Hidden Layers And Hidden Neurons.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Keras Tuner- Decide Number of Hidden Layers And Neuron In Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from kerastuner.tuners import RandomSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('Real_Combine.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>TM</th>\n",
       "      <th>Tm</th>\n",
       "      <th>SLP</th>\n",
       "      <th>H</th>\n",
       "      <th>VV</th>\n",
       "      <th>V</th>\n",
       "      <th>VM</th>\n",
       "      <th>PM 2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>9.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.3</td>\n",
       "      <td>9.4</td>\n",
       "      <td>219.720833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>12.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1018.5</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>11.1</td>\n",
       "      <td>182.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.7</td>\n",
       "      <td>13.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1019.4</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>11.1</td>\n",
       "      <td>154.037500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.6</td>\n",
       "      <td>15.5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1018.7</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>20.6</td>\n",
       "      <td>223.208333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.4</td>\n",
       "      <td>20.9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1017.3</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>22.2</td>\n",
       "      <td>200.645833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "      T    TM   Tm     SLP     H   VV    V    VM      PM 2.5\n",
       "0   7.4   9.8  4.8  1017.6  93.0  0.5  4.3   9.4  219.720833\n",
       "1   7.8  12.7  4.4  1018.5  87.0  0.6  4.4  11.1  182.187500\n",
       "2   6.7  13.4  2.4  1019.4  82.0  0.6  4.8  11.1  154.037500\n",
       "3   8.6  15.5  3.3  1018.7  72.0  0.8  8.1  20.6  223.208333\n",
       "4  12.4  20.9  4.4  1017.3  61.0  1.3  8.7  22.2  200.645833"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.iloc[:,:-1] ## independent features\n",
    "y=df.iloc[:,-1] ## dependent features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyperparameters\n",
    "1. How many number of hidden layers we should have?\n",
    "2. How many number of neurons we should have in hidden layers?\n",
    "3. Learning Rate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(hp):\n",
    "    model = keras.Sequential()\n",
    "    for i in range(hp.Int('num_layers', 2, 20)):\n",
    "        model.add(layers.Dense(units=hp.Int('units_' + str(i),\n",
    "                                            min_value=32,\n",
    "                                            max_value=512,\n",
    "                                            step=32),\n",
    "                               activation='relu'))\n",
    "    model.add(layers.Dense(1, activation='linear'))\n",
    "    model.compile(\n",
    "        optimizer=keras.optimizers.Adam(\n",
    "            hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])),\n",
    "        loss='mean_absolute_error',\n",
    "        metrics=['mean_absolute_error'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tuner = RandomSearch(\n",
    "    build_model,\n",
    "    objective='val_mean_absolute_error',\n",
    "    max_trials=5,\n",
    "    executions_per_trial=3,\n",
    "    directory='project',\n",
    "    project_name='Air Quality Index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
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    {
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     "text": [
      "Epoch 1/5\n",
      "WARNING:tensorflow:Layer dense is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "24/24 [==============================] - ETA: 0s - loss: 116.8132 - mean_absolute_error: 116.813 - ETA: 0s - loss: 76.3447 - mean_absolute_error: 76.3447  - 0s 18ms/step - loss: nan - mean_absolute_error: nan - val_loss: 63.4238 - val_mean_absolute_error: 63.4238\n",
      "Epoch 2/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 72.3939 - mean_absolute_error: 72.393 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 16ms/step - loss: nan - mean_absolute_error: nan - val_loss: 62.7431 - val_mean_absolute_error: 62.7431\n",
      "Epoch 3/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 78.7923 - mean_absolute_error: 78.792 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 19ms/step - loss: nan - mean_absolute_error: nan - val_loss: 47.5451 - val_mean_absolute_error: 47.5451\n",
      "Epoch 4/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 48.1353 - mean_absolute_error: 48.135 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 72.8017 - val_mean_absolute_error: 72.8017\n",
      "Epoch 5/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 58.7824 - mean_absolute_error: 58.782 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 55.2045 - val_mean_absolute_error: 55.2045\n",
      "Epoch 1/5\n",
      "WARNING:tensorflow:Layer dense is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "24/24 [==============================] - ETA: 0s - loss: 127.8929 - mean_absolute_error: 127.892 - 0s 6ms/step - loss: nan - mean_absolute_error: nan - val_loss: 71.2169 - val_mean_absolute_error: 71.2169\n",
      "Epoch 2/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 81.4796 - mean_absolute_error: 81.479 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 59.2815 - val_mean_absolute_error: 59.2815\n",
      "Epoch 3/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 65.3511 - mean_absolute_error: 65.351 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 47.8693 - val_mean_absolute_error: 47.8693\n",
      "Epoch 4/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 26.0163 - mean_absolute_error: 26.016 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 57.7024 - val_mean_absolute_error: 57.7024\n",
      "Epoch 5/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 48.9063 - mean_absolute_error: 48.906 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 49.2807 - val_mean_absolute_error: 49.2807\n",
      "Epoch 1/5\n",
      "WARNING:tensorflow:Layer dense is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "24/24 [==============================] - ETA: 0s - loss: 132.7942 - mean_absolute_error: 132.794 - ETA: 0s - loss: nan - mean_absolute_error: nan          - 0s 7ms/step - loss: nan - mean_absolute_error: nan - val_loss: 66.7852 - val_mean_absolute_error: 66.7852\n",
      "Epoch 2/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 91.3190 - mean_absolute_error: 91.319 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 65.7265 - val_mean_absolute_error: 65.7265\n",
      "Epoch 3/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 65.8415 - mean_absolute_error: 65.841 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 60.6193 - val_mean_absolute_error: 60.6193\n",
      "Epoch 4/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 64.0055 - mean_absolute_error: 64.005 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 47.9010 - val_mean_absolute_error: 47.9010\n",
      "Epoch 5/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 53.0121 - mean_absolute_error: 53.012 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 51.7172 - val_mean_absolute_error: 51.7172\n"
     ]
    },
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     "text": [
      "C:\\Users\\win10\\anaconda3\\envs\\myenv\\lib\\site-packages\\kerastuner\\engine\\metrics_tracking.py:92: RuntimeWarning: All-NaN axis encountered\n",
      "  return np.nanmin(values)\n"
     ]
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      "Epoch 1/5\n",
      "WARNING:tensorflow:Layer dense is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "24/24 [==============================] - ETA: 0s - loss: 115.1197 - mean_absolute_error: 115.119 - ETA: 0s - loss: nan - mean_absolute_error: nan          - 1s 29ms/step - loss: nan - mean_absolute_error: nan - val_loss: 64.2618 - val_mean_absolute_error: 64.2618\n",
      "Epoch 2/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 70.6723 - mean_absolute_error: 70.672 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 1s 27ms/step - loss: nan - mean_absolute_error: nan - val_loss: 63.4300 - val_mean_absolute_error: 63.4300\n",
      "Epoch 3/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 50.7973 - mean_absolute_error: 50.797 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 66.6015 - val_mean_absolute_error: 66.6015\n",
      "Epoch 4/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 84.8132 - mean_absolute_error: 84.813 - ETA: 0s - loss: 70.3534 - mean_absolute_error: 70.353 - 1s 27ms/step - loss: nan - mean_absolute_error: nan - val_loss: 63.2408 - val_mean_absolute_error: 63.2408\n",
      "Epoch 5/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 52.6199 - mean_absolute_error: 52.619 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 69.6594 - val_mean_absolute_error: 69.6594\n",
      "Epoch 1/5\n",
      "WARNING:tensorflow:Layer dense is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "24/24 [==============================] - ETA: 0s - loss: 130.5468 - mean_absolute_error: 130.546 - ETA: 0s - loss: nan - mean_absolute_error: nan          - 0s 8ms/step - loss: nan - mean_absolute_error: nan - val_loss: 64.0518 - val_mean_absolute_error: 64.0518\n",
      "Epoch 2/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 82.7737 - mean_absolute_error: 82.773 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 64.5639 - val_mean_absolute_error: 64.5639\n",
      "Epoch 3/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 55.6748 - mean_absolute_error: 55.674 - ETA: 0s - loss: 71.1010 - mean_absolute_error: 71.101 - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 63.5915 - val_mean_absolute_error: 63.5915\n",
      "Epoch 4/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 69.2862 - mean_absolute_error: 69.286 - ETA: 0s - loss: 67.9955 - mean_absolute_error: 67.995 - 0s 5ms/step - loss: nan - mean_absolute_error: nan - val_loss: 65.5058 - val_mean_absolute_error: 65.5058\n",
      "Epoch 5/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 64.8581 - mean_absolute_error: 64.858 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 5ms/step - loss: nan - mean_absolute_error: nan - val_loss: 66.2052 - val_mean_absolute_error: 66.2052\n",
      "Epoch 1/5\n",
      "WARNING:tensorflow:Layer dense is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "24/24 [==============================] - ETA: 0s - loss: 93.9155 - mean_absolute_error: 93.915 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 8ms/step - loss: nan - mean_absolute_error: nan - val_loss: 63.8856 - val_mean_absolute_error: 63.8856\n",
      "Epoch 2/5\n",
      "24/24 [==============================] - ETA: 0s - loss: nan - mean_absolute_error: na - ETA: 0s - loss: nan - mean_absolute_error: na - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 64.4444 - val_mean_absolute_error: 64.4444\n",
      "Epoch 3/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 56.2157 - mean_absolute_error: 56.215 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 64.0128 - val_mean_absolute_error: 64.0128\n",
      "Epoch 4/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 77.8994 - mean_absolute_error: 77.899 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 72.6649 - val_mean_absolute_error: 72.6649\n",
      "Epoch 5/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 65.4756 - mean_absolute_error: 65.475 - ETA: 0s - loss: 67.4593 - mean_absolute_error: 67.459 - 1s 27ms/step - loss: nan - mean_absolute_error: nan - val_loss: 62.9103 - val_mean_absolute_error: 62.9103\n"
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      "C:\\Users\\win10\\anaconda3\\envs\\myenv\\lib\\site-packages\\kerastuner\\engine\\metrics_tracking.py:92: RuntimeWarning: All-NaN axis encountered\n",
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      "Epoch 1/5\n",
      "WARNING:tensorflow:Layer dense is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "24/24 [==============================] - ETA: 0s - loss: 116.3867 - mean_absolute_error: 116.386 - ETA: 0s - loss: nan - mean_absolute_error: nan          - 1s 24ms/step - loss: nan - mean_absolute_error: nan - val_loss: 68.6886 - val_mean_absolute_error: 68.6886\n",
      "Epoch 2/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 83.5364 - mean_absolute_error: 83.536 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 1s 23ms/step - loss: nan - mean_absolute_error: nan - val_loss: 63.6457 - val_mean_absolute_error: 63.6457\n",
      "Epoch 3/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 68.2929 - mean_absolute_error: 68.292 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 65.1698 - val_mean_absolute_error: 65.1698\n",
      "Epoch 4/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 56.6373 - mean_absolute_error: 56.637 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 64.3594 - val_mean_absolute_error: 64.3594\n",
      "Epoch 5/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 61.8076 - mean_absolute_error: 61.807 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 20ms/step - loss: nan - mean_absolute_error: nan - val_loss: 63.5364 - val_mean_absolute_error: 63.5364\n",
      "Epoch 1/5\n",
      "WARNING:tensorflow:Layer dense is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "24/24 [==============================] - ETA: 0s - loss: 118.2550 - mean_absolute_error: 118.255 - ETA: 0s - loss: nan - mean_absolute_error: nan          - 0s 7ms/step - loss: nan - mean_absolute_error: nan - val_loss: 65.2420 - val_mean_absolute_error: 65.2420\n",
      "Epoch 2/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 69.8192 - mean_absolute_error: 69.819 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 63.6967 - val_mean_absolute_error: 63.6967\n",
      "Epoch 3/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 52.9929 - mean_absolute_error: 52.992 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 66.0549 - val_mean_absolute_error: 66.0549\n",
      "Epoch 4/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 65.4321 - mean_absolute_error: 65.432 - ETA: 0s - loss: 66.1418 - mean_absolute_error: 66.141 - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 64.8149 - val_mean_absolute_error: 64.8149\n",
      "Epoch 5/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 70.4234 - mean_absolute_error: 70.423 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 64.2624 - val_mean_absolute_error: 64.2624\n",
      "Epoch 1/5\n",
      "WARNING:tensorflow:Layer dense is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "24/24 [==============================] - ETA: 0s - loss: 71.8871 - mean_absolute_error: 71.887 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 7ms/step - loss: nan - mean_absolute_error: nan - val_loss: 69.0089 - val_mean_absolute_error: 69.0089\n",
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      "Epoch 1/5\n",
      "WARNING:tensorflow:Layer dense is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "24/24 [==============================] - ETA: 0s - loss: 178.2827 - mean_absolute_error: 178.282 - 1s 22ms/step - loss: nan - mean_absolute_error: nan - val_loss: 64.0686 - val_mean_absolute_error: 64.0686\n",
      "Epoch 2/5\n",
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      "Epoch 3/5\n",
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      "Epoch 4/5\n",
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      "Epoch 5/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 57.2574 - mean_absolute_error: 57.257 - 0s 19ms/step - loss: nan - mean_absolute_error: nan - val_loss: 56.0679 - val_mean_absolute_error: 56.0679\n",
      "Epoch 1/5\n",
      "WARNING:tensorflow:Layer dense is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "24/24 [==============================] - ETA: 0s - loss: 98.6499 - mean_absolute_error: 98.649 - 0s 5ms/step - loss: nan - mean_absolute_error: nan - val_loss: 66.8493 - val_mean_absolute_error: 66.8493\n",
      "Epoch 2/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 71.4547 - mean_absolute_error: 71.454 - 0s 3ms/step - loss: nan - mean_absolute_error: nan - val_loss: 63.0680 - val_mean_absolute_error: 63.0680\n",
      "Epoch 3/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 68.1744 - mean_absolute_error: 68.174 - 0s 3ms/step - loss: nan - mean_absolute_error: nan - val_loss: 61.9647 - val_mean_absolute_error: 61.9647\n",
      "Epoch 4/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 67.6296 - mean_absolute_error: 67.629 - 0s 3ms/step - loss: nan - mean_absolute_error: nan - val_loss: 64.9284 - val_mean_absolute_error: 64.9284\n",
      "Epoch 5/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 64.9716 - mean_absolute_error: 64.971 - 0s 3ms/step - loss: nan - mean_absolute_error: nan - val_loss: 64.2758 - val_mean_absolute_error: 64.2758\n",
      "Epoch 1/5\n",
      "WARNING:tensorflow:Layer dense is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "24/24 [==============================] - ETA: 0s - loss: 115.2061 - mean_absolute_error: 115.206 - 0s 5ms/step - loss: nan - mean_absolute_error: nan - val_loss: 63.7263 - val_mean_absolute_error: 63.7263\n",
      "Epoch 2/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 76.3914 - mean_absolute_error: 76.391 - 0s 3ms/step - loss: nan - mean_absolute_error: nan - val_loss: 63.5010 - val_mean_absolute_error: 63.5010\n",
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      "Epoch 1/5\n",
      "WARNING:tensorflow:Layer dense is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
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      "\n",
      "24/24 [==============================] - ETA: 0s - loss: 81.2644 - mean_absolute_error: 81.264 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 1s 27ms/step - loss: nan - mean_absolute_error: nan - val_loss: 64.7037 - val_mean_absolute_error: 64.7037\n",
      "Epoch 2/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 83.1217 - mean_absolute_error: 83.121 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 1s 23ms/step - loss: nan - mean_absolute_error: nan - val_loss: 63.5881 - val_mean_absolute_error: 63.5881\n",
      "Epoch 3/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 71.5101 - mean_absolute_error: 71.510 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 64.8098 - val_mean_absolute_error: 64.8098\n",
      "Epoch 4/5\n",
      "24/24 [==============================] - ETA: 0s - loss: nan - mean_absolute_error: na - ETA: 0s - loss: nan - mean_absolute_error: na - 1s 24ms/step - loss: nan - mean_absolute_error: nan - val_loss: 63.5553 - val_mean_absolute_error: 63.5553\n",
      "Epoch 5/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 66.2654 - mean_absolute_error: 66.265 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 64.5349 - val_mean_absolute_error: 64.5349\n",
      "Epoch 1/5\n",
      "WARNING:tensorflow:Layer dense is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "24/24 [==============================] - ETA: 0s - loss: 130.7908 - mean_absolute_error: 130.790 - ETA: 0s - loss: nan - mean_absolute_error: nan          - 0s 7ms/step - loss: nan - mean_absolute_error: nan - val_loss: 71.2532 - val_mean_absolute_error: 71.2532\n",
      "Epoch 2/5\n",
      "24/24 [==============================] - ETA: 0s - loss: nan - mean_absolute_error: na - ETA: 0s - loss: nan - mean_absolute_error: na - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 64.0055 - val_mean_absolute_error: 64.0055\n",
      "Epoch 3/5\n",
      "24/24 [==============================] - ETA: 0s - loss: 70.5603 - mean_absolute_error: 70.560 - ETA: 0s - loss: nan - mean_absolute_error: nan        - 0s 4ms/step - loss: nan - mean_absolute_error: nan - val_loss: 63.7454 - val_mean_absolute_error: 63.7454\n",
      "Epoch 4/5\n",
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      "Epoch 5/5\n",
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      "Epoch 1/5\n",
      "WARNING:tensorflow:Layer dense is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "24/24 [==============================] - ETA: 0s - loss: 101.1746 - mean_absolute_error: 101.174 - ETA: 0s - loss: 98.0866 - mean_absolute_error: 98.0866  - 0s 7ms/step - loss: nan - mean_absolute_error: nan - val_loss: 63.6381 - val_mean_absolute_error: 63.6381\n",
      "Epoch 2/5\n",
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      "Epoch 3/5\n",
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      "Epoch 5/5\n",
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