3_from_numpy_to_tensorflow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far, we have use a mixture of libraries: numpy, scipy and matplotlib to build, training and analyse our models. \n",
    "\n",
    "Tensorflow and Pytorch deep learning frameworks which do much of the heavy lifting. E.g. we do not need to implement our own optimisation routine with these frameworks. Much boilerplat code is also available in there respective ecosystems \n",
    "\n",
    "Generally speaking, Pytorch lends itself well to apply DL to Natural Langauge Processing whereas Tensorflow is more suited for computer vision and non-euclidean data structures; social network graphs etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf \n",
    "from tensorflow import keras \n",
    "from tensorflow.keras import layers \n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "warnings.filterwarnings('ignore')\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "tf.random.set_seed(1)\n",
    "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the **functional API** of Keras; Tensorflow's  medium to high level API for building computational graphs. \n",
    "\n",
    "**NOTE**\n",
    "If I were you I would avoid using the *sequential* method for constructing the graphs in Keras. Instead use the functional API. \n",
    "\n",
    "_Why?_ \n",
    "\n",
    "You may want to add inputs into a network that are inputted **after** the first layer; you cannot do this with the [sequential class](https://keras.io/api/models/sequential/). Hence, use the [functional one](https://keras.io/guides/functional_api/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"XOR_Gate_model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " binary_value (InputLayer)   [(None, 2)]               0         \n",
      "                                                                 \n",
      " hidden_layer_1 (Dense)      (None, 2)                 6         \n",
      "                                                                 \n",
      " hidden_layer_2 (Dense)      (None, 2)                 6         \n",
      "                                                                 \n",
      " output_layer (Dense)        (None, 1)                 3         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 15\n",
      "Trainable params: 15\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def construct_model(model_name=\"XOR_Gate_model\"):\n",
    "    input_nodes = keras.Input(shape=(2,), name=\"binary_value\")\n",
    "    dense_hidden1 = layers.Dense(units=2,activation=\"relu\", name=\"hidden_layer_1\")(input_nodes)\n",
    "    dense_hidden2 = layers.Dense(units=2,activation=\"relu\", name=\"hidden_layer_2\")(dense_hidden1)\n",
    "    output = layers.Dense(units=1, activation=\"relu\", name=\"output_layer\")(dense_hidden2)\n",
    "    return keras.Model(inputs=input_nodes,\n",
    "                       outputs=output,\n",
    "                       name=model_name)\n",
    "\n",
    "\n",
    "model = construct_model()\n",
    "model_name=\"XOR_Gate_model\"\n",
    "\n",
    "keras.utils.plot_model(model,\n",
    "                       f\"media/{model_name}_diagram.png\",\n",
    "                      show_shapes=True)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "LR = 0.001\n",
    "def compile_model(model):\n",
    "    optimizer = keras.optimizers.RMSprop(learning_rate=LR)\n",
    "    model.compile(loss='mse',\n",
    "                    optimizer=optimizer,\n",
    "                    metrics=['mae', 'mse'])\n",
    "    return model \n",
    "\n",
    "\n",
    "M = compile_model(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting up the training routine in TF. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "1/1 [==============================] - 0s 416ms/step - loss: 0.2046 - mae: 0.3347 - mse: 0.2046\n",
      "Epoch 2/2000\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.1931 - mae: 0.3319 - mse: 0.1931\n",
      "Epoch 3/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.1852 - mae: 0.3298 - mse: 0.1852\n",
      "Epoch 4/2000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.1789 - mae: 0.3280 - mse: 0.1789\n",
      "Epoch 5/2000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.1735 - mae: 0.3264 - mse: 0.1735\n",
      "Epoch 6/2000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.1687 - mae: 0.3249 - mse: 0.1687\n",
      "Epoch 7/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.1644 - mae: 0.3235 - mse: 0.1644\n",
      "Epoch 8/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.1604 - mae: 0.3222 - mse: 0.1604\n",
      "Epoch 9/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.1566 - mae: 0.3209 - mse: 0.1566\n",
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      "Epoch 15/2000\n",
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      "Epoch 16/2000\n",
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      "Epoch 18/2000\n",
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      "Epoch 19/2000\n",
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      "Epoch 20/2000\n",
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      "Epoch 21/2000\n",
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      "Epoch 22/2000\n",
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      "Epoch 23/2000\n",
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      "Epoch 24/2000\n",
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      "Epoch 25/2000\n",
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      "Epoch 26/2000\n",
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      "Epoch 27/2000\n",
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      "Epoch 28/2000\n",
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      "Epoch 29/2000\n",
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      "Epoch 30/2000\n",
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      "Epoch 31/2000\n",
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      "Epoch 32/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.1032 - mae: 0.2943 - mse: 0.1032\n",
      "Epoch 33/2000\n",
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      "Epoch 34/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0998 - mae: 0.2907 - mse: 0.0998\n",
      "Epoch 35/2000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0981 - mae: 0.2889 - mse: 0.0981\n",
      "Epoch 36/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0965 - mae: 0.2871 - mse: 0.0965\n",
      "Epoch 37/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0948 - mae: 0.2852 - mse: 0.0948\n",
      "Epoch 38/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0932 - mae: 0.2834 - mse: 0.0932\n",
      "Epoch 39/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0916 - mae: 0.2816 - mse: 0.0916\n",
      "Epoch 40/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0900 - mae: 0.2797 - mse: 0.0900\n",
      "Epoch 41/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0885 - mae: 0.2779 - mse: 0.0885\n",
      "Epoch 42/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0869 - mae: 0.2760 - mse: 0.0869\n",
      "Epoch 43/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0854 - mae: 0.2742 - mse: 0.0854\n",
      "Epoch 44/2000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0839 - mae: 0.2723 - mse: 0.0839\n",
      "Epoch 45/2000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0825 - mae: 0.2705 - mse: 0.0825\n",
      "Epoch 46/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0810 - mae: 0.2687 - mse: 0.0810\n",
      "Epoch 47/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0796 - mae: 0.2668 - mse: 0.0796\n",
      "Epoch 48/2000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0782 - mae: 0.2650 - mse: 0.0782\n",
      "Epoch 49/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0768 - mae: 0.2631 - mse: 0.0768\n",
      "Epoch 50/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0755 - mae: 0.2613 - mse: 0.0755\n",
      "Epoch 51/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0741 - mae: 0.2594 - mse: 0.0741\n",
      "Epoch 52/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0728 - mae: 0.2576 - mse: 0.0728\n",
      "Epoch 53/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0715 - mae: 0.2558 - mse: 0.0715\n",
      "Epoch 54/2000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0705 - mae: 0.2545 - mse: 0.0705\n",
      "Epoch 55/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0695 - mae: 0.2533 - mse: 0.0695\n",
      "Epoch 56/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0685 - mae: 0.2521 - mse: 0.0685\n",
      "Epoch 57/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0676 - mae: 0.2509 - mse: 0.0676\n",
      "Epoch 58/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0666 - mae: 0.2497 - mse: 0.0666\n",
      "Epoch 59/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0658 - mae: 0.2485 - mse: 0.0658\n",
      "Epoch 60/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0649 - mae: 0.2473 - mse: 0.0649\n",
      "Epoch 61/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0641 - mae: 0.2461 - mse: 0.0641\n",
      "Epoch 62/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0632 - mae: 0.2448 - mse: 0.0632\n",
      "Epoch 63/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0624 - mae: 0.2436 - mse: 0.0624\n",
      "Epoch 64/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0616 - mae: 0.2423 - mse: 0.0616\n",
      "Epoch 65/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0605 - mae: 0.2402 - mse: 0.0605\n",
      "Epoch 66/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0594 - mae: 0.2382 - mse: 0.0594\n",
      "Epoch 67/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0584 - mae: 0.2363 - mse: 0.0584\n",
      "Epoch 68/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0574 - mae: 0.2344 - mse: 0.0574\n",
      "Epoch 69/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0564 - mae: 0.2326 - mse: 0.0564\n",
      "Epoch 70/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0555 - mae: 0.2308 - mse: 0.0555\n",
      "Epoch 71/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0546 - mae: 0.2290 - mse: 0.0546\n",
      "Epoch 72/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0537 - mae: 0.2273 - mse: 0.0537\n",
      "Epoch 73/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0529 - mae: 0.2256 - mse: 0.0529\n",
      "Epoch 74/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0521 - mae: 0.2238 - mse: 0.0521\n",
      "Epoch 75/2000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0513 - mae: 0.2221 - mse: 0.0513\n",
      "Epoch 76/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0505 - mae: 0.2204 - mse: 0.0505\n",
      "Epoch 77/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0498 - mae: 0.2188 - mse: 0.0498\n",
      "Epoch 78/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0490 - mae: 0.2171 - mse: 0.0490\n",
      "Epoch 79/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0483 - mae: 0.2154 - mse: 0.0483\n",
      "Epoch 80/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0476 - mae: 0.2138 - mse: 0.0476\n",
      "Epoch 81/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0472 - mae: 0.2129 - mse: 0.0472\n",
      "Epoch 82/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0466 - mae: 0.2117 - mse: 0.0466\n",
      "Epoch 83/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0459 - mae: 0.2100 - mse: 0.0459\n",
      "Epoch 84/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0456 - mae: 0.2093 - mse: 0.0456\n",
      "Epoch 85/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0450 - mae: 0.2078 - mse: 0.0450\n",
      "Epoch 86/2000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0445 - mae: 0.2066 - mse: 0.0445\n",
      "Epoch 87/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0440 - mae: 0.2055 - mse: 0.0440\n",
      "Epoch 88/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0435 - mae: 0.2040 - mse: 0.0435\n",
      "Epoch 89/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0431 - mae: 0.2031 - mse: 0.0431\n",
      "Epoch 90/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0425 - mae: 0.2016 - mse: 0.0425\n",
      "Epoch 91/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0422 - mae: 0.2007 - mse: 0.0422\n",
      "Epoch 92/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0416 - mae: 0.1992 - mse: 0.0416\n",
      "Epoch 93/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0413 - mae: 0.1982 - mse: 0.0413\n",
      "Epoch 94/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0407 - mae: 0.1968 - mse: 0.0407\n",
      "Epoch 95/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0404 - mae: 0.1957 - mse: 0.0404\n",
      "Epoch 96/2000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.0398 - mae: 0.1944 - mse: 0.0398\n",
      "Epoch 97/2000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.0394 - mae: 0.1931 - mse: 0.0394\n",
      "Epoch 98/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0390 - mae: 0.1920 - mse: 0.0390\n",
      "Epoch 99/2000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0385 - mae: 0.1905 - mse: 0.0385\n",
      "Epoch 100/2000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0381 - mae: 0.1896 - mse: 0.0381\n",
      "Epoch 101/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0375 - mae: 0.1879 - mse: 0.0375\n",
      "Epoch 102/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0372 - mae: 0.1872 - mse: 0.0372\n",
      "Epoch 103/2000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0366 - mae: 0.1853 - mse: 0.0366\n",
      "Epoch 104/2000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0363 - mae: 0.1845 - mse: 0.0363\n",
      "Epoch 105/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0358 - mae: 0.1830 - mse: 0.0358\n",
      "Epoch 106/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0354 - mae: 0.1817 - mse: 0.0354\n",
      "Epoch 107/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0349 - mae: 0.1806 - mse: 0.0349\n",
      "Epoch 108/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0344 - mae: 0.1790 - mse: 0.0344\n",
      "Epoch 109/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0341 - mae: 0.1781 - mse: 0.0341\n",
      "Epoch 110/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0335 - mae: 0.1763 - mse: 0.0335\n",
      "Epoch 111/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0332 - mae: 0.1754 - mse: 0.0332\n",
      "Epoch 112/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0327 - mae: 0.1739 - mse: 0.0327\n",
      "Epoch 113/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0323 - mae: 0.1726 - mse: 0.0323\n",
      "Epoch 114/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0318 - mae: 0.1714 - mse: 0.0318\n",
      "Epoch 115/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0314 - mae: 0.1698 - mse: 0.0314\n",
      "Epoch 116/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0310 - mae: 0.1690 - mse: 0.0310\n",
      "Epoch 117/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0305 - mae: 0.1671 - mse: 0.0305\n",
      "Epoch 118/2000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0302 - mae: 0.1661 - mse: 0.0302\n",
      "Epoch 119/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0297 - mae: 0.1647 - mse: 0.0297\n",
      "Epoch 120/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0293 - mae: 0.1633 - mse: 0.0293\n",
      "Epoch 121/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0289 - mae: 0.1622 - mse: 0.0289\n",
      "Epoch 122/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0284 - mae: 0.1605 - mse: 0.0284\n",
      "Epoch 123/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0281 - mae: 0.1597 - mse: 0.0281\n",
      "Epoch 124/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0276 - mae: 0.1578 - mse: 0.0276\n",
      "Epoch 125/2000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0274 - mae: 0.1570 - mse: 0.0274\n",
      "Epoch 126/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0270 - mae: 0.1560 - mse: 0.0270\n",
      "Epoch 127/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0265 - mae: 0.1542 - mse: 0.0265\n",
      "Epoch 128/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0261 - mae: 0.1527 - mse: 0.0261\n",
      "Epoch 129/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0258 - mae: 0.1517 - mse: 0.0258\n",
      "Epoch 130/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0253 - mae: 0.1500 - mse: 0.0253\n",
      "Epoch 131/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0251 - mae: 0.1491 - mse: 0.0251\n",
      "Epoch 132/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0247 - mae: 0.1481 - mse: 0.0247\n",
      "Epoch 133/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0242 - mae: 0.1463 - mse: 0.0242\n",
      "Epoch 134/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0239 - mae: 0.1448 - mse: 0.0239\n",
      "Epoch 135/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0235 - mae: 0.1438 - mse: 0.0235\n",
      "Epoch 136/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0231 - mae: 0.1421 - mse: 0.0231\n",
      "Epoch 137/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0229 - mae: 0.1411 - mse: 0.0229\n",
      "Epoch 138/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0225 - mae: 0.1402 - mse: 0.0225\n",
      "Epoch 139/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0221 - mae: 0.1385 - mse: 0.0221\n",
      "Epoch 140/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0217 - mae: 0.1369 - mse: 0.0217\n",
      "Epoch 141/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0214 - mae: 0.1359 - mse: 0.0214\n",
      "Epoch 142/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0211 - mae: 0.1345 - mse: 0.0211\n",
      "Epoch 143/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0208 - mae: 0.1333 - mse: 0.0208\n",
      "Epoch 144/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0205 - mae: 0.1324 - mse: 0.0205\n",
      "Epoch 145/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0201 - mae: 0.1306 - mse: 0.0201\n",
      "Epoch 146/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0198 - mae: 0.1291 - mse: 0.0198\n",
      "Epoch 147/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0195 - mae: 0.1283 - mse: 0.0195\n",
      "Epoch 148/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0192 - mae: 0.1266 - mse: 0.0192\n",
      "Epoch 149/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0189 - mae: 0.1253 - mse: 0.0189\n",
      "Epoch 150/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0186 - mae: 0.1241 - mse: 0.0186\n",
      "Epoch 151/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0182 - mae: 0.1226 - mse: 0.0182\n",
      "Epoch 152/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0180 - mae: 0.1216 - mse: 0.0180\n",
      "Epoch 153/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0177 - mae: 0.1202 - mse: 0.0177\n",
      "Epoch 154/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0174 - mae: 0.1192 - mse: 0.0174\n",
      "Epoch 155/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0171 - mae: 0.1178 - mse: 0.0171\n",
      "Epoch 156/2000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0168 - mae: 0.1164 - mse: 0.0168\n",
      "Epoch 157/2000\n",
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      "Epoch 225/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0039 - mae: 0.0453 - mse: 0.0039\n",
      "Epoch 226/2000\n",
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      "Epoch 227/2000\n",
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      "Epoch 228/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0036 - mae: 0.0437 - mse: 0.0036\n",
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      "1/1 [==============================] - 0s 2ms/step - loss: 0.0034 - mae: 0.0425 - mse: 0.0034\n",
      "Epoch 231/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0033 - mae: 0.0411 - mse: 0.0033\n",
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      "Epoch 235/2000\n",
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      "1/1 [==============================] - 0s 4ms/step - loss: 0.0027 - mae: 0.0375 - mse: 0.0027\n",
      "Epoch 238/2000\n",
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      "Epoch 245/2000\n",
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      "Epoch 246/2000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.0019 - mae: 0.0319 - mse: 0.0019\n",
      "Epoch 247/2000\n",
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      "Epoch 249/2000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.0017 - mae: 0.0303 - mse: 0.0017\n",
      "Epoch 250/2000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.0016 - mae: 0.0296 - mse: 0.0016\n",
      "Epoch 251/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0016 - mae: 0.0290 - mse: 0.0016\n",
      "Epoch 252/2000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0015 - mae: 0.0282 - mse: 0.0015\n",
      "Epoch 253/2000\n",
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      "Epoch 254/2000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0014 - mae: 0.0269 - mse: 0.0014\n",
      "Epoch 255/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0013 - mae: 0.0263 - mse: 0.0013\n",
      "Epoch 256/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0012 - mae: 0.0256 - mse: 0.0012\n",
      "Epoch 257/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0012 - mae: 0.0250 - mse: 0.0012\n",
      "Epoch 258/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0011 - mae: 0.0244 - mse: 0.0011\n",
      "Epoch 259/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0011 - mae: 0.0239 - mse: 0.0011\n",
      "Epoch 260/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0010 - mae: 0.0233 - mse: 0.0010\n",
      "Epoch 261/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 9.4990e-04 - mae: 0.0228 - mse: 9.4990e-04\n",
      "Epoch 262/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 8.9931e-04 - mae: 0.0222 - mse: 8.9931e-04\n",
      "Epoch 263/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.4952e-04 - mae: 0.0216 - mse: 8.4952e-04\n",
      "Epoch 264/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 8.0177e-04 - mae: 0.0210 - mse: 8.0177e-04\n",
      "Epoch 265/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.5478e-04 - mae: 0.0204 - mse: 7.5478e-04\n",
      "Epoch 266/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.0992e-04 - mae: 0.0198 - mse: 7.0992e-04\n",
      "Epoch 267/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 6.6610e-04 - mae: 0.0192 - mse: 6.6610e-04\n",
      "Epoch 268/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 6.2450e-04 - mae: 0.0185 - mse: 6.2450e-04\n",
      "Epoch 269/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 5.8416e-04 - mae: 0.0180 - mse: 5.8416e-04\n",
      "Epoch 270/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 5.4600e-04 - mae: 0.0173 - mse: 5.4600e-04\n",
      "Epoch 271/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 5.0914e-04 - mae: 0.0168 - mse: 5.0914e-04\n",
      "Epoch 272/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 4.7440e-04 - mae: 0.0162 - mse: 4.7440e-04\n",
      "Epoch 273/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 4.4088e-04 - mae: 0.0158 - mse: 4.4088e-04\n",
      "Epoch 274/2000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 4.0940e-04 - mae: 0.0152 - mse: 4.0940e-04\n",
      "Epoch 275/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 3.7897e-04 - mae: 0.0147 - mse: 3.7897e-04\n",
      "Epoch 276/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 3.5047e-04 - mae: 0.0141 - mse: 3.5047e-04\n",
      "Epoch 277/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 3.2282e-04 - mae: 0.0137 - mse: 3.2282e-04\n",
      "Epoch 278/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 2.9700e-04 - mae: 0.0131 - mse: 2.9700e-04\n",
      "Epoch 279/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 2.7201e-04 - mae: 0.0126 - mse: 2.7201e-04\n",
      "Epoch 280/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.4882e-04 - mae: 0.0120 - mse: 2.4882e-04\n",
      "Epoch 281/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.2654e-04 - mae: 0.0116 - mse: 2.2654e-04\n",
      "Epoch 282/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 2.0605e-04 - mae: 0.0109 - mse: 2.0605e-04\n",
      "Epoch 283/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.8654e-04 - mae: 0.0105 - mse: 1.8654e-04\n",
      "Epoch 284/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 1.6876e-04 - mae: 0.0100 - mse: 1.6876e-04\n",
      "Epoch 285/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 1.5194e-04 - mae: 0.0096 - mse: 1.5194e-04\n",
      "Epoch 286/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.3675e-04 - mae: 0.0090 - mse: 1.3675e-04\n",
      "Epoch 287/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.2242e-04 - mae: 0.0087 - mse: 1.2242e-04\n",
      "Epoch 288/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 1.0960e-04 - mae: 0.0081 - mse: 1.0960e-04\n",
      "Epoch 289/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 9.7501e-05 - mae: 0.0078 - mse: 9.7501e-05\n",
      "Epoch 290/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.6752e-05 - mae: 0.0073 - mse: 8.6752e-05\n",
      "Epoch 291/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.6601e-05 - mae: 0.0070 - mse: 7.6601e-05\n",
      "Epoch 292/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6.7663e-05 - mae: 0.0065 - mse: 6.7663e-05\n",
      "Epoch 293/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 5.9261e-05 - mae: 0.0062 - mse: 5.9261e-05\n",
      "Epoch 294/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 5.1971e-05 - mae: 0.0057 - mse: 5.1971e-05\n",
      "Epoch 295/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 4.5202e-05 - mae: 0.0055 - mse: 4.5202e-05\n",
      "Epoch 296/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 3.9451e-05 - mae: 0.0050 - mse: 3.9451e-05\n",
      "Epoch 297/2000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 3.4195e-05 - mae: 0.0048 - mse: 3.4195e-05\n",
      "Epoch 298/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 2.9844e-05 - mae: 0.0043 - mse: 2.9844e-05\n",
      "Epoch 299/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 2.5923e-05 - mae: 0.0042 - mse: 2.5923e-05\n",
      "Epoch 300/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 2.2770e-05 - mae: 0.0037 - mse: 2.2770e-05\n",
      "Epoch 301/2000\n",
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      "Epoch 302/2000\n",
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      "Epoch 750/2000\n",
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      "Epoch 768/2000\n",
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      "Epoch 777/2000\n",
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      "Epoch 778/2000\n",
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      "Epoch 780/2000\n",
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      "Epoch 800/2000\n",
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      "Epoch 801/2000\n",
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      "Epoch 802/2000\n",
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      "Epoch 820/2000\n",
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      "Epoch 839/2000\n",
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      "Epoch 900/2000\n",
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      "Epoch 924/2000\n",
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      "Epoch 950/2000\n",
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      "Epoch 953/2000\n",
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      "Epoch 960/2000\n",
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      "Epoch 961/2000\n",
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      "Epoch 962/2000\n",
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      "Epoch 964/2000\n",
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      "Epoch 965/2000\n",
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      "Epoch 968/2000\n",
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      "Epoch 969/2000\n",
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      "Epoch 970/2000\n",
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      "Epoch 971/2000\n",
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      "Epoch 972/2000\n",
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      "Epoch 978/2000\n",
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      "Epoch 999/2000\n",
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      "Epoch 1000/2000\n",
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      "Epoch 1001/2000\n",
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      "Epoch 1002/2000\n",
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      "Epoch 1008/2000\n",
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      "Epoch 1010/2000\n",
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      "Epoch 1011/2000\n",
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      "Epoch 1019/2000\n",
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      "Epoch 1020/2000\n",
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      "Epoch 1021/2000\n",
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      "Epoch 1022/2000\n",
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      "Epoch 1023/2000\n",
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      "Epoch 1024/2000\n",
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      "Epoch 1025/2000\n",
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      "Epoch 1026/2000\n",
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      "Epoch 1028/2000\n",
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      "Epoch 1029/2000\n",
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      "Epoch 1055/2000\n",
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      "Epoch 1056/2000\n",
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      "Epoch 1058/2000\n",
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      "Epoch 1059/2000\n",
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      "Epoch 1068/2000\n",
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      "Epoch 1070/2000\n",
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      "Epoch 1072/2000\n",
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      "Epoch 1073/2000\n",
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      "Epoch 1074/2000\n",
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      "Epoch 1075/2000\n",
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      "Epoch 1079/2000\n",
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      "Epoch 1080/2000\n",
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      "Epoch 1081/2000\n",
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      "Epoch 1083/2000\n",
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      "Epoch 1084/2000\n",
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      "Epoch 1085/2000\n",
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      "Epoch 1086/2000\n",
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      "Epoch 1087/2000\n",
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      "Epoch 1088/2000\n",
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      "Epoch 1089/2000\n",
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      "Epoch 1091/2000\n",
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      "Epoch 1095/2000\n",
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      "Epoch 1096/2000\n",
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      "Epoch 1097/2000\n",
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      "Epoch 1098/2000\n",
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      "Epoch 1099/2000\n",
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      "Epoch 1100/2000\n",
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      "Epoch 1101/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 8.1971e-06 - mae: 0.0020 - mse: 8.1971e-06\n",
      "Epoch 1102/2000\n",
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      "Epoch 1104/2000\n",
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      "Epoch 1105/2000\n",
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      "Epoch 1199/2000\n",
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      "Epoch 1200/2000\n",
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      "Epoch 1770/2000\n",
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      "Epoch 1800/2000\n",
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      "Epoch 1830/2000\n",
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      "Epoch 1848/2000\n",
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      "Epoch 1850/2000\n",
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      "Epoch 1860/2000\n",
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      "Epoch 1861/2000\n",
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      "Epoch 1862/2000\n",
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      "Epoch 1863/2000\n",
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      "Epoch 1864/2000\n",
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      "Epoch 1865/2000\n",
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      "Epoch 1866/2000\n",
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      "Epoch 1867/2000\n",
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      "Epoch 1868/2000\n",
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      "Epoch 1869/2000\n",
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      "Epoch 1870/2000\n",
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      "Epoch 1871/2000\n",
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      "Epoch 1872/2000\n",
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      "Epoch 1873/2000\n",
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      "Epoch 1874/2000\n",
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      "Epoch 1875/2000\n",
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      "Epoch 1876/2000\n",
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      "Epoch 1877/2000\n",
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      "Epoch 1878/2000\n",
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      "Epoch 1879/2000\n",
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      "Epoch 1880/2000\n",
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      "Epoch 1881/2000\n",
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      "Epoch 1882/2000\n",
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      "Epoch 1883/2000\n",
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      "Epoch 1884/2000\n",
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      "Epoch 1885/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 8.4141e-06 - mae: 0.0020 - mse: 8.4141e-06\n",
      "Epoch 1886/2000\n",
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      "Epoch 1887/2000\n",
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      "Epoch 1888/2000\n",
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      "Epoch 1889/2000\n",
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      "Epoch 1890/2000\n",
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      "Epoch 1891/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.1028e-06 - mae: 0.0020 - mse: 8.1028e-06\n",
      "Epoch 1892/2000\n",
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      "Epoch 1893/2000\n",
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      "Epoch 1894/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.1536e-06 - mae: 0.0020 - mse: 8.1536e-06\n",
      "Epoch 1895/2000\n",
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      "Epoch 1896/2000\n",
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      "Epoch 1897/2000\n",
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      "Epoch 1898/2000\n",
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      "Epoch 1899/2000\n",
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      "Epoch 1900/2000\n",
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      "Epoch 1901/2000\n",
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      "Epoch 1902/2000\n",
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      "Epoch 1903/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1514e-06 - mae: 0.0020 - mse: 8.1514e-06\n",
      "Epoch 1904/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.0795e-06 - mae: 0.0020 - mse: 8.0795e-06\n",
      "Epoch 1905/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1009e-06 - mae: 0.0020 - mse: 8.1009e-06\n",
      "Epoch 1906/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.0900e-06 - mae: 0.0020 - mse: 8.0900e-06\n",
      "Epoch 1907/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1642e-06 - mae: 0.0020 - mse: 8.1642e-06\n",
      "Epoch 1908/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1877e-06 - mae: 0.0020 - mse: 8.1877e-06\n",
      "Epoch 1909/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.2976e-06 - mae: 0.0020 - mse: 8.2976e-06\n",
      "Epoch 1910/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.3034e-06 - mae: 0.0020 - mse: 8.3034e-06\n",
      "Epoch 1911/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.4019e-06 - mae: 0.0020 - mse: 8.4019e-06\n",
      "Epoch 1912/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 8.3475e-06 - mae: 0.0020 - mse: 8.3475e-06\n",
      "Epoch 1913/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 8.3841e-06 - mae: 0.0020 - mse: 8.3841e-06\n",
      "Epoch 1914/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 8.2764e-06 - mae: 0.0020 - mse: 8.2764e-06\n",
      "Epoch 1915/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.2517e-06 - mae: 0.0020 - mse: 8.2517e-06\n",
      "Epoch 1916/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1464e-06 - mae: 0.0020 - mse: 8.1464e-06\n",
      "Epoch 1917/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1253e-06 - mae: 0.0020 - mse: 8.1253e-06\n",
      "Epoch 1918/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.0701e-06 - mae: 0.0020 - mse: 8.0701e-06\n",
      "Epoch 1919/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.1081e-06 - mae: 0.0020 - mse: 8.1081e-06\n",
      "Epoch 1920/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.1110e-06 - mae: 0.0020 - mse: 8.1110e-06\n",
      "Epoch 1921/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.1932e-06 - mae: 0.0020 - mse: 8.1932e-06\n",
      "Epoch 1922/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.2190e-06 - mae: 0.0020 - mse: 8.2190e-06\n",
      "Epoch 1923/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.3333e-06 - mae: 0.0020 - mse: 8.3333e-06\n",
      "Epoch 1924/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.3261e-06 - mae: 0.0020 - mse: 8.3261e-06\n",
      "Epoch 1925/2000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 8.4125e-06 - mae: 0.0020 - mse: 8.4125e-06\n",
      "Epoch 1926/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.3404e-06 - mae: 0.0020 - mse: 8.3404e-06\n",
      "Epoch 1927/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.3579e-06 - mae: 0.0020 - mse: 8.3579e-06\n",
      "Epoch 1928/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.2441e-06 - mae: 0.0020 - mse: 8.2441e-06\n",
      "Epoch 1929/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.2104e-06 - mae: 0.0020 - mse: 8.2104e-06\n",
      "Epoch 1930/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1148e-06 - mae: 0.0020 - mse: 8.1148e-06\n",
      "Epoch 1931/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1086e-06 - mae: 0.0020 - mse: 8.1086e-06\n",
      "Epoch 1932/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.0705e-06 - mae: 0.0020 - mse: 8.0705e-06\n",
      "Epoch 1933/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.1240e-06 - mae: 0.0020 - mse: 8.1240e-06\n",
      "Epoch 1934/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1372e-06 - mae: 0.0020 - mse: 8.1372e-06\n",
      "Epoch 1935/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.2300e-06 - mae: 0.0020 - mse: 8.2300e-06\n",
      "Epoch 1936/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.2511e-06 - mae: 0.0020 - mse: 8.2511e-06\n",
      "Epoch 1937/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.3628e-06 - mae: 0.0020 - mse: 8.3628e-06\n",
      "Epoch 1938/2000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 8.3398e-06 - mae: 0.0020 - mse: 8.3398e-06\n",
      "Epoch 1939/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.4087e-06 - mae: 0.0020 - mse: 8.4087e-06\n",
      "Epoch 1940/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.3219e-06 - mae: 0.0020 - mse: 8.3219e-06\n",
      "Epoch 1941/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.3219e-06 - mae: 0.0020 - mse: 8.3219e-06\n",
      "Epoch 1942/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.2075e-06 - mae: 0.0020 - mse: 8.2075e-06\n",
      "Epoch 1943/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.1710e-06 - mae: 0.0020 - mse: 8.1710e-06\n",
      "Epoch 1944/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.0894e-06 - mae: 0.0020 - mse: 8.0894e-06\n",
      "Epoch 1945/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1003e-06 - mae: 0.0020 - mse: 8.1003e-06\n",
      "Epoch 1946/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.0795e-06 - mae: 0.0020 - mse: 8.0795e-06\n",
      "Epoch 1947/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.1464e-06 - mae: 0.0020 - mse: 8.1464e-06\n",
      "Epoch 1948/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1673e-06 - mae: 0.0020 - mse: 8.1673e-06\n",
      "Epoch 1949/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.2725e-06 - mae: 0.0020 - mse: 8.2725e-06\n",
      "Epoch 1950/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.2851e-06 - mae: 0.0020 - mse: 8.2851e-06\n",
      "Epoch 1951/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.3899e-06 - mae: 0.0020 - mse: 8.3899e-06\n",
      "Epoch 1952/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.3462e-06 - mae: 0.0020 - mse: 8.3462e-06\n",
      "Epoch 1953/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.3947e-06 - mae: 0.0020 - mse: 8.3947e-06\n",
      "Epoch 1954/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.2944e-06 - mae: 0.0020 - mse: 8.2944e-06\n",
      "Epoch 1955/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.2775e-06 - mae: 0.0020 - mse: 8.2775e-06\n",
      "Epoch 1956/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 8.1678e-06 - mae: 0.0020 - mse: 8.1678e-06\n",
      "Epoch 1957/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1393e-06 - mae: 0.0020 - mse: 8.1393e-06\n",
      "Epoch 1958/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.0747e-06 - mae: 0.0020 - mse: 8.0747e-06\n",
      "Epoch 1959/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1031e-06 - mae: 0.0020 - mse: 8.1031e-06\n",
      "Epoch 1960/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.0982e-06 - mae: 0.0020 - mse: 8.0982e-06\n",
      "Epoch 1961/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1758e-06 - mae: 0.0020 - mse: 8.1758e-06\n",
      "Epoch 1962/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.2003e-06 - mae: 0.0020 - mse: 8.2003e-06\n",
      "Epoch 1963/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.3123e-06 - mae: 0.0020 - mse: 8.3123e-06\n",
      "Epoch 1964/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.3129e-06 - mae: 0.0020 - mse: 8.3129e-06\n",
      "Epoch 1965/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.4065e-06 - mae: 0.0020 - mse: 8.4065e-06\n",
      "Epoch 1966/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 8.3443e-06 - mae: 0.0020 - mse: 8.3443e-06\n",
      "Epoch 1967/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 8.3717e-06 - mae: 0.0020 - mse: 8.3717e-06\n",
      "Epoch 1968/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.2608e-06 - mae: 0.0020 - mse: 8.2608e-06\n",
      "Epoch 1969/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.2328e-06 - mae: 0.0020 - mse: 8.2328e-06\n",
      "Epoch 1970/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1314e-06 - mae: 0.0020 - mse: 8.1314e-06\n",
      "Epoch 1971/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.1163e-06 - mae: 0.0020 - mse: 8.1163e-06\n",
      "Epoch 1972/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 8.0686e-06 - mae: 0.0020 - mse: 8.0686e-06\n",
      "Epoch 1973/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1134e-06 - mae: 0.0020 - mse: 8.1134e-06\n",
      "Epoch 1974/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1206e-06 - mae: 0.0020 - mse: 8.1206e-06\n",
      "Epoch 1975/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.2066e-06 - mae: 0.0020 - mse: 8.2066e-06\n",
      "Epoch 1976/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.2313e-06 - mae: 0.0020 - mse: 8.2313e-06\n",
      "Epoch 1977/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.3465e-06 - mae: 0.0020 - mse: 8.3465e-06\n",
      "Epoch 1978/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.3334e-06 - mae: 0.0020 - mse: 8.3334e-06\n",
      "Epoch 1979/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 8.4135e-06 - mae: 0.0020 - mse: 8.4135e-06\n",
      "Epoch 1980/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.3341e-06 - mae: 0.0020 - mse: 8.3341e-06\n",
      "Epoch 1981/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.3426e-06 - mae: 0.0020 - mse: 8.3426e-06\n",
      "Epoch 1982/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.2273e-06 - mae: 0.0020 - mse: 8.2273e-06\n",
      "Epoch 1983/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.1919e-06 - mae: 0.0020 - mse: 8.1919e-06\n",
      "Epoch 1984/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1017e-06 - mae: 0.0020 - mse: 8.1017e-06\n",
      "Epoch 1985/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1027e-06 - mae: 0.0020 - mse: 8.1027e-06\n",
      "Epoch 1986/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.0718e-06 - mae: 0.0020 - mse: 8.0718e-06\n",
      "Epoch 1987/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.1309e-06 - mae: 0.0020 - mse: 8.1309e-06\n",
      "Epoch 1988/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1478e-06 - mae: 0.0020 - mse: 8.1478e-06\n",
      "Epoch 1989/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.2463e-06 - mae: 0.0020 - mse: 8.2463e-06\n",
      "Epoch 1990/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.2646e-06 - mae: 0.0020 - mse: 8.2646e-06\n",
      "Epoch 1991/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.3742e-06 - mae: 0.0020 - mse: 8.3742e-06\n",
      "Epoch 1992/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.3427e-06 - mae: 0.0020 - mse: 8.3427e-06\n",
      "Epoch 1993/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.4039e-06 - mae: 0.0020 - mse: 8.4039e-06\n",
      "Epoch 1994/2000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 8.3112e-06 - mae: 0.0020 - mse: 8.3112e-06\n",
      "Epoch 1995/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.3038e-06 - mae: 0.0020 - mse: 8.3038e-06\n",
      "Epoch 1996/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1901e-06 - mae: 0.0020 - mse: 8.1901e-06\n",
      "Epoch 1997/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1538e-06 - mae: 0.0020 - mse: 8.1538e-06\n",
      "Epoch 1998/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.0800e-06 - mae: 0.0020 - mse: 8.0800e-06\n",
      "Epoch 1999/2000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.0991e-06 - mae: 0.0020 - mse: 8.0991e-06\n",
      "Epoch 2000/2000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.0855e-06 - mae: 0.0020 - mse: 8.0855e-06\n"
     ]
    }
   ],
   "source": [
    "MAX_EPOCHS = 2000\n",
    "\n",
    "X = tf.constant([[1, 1],\n",
    "                 [0, 1],\n",
    "                 [1,0],\n",
    "                 [0,0]])\n",
    "\n",
    "Yxor = tf.constant( [\n",
    "    [0],\n",
    "    [1],\n",
    "    [1],\n",
    "    [0]\n",
    "    ])\n",
    "\n",
    "result = M.fit(X, Yxor, epochs=MAX_EPOCHS, verbose=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_training_loss_hist(hist):\n",
    "    fig = plt.figure(figsize=(10,8))\n",
    "    plt.plot(hist['loss'])\n",
    "    plt.title('MSE XOR Gate')\n",
    "    plt.ylabel('Loss [MSE]')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.show()\n",
    "    \n",
    "plot_training_loss_hist(result.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        ],\n",
       "       [0.99564004],\n",
       "       [0.99630916],\n",
       "       [0.        ]], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = result.model\n",
    "clf.predict(X)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ground-up-Awl4p5GG",
   "language": "python",
   "name": "ground-up-awl4p5gg"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}