
            "Epoch 62/100\n",
            "33/33 [==============================] - 1s 21ms/step - loss: 13.8915 - mae: 14.3801\n",
            "Epoch 63/100\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 14.1473 - mae: 14.6398\n",
            "Epoch 64/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 9.7588 - mae: 10.2471\n",
            "Epoch 65/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 20.0374 - mae: 20.5324\n",
            "Epoch 66/100\n",
            "33/33 [==============================] - 0s 15ms/step - loss: 11.0544 - mae: 11.5475\n",
            "Epoch 67/100\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 10.3344 - mae: 10.8213\n",
            "Epoch 68/100\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 13.2738 - mae: 13.7665\n",
            "Epoch 69/100\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 11.7537 - mae: 12.2445\n",
            "Epoch 70/100\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 14.3823 - mae: 14.8772\n",
            "Epoch 71/100\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 12.9174 - mae: 13.4111\n",
            "Epoch 72/100\n",
            "33/33 [==============================] - 0s 14ms/step - loss: 25.2012 - mae: 25.6979\n",
            "Epoch 73/100\n",
            "33/33 [==============================] - 0s 14ms/step - loss: 21.6321 - mae: 22.1287\n",
            "Epoch 74/100\n",
            "33/33 [==============================] - 0s 14ms/step - loss: 12.1974 - mae: 12.6863\n",
            "Epoch 75/100\n",
            "33/33 [==============================] - 1s 15ms/step - loss: 8.2519 - mae: 8.7384\n",
            "Epoch 76/100\n",
            "33/33 [==============================] - 1s 15ms/step - loss: 26.8818 - mae: 27.3778\n",
            "Epoch 77/100\n",
            "33/33 [==============================] - 0s 15ms/step - loss: 12.8940 - mae: 13.3868\n",
            "Epoch 78/100\n",
            "33/33 [==============================] - 0s 14ms/step - loss: 10.5070 - mae: 10.9954\n",
            "Epoch 79/100\n",
            "33/33 [==============================] - 0s 13ms/step - loss: 11.2675 - mae: 11.7560\n",
            "Epoch 80/100\n",
            "33/33 [==============================] - 0s 14ms/step - loss: 19.9350 - mae: 20.4302\n",
            "Epoch 81/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 14.7210 - mae: 15.2154\n",
            "Epoch 82/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 16.3839 - mae: 16.8756\n",
            "Epoch 83/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 13.1246 - mae: 13.6159\n",
            "Epoch 84/100\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 15.1986 - mae: 15.6932\n",
            "Epoch 85/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 21.8978 - mae: 22.3929\n",
            "Epoch 86/100\n",
            "33/33 [==============================] - 0s 15ms/step - loss: 23.7963 - mae: 24.2939\n",
            "Epoch 87/100\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 22.8037 - mae: 23.2998\n",
            "Epoch 88/100\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 26.2572 - mae: 26.7556\n",
            "Epoch 89/100\n",
            "33/33 [==============================] - 0s 15ms/step - loss: 40.5868 - mae: 41.0838\n",
            "Epoch 90/100\n",
            "33/33 [==============================] - 0s 14ms/step - loss: 32.1696 - mae: 32.6679\n",
            "Epoch 91/100\n",
            "33/33 [==============================] - 0s 13ms/step - loss: 23.1459 - mae: 23.6421\n",
            "Epoch 92/100\n",
            "33/33 [==============================] - 0s 14ms/step - loss: 49.7284 - mae: 50.2244\n",
            "Epoch 93/100\n",
            "33/33 [==============================] - 0s 14ms/step - loss: 38.4224 - mae: 38.9214\n",
            "Epoch 94/100\n",
            "33/33 [==============================] - 0s 14ms/step - loss: 45.0525 - mae: 45.5519\n",
            "Epoch 95/100\n",
            "33/33 [==============================] - 0s 14ms/step - loss: 40.5402 - mae: 41.0384\n",
            "Epoch 96/100\n",
            "33/33 [==============================] - 0s 14ms/step - loss: 40.9928 - mae: 41.4898\n",
            "Epoch 97/100\n",
            "33/33 [==============================] - 1s 15ms/step - loss: 59.3428 - mae: 59.8427\n",
            "Epoch 98/100\n",
            "33/33 [==============================] - 1s 15ms/step - loss: 54.6160 - mae: 55.1147\n",
            "Epoch 99/100\n",
            "33/33 [==============================] - 0s 15ms/step - loss: 34.3453 - mae: 34.8416\n",
            "Epoch 100/100\n",
            "33/33 [==============================] - 1s 15ms/step - loss: 161.7227 - mae: 162.2227\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KA0GM9sQ8OJO"
      },
      "source": [
        "plt.semilogx(history.history[\"lr\"], history.history[\"loss\"])\n",
        "plt.axis([1e-8, 1e-4, 0, 30])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1e-08, 0.0001, 0.0, 30.0)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hiHR5pPL8OJP",
        "tags": [
          "outputPrepend"
        ]
      },
      "source": [
        "keras.backend.clear_session()\n",
        "tf.random.set_seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "window_size = 30\n",
        "train_set = sequential_window_dataset(x_train, window_size)\n",
        "valid_set = sequential_window_dataset(x_valid, window_size)\n",
        "\n",
        "model = keras.models.Sequential([\n",
        "  keras.layers.LSTM(100, return_sequences=True, stateful=True,\n",
        "                         batch_input_shape=[1, None, 1]),\n",
        "  keras.layers.LSTM(100, return_sequences=True, stateful=True),\n",
        "  keras.layers.Dense(1),\n",
        "  keras.layers.Lambda(lambda x: x * 200.0)\n",
        "])\n",
        "optimizer = keras.optimizers.SGD(lr=5e-7, momentum=0.9)\n",
        "model.compile(loss=keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])\n",
        "reset_states = ResetStatesCallback()\n",
        "model_checkpoint = keras.callbacks.ModelCheckpoint(\n",
        "    \"my_checkpoint.h5\", save_best_only=True)\n",
        "early_stopping = keras.callbacks.EarlyStopping(patience=50)\n",
        "model.fit(train_set, epochs=500,\n",
        "          validation_data=valid_set,\n",
        "          callbacks=[early_stopping, model_checkpoint, reset_states])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "al_loss: 5.8581 - val_mae: 6.3357\n",
            "Epoch 249/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3972 - mae: 4.8737 - val_loss: 5.5920 - val_mae: 6.0704\n",
            "Epoch 250/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.4102 - mae: 4.8866 - val_loss: 5.9205 - val_mae: 6.3994\n",
            "Epoch 251/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3933 - mae: 4.8706 - val_loss: 5.5486 - val_mae: 6.0274\n",
            "Epoch 252/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.4141 - mae: 4.8910 - val_loss: 5.7430 - val_mae: 6.2200\n",
            "Epoch 253/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3968 - mae: 4.8737 - val_loss: 5.6224 - val_mae: 6.1012\n",
            "Epoch 254/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3966 - mae: 4.8724 - val_loss: 5.8626 - val_mae: 6.3402\n",
            "Epoch 255/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3857 - mae: 4.8624 - val_loss: 5.5363 - val_mae: 6.0153\n",
            "Epoch 256/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.4060 - mae: 4.8829 - val_loss: 5.7488 - val_mae: 6.2258\n",
            "Epoch 257/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3907 - mae: 4.8673 - val_loss: 5.5937 - val_mae: 6.0727\n",
            "Epoch 258/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3975 - mae: 4.8739 - val_loss: 5.8875 - val_mae: 6.3657\n",
            "Epoch 259/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3855 - mae: 4.8622 - val_loss: 5.5271 - val_mae: 6.0062\n",
            "Epoch 260/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.4097 - mae: 4.8869 - val_loss: 5.7078 - val_mae: 6.1856\n",
            "Epoch 261/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3923 - mae: 4.8694 - val_loss: 5.7467 - val_mae: 6.2246\n",
            "Epoch 262/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3842 - mae: 4.8607 - val_loss: 5.6059 - val_mae: 6.0850\n",
            "Epoch 263/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3878 - mae: 4.8634 - val_loss: 5.8217 - val_mae: 6.2985\n",
            "Epoch 264/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3776 - mae: 4.8538 - val_loss: 5.5057 - val_mae: 5.9854\n",
            "Epoch 265/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.4025 - mae: 4.8798 - val_loss: 5.7009 - val_mae: 6.1788\n",
            "Epoch 266/500\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 4.3863 - mae: 4.8633 - val_loss: 5.7467 - val_mae: 6.2247\n",
            "Epoch 267/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3802 - mae: 4.8568 - val_loss: 5.6034 - val_mae: 6.0824\n",
            "Epoch 268/500\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 4.3864 - mae: 4.8623 - val_loss: 5.8528 - val_mae: 6.3301\n",
            "Epoch 269/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3775 - mae: 4.8538 - val_loss: 5.5109 - val_mae: 5.9906\n",
            "Epoch 270/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.4024 - mae: 4.8798 - val_loss: 5.7662 - val_mae: 6.2433\n",
            "Epoch 271/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3853 - mae: 4.8625 - val_loss: 5.6061 - val_mae: 6.0851\n",
            "Epoch 272/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3894 - mae: 4.8661 - val_loss: 5.8951 - val_mae: 6.3739\n",
            "Epoch 273/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3826 - mae: 4.8592 - val_loss: 5.5336 - val_mae: 6.0126\n",
            "Epoch 274/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.4018 - mae: 4.8787 - val_loss: 5.9018 - val_mae: 6.3803\n",
            "Epoch 275/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3830 - mae: 4.8604 - val_loss: 5.4925 - val_mae: 5.9726\n",
            "Epoch 276/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.4025 - mae: 4.8801 - val_loss: 5.8301 - val_mae: 6.3072\n",
            "Epoch 277/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3823 - mae: 4.8592 - val_loss: 5.5358 - val_mae: 6.0147\n",
            "Epoch 278/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3947 - mae: 4.8733 - val_loss: 5.8860 - val_mae: 6.3646\n",
            "Epoch 279/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3854 - mae: 4.8619 - val_loss: 5.5320 - val_mae: 6.0110\n",
            "Epoch 280/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.4025 - mae: 4.8796 - val_loss: 5.8713 - val_mae: 6.3493\n",
            "Epoch 281/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3837 - mae: 4.8616 - val_loss: 5.4492 - val_mae: 5.9300\n",
            "Epoch 282/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3982 - mae: 4.8759 - val_loss: 5.7840 - val_mae: 6.2614\n",
            "Epoch 283/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3751 - mae: 4.8518 - val_loss: 5.5096 - val_mae: 5.9891\n",
            "Epoch 284/500\n",
            "33/33 [==============================] - 1s 20ms/step - loss: 4.3855 - mae: 4.8634 - val_loss: 5.8729 - val_mae: 6.3511\n",
            "Epoch 285/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3767 - mae: 4.8529 - val_loss: 5.5068 - val_mae: 5.9864\n",
            "Epoch 286/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3953 - mae: 4.8719 - val_loss: 5.8257 - val_mae: 6.3037\n",
            "Epoch 287/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3774 - mae: 4.8551 - val_loss: 5.4318 - val_mae: 5.9126\n",
            "Epoch 288/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3913 - mae: 4.8691 - val_loss: 5.7517 - val_mae: 6.2296\n",
            "Epoch 289/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3686 - mae: 4.8454 - val_loss: 5.4984 - val_mae: 5.9784\n",
            "Epoch 290/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3791 - mae: 4.8567 - val_loss: 5.8624 - val_mae: 6.3403\n",
            "Epoch 291/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3732 - mae: 4.8493 - val_loss: 5.5050 - val_mae: 5.9847\n",
            "Epoch 292/500\n",
            "33/33 [==============================] - 1s 19ms/step - loss: 4.3930 - mae: 4.8695 - val_loss: 5.8146 - val_mae: 6.2930\n",
            "Epoch 293/500\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 4.3759 - mae: 4.8537 - val_loss: 5.4218 - val_mae: 5.9026\n",
            "Epoch 294/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3884 - mae: 4.8662 - val_loss: 5.7617 - val_mae: 6.2395\n",
            "Epoch 295/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3650 - mae: 4.8418 - val_loss: 5.4840 - val_mae: 5.9642\n",
            "Epoch 296/500\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 4.3777 - mae: 4.8559 - val_loss: 5.8121 - val_mae: 6.2887\n",
            "Epoch 297/500\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 4.3739 - mae: 4.8501 - val_loss: 5.5143 - val_mae: 5.9939\n",
            "Epoch 298/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3921 - mae: 4.8689 - val_loss: 5.9021 - val_mae: 6.3818\n",
            "Epoch 299/500\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 4.3751 - mae: 4.8528 - val_loss: 5.4398 - val_mae: 5.9205\n",
            "Epoch 300/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3889 - mae: 4.8657 - val_loss: 5.8363 - val_mae: 6.3150\n",
            "Epoch 301/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3662 - mae: 4.8436 - val_loss: 5.4118 - val_mae: 5.8924\n",
            "Epoch 302/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3792 - mae: 4.8560 - val_loss: 5.8305 - val_mae: 6.3093\n",
            "Epoch 303/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3565 - mae: 4.8330 - val_loss: 5.4201 - val_mae: 5.9008\n",
            "Epoch 304/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3748 - mae: 4.8511 - val_loss: 5.7635 - val_mae: 6.2432\n",
            "Epoch 305/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3531 - mae: 4.8293 - val_loss: 5.3788 - val_mae: 5.8598\n",
            "Epoch 306/500\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 4.3755 - mae: 4.8528 - val_loss: 5.5800 - val_mae: 6.0593\n",
            "Epoch 307/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3606 - mae: 4.8364 - val_loss: 5.6763 - val_mae: 6.1575\n",
            "Epoch 308/500\n",
            "33/33 [==============================] - 1s 19ms/step - loss: 4.3522 - mae: 4.8282 - val_loss: 5.3801 - val_mae: 5.8612\n",
            "Epoch 309/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3721 - mae: 4.8490 - val_loss: 5.5760 - val_mae: 6.0555\n",
            "Epoch 310/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3596 - mae: 4.8353 - val_loss: 5.6439 - val_mae: 6.1255\n",
            "Epoch 311/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3518 - mae: 4.8279 - val_loss: 5.3755 - val_mae: 5.8567\n",
            "Epoch 312/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3747 - mae: 4.8520 - val_loss: 5.5522 - val_mae: 6.0320\n",
            "Epoch 313/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3666 - mae: 4.8421 - val_loss: 5.6639 - val_mae: 6.1455\n",
            "Epoch 314/500\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 4.3539 - mae: 4.8302 - val_loss: 5.3741 - val_mae: 5.8554\n",
            "Epoch 315/500\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 4.3779 - mae: 4.8558 - val_loss: 5.5678 - val_mae: 6.0472\n",
            "Epoch 316/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3678 - mae: 4.8430 - val_loss: 5.9225 - val_mae: 6.4033\n",
            "Epoch 317/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3519 - mae: 4.8286 - val_loss: 5.4552 - val_mae: 5.9365\n",
            "Epoch 318/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3727 - mae: 4.8502 - val_loss: 5.6763 - val_mae: 6.1553\n",
            "Epoch 319/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3574 - mae: 4.8333 - val_loss: 5.8981 - val_mae: 6.3786\n",
            "Epoch 320/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3511 - mae: 4.8274 - val_loss: 5.4701 - val_mae: 5.9512\n",
            "Epoch 321/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3703 - mae: 4.8475 - val_loss: 5.8578 - val_mae: 6.3369\n",
            "Epoch 322/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3544 - mae: 4.8310 - val_loss: 5.5185 - val_mae: 5.9986\n",
            "Epoch 323/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3724 - mae: 4.8500 - val_loss: 6.0160 - val_mae: 6.4976\n",
            "Epoch 324/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3584 - mae: 4.8355 - val_loss: 5.4581 - val_mae: 5.9395\n",
            "Epoch 325/500\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 4.3878 - mae: 4.8657 - val_loss: 5.6257 - val_mae: 6.1048\n",
            "Epoch 326/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3746 - mae: 4.8511 - val_loss: 5.9441 - val_mae: 6.4248\n",
            "Epoch 327/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3635 - mae: 4.8414 - val_loss: 5.4504 - val_mae: 5.9314\n",
            "Epoch 328/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3757 - mae: 4.8531 - val_loss: 5.8680 - val_mae: 6.3478\n",
            "Epoch 329/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3545 - mae: 4.8310 - val_loss: 5.4930 - val_mae: 5.9733\n",
            "Epoch 330/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3689 - mae: 4.8474 - val_loss: 5.9842 - val_mae: 6.4657\n",
            "Epoch 331/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3606 - mae: 4.8370 - val_loss: 5.4953 - val_mae: 5.9759\n",
            "Epoch 332/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3812 - mae: 4.8581 - val_loss: 5.9374 - val_mae: 6.4190\n",
            "Epoch 333/500\n",
            "33/33 [==============================] - 1s 19ms/step - loss: 4.3589 - mae: 4.8361 - val_loss: 5.4725 - val_mae: 5.9531\n",
            "Epoch 334/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3740 - mae: 4.8533 - val_loss: 5.8753 - val_mae: 6.3549\n",
            "Epoch 335/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3645 - mae: 4.8407 - val_loss: 5.5045 - val_mae: 5.9848\n",
            "Epoch 336/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3807 - mae: 4.8593 - val_loss: 5.5806 - val_mae: 6.0601\n",
            "Epoch 337/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3822 - mae: 4.8572 - val_loss: 5.8990 - val_mae: 6.3793\n",
            "Epoch 338/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3666 - mae: 4.8449 - val_loss: 5.3938 - val_mae: 5.8744\n",
            "Epoch 339/500\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 4.3693 - mae: 4.8476 - val_loss: 5.9319 - val_mae: 6.4132\n",
            "Epoch 340/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3497 - mae: 4.8257 - val_loss: 5.4094 - val_mae: 5.8902\n",
            "Epoch 341/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3686 - mae: 4.8449 - val_loss: 5.8535 - val_mae: 6.3334\n",
            "Epoch 342/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3456 - mae: 4.8223 - val_loss: 5.3805 - val_mae: 5.8612\n",
            "Epoch 343/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3616 - mae: 4.8384 - val_loss: 5.6956 - val_mae: 6.1773\n",
            "Epoch 344/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3406 - mae: 4.8166 - val_loss: 5.3068 - val_mae: 5.7867\n",
            "Epoch 345/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3575 - mae: 4.8342 - val_loss: 5.4798 - val_mae: 5.9613\n",
            "Epoch 346/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3478 - mae: 4.8235 - val_loss: 5.5616 - val_mae: 6.0438\n",
            "Epoch 347/500\n",
            "33/33 [==============================] - 1s 19ms/step - loss: 4.3383 - mae: 4.8148 - val_loss: 5.2838 - val_mae: 5.7642\n",
            "Epoch 348/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3572 - mae: 4.8332 - val_loss: 5.4647 - val_mae: 5.9460\n",
            "Epoch 349/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3464 - mae: 4.8219 - val_loss: 5.8400 - val_mae: 6.3204\n",
            "Epoch 350/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3292 - mae: 4.8050 - val_loss: 5.3439 - val_mae: 5.8245\n",
            "Epoch 351/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3484 - mae: 4.8243 - val_loss: 5.5462 - val_mae: 6.0267\n",
            "Epoch 352/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3415 - mae: 4.8175 - val_loss: 5.4451 - val_mae: 5.9265\n",
            "Epoch 353/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3387 - mae: 4.8156 - val_loss: 5.2892 - val_mae: 5.7697\n",
            "Epoch 354/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3529 - mae: 4.8288 - val_loss: 5.4908 - val_mae: 5.9720\n",
            "Epoch 355/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3453 - mae: 4.8207 - val_loss: 5.5629 - val_mae: 6.0433\n",
            "Epoch 356/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3436 - mae: 4.8194 - val_loss: 5.6217 - val_mae: 6.1044\n",
            "Epoch 357/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3373 - mae: 4.8141 - val_loss: 5.3213 - val_mae: 5.8019\n",
            "Epoch 358/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3556 - mae: 4.8321 - val_loss: 5.5327 - val_mae: 6.0131\n",
            "Epoch 359/500\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 4.3496 - mae: 4.8252 - val_loss: 5.5576 - val_mae: 6.0379\n",
            "Epoch 360/500\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 4.3493 - mae: 4.8247 - val_loss: 5.8081 - val_mae: 6.2896\n",
            "Epoch 361/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3378 - mae: 4.8143 - val_loss: 5.3483 - val_mae: 5.8292\n",
            "Epoch 362/500\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 4.3600 - mae: 4.8370 - val_loss: 5.5698 - val_mae: 6.0495\n",
            "Epoch 363/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3547 - mae: 4.8307 - val_loss: 5.5869 - val_mae: 6.0667\n",
            "Epoch 364/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3526 - mae: 4.8277 - val_loss: 5.9121 - val_mae: 6.3924\n",
            "Epoch 365/500\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 4.3393 - mae: 4.8157 - val_loss: 5.3666 - val_mae: 5.8475\n",
            "Epoch 366/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3615 - mae: 4.8387 - val_loss: 5.5865 - val_mae: 6.0661\n",
            "Epoch 367/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3542 - mae: 4.8302 - val_loss: 5.6235 - val_mae: 6.1038\n",
            "Epoch 368/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3489 - mae: 4.8241 - val_loss: 5.9567 - val_mae: 6.4375\n",
            "Epoch 369/500\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 4.3381 - mae: 4.8142 - val_loss: 5.3799 - val_mae: 5.8607\n",
            "Epoch 370/500\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 4.3605 - mae: 4.8375 - val_loss: 5.5902 - val_mae: 6.0698\n",
            "Epoch 371/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3507 - mae: 4.8266 - val_loss: 5.8497 - val_mae: 6.3294\n",
            "Epoch 372/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3361 - mae: 4.8119 - val_loss: 5.7482 - val_mae: 6.2289\n",
            "Epoch 373/500\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 4.3367 - mae: 4.8117 - val_loss: 5.9942 - val_mae: 6.4761\n",
            "Epoch 374/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3324 - mae: 4.8080 - val_loss: 5.4281 - val_mae: 5.9096\n",
            "Epoch 375/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3555 - mae: 4.8321 - val_loss: 5.5820 - val_mae: 6.0618\n",
            "Epoch 376/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3514 - mae: 4.8267 - val_loss: 6.0174 - val_mae: 6.4987\n",
            "Epoch 377/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3389 - mae: 4.8155 - val_loss: 5.3963 - val_mae: 5.8775\n",
            "Epoch 378/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3621 - mae: 4.8391 - val_loss: 5.6045 - val_mae: 6.0842\n",
            "Epoch 379/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3542 - mae: 4.8290 - val_loss: 6.1200 - val_mae: 6.6032\n",
            "Epoch 380/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3374 - mae: 4.8138 - val_loss: 5.4808 - val_mae: 5.9619\n",
            "Epoch 381/500\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 4.3589 - mae: 4.8359 - val_loss: 5.6184 - val_mae: 6.0985\n",
            "Epoch 382/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3517 - mae: 4.8270 - val_loss: 6.0780 - val_mae: 6.5599\n",
            "Epoch 383/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3402 - mae: 4.8169 - val_loss: 5.4240 - val_mae: 5.9053\n",
            "Epoch 384/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3587 - mae: 4.8355 - val_loss: 5.5902 - val_mae: 6.0701\n",
            "Epoch 385/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3503 - mae: 4.8254 - val_loss: 6.0539 - val_mae: 6.5355\n",
            "Epoch 386/500\n",
            "33/33 [==============================] - 1s 16ms/step - loss: 4.3374 - mae: 4.8140 - val_loss: 5.4038 - val_mae: 5.8848\n",
            "Epoch 387/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3561 - mae: 4.8326 - val_loss: 5.5741 - val_mae: 6.0539\n",
            "Epoch 388/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3478 - mae: 4.8229 - val_loss: 6.0314 - val_mae: 6.5129\n",
            "Epoch 389/500\n",
            "33/33 [==============================] - 1s 19ms/step - loss: 4.3347 - mae: 4.8112 - val_loss: 5.3889 - val_mae: 5.8697\n",
            "Epoch 390/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3535 - mae: 4.8297 - val_loss: 5.5614 - val_mae: 6.0416\n",
            "Epoch 391/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3457 - mae: 4.8209 - val_loss: 6.0233 - val_mae: 6.5047\n",
            "Epoch 392/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3321 - mae: 4.8085 - val_loss: 5.3777 - val_mae: 5.8582\n",
            "Epoch 393/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3513 - mae: 4.8273 - val_loss: 5.5537 - val_mae: 6.0340\n",
            "Epoch 394/500\n",
            "33/33 [==============================] - 1s 18ms/step - loss: 4.3436 - mae: 4.8188 - val_loss: 6.0285 - val_mae: 6.5101\n",
            "Epoch 395/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3297 - mae: 4.8062 - val_loss: 5.3775 - val_mae: 5.8580\n",
            "Epoch 396/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3493 - mae: 4.8251 - val_loss: 5.5495 - val_mae: 6.0299\n",
            "Epoch 397/500\n",
            "33/33 [==============================] - 1s 17ms/step - loss: 4.3419 - mae: 4.8171 - val_loss: 6.0412 - val_mae: 6.5231\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fdd487ac3d0>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPeZUfQy8OJQ"
      },
      "source": [
        "model = keras.models.load_model(\"my_checkpoint.h5\")"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4tFrq5uW8OJR"
      },
      "source": [
        "rnn_forecast = model.predict(series[np.newaxis, :, np.newaxis])\n",
        "rnn_forecast = rnn_forecast[0, split_time - 1:-1, 0]"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZfaR6nqj8OJT"
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(time_valid, x_valid)\n",
        "plot_series(time_valid, rnn_forecast)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wgf2u2Tp8OJV"
      },
      "source": [
        "keras.metrics.mean_absolute_error(x_valid, rnn_forecast).numpy()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.8760943"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ]
}