Untitled-1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf \n",
    "from tensorflow import keras\n",
    "from keras import layers \n",
    "from keras.models import Sequential\n",
    "import keras.optimizers\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.layers import Dense\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import multilabel_confusion_matrix\n",
    "from tensorflow.keras.activations import swish\n",
    "from tensorflow.keras.layers import LeakyReLU\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     layer1    layer2    layer3    layer4    layer5    layer6      var2\n",
      "0  0.248512  0.254464  0.223214  0.220238  0.187500  0.181548  0.701097\n",
      "1  0.302679  0.275000  0.270408  0.236607  0.213010  0.185714  0.706118\n",
      "2  0.625000  0.589286  0.574405  0.525298  0.468750  0.425595  0.689335\n",
      "3  0.422619  0.414286  0.308036  0.298469  0.031888  0.022557  0.706299\n",
      "4  0.462798  0.456827  0.419643  0.406250  0.342262  0.328571  0.692404\n",
      "           var2\n",
      "70000  0.715162\n",
      "70001  0.718581\n",
      "70002  0.719550\n",
      "70003  0.717562\n",
      "70004  0.694195\n",
      "         layer1    layer2    layer3    layer4    layer5    layer6\n",
      "70000  0.272321  0.262755  0.238095  0.218112  0.168155  0.148810\n",
      "70001  0.151786  0.146684  0.130102  0.114583  0.087798  0.071429\n",
      "70002  0.184524  0.180357  0.162202  0.147817  0.112723  0.098214\n",
      "70003  0.140306  0.157738  0.121173  0.127976  0.085459  0.085714\n",
      "70004  0.562500  0.581845  0.505357  0.511905  0.410714  0.407738\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-09 19:52:08.584144: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "random.seed(42)\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "def create_model(act1, act2, train_input_shape):\n",
    "    # Define your model architecture here\n",
    "    model = tf.keras.Sequential([\n",
    "        tf.keras.layers.InputLayer(input_shape= train_input_shape),\n",
    "        tf.keras.layers.Dense(6, activation=act1),\n",
    "        tf.keras.layers.Dense(32, activation=act1),\n",
    "        tf.keras.layers.Dense(64, activation=act1),\n",
    "        tf.keras.layers.Dense(128, activation=act1),\n",
    "        tf.keras.layers.Dense(1, activation=act2)])\n",
    "    \n",
    "    return model\n",
    "\n",
    "colnames = ['layer1', 'layer2', 'layer3', 'layer4', 'layer5', 'layer6', 'var1', 'var2', 'var3']\n",
    "df = pd.read_csv(\"train_cf_onlyFTOF.csv\", usecols=[i for i in range(9)], names = colnames, skiprows=0, sep=\",\", header=None, skip_blank_lines=True)\n",
    "df = df.sample(frac=1).reset_index(drop=True)\n",
    "df.drop(df.columns[[6,8]], axis=1, inplace=True)\n",
    "print(df.head(5))\n",
    "train_frac = 0.7\n",
    "\n",
    "train_arr = df.iloc[:int(train_frac*len(df)), 0:]\n",
    "test_arr = df.iloc[int(train_frac*len(df)):, 0:]\n",
    "\n",
    "# Fix this to fit different shapes of df.\n",
    "train_x = train_arr.iloc[:, :-1] \n",
    "train_y = train_arr.iloc[:, -1:]\n",
    "\n",
    "\n",
    "test_x = test_arr.iloc[:, :-1] \n",
    "test_y = test_arr.iloc[:, -1:]\n",
    "print(test_y.head(5))\n",
    "print(test_x.head(5))\n",
    "train_x = train_x.to_numpy()\n",
    "train_y = train_y.to_numpy()\n",
    "test_x = test_x.to_numpy()\n",
    "test_y = test_y.to_numpy()\n",
    "\n",
    "train_dataset = tf.data.Dataset.from_tensor_slices((train_x,train_y))\n",
    "test_dataset = tf.data.Dataset.from_tensor_slices((test_x,test_y))\n",
    "\n",
    "train_dataset = train_dataset.batch(64)\n",
    "test_dataset = test_dataset.batch(1)\n",
    "#tf.data.experimental.AUTOTUNE: I don't know what this does.\n",
    "train_input_shape = train_x[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/118\n",
      "1094/1094 [==============================] - 13s 11ms/step - loss: 0.0066 - mse: 0.0066 - mae: 0.0197 - val_loss: 1.2190e-05 - val_mse: 1.2190e-05 - val_mae: 0.0024\n",
      "Epoch 2/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2325e-05 - mse: 1.2325e-05 - mae: 0.0025 - val_loss: 1.2431e-05 - val_mse: 1.2431e-05 - val_mae: 0.0025\n",
      "Epoch 3/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1702e-05 - mse: 1.1702e-05 - mae: 0.0024 - val_loss: 1.0471e-05 - val_mse: 1.0471e-05 - val_mae: 0.0022\n",
      "Epoch 4/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2139e-05 - mse: 1.2139e-05 - mae: 0.0025 - val_loss: 1.1398e-05 - val_mse: 1.1398e-05 - val_mae: 0.0024\n",
      "Epoch 5/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.3110e-05 - mse: 1.3110e-05 - mae: 0.0027 - val_loss: 1.2934e-05 - val_mse: 1.2934e-05 - val_mae: 0.0026\n",
      "Epoch 6/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.3027e-05 - mse: 1.3027e-05 - mae: 0.0027 - val_loss: 1.0543e-05 - val_mse: 1.0543e-05 - val_mae: 0.0022\n",
      "Epoch 7/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.3335e-05 - mse: 1.3335e-05 - mae: 0.0027 - val_loss: 1.0481e-05 - val_mse: 1.0481e-05 - val_mae: 0.0022\n",
      "Epoch 8/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.3140e-05 - mse: 1.3140e-05 - mae: 0.0027 - val_loss: 1.0636e-05 - val_mse: 1.0636e-05 - val_mae: 0.0022\n",
      "Epoch 9/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2821e-05 - mse: 1.2821e-05 - mae: 0.0027 - val_loss: 1.0531e-05 - val_mse: 1.0531e-05 - val_mae: 0.0022\n",
      "Epoch 10/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2587e-05 - mse: 1.2587e-05 - mae: 0.0026 - val_loss: 1.1690e-05 - val_mse: 1.1690e-05 - val_mae: 0.0024\n",
      "Epoch 11/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2414e-05 - mse: 1.2414e-05 - mae: 0.0026 - val_loss: 1.2932e-05 - val_mse: 1.2932e-05 - val_mae: 0.0026\n",
      "Epoch 12/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2293e-05 - mse: 1.2293e-05 - mae: 0.0026 - val_loss: 1.3377e-05 - val_mse: 1.3377e-05 - val_mae: 0.0027\n",
      "Epoch 13/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2202e-05 - mse: 1.2202e-05 - mae: 0.0026 - val_loss: 1.0899e-05 - val_mse: 1.0899e-05 - val_mae: 0.0023\n",
      "Epoch 14/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2044e-05 - mse: 1.2044e-05 - mae: 0.0025 - val_loss: 9.6859e-06 - val_mse: 9.6859e-06 - val_mae: 0.0021\n",
      "Epoch 15/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2191e-05 - mse: 1.2191e-05 - mae: 0.0026 - val_loss: 1.0233e-05 - val_mse: 1.0233e-05 - val_mae: 0.0022\n",
      "Epoch 16/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1934e-05 - mse: 1.1934e-05 - mae: 0.0025 - val_loss: 1.0044e-05 - val_mse: 1.0044e-05 - val_mae: 0.0022\n",
      "Epoch 17/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1742e-05 - mse: 1.1742e-05 - mae: 0.0025 - val_loss: 9.4659e-06 - val_mse: 9.4659e-06 - val_mae: 0.0021\n",
      "Epoch 18/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1563e-05 - mse: 1.1563e-05 - mae: 0.0025 - val_loss: 9.2530e-06 - val_mse: 9.2530e-06 - val_mae: 0.0021\n",
      "Epoch 19/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1518e-05 - mse: 1.1518e-05 - mae: 0.0025 - val_loss: 9.2571e-06 - val_mse: 9.2571e-06 - val_mae: 0.0021\n",
      "Epoch 20/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1292e-05 - mse: 1.1292e-05 - mae: 0.0024 - val_loss: 9.5331e-06 - val_mse: 9.5331e-06 - val_mae: 0.0022\n",
      "Epoch 21/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1004e-05 - mse: 1.1004e-05 - mae: 0.0024 - val_loss: 9.6564e-06 - val_mse: 9.6564e-06 - val_mae: 0.0023\n",
      "Epoch 22/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0831e-05 - mse: 1.0831e-05 - mae: 0.0024 - val_loss: 9.9820e-06 - val_mse: 9.9820e-06 - val_mae: 0.0023\n",
      "Epoch 23/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0688e-05 - mse: 1.0688e-05 - mae: 0.0024 - val_loss: 1.0297e-05 - val_mse: 1.0297e-05 - val_mae: 0.0024\n",
      "Epoch 24/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0582e-05 - mse: 1.0582e-05 - mae: 0.0023 - val_loss: 1.0505e-05 - val_mse: 1.0505e-05 - val_mae: 0.0024\n",
      "Epoch 25/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0513e-05 - mse: 1.0513e-05 - mae: 0.0023 - val_loss: 1.0728e-05 - val_mse: 1.0728e-05 - val_mae: 0.0025\n",
      "Epoch 26/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0384e-05 - mse: 1.0384e-05 - mae: 0.0023 - val_loss: 1.0928e-05 - val_mse: 1.0928e-05 - val_mae: 0.0025\n",
      "Epoch 27/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0295e-05 - mse: 1.0295e-05 - mae: 0.0023 - val_loss: 1.0899e-05 - val_mse: 1.0899e-05 - val_mae: 0.0025\n",
      "Epoch 28/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0165e-05 - mse: 1.0165e-05 - mae: 0.0023 - val_loss: 1.0916e-05 - val_mse: 1.0916e-05 - val_mae: 0.0025\n",
      "Epoch 29/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0053e-05 - mse: 1.0053e-05 - mae: 0.0023 - val_loss: 1.0983e-05 - val_mse: 1.0983e-05 - val_mae: 0.0025\n",
      "Epoch 30/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0028e-05 - mse: 1.0028e-05 - mae: 0.0023 - val_loss: 1.0798e-05 - val_mse: 1.0798e-05 - val_mae: 0.0025\n",
      "Epoch 31/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.8914e-06 - mse: 9.8914e-06 - mae: 0.0023 - val_loss: 1.0617e-05 - val_mse: 1.0617e-05 - val_mae: 0.0025\n",
      "Epoch 32/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.9107e-06 - mse: 9.9107e-06 - mae: 0.0023 - val_loss: 1.0376e-05 - val_mse: 1.0376e-05 - val_mae: 0.0024\n",
      "Epoch 33/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.7923e-06 - mse: 9.7923e-06 - mae: 0.0022 - val_loss: 9.9156e-06 - val_mse: 9.9156e-06 - val_mae: 0.0023\n",
      "Epoch 34/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.8243e-06 - mse: 9.8243e-06 - mae: 0.0022 - val_loss: 1.0174e-05 - val_mse: 1.0174e-05 - val_mae: 0.0023\n",
      "Epoch 35/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.7986e-06 - mse: 9.7986e-06 - mae: 0.0022 - val_loss: 1.0444e-05 - val_mse: 1.0444e-05 - val_mae: 0.0024\n",
      "Epoch 36/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.6580e-06 - mse: 9.6580e-06 - mae: 0.0022 - val_loss: 9.9913e-06 - val_mse: 9.9913e-06 - val_mae: 0.0023\n",
      "Epoch 37/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.6476e-06 - mse: 9.6476e-06 - mae: 0.0022 - val_loss: 9.7108e-06 - val_mse: 9.7108e-06 - val_mae: 0.0023\n",
      "Epoch 38/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.7441e-06 - mse: 9.7441e-06 - mae: 0.0022 - val_loss: 1.0289e-05 - val_mse: 1.0289e-05 - val_mae: 0.0023\n",
      "Epoch 39/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.6269e-06 - mse: 9.6269e-06 - mae: 0.0022 - val_loss: 9.7850e-06 - val_mse: 9.7850e-06 - val_mae: 0.0023\n",
      "Epoch 40/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.5116e-06 - mse: 9.5116e-06 - mae: 0.0022 - val_loss: 1.0047e-05 - val_mse: 1.0047e-05 - val_mae: 0.0024\n",
      "Epoch 41/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.5089e-06 - mse: 9.5089e-06 - mae: 0.0022 - val_loss: 9.7955e-06 - val_mse: 9.7955e-06 - val_mae: 0.0023\n",
      "Epoch 42/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.4624e-06 - mse: 9.4624e-06 - mae: 0.0022 - val_loss: 9.6351e-06 - val_mse: 9.6351e-06 - val_mae: 0.0023\n",
      "Epoch 43/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.4385e-06 - mse: 9.4385e-06 - mae: 0.0022 - val_loss: 9.4411e-06 - val_mse: 9.4411e-06 - val_mae: 0.0023\n",
      "Epoch 44/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.3931e-06 - mse: 9.3931e-06 - mae: 0.0022 - val_loss: 9.3699e-06 - val_mse: 9.3699e-06 - val_mae: 0.0022\n",
      "Epoch 45/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.3434e-06 - mse: 9.3434e-06 - mae: 0.0022 - val_loss: 9.4113e-06 - val_mse: 9.4113e-06 - val_mae: 0.0023\n",
      "Epoch 46/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.4466e-06 - mse: 9.4466e-06 - mae: 0.0022 - val_loss: 9.4551e-06 - val_mse: 9.4551e-06 - val_mae: 0.0023\n",
      "Epoch 47/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.2306e-06 - mse: 9.2306e-06 - mae: 0.0022 - val_loss: 8.9930e-06 - val_mse: 8.9930e-06 - val_mae: 0.0022\n",
      "Epoch 48/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.2944e-06 - mse: 9.2944e-06 - mae: 0.0022 - val_loss: 9.1899e-06 - val_mse: 9.1899e-06 - val_mae: 0.0022\n",
      "Epoch 49/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.2334e-06 - mse: 9.2334e-06 - mae: 0.0021 - val_loss: 8.9837e-06 - val_mse: 8.9837e-06 - val_mae: 0.0022\n",
      "Epoch 50/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.2073e-06 - mse: 9.2073e-06 - mae: 0.0021 - val_loss: 8.8398e-06 - val_mse: 8.8398e-06 - val_mae: 0.0021\n",
      "Epoch 51/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.1520e-06 - mse: 9.1520e-06 - mae: 0.0021 - val_loss: 8.7364e-06 - val_mse: 8.7364e-06 - val_mae: 0.0021\n",
      "Epoch 52/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.1653e-06 - mse: 9.1653e-06 - mae: 0.0021 - val_loss: 8.5895e-06 - val_mse: 8.5895e-06 - val_mae: 0.0021\n",
      "Epoch 53/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.0533e-06 - mse: 9.0533e-06 - mae: 0.0021 - val_loss: 8.4876e-06 - val_mse: 8.4876e-06 - val_mae: 0.0021\n",
      "Epoch 54/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.1222e-06 - mse: 9.1222e-06 - mae: 0.0021 - val_loss: 8.3068e-06 - val_mse: 8.3068e-06 - val_mae: 0.0020\n",
      "Epoch 55/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.0130e-06 - mse: 9.0130e-06 - mae: 0.0021 - val_loss: 8.0592e-06 - val_mse: 8.0592e-06 - val_mae: 0.0020\n",
      "Epoch 56/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.0613e-06 - mse: 9.0613e-06 - mae: 0.0021 - val_loss: 8.0002e-06 - val_mse: 8.0002e-06 - val_mae: 0.0020\n",
      "Epoch 57/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.9620e-06 - mse: 8.9620e-06 - mae: 0.0021 - val_loss: 7.8907e-06 - val_mse: 7.8907e-06 - val_mae: 0.0019\n",
      "Epoch 58/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.9606e-06 - mse: 8.9606e-06 - mae: 0.0021 - val_loss: 7.9749e-06 - val_mse: 7.9749e-06 - val_mae: 0.0020\n",
      "Epoch 59/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.9310e-06 - mse: 8.9310e-06 - mae: 0.0021 - val_loss: 7.8786e-06 - val_mse: 7.8786e-06 - val_mae: 0.0019\n",
      "Epoch 60/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.9171e-06 - mse: 8.9171e-06 - mae: 0.0021 - val_loss: 7.8134e-06 - val_mse: 7.8134e-06 - val_mae: 0.0019\n",
      "Epoch 61/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.8877e-06 - mse: 8.8877e-06 - mae: 0.0021 - val_loss: 7.8172e-06 - val_mse: 7.8172e-06 - val_mae: 0.0019\n",
      "Epoch 62/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.8796e-06 - mse: 8.8796e-06 - mae: 0.0021 - val_loss: 7.7303e-06 - val_mse: 7.7303e-06 - val_mae: 0.0019\n",
      "Epoch 63/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.8636e-06 - mse: 8.8636e-06 - mae: 0.0021 - val_loss: 7.7498e-06 - val_mse: 7.7498e-06 - val_mae: 0.0019\n",
      "Epoch 64/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.8578e-06 - mse: 8.8578e-06 - mae: 0.0021 - val_loss: 7.8595e-06 - val_mse: 7.8595e-06 - val_mae: 0.0019\n",
      "Epoch 65/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.8136e-06 - mse: 8.8136e-06 - mae: 0.0021 - val_loss: 7.7599e-06 - val_mse: 7.7599e-06 - val_mae: 0.0019\n",
      "Epoch 66/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.8033e-06 - mse: 8.8033e-06 - mae: 0.0021 - val_loss: 7.7873e-06 - val_mse: 7.7873e-06 - val_mae: 0.0019\n",
      "Epoch 67/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.7902e-06 - mse: 8.7902e-06 - mae: 0.0021 - val_loss: 7.7366e-06 - val_mse: 7.7366e-06 - val_mae: 0.0019\n",
      "Epoch 68/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 8.7685e-06 - mse: 8.7685e-06 - mae: 0.0021 - val_loss: 7.7270e-06 - val_mse: 7.7270e-06 - val_mae: 0.0019\n",
      "Epoch 69/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.7605e-06 - mse: 8.7605e-06 - mae: 0.0021 - val_loss: 7.8036e-06 - val_mse: 7.8036e-06 - val_mae: 0.0019\n",
      "Epoch 70/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.7423e-06 - mse: 8.7423e-06 - mae: 0.0021 - val_loss: 7.7761e-06 - val_mse: 7.7761e-06 - val_mae: 0.0019\n",
      "Epoch 71/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.7302e-06 - mse: 8.7302e-06 - mae: 0.0021 - val_loss: 7.8297e-06 - val_mse: 7.8297e-06 - val_mae: 0.0019\n",
      "Epoch 72/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.7013e-06 - mse: 8.7013e-06 - mae: 0.0021 - val_loss: 7.7397e-06 - val_mse: 7.7397e-06 - val_mae: 0.0019\n",
      "Epoch 73/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.7340e-06 - mse: 8.7340e-06 - mae: 0.0021 - val_loss: 7.8909e-06 - val_mse: 7.8909e-06 - val_mae: 0.0020\n",
      "Epoch 74/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.6472e-06 - mse: 8.6472e-06 - mae: 0.0021 - val_loss: 7.8425e-06 - val_mse: 7.8425e-06 - val_mae: 0.0020\n",
      "Epoch 75/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.6932e-06 - mse: 8.6932e-06 - mae: 0.0021 - val_loss: 8.0011e-06 - val_mse: 8.0011e-06 - val_mae: 0.0020\n",
      "Epoch 76/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.6364e-06 - mse: 8.6364e-06 - mae: 0.0021 - val_loss: 7.9476e-06 - val_mse: 7.9476e-06 - val_mae: 0.0020\n",
      "Epoch 77/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.6244e-06 - mse: 8.6244e-06 - mae: 0.0021 - val_loss: 8.0663e-06 - val_mse: 8.0663e-06 - val_mae: 0.0020\n",
      "Epoch 78/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.6077e-06 - mse: 8.6077e-06 - mae: 0.0021 - val_loss: 8.0561e-06 - val_mse: 8.0561e-06 - val_mae: 0.0020\n",
      "Epoch 79/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.6307e-06 - mse: 8.6307e-06 - mae: 0.0021 - val_loss: 7.9344e-06 - val_mse: 7.9344e-06 - val_mae: 0.0020\n",
      "Epoch 80/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 8.6325e-06 - mse: 8.6325e-06 - mae: 0.0021 - val_loss: 8.1355e-06 - val_mse: 8.1355e-06 - val_mae: 0.0020\n",
      "Epoch 81/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.5738e-06 - mse: 8.5738e-06 - mae: 0.0021 - val_loss: 8.0866e-06 - val_mse: 8.0866e-06 - val_mae: 0.0020\n",
      "Epoch 82/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.5815e-06 - mse: 8.5815e-06 - mae: 0.0021 - val_loss: 8.0990e-06 - val_mse: 8.0990e-06 - val_mae: 0.0020\n",
      "Epoch 83/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 8.5296e-06 - mse: 8.5296e-06 - mae: 0.0021 - val_loss: 7.9493e-06 - val_mse: 7.9493e-06 - val_mae: 0.0020\n",
      "Epoch 84/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 8.5798e-06 - mse: 8.5798e-06 - mae: 0.0021 - val_loss: 8.2344e-06 - val_mse: 8.2344e-06 - val_mae: 0.0021\n",
      "Epoch 85/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.5367e-06 - mse: 8.5367e-06 - mae: 0.0020 - val_loss: 8.1586e-06 - val_mse: 8.1586e-06 - val_mae: 0.0020\n",
      "Epoch 86/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.5086e-06 - mse: 8.5086e-06 - mae: 0.0020 - val_loss: 8.2000e-06 - val_mse: 8.2000e-06 - val_mae: 0.0020\n",
      "Epoch 87/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.4793e-06 - mse: 8.4793e-06 - mae: 0.0020 - val_loss: 8.0847e-06 - val_mse: 8.0847e-06 - val_mae: 0.0020\n",
      "Epoch 88/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.5258e-06 - mse: 8.5258e-06 - mae: 0.0020 - val_loss: 8.1880e-06 - val_mse: 8.1880e-06 - val_mae: 0.0020\n",
      "Epoch 89/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.4957e-06 - mse: 8.4957e-06 - mae: 0.0020 - val_loss: 8.1626e-06 - val_mse: 8.1626e-06 - val_mae: 0.0020\n",
      "Epoch 90/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.5037e-06 - mse: 8.5037e-06 - mae: 0.0020 - val_loss: 8.4292e-06 - val_mse: 8.4292e-06 - val_mae: 0.0021\n",
      "Epoch 91/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.4274e-06 - mse: 8.4274e-06 - mae: 0.0020 - val_loss: 8.0992e-06 - val_mse: 8.0992e-06 - val_mae: 0.0020\n",
      "Epoch 92/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.4397e-06 - mse: 8.4397e-06 - mae: 0.0020 - val_loss: 8.1672e-06 - val_mse: 8.1672e-06 - val_mae: 0.0020\n",
      "Epoch 93/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.4687e-06 - mse: 8.4687e-06 - mae: 0.0020 - val_loss: 8.2748e-06 - val_mse: 8.2748e-06 - val_mae: 0.0021\n",
      "Epoch 94/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.4334e-06 - mse: 8.4334e-06 - mae: 0.0020 - val_loss: 8.2762e-06 - val_mse: 8.2762e-06 - val_mae: 0.0021\n",
      "Epoch 95/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.4348e-06 - mse: 8.4348e-06 - mae: 0.0020 - val_loss: 8.3057e-06 - val_mse: 8.3057e-06 - val_mae: 0.0021\n",
      "Epoch 96/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.4210e-06 - mse: 8.4210e-06 - mae: 0.0020 - val_loss: 8.3245e-06 - val_mse: 8.3245e-06 - val_mae: 0.0021\n",
      "Epoch 97/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.4200e-06 - mse: 8.4200e-06 - mae: 0.0020 - val_loss: 8.3277e-06 - val_mse: 8.3277e-06 - val_mae: 0.0021\n",
      "Epoch 98/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.4148e-06 - mse: 8.4148e-06 - mae: 0.0020 - val_loss: 8.3468e-06 - val_mse: 8.3468e-06 - val_mae: 0.0021\n",
      "Epoch 99/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.4126e-06 - mse: 8.4126e-06 - mae: 0.0020 - val_loss: 8.3580e-06 - val_mse: 8.3580e-06 - val_mae: 0.0021\n",
      "Epoch 100/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3866e-06 - mse: 8.3866e-06 - mae: 0.0020 - val_loss: 8.3700e-06 - val_mse: 8.3700e-06 - val_mae: 0.0021\n",
      "Epoch 101/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3731e-06 - mse: 8.3731e-06 - mae: 0.0020 - val_loss: 8.3039e-06 - val_mse: 8.3039e-06 - val_mae: 0.0021\n",
      "Epoch 102/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3588e-06 - mse: 8.3588e-06 - mae: 0.0020 - val_loss: 8.4842e-06 - val_mse: 8.4842e-06 - val_mae: 0.0021\n",
      "Epoch 103/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3496e-06 - mse: 8.3496e-06 - mae: 0.0020 - val_loss: 8.4704e-06 - val_mse: 8.4704e-06 - val_mae: 0.0021\n",
      "Epoch 104/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3347e-06 - mse: 8.3347e-06 - mae: 0.0020 - val_loss: 8.4549e-06 - val_mse: 8.4549e-06 - val_mae: 0.0021\n",
      "Epoch 105/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3336e-06 - mse: 8.3336e-06 - mae: 0.0020 - val_loss: 8.4279e-06 - val_mse: 8.4279e-06 - val_mae: 0.0021\n",
      "Epoch 106/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3184e-06 - mse: 8.3184e-06 - mae: 0.0020 - val_loss: 8.5213e-06 - val_mse: 8.5213e-06 - val_mae: 0.0021\n",
      "Epoch 107/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3201e-06 - mse: 8.3201e-06 - mae: 0.0020 - val_loss: 8.4515e-06 - val_mse: 8.4515e-06 - val_mae: 0.0021\n",
      "Epoch 108/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3057e-06 - mse: 8.3057e-06 - mae: 0.0020 - val_loss: 8.4436e-06 - val_mse: 8.4436e-06 - val_mae: 0.0021\n",
      "Epoch 109/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3040e-06 - mse: 8.3040e-06 - mae: 0.0020 - val_loss: 8.4401e-06 - val_mse: 8.4401e-06 - val_mae: 0.0021\n",
      "Epoch 110/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3175e-06 - mse: 8.3175e-06 - mae: 0.0020 - val_loss: 8.4192e-06 - val_mse: 8.4192e-06 - val_mae: 0.0021\n",
      "Epoch 111/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.2985e-06 - mse: 8.2985e-06 - mae: 0.0020 - val_loss: 8.4040e-06 - val_mse: 8.4040e-06 - val_mae: 0.0021\n",
      "Epoch 112/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3041e-06 - mse: 8.3041e-06 - mae: 0.0020 - val_loss: 8.3046e-06 - val_mse: 8.3046e-06 - val_mae: 0.0021\n",
      "Epoch 113/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3020e-06 - mse: 8.3020e-06 - mae: 0.0020 - val_loss: 8.4136e-06 - val_mse: 8.4136e-06 - val_mae: 0.0021\n",
      "Epoch 114/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.2684e-06 - mse: 8.2684e-06 - mae: 0.0020 - val_loss: 8.3805e-06 - val_mse: 8.3805e-06 - val_mae: 0.0021\n",
      "Epoch 115/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.2720e-06 - mse: 8.2720e-06 - mae: 0.0020 - val_loss: 8.2218e-06 - val_mse: 8.2218e-06 - val_mae: 0.0021\n",
      "Epoch 116/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.2553e-06 - mse: 8.2553e-06 - mae: 0.0020 - val_loss: 8.2149e-06 - val_mse: 8.2149e-06 - val_mae: 0.0021\n",
      "Epoch 117/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.2635e-06 - mse: 8.2635e-06 - mae: 0.0020 - val_loss: 8.0871e-06 - val_mse: 8.0871e-06 - val_mae: 0.0020\n",
      "Epoch 118/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.2518e-06 - mse: 8.2518e-06 - mae: 0.0020 - val_loss: 8.0635e-06 - val_mse: 8.0635e-06 - val_mae: 0.0020\n",
      "938/938 [==============================] - 1s 508us/step\n",
      "Epoch 1/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 0.0053 - mse: 0.0053 - mae: 0.0163 - val_loss: 1.9928e-05 - val_mse: 1.9928e-05 - val_mae: 0.0034\n",
      "Epoch 2/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2280e-05 - mse: 1.2280e-05 - mae: 0.0025 - val_loss: 1.2671e-05 - val_mse: 1.2671e-05 - val_mae: 0.0025\n",
      "Epoch 3/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1527e-05 - mse: 1.1527e-05 - mae: 0.0024 - val_loss: 1.1119e-05 - val_mse: 1.1119e-05 - val_mae: 0.0023\n",
      "Epoch 4/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2701e-05 - mse: 1.2701e-05 - mae: 0.0026 - val_loss: 1.3496e-05 - val_mse: 1.3496e-05 - val_mae: 0.0026\n",
      "Epoch 5/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2698e-05 - mse: 1.2698e-05 - mae: 0.0026 - val_loss: 1.0875e-05 - val_mse: 1.0875e-05 - val_mae: 0.0023\n",
      "Epoch 6/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.3610e-05 - mse: 1.3610e-05 - mae: 0.0028 - val_loss: 1.0875e-05 - val_mse: 1.0875e-05 - val_mae: 0.0023\n",
      "Epoch 7/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.3498e-05 - mse: 1.3498e-05 - mae: 0.0027 - val_loss: 1.0689e-05 - val_mse: 1.0689e-05 - val_mae: 0.0023\n",
      "Epoch 8/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2903e-05 - mse: 1.2903e-05 - mae: 0.0027 - val_loss: 1.2671e-05 - val_mse: 1.2671e-05 - val_mae: 0.0026\n",
      "Epoch 9/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2413e-05 - mse: 1.2413e-05 - mae: 0.0026 - val_loss: 1.1244e-05 - val_mse: 1.1244e-05 - val_mae: 0.0023\n",
      "Epoch 10/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2006e-05 - mse: 1.2006e-05 - mae: 0.0026 - val_loss: 1.0520e-05 - val_mse: 1.0520e-05 - val_mae: 0.0022\n",
      "Epoch 11/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1676e-05 - mse: 1.1676e-05 - mae: 0.0025 - val_loss: 1.0210e-05 - val_mse: 1.0210e-05 - val_mae: 0.0022\n",
      "Epoch 12/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1316e-05 - mse: 1.1316e-05 - mae: 0.0025 - val_loss: 1.0129e-05 - val_mse: 1.0129e-05 - val_mae: 0.0022\n",
      "Epoch 13/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1185e-05 - mse: 1.1185e-05 - mae: 0.0025 - val_loss: 1.1377e-05 - val_mse: 1.1377e-05 - val_mae: 0.0024\n",
      "Epoch 14/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1080e-05 - mse: 1.1080e-05 - mae: 0.0024 - val_loss: 1.0205e-05 - val_mse: 1.0205e-05 - val_mae: 0.0022\n",
      "Epoch 15/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0959e-05 - mse: 1.0959e-05 - mae: 0.0024 - val_loss: 9.5305e-06 - val_mse: 9.5305e-06 - val_mae: 0.0021\n",
      "Epoch 16/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0917e-05 - mse: 1.0917e-05 - mae: 0.0024 - val_loss: 9.3887e-06 - val_mse: 9.3887e-06 - val_mae: 0.0021\n",
      "Epoch 17/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0827e-05 - mse: 1.0827e-05 - mae: 0.0024 - val_loss: 9.0599e-06 - val_mse: 9.0599e-06 - val_mae: 0.0021\n",
      "Epoch 18/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0606e-05 - mse: 1.0606e-05 - mae: 0.0024 - val_loss: 9.2159e-06 - val_mse: 9.2159e-06 - val_mae: 0.0021\n",
      "Epoch 19/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0443e-05 - mse: 1.0443e-05 - mae: 0.0024 - val_loss: 9.8068e-06 - val_mse: 9.8068e-06 - val_mae: 0.0023\n",
      "Epoch 20/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0347e-05 - mse: 1.0347e-05 - mae: 0.0023 - val_loss: 1.1138e-05 - val_mse: 1.1138e-05 - val_mae: 0.0025\n",
      "Epoch 21/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0275e-05 - mse: 1.0275e-05 - mae: 0.0023 - val_loss: 1.1200e-05 - val_mse: 1.1200e-05 - val_mae: 0.0026\n",
      "Epoch 22/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0152e-05 - mse: 1.0152e-05 - mae: 0.0023 - val_loss: 1.0060e-05 - val_mse: 1.0060e-05 - val_mae: 0.0024\n",
      "Epoch 23/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0037e-05 - mse: 1.0037e-05 - mae: 0.0023 - val_loss: 9.4693e-06 - val_mse: 9.4693e-06 - val_mae: 0.0023\n",
      "Epoch 24/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.9374e-06 - mse: 9.9374e-06 - mae: 0.0023 - val_loss: 9.5410e-06 - val_mse: 9.5410e-06 - val_mae: 0.0023\n",
      "Epoch 25/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.8458e-06 - mse: 9.8458e-06 - mae: 0.0023 - val_loss: 9.8365e-06 - val_mse: 9.8365e-06 - val_mae: 0.0024\n",
      "Epoch 26/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.8469e-06 - mse: 9.8469e-06 - mae: 0.0023 - val_loss: 9.7858e-06 - val_mse: 9.7858e-06 - val_mae: 0.0023\n",
      "Epoch 27/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.7495e-06 - mse: 9.7495e-06 - mae: 0.0022 - val_loss: 9.7207e-06 - val_mse: 9.7207e-06 - val_mae: 0.0023\n",
      "Epoch 28/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.7193e-06 - mse: 9.7193e-06 - mae: 0.0022 - val_loss: 9.6762e-06 - val_mse: 9.6762e-06 - val_mae: 0.0023\n",
      "Epoch 29/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.6691e-06 - mse: 9.6691e-06 - mae: 0.0022 - val_loss: 9.7852e-06 - val_mse: 9.7852e-06 - val_mae: 0.0024\n",
      "Epoch 30/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.6291e-06 - mse: 9.6291e-06 - mae: 0.0022 - val_loss: 9.9919e-06 - val_mse: 9.9919e-06 - val_mae: 0.0024\n",
      "Epoch 31/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.6351e-06 - mse: 9.6351e-06 - mae: 0.0022 - val_loss: 9.7884e-06 - val_mse: 9.7884e-06 - val_mae: 0.0024\n",
      "Epoch 32/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.5754e-06 - mse: 9.5754e-06 - mae: 0.0022 - val_loss: 9.7907e-06 - val_mse: 9.7907e-06 - val_mae: 0.0024\n",
      "Epoch 33/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.5797e-06 - mse: 9.5797e-06 - mae: 0.0022 - val_loss: 8.8862e-06 - val_mse: 8.8862e-06 - val_mae: 0.0022\n",
      "Epoch 34/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.4683e-06 - mse: 9.4683e-06 - mae: 0.0022 - val_loss: 8.8363e-06 - val_mse: 8.8363e-06 - val_mae: 0.0022\n",
      "Epoch 35/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.4567e-06 - mse: 9.4567e-06 - mae: 0.0022 - val_loss: 8.6164e-06 - val_mse: 8.6164e-06 - val_mae: 0.0021\n",
      "Epoch 36/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.4191e-06 - mse: 9.4191e-06 - mae: 0.0022 - val_loss: 8.4492e-06 - val_mse: 8.4492e-06 - val_mae: 0.0021\n",
      "Epoch 37/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.3470e-06 - mse: 9.3470e-06 - mae: 0.0022 - val_loss: 8.3027e-06 - val_mse: 8.3027e-06 - val_mae: 0.0020\n",
      "Epoch 38/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.3377e-06 - mse: 9.3377e-06 - mae: 0.0022 - val_loss: 8.2638e-06 - val_mse: 8.2638e-06 - val_mae: 0.0020\n",
      "Epoch 39/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.3529e-06 - mse: 9.3529e-06 - mae: 0.0022 - val_loss: 8.1674e-06 - val_mse: 8.1674e-06 - val_mae: 0.0020\n",
      "Epoch 40/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.3142e-06 - mse: 9.3142e-06 - mae: 0.0022 - val_loss: 8.3364e-06 - val_mse: 8.3364e-06 - val_mae: 0.0021\n",
      "Epoch 41/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.2832e-06 - mse: 9.2832e-06 - mae: 0.0022 - val_loss: 8.3499e-06 - val_mse: 8.3499e-06 - val_mae: 0.0021\n",
      "Epoch 42/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.2644e-06 - mse: 9.2644e-06 - mae: 0.0022 - val_loss: 8.3497e-06 - val_mse: 8.3497e-06 - val_mae: 0.0021\n",
      "Epoch 43/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 9.2110e-06 - mse: 9.2110e-06 - mae: 0.0022 - val_loss: 8.3076e-06 - val_mse: 8.3076e-06 - val_mae: 0.0021\n",
      "Epoch 44/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 9.1894e-06 - mse: 9.1894e-06 - mae: 0.0022 - val_loss: 8.4034e-06 - val_mse: 8.4034e-06 - val_mae: 0.0021\n",
      "Epoch 45/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.1344e-06 - mse: 9.1344e-06 - mae: 0.0022 - val_loss: 8.3366e-06 - val_mse: 8.3366e-06 - val_mae: 0.0021\n",
      "Epoch 46/118\n",
      "1094/1094 [==============================] - 13s 11ms/step - loss: 9.1255e-06 - mse: 9.1255e-06 - mae: 0.0022 - val_loss: 8.5088e-06 - val_mse: 8.5088e-06 - val_mae: 0.0021\n",
      "Epoch 47/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.0850e-06 - mse: 9.0850e-06 - mae: 0.0021 - val_loss: 8.5874e-06 - val_mse: 8.5874e-06 - val_mae: 0.0021\n",
      "Epoch 48/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.0807e-06 - mse: 9.0807e-06 - mae: 0.0021 - val_loss: 8.5519e-06 - val_mse: 8.5519e-06 - val_mae: 0.0021\n",
      "Epoch 49/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.0497e-06 - mse: 9.0497e-06 - mae: 0.0021 - val_loss: 8.6114e-06 - val_mse: 8.6114e-06 - val_mae: 0.0021\n",
      "Epoch 50/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 9.0379e-06 - mse: 9.0379e-06 - mae: 0.0021 - val_loss: 8.6519e-06 - val_mse: 8.6519e-06 - val_mae: 0.0021\n",
      "Epoch 51/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 9.0115e-06 - mse: 9.0115e-06 - mae: 0.0021 - val_loss: 8.5280e-06 - val_mse: 8.5280e-06 - val_mae: 0.0021\n",
      "Epoch 52/118\n",
      "1094/1094 [==============================] - 14s 12ms/step - loss: 8.9940e-06 - mse: 8.9940e-06 - mae: 0.0021 - val_loss: 8.6633e-06 - val_mse: 8.6633e-06 - val_mae: 0.0021\n",
      "Epoch 53/118\n",
      "1094/1094 [==============================] - 13s 11ms/step - loss: 8.9610e-06 - mse: 8.9610e-06 - mae: 0.0021 - val_loss: 8.6137e-06 - val_mse: 8.6137e-06 - val_mae: 0.0021\n",
      "Epoch 54/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 8.9478e-06 - mse: 8.9478e-06 - mae: 0.0021 - val_loss: 8.9075e-06 - val_mse: 8.9075e-06 - val_mae: 0.0022\n",
      "Epoch 55/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 8.9320e-06 - mse: 8.9320e-06 - mae: 0.0021 - val_loss: 8.7692e-06 - val_mse: 8.7692e-06 - val_mae: 0.0022\n",
      "Epoch 56/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 8.9088e-06 - mse: 8.9088e-06 - mae: 0.0021 - val_loss: 8.9278e-06 - val_mse: 8.9278e-06 - val_mae: 0.0022\n",
      "Epoch 57/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 8.8806e-06 - mse: 8.8806e-06 - mae: 0.0021 - val_loss: 9.0376e-06 - val_mse: 9.0376e-06 - val_mae: 0.0022\n",
      "Epoch 58/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 8.8786e-06 - mse: 8.8786e-06 - mae: 0.0021 - val_loss: 9.0100e-06 - val_mse: 9.0100e-06 - val_mae: 0.0022\n",
      "Epoch 59/118\n",
      "1094/1094 [==============================] - 13s 11ms/step - loss: 8.8454e-06 - mse: 8.8454e-06 - mae: 0.0021 - val_loss: 9.1781e-06 - val_mse: 9.1781e-06 - val_mae: 0.0023\n",
      "Epoch 60/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 8.8442e-06 - mse: 8.8442e-06 - mae: 0.0021 - val_loss: 9.3712e-06 - val_mse: 9.3712e-06 - val_mae: 0.0023\n",
      "Epoch 61/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.8085e-06 - mse: 8.8085e-06 - mae: 0.0021 - val_loss: 9.1145e-06 - val_mse: 9.1145e-06 - val_mae: 0.0022\n",
      "Epoch 62/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.8125e-06 - mse: 8.8125e-06 - mae: 0.0021 - val_loss: 9.0064e-06 - val_mse: 9.0064e-06 - val_mae: 0.0022\n",
      "Epoch 63/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.7787e-06 - mse: 8.7787e-06 - mae: 0.0021 - val_loss: 9.0265e-06 - val_mse: 9.0265e-06 - val_mae: 0.0022\n",
      "Epoch 64/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.7620e-06 - mse: 8.7620e-06 - mae: 0.0021 - val_loss: 9.0656e-06 - val_mse: 9.0656e-06 - val_mae: 0.0022\n",
      "Epoch 65/118\n",
      "1094/1094 [==============================] - 13s 11ms/step - loss: 8.7367e-06 - mse: 8.7367e-06 - mae: 0.0021 - val_loss: 8.8760e-06 - val_mse: 8.8760e-06 - val_mae: 0.0022\n",
      "Epoch 66/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.7397e-06 - mse: 8.7397e-06 - mae: 0.0021 - val_loss: 9.1164e-06 - val_mse: 9.1164e-06 - val_mae: 0.0023\n",
      "Epoch 67/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.7244e-06 - mse: 8.7244e-06 - mae: 0.0021 - val_loss: 9.0896e-06 - val_mse: 9.0896e-06 - val_mae: 0.0023\n",
      "Epoch 68/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.6917e-06 - mse: 8.6917e-06 - mae: 0.0021 - val_loss: 9.1096e-06 - val_mse: 9.1096e-06 - val_mae: 0.0023\n",
      "Epoch 69/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.6842e-06 - mse: 8.6842e-06 - mae: 0.0021 - val_loss: 9.0635e-06 - val_mse: 9.0635e-06 - val_mae: 0.0022\n",
      "Epoch 70/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.6595e-06 - mse: 8.6595e-06 - mae: 0.0021 - val_loss: 8.9409e-06 - val_mse: 8.9409e-06 - val_mae: 0.0022\n",
      "Epoch 71/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.6498e-06 - mse: 8.6498e-06 - mae: 0.0021 - val_loss: 8.9429e-06 - val_mse: 8.9429e-06 - val_mae: 0.0022\n",
      "Epoch 72/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.6317e-06 - mse: 8.6317e-06 - mae: 0.0021 - val_loss: 8.7420e-06 - val_mse: 8.7420e-06 - val_mae: 0.0022\n",
      "Epoch 73/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.6169e-06 - mse: 8.6169e-06 - mae: 0.0021 - val_loss: 8.9199e-06 - val_mse: 8.9199e-06 - val_mae: 0.0022\n",
      "Epoch 74/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.6072e-06 - mse: 8.6072e-06 - mae: 0.0021 - val_loss: 8.8430e-06 - val_mse: 8.8430e-06 - val_mae: 0.0022\n",
      "Epoch 75/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.5774e-06 - mse: 8.5774e-06 - mae: 0.0021 - val_loss: 8.6851e-06 - val_mse: 8.6851e-06 - val_mae: 0.0022\n",
      "Epoch 76/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.5956e-06 - mse: 8.5956e-06 - mae: 0.0021 - val_loss: 8.9982e-06 - val_mse: 8.9982e-06 - val_mae: 0.0022\n",
      "Epoch 77/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.5741e-06 - mse: 8.5741e-06 - mae: 0.0021 - val_loss: 8.6383e-06 - val_mse: 8.6383e-06 - val_mae: 0.0022\n",
      "Epoch 78/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.5615e-06 - mse: 8.5615e-06 - mae: 0.0021 - val_loss: 8.9764e-06 - val_mse: 8.9764e-06 - val_mae: 0.0022\n",
      "Epoch 79/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 8.5373e-06 - mse: 8.5373e-06 - mae: 0.0021 - val_loss: 9.0341e-06 - val_mse: 9.0341e-06 - val_mae: 0.0022\n",
      "Epoch 80/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 8.5426e-06 - mse: 8.5426e-06 - mae: 0.0021 - val_loss: 8.8180e-06 - val_mse: 8.8180e-06 - val_mae: 0.0022\n",
      "Epoch 81/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.5303e-06 - mse: 8.5303e-06 - mae: 0.0021 - val_loss: 8.8373e-06 - val_mse: 8.8373e-06 - val_mae: 0.0022\n",
      "Epoch 82/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.5208e-06 - mse: 8.5208e-06 - mae: 0.0021 - val_loss: 8.8101e-06 - val_mse: 8.8101e-06 - val_mae: 0.0022\n",
      "Epoch 83/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.5071e-06 - mse: 8.5071e-06 - mae: 0.0021 - val_loss: 8.9022e-06 - val_mse: 8.9022e-06 - val_mae: 0.0022\n",
      "Epoch 84/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.4720e-06 - mse: 8.4720e-06 - mae: 0.0021 - val_loss: 8.8999e-06 - val_mse: 8.8999e-06 - val_mae: 0.0022\n",
      "Epoch 85/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 8.4735e-06 - mse: 8.4735e-06 - mae: 0.0021 - val_loss: 8.9605e-06 - val_mse: 8.9605e-06 - val_mae: 0.0022\n",
      "Epoch 86/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.4580e-06 - mse: 8.4580e-06 - mae: 0.0020 - val_loss: 8.6895e-06 - val_mse: 8.6895e-06 - val_mae: 0.0022\n",
      "Epoch 87/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 8.4514e-06 - mse: 8.4514e-06 - mae: 0.0020 - val_loss: 8.6516e-06 - val_mse: 8.6516e-06 - val_mae: 0.0022\n",
      "Epoch 88/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 8.4374e-06 - mse: 8.4374e-06 - mae: 0.0020 - val_loss: 8.6327e-06 - val_mse: 8.6327e-06 - val_mae: 0.0022\n",
      "Epoch 89/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.4241e-06 - mse: 8.4241e-06 - mae: 0.0020 - val_loss: 8.6787e-06 - val_mse: 8.6787e-06 - val_mae: 0.0022\n",
      "Epoch 90/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 8.4211e-06 - mse: 8.4211e-06 - mae: 0.0020 - val_loss: 8.8427e-06 - val_mse: 8.8427e-06 - val_mae: 0.0022\n",
      "Epoch 91/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.4145e-06 - mse: 8.4145e-06 - mae: 0.0020 - val_loss: 8.4840e-06 - val_mse: 8.4840e-06 - val_mae: 0.0021\n",
      "Epoch 92/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.4054e-06 - mse: 8.4054e-06 - mae: 0.0020 - val_loss: 8.5831e-06 - val_mse: 8.5831e-06 - val_mae: 0.0022\n",
      "Epoch 93/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3848e-06 - mse: 8.3848e-06 - mae: 0.0020 - val_loss: 8.4328e-06 - val_mse: 8.4328e-06 - val_mae: 0.0021\n",
      "Epoch 94/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3953e-06 - mse: 8.3953e-06 - mae: 0.0020 - val_loss: 8.6771e-06 - val_mse: 8.6771e-06 - val_mae: 0.0022\n",
      "Epoch 95/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3733e-06 - mse: 8.3733e-06 - mae: 0.0020 - val_loss: 8.3627e-06 - val_mse: 8.3627e-06 - val_mae: 0.0021\n",
      "Epoch 96/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3556e-06 - mse: 8.3556e-06 - mae: 0.0020 - val_loss: 8.4675e-06 - val_mse: 8.4675e-06 - val_mae: 0.0021\n",
      "Epoch 97/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3425e-06 - mse: 8.3425e-06 - mae: 0.0020 - val_loss: 8.5988e-06 - val_mse: 8.5988e-06 - val_mae: 0.0022\n",
      "Epoch 98/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3345e-06 - mse: 8.3345e-06 - mae: 0.0020 - val_loss: 8.5130e-06 - val_mse: 8.5130e-06 - val_mae: 0.0021\n",
      "Epoch 99/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3288e-06 - mse: 8.3288e-06 - mae: 0.0020 - val_loss: 8.4369e-06 - val_mse: 8.4369e-06 - val_mae: 0.0021\n",
      "Epoch 100/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3220e-06 - mse: 8.3220e-06 - mae: 0.0020 - val_loss: 8.4701e-06 - val_mse: 8.4701e-06 - val_mae: 0.0021\n",
      "Epoch 101/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.3155e-06 - mse: 8.3155e-06 - mae: 0.0020 - val_loss: 8.3043e-06 - val_mse: 8.3043e-06 - val_mae: 0.0021\n",
      "Epoch 102/118\n",
      "1094/1094 [==============================] - 13s 11ms/step - loss: 8.2968e-06 - mse: 8.2968e-06 - mae: 0.0020 - val_loss: 8.2781e-06 - val_mse: 8.2781e-06 - val_mae: 0.0021\n",
      "Epoch 103/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.2888e-06 - mse: 8.2888e-06 - mae: 0.0020 - val_loss: 8.3367e-06 - val_mse: 8.3367e-06 - val_mae: 0.0021\n",
      "Epoch 104/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.2863e-06 - mse: 8.2863e-06 - mae: 0.0020 - val_loss: 8.3663e-06 - val_mse: 8.3663e-06 - val_mae: 0.0021\n",
      "Epoch 105/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.2727e-06 - mse: 8.2727e-06 - mae: 0.0020 - val_loss: 8.2764e-06 - val_mse: 8.2764e-06 - val_mae: 0.0021\n",
      "Epoch 106/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.2622e-06 - mse: 8.2622e-06 - mae: 0.0020 - val_loss: 8.2151e-06 - val_mse: 8.2151e-06 - val_mae: 0.0021\n",
      "Epoch 107/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.2529e-06 - mse: 8.2529e-06 - mae: 0.0020 - val_loss: 8.0927e-06 - val_mse: 8.0927e-06 - val_mae: 0.0021\n",
      "Epoch 108/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.2367e-06 - mse: 8.2367e-06 - mae: 0.0020 - val_loss: 8.0808e-06 - val_mse: 8.0808e-06 - val_mae: 0.0021\n",
      "Epoch 109/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.2322e-06 - mse: 8.2322e-06 - mae: 0.0020 - val_loss: 8.1912e-06 - val_mse: 8.1912e-06 - val_mae: 0.0021\n",
      "Epoch 110/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 8.2387e-06 - mse: 8.2387e-06 - mae: 0.0020 - val_loss: 8.0142e-06 - val_mse: 8.0142e-06 - val_mae: 0.0020\n",
      "Epoch 111/118\n",
      "1094/1094 [==============================] - 13s 11ms/step - loss: 8.2294e-06 - mse: 8.2294e-06 - mae: 0.0020 - val_loss: 8.0123e-06 - val_mse: 8.0123e-06 - val_mae: 0.0020\n",
      "Epoch 112/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 8.2381e-06 - mse: 8.2381e-06 - mae: 0.0020 - val_loss: 7.8201e-06 - val_mse: 7.8201e-06 - val_mae: 0.0020\n",
      "Epoch 113/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.2201e-06 - mse: 8.2201e-06 - mae: 0.0020 - val_loss: 7.8390e-06 - val_mse: 7.8390e-06 - val_mae: 0.0020\n",
      "Epoch 114/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.1295e-06 - mse: 8.1295e-06 - mae: 0.0020 - val_loss: 7.8679e-06 - val_mse: 7.8679e-06 - val_mae: 0.0020\n",
      "Epoch 115/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.0988e-06 - mse: 8.0988e-06 - mae: 0.0020 - val_loss: 7.9729e-06 - val_mse: 7.9729e-06 - val_mae: 0.0021\n",
      "Epoch 116/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.0363e-06 - mse: 8.0363e-06 - mae: 0.0020 - val_loss: 7.9121e-06 - val_mse: 7.9121e-06 - val_mae: 0.0020\n",
      "Epoch 117/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 8.0151e-06 - mse: 8.0151e-06 - mae: 0.0020 - val_loss: 8.0016e-06 - val_mse: 8.0016e-06 - val_mae: 0.0021\n",
      "Epoch 118/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 7.9656e-06 - mse: 7.9656e-06 - mae: 0.0020 - val_loss: 8.0272e-06 - val_mse: 8.0272e-06 - val_mae: 0.0021\n",
      "938/938 [==============================] - 1s 641us/step\n",
      "Epoch 1/118\n",
      "1094/1094 [==============================] - 13s 11ms/step - loss: 0.0034 - mse: 0.0034 - mae: 0.0145 - val_loss: 2.6822e-05 - val_mse: 2.6822e-05 - val_mae: 0.0040\n",
      "Epoch 2/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 2.6747e-05 - mse: 2.6747e-05 - mae: 0.0038 - val_loss: 2.1217e-05 - val_mse: 2.1217e-05 - val_mae: 0.0032\n",
      "Epoch 3/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 2.3633e-05 - mse: 2.3633e-05 - mae: 0.0036 - val_loss: 2.1356e-05 - val_mse: 2.1356e-05 - val_mae: 0.0034\n",
      "Epoch 4/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 2.4444e-05 - mse: 2.4444e-05 - mae: 0.0037 - val_loss: 2.7414e-05 - val_mse: 2.7414e-05 - val_mae: 0.0042\n",
      "Epoch 5/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 2.6199e-05 - mse: 2.6199e-05 - mae: 0.0038 - val_loss: 2.0874e-05 - val_mse: 2.0874e-05 - val_mae: 0.0030\n",
      "Epoch 6/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 2.5241e-05 - mse: 2.5241e-05 - mae: 0.0037 - val_loss: 1.6544e-05 - val_mse: 1.6544e-05 - val_mae: 0.0028\n",
      "Epoch 7/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 2.3380e-05 - mse: 2.3380e-05 - mae: 0.0036 - val_loss: 1.6157e-05 - val_mse: 1.6157e-05 - val_mae: 0.0028\n",
      "Epoch 8/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 2.2615e-05 - mse: 2.2615e-05 - mae: 0.0035 - val_loss: 2.9087e-05 - val_mse: 2.9087e-05 - val_mae: 0.0041\n",
      "Epoch 9/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 2.1377e-05 - mse: 2.1377e-05 - mae: 0.0034 - val_loss: 2.8877e-05 - val_mse: 2.8877e-05 - val_mae: 0.0042\n",
      "Epoch 10/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 2.0418e-05 - mse: 2.0418e-05 - mae: 0.0033 - val_loss: 1.9628e-05 - val_mse: 1.9628e-05 - val_mae: 0.0033\n",
      "Epoch 11/118\n",
      "1094/1094 [==============================] - 13s 11ms/step - loss: 2.0014e-05 - mse: 2.0014e-05 - mae: 0.0033 - val_loss: 1.9459e-05 - val_mse: 1.9459e-05 - val_mae: 0.0030\n",
      "Epoch 12/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.8963e-05 - mse: 1.8963e-05 - mae: 0.0032 - val_loss: 1.8955e-05 - val_mse: 1.8955e-05 - val_mae: 0.0029\n",
      "Epoch 13/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.8136e-05 - mse: 1.8136e-05 - mae: 0.0031 - val_loss: 1.7597e-05 - val_mse: 1.7597e-05 - val_mae: 0.0028\n",
      "Epoch 14/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.7607e-05 - mse: 1.7607e-05 - mae: 0.0030 - val_loss: 1.6557e-05 - val_mse: 1.6557e-05 - val_mae: 0.0027\n",
      "Epoch 15/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.7320e-05 - mse: 1.7320e-05 - mae: 0.0030 - val_loss: 1.5717e-05 - val_mse: 1.5717e-05 - val_mae: 0.0027\n",
      "Epoch 16/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.7186e-05 - mse: 1.7186e-05 - mae: 0.0030 - val_loss: 1.6365e-05 - val_mse: 1.6365e-05 - val_mae: 0.0028\n",
      "Epoch 17/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.6857e-05 - mse: 1.6857e-05 - mae: 0.0029 - val_loss: 1.5971e-05 - val_mse: 1.5971e-05 - val_mae: 0.0027\n",
      "Epoch 18/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.6602e-05 - mse: 1.6602e-05 - mae: 0.0029 - val_loss: 1.5780e-05 - val_mse: 1.5780e-05 - val_mae: 0.0027\n",
      "Epoch 19/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.6429e-05 - mse: 1.6429e-05 - mae: 0.0029 - val_loss: 1.5487e-05 - val_mse: 1.5487e-05 - val_mae: 0.0026\n",
      "Epoch 20/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.6389e-05 - mse: 1.6389e-05 - mae: 0.0029 - val_loss: 1.5186e-05 - val_mse: 1.5186e-05 - val_mae: 0.0026\n",
      "Epoch 21/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.6298e-05 - mse: 1.6298e-05 - mae: 0.0029 - val_loss: 1.5348e-05 - val_mse: 1.5348e-05 - val_mae: 0.0026\n",
      "Epoch 22/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.6153e-05 - mse: 1.6153e-05 - mae: 0.0029 - val_loss: 1.5419e-05 - val_mse: 1.5419e-05 - val_mae: 0.0027\n",
      "Epoch 23/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.6058e-05 - mse: 1.6058e-05 - mae: 0.0029 - val_loss: 1.5326e-05 - val_mse: 1.5326e-05 - val_mae: 0.0026\n",
      "Epoch 24/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.5938e-05 - mse: 1.5938e-05 - mae: 0.0028 - val_loss: 1.5307e-05 - val_mse: 1.5307e-05 - val_mae: 0.0026\n",
      "Epoch 25/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.5803e-05 - mse: 1.5803e-05 - mae: 0.0028 - val_loss: 1.5288e-05 - val_mse: 1.5288e-05 - val_mae: 0.0026\n",
      "Epoch 26/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.5716e-05 - mse: 1.5716e-05 - mae: 0.0028 - val_loss: 1.5254e-05 - val_mse: 1.5254e-05 - val_mae: 0.0026\n",
      "Epoch 27/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.5620e-05 - mse: 1.5620e-05 - mae: 0.0028 - val_loss: 1.5192e-05 - val_mse: 1.5192e-05 - val_mae: 0.0026\n",
      "Epoch 28/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.5484e-05 - mse: 1.5484e-05 - mae: 0.0028 - val_loss: 1.4969e-05 - val_mse: 1.4969e-05 - val_mae: 0.0026\n",
      "Epoch 29/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.5333e-05 - mse: 1.5333e-05 - mae: 0.0028 - val_loss: 1.4687e-05 - val_mse: 1.4687e-05 - val_mae: 0.0026\n",
      "Epoch 30/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.5219e-05 - mse: 1.5219e-05 - mae: 0.0028 - val_loss: 1.4260e-05 - val_mse: 1.4260e-05 - val_mae: 0.0026\n",
      "Epoch 31/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.5151e-05 - mse: 1.5151e-05 - mae: 0.0028 - val_loss: 1.4056e-05 - val_mse: 1.4056e-05 - val_mae: 0.0025\n",
      "Epoch 32/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.5027e-05 - mse: 1.5027e-05 - mae: 0.0028 - val_loss: 1.3829e-05 - val_mse: 1.3829e-05 - val_mae: 0.0025\n",
      "Epoch 33/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.4988e-05 - mse: 1.4988e-05 - mae: 0.0028 - val_loss: 1.3699e-05 - val_mse: 1.3699e-05 - val_mae: 0.0025\n",
      "Epoch 34/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.4839e-05 - mse: 1.4839e-05 - mae: 0.0027 - val_loss: 1.3619e-05 - val_mse: 1.3619e-05 - val_mae: 0.0025\n",
      "Epoch 35/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.4772e-05 - mse: 1.4772e-05 - mae: 0.0027 - val_loss: 1.3468e-05 - val_mse: 1.3468e-05 - val_mae: 0.0025\n",
      "Epoch 36/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.4731e-05 - mse: 1.4731e-05 - mae: 0.0027 - val_loss: 1.3384e-05 - val_mse: 1.3384e-05 - val_mae: 0.0025\n",
      "Epoch 37/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.4644e-05 - mse: 1.4644e-05 - mae: 0.0027 - val_loss: 1.3424e-05 - val_mse: 1.3424e-05 - val_mae: 0.0025\n",
      "Epoch 38/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.4557e-05 - mse: 1.4557e-05 - mae: 0.0027 - val_loss: 1.3342e-05 - val_mse: 1.3342e-05 - val_mae: 0.0025\n",
      "Epoch 39/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.4502e-05 - mse: 1.4502e-05 - mae: 0.0027 - val_loss: 1.3148e-05 - val_mse: 1.3148e-05 - val_mae: 0.0025\n",
      "Epoch 40/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.4439e-05 - mse: 1.4439e-05 - mae: 0.0027 - val_loss: 1.3238e-05 - val_mse: 1.3238e-05 - val_mae: 0.0025\n",
      "Epoch 41/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.4360e-05 - mse: 1.4360e-05 - mae: 0.0027 - val_loss: 1.3273e-05 - val_mse: 1.3273e-05 - val_mae: 0.0025\n",
      "Epoch 42/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.4286e-05 - mse: 1.4286e-05 - mae: 0.0027 - val_loss: 1.3100e-05 - val_mse: 1.3100e-05 - val_mae: 0.0025\n",
      "Epoch 43/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.4246e-05 - mse: 1.4246e-05 - mae: 0.0027 - val_loss: 1.3151e-05 - val_mse: 1.3151e-05 - val_mae: 0.0025\n",
      "Epoch 44/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.4188e-05 - mse: 1.4188e-05 - mae: 0.0027 - val_loss: 1.3119e-05 - val_mse: 1.3119e-05 - val_mae: 0.0025\n",
      "Epoch 45/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.4097e-05 - mse: 1.4097e-05 - mae: 0.0027 - val_loss: 1.3023e-05 - val_mse: 1.3023e-05 - val_mae: 0.0025\n",
      "Epoch 46/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.4041e-05 - mse: 1.4041e-05 - mae: 0.0027 - val_loss: 1.2898e-05 - val_mse: 1.2898e-05 - val_mae: 0.0025\n",
      "Epoch 47/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.3964e-05 - mse: 1.3964e-05 - mae: 0.0026 - val_loss: 1.2789e-05 - val_mse: 1.2789e-05 - val_mae: 0.0025\n",
      "Epoch 48/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.3863e-05 - mse: 1.3863e-05 - mae: 0.0026 - val_loss: 1.2675e-05 - val_mse: 1.2675e-05 - val_mae: 0.0024\n",
      "Epoch 49/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.3787e-05 - mse: 1.3787e-05 - mae: 0.0026 - val_loss: 1.2589e-05 - val_mse: 1.2589e-05 - val_mae: 0.0024\n",
      "Epoch 50/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.3658e-05 - mse: 1.3658e-05 - mae: 0.0026 - val_loss: 1.2388e-05 - val_mse: 1.2388e-05 - val_mae: 0.0024\n",
      "Epoch 51/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.3540e-05 - mse: 1.3540e-05 - mae: 0.0026 - val_loss: 1.2256e-05 - val_mse: 1.2256e-05 - val_mae: 0.0024\n",
      "Epoch 52/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.3387e-05 - mse: 1.3387e-05 - mae: 0.0026 - val_loss: 1.2207e-05 - val_mse: 1.2207e-05 - val_mae: 0.0024\n",
      "Epoch 53/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.3243e-05 - mse: 1.3243e-05 - mae: 0.0026 - val_loss: 1.2227e-05 - val_mse: 1.2227e-05 - val_mae: 0.0024\n",
      "Epoch 54/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.3080e-05 - mse: 1.3080e-05 - mae: 0.0026 - val_loss: 1.2229e-05 - val_mse: 1.2229e-05 - val_mae: 0.0024\n",
      "Epoch 55/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2928e-05 - mse: 1.2928e-05 - mae: 0.0025 - val_loss: 1.2289e-05 - val_mse: 1.2289e-05 - val_mae: 0.0024\n",
      "Epoch 56/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2796e-05 - mse: 1.2796e-05 - mae: 0.0025 - val_loss: 1.2291e-05 - val_mse: 1.2291e-05 - val_mae: 0.0024\n",
      "Epoch 57/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2642e-05 - mse: 1.2642e-05 - mae: 0.0025 - val_loss: 1.2183e-05 - val_mse: 1.2183e-05 - val_mae: 0.0024\n",
      "Epoch 58/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2527e-05 - mse: 1.2527e-05 - mae: 0.0025 - val_loss: 1.2063e-05 - val_mse: 1.2063e-05 - val_mae: 0.0023\n",
      "Epoch 59/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2429e-05 - mse: 1.2429e-05 - mae: 0.0025 - val_loss: 1.1897e-05 - val_mse: 1.1897e-05 - val_mae: 0.0023\n",
      "Epoch 60/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2329e-05 - mse: 1.2329e-05 - mae: 0.0025 - val_loss: 1.1765e-05 - val_mse: 1.1765e-05 - val_mae: 0.0023\n",
      "Epoch 61/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2219e-05 - mse: 1.2219e-05 - mae: 0.0025 - val_loss: 1.1584e-05 - val_mse: 1.1584e-05 - val_mae: 0.0023\n",
      "Epoch 62/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2164e-05 - mse: 1.2164e-05 - mae: 0.0025 - val_loss: 1.1492e-05 - val_mse: 1.1492e-05 - val_mae: 0.0023\n",
      "Epoch 63/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2079e-05 - mse: 1.2079e-05 - mae: 0.0025 - val_loss: 1.1396e-05 - val_mse: 1.1396e-05 - val_mae: 0.0023\n",
      "Epoch 64/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.2047e-05 - mse: 1.2047e-05 - mae: 0.0025 - val_loss: 1.1341e-05 - val_mse: 1.1341e-05 - val_mae: 0.0023\n",
      "Epoch 65/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1977e-05 - mse: 1.1977e-05 - mae: 0.0025 - val_loss: 1.1182e-05 - val_mse: 1.1182e-05 - val_mae: 0.0023\n",
      "Epoch 66/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1927e-05 - mse: 1.1927e-05 - mae: 0.0025 - val_loss: 1.1098e-05 - val_mse: 1.1098e-05 - val_mae: 0.0023\n",
      "Epoch 67/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1873e-05 - mse: 1.1873e-05 - mae: 0.0024 - val_loss: 1.1069e-05 - val_mse: 1.1069e-05 - val_mae: 0.0023\n",
      "Epoch 68/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1854e-05 - mse: 1.1854e-05 - mae: 0.0024 - val_loss: 1.1001e-05 - val_mse: 1.1001e-05 - val_mae: 0.0023\n",
      "Epoch 69/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1809e-05 - mse: 1.1809e-05 - mae: 0.0024 - val_loss: 1.0983e-05 - val_mse: 1.0983e-05 - val_mae: 0.0023\n",
      "Epoch 70/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1756e-05 - mse: 1.1756e-05 - mae: 0.0024 - val_loss: 1.0975e-05 - val_mse: 1.0975e-05 - val_mae: 0.0023\n",
      "Epoch 71/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1738e-05 - mse: 1.1738e-05 - mae: 0.0024 - val_loss: 1.1027e-05 - val_mse: 1.1027e-05 - val_mae: 0.0023\n",
      "Epoch 72/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1700e-05 - mse: 1.1700e-05 - mae: 0.0024 - val_loss: 1.1081e-05 - val_mse: 1.1081e-05 - val_mae: 0.0023\n",
      "Epoch 73/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1649e-05 - mse: 1.1649e-05 - mae: 0.0024 - val_loss: 1.1068e-05 - val_mse: 1.1068e-05 - val_mae: 0.0023\n",
      "Epoch 74/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1615e-05 - mse: 1.1615e-05 - mae: 0.0024 - val_loss: 1.1138e-05 - val_mse: 1.1138e-05 - val_mae: 0.0024\n",
      "Epoch 75/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1580e-05 - mse: 1.1580e-05 - mae: 0.0024 - val_loss: 1.1236e-05 - val_mse: 1.1236e-05 - val_mae: 0.0024\n",
      "Epoch 76/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1549e-05 - mse: 1.1549e-05 - mae: 0.0024 - val_loss: 1.1252e-05 - val_mse: 1.1252e-05 - val_mae: 0.0024\n",
      "Epoch 77/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1513e-05 - mse: 1.1513e-05 - mae: 0.0024 - val_loss: 1.1225e-05 - val_mse: 1.1225e-05 - val_mae: 0.0024\n",
      "Epoch 78/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1482e-05 - mse: 1.1482e-05 - mae: 0.0024 - val_loss: 1.1342e-05 - val_mse: 1.1342e-05 - val_mae: 0.0024\n",
      "Epoch 79/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1452e-05 - mse: 1.1452e-05 - mae: 0.0024 - val_loss: 1.1356e-05 - val_mse: 1.1356e-05 - val_mae: 0.0024\n",
      "Epoch 80/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1413e-05 - mse: 1.1413e-05 - mae: 0.0024 - val_loss: 1.1383e-05 - val_mse: 1.1383e-05 - val_mae: 0.0024\n",
      "Epoch 81/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1388e-05 - mse: 1.1388e-05 - mae: 0.0024 - val_loss: 1.1465e-05 - val_mse: 1.1465e-05 - val_mae: 0.0024\n",
      "Epoch 82/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1347e-05 - mse: 1.1347e-05 - mae: 0.0024 - val_loss: 1.1491e-05 - val_mse: 1.1491e-05 - val_mae: 0.0024\n",
      "Epoch 83/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1336e-05 - mse: 1.1336e-05 - mae: 0.0024 - val_loss: 1.1562e-05 - val_mse: 1.1562e-05 - val_mae: 0.0024\n",
      "Epoch 84/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1284e-05 - mse: 1.1284e-05 - mae: 0.0024 - val_loss: 1.1605e-05 - val_mse: 1.1605e-05 - val_mae: 0.0025\n",
      "Epoch 85/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1273e-05 - mse: 1.1273e-05 - mae: 0.0024 - val_loss: 1.1634e-05 - val_mse: 1.1634e-05 - val_mae: 0.0025\n",
      "Epoch 86/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1243e-05 - mse: 1.1243e-05 - mae: 0.0024 - val_loss: 1.1687e-05 - val_mse: 1.1687e-05 - val_mae: 0.0025\n",
      "Epoch 87/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1219e-05 - mse: 1.1219e-05 - mae: 0.0024 - val_loss: 1.1662e-05 - val_mse: 1.1662e-05 - val_mae: 0.0025\n",
      "Epoch 88/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1220e-05 - mse: 1.1220e-05 - mae: 0.0024 - val_loss: 1.1748e-05 - val_mse: 1.1748e-05 - val_mae: 0.0025\n",
      "Epoch 89/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1186e-05 - mse: 1.1186e-05 - mae: 0.0024 - val_loss: 1.1738e-05 - val_mse: 1.1738e-05 - val_mae: 0.0025\n",
      "Epoch 90/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1177e-05 - mse: 1.1177e-05 - mae: 0.0024 - val_loss: 1.1764e-05 - val_mse: 1.1764e-05 - val_mae: 0.0025\n",
      "Epoch 91/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1148e-05 - mse: 1.1148e-05 - mae: 0.0024 - val_loss: 1.1809e-05 - val_mse: 1.1809e-05 - val_mae: 0.0025\n",
      "Epoch 92/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1141e-05 - mse: 1.1141e-05 - mae: 0.0024 - val_loss: 1.1825e-05 - val_mse: 1.1825e-05 - val_mae: 0.0025\n",
      "Epoch 93/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1119e-05 - mse: 1.1119e-05 - mae: 0.0024 - val_loss: 1.1841e-05 - val_mse: 1.1841e-05 - val_mae: 0.0025\n",
      "Epoch 94/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1107e-05 - mse: 1.1107e-05 - mae: 0.0024 - val_loss: 1.1843e-05 - val_mse: 1.1843e-05 - val_mae: 0.0025\n",
      "Epoch 95/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1076e-05 - mse: 1.1076e-05 - mae: 0.0023 - val_loss: 1.1835e-05 - val_mse: 1.1835e-05 - val_mae: 0.0025\n",
      "Epoch 96/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1069e-05 - mse: 1.1069e-05 - mae: 0.0023 - val_loss: 1.1849e-05 - val_mse: 1.1849e-05 - val_mae: 0.0025\n",
      "Epoch 97/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1053e-05 - mse: 1.1053e-05 - mae: 0.0023 - val_loss: 1.1860e-05 - val_mse: 1.1860e-05 - val_mae: 0.0025\n",
      "Epoch 98/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1039e-05 - mse: 1.1039e-05 - mae: 0.0023 - val_loss: 1.1830e-05 - val_mse: 1.1830e-05 - val_mae: 0.0025\n",
      "Epoch 99/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1030e-05 - mse: 1.1030e-05 - mae: 0.0023 - val_loss: 1.1850e-05 - val_mse: 1.1850e-05 - val_mae: 0.0025\n",
      "Epoch 100/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1061e-05 - mse: 1.1061e-05 - mae: 0.0023 - val_loss: 1.1904e-05 - val_mse: 1.1904e-05 - val_mae: 0.0025\n",
      "Epoch 101/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1042e-05 - mse: 1.1042e-05 - mae: 0.0023 - val_loss: 1.1882e-05 - val_mse: 1.1882e-05 - val_mae: 0.0025\n",
      "Epoch 102/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1038e-05 - mse: 1.1038e-05 - mae: 0.0023 - val_loss: 1.1834e-05 - val_mse: 1.1834e-05 - val_mae: 0.0025\n",
      "Epoch 103/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1038e-05 - mse: 1.1038e-05 - mae: 0.0023 - val_loss: 1.1706e-05 - val_mse: 1.1706e-05 - val_mae: 0.0025\n",
      "Epoch 104/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1018e-05 - mse: 1.1018e-05 - mae: 0.0023 - val_loss: 1.1635e-05 - val_mse: 1.1635e-05 - val_mae: 0.0025\n",
      "Epoch 105/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1029e-05 - mse: 1.1029e-05 - mae: 0.0023 - val_loss: 1.1469e-05 - val_mse: 1.1469e-05 - val_mae: 0.0024\n",
      "Epoch 106/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1006e-05 - mse: 1.1006e-05 - mae: 0.0023 - val_loss: 1.1402e-05 - val_mse: 1.1402e-05 - val_mae: 0.0024\n",
      "Epoch 107/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0994e-05 - mse: 1.0994e-05 - mae: 0.0023 - val_loss: 1.1427e-05 - val_mse: 1.1427e-05 - val_mae: 0.0024\n",
      "Epoch 108/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.1002e-05 - mse: 1.1002e-05 - mae: 0.0023 - val_loss: 1.1334e-05 - val_mse: 1.1334e-05 - val_mae: 0.0024\n",
      "Epoch 109/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0989e-05 - mse: 1.0989e-05 - mae: 0.0023 - val_loss: 1.1247e-05 - val_mse: 1.1247e-05 - val_mae: 0.0024\n",
      "Epoch 110/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0995e-05 - mse: 1.0995e-05 - mae: 0.0023 - val_loss: 1.1115e-05 - val_mse: 1.1115e-05 - val_mae: 0.0024\n",
      "Epoch 111/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0995e-05 - mse: 1.0995e-05 - mae: 0.0023 - val_loss: 1.0939e-05 - val_mse: 1.0939e-05 - val_mae: 0.0024\n",
      "Epoch 112/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0991e-05 - mse: 1.0991e-05 - mae: 0.0023 - val_loss: 1.0841e-05 - val_mse: 1.0841e-05 - val_mae: 0.0023\n",
      "Epoch 113/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0972e-05 - mse: 1.0972e-05 - mae: 0.0023 - val_loss: 1.0813e-05 - val_mse: 1.0813e-05 - val_mae: 0.0023\n",
      "Epoch 114/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 1.0973e-05 - mse: 1.0973e-05 - mae: 0.0023 - val_loss: 1.0782e-05 - val_mse: 1.0782e-05 - val_mae: 0.0023\n",
      "Epoch 115/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0958e-05 - mse: 1.0958e-05 - mae: 0.0023 - val_loss: 1.0774e-05 - val_mse: 1.0774e-05 - val_mae: 0.0023\n",
      "Epoch 116/118\n",
      "1094/1094 [==============================] - 13s 12ms/step - loss: 1.0949e-05 - mse: 1.0949e-05 - mae: 0.0023 - val_loss: 1.0774e-05 - val_mse: 1.0774e-05 - val_mae: 0.0023\n",
      "Epoch 117/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0960e-05 - mse: 1.0960e-05 - mae: 0.0023 - val_loss: 1.0812e-05 - val_mse: 1.0812e-05 - val_mae: 0.0023\n",
      "Epoch 118/118\n",
      "1094/1094 [==============================] - 12s 11ms/step - loss: 1.0925e-05 - mse: 1.0925e-05 - mae: 0.0023 - val_loss: 1.0803e-05 - val_mse: 1.0803e-05 - val_mae: 0.0023\n",
      "938/938 [==============================] - 1s 421us/step\n",
      "Results for act1 = relu, act2 = linear:\n",
      "Training loss: [0.006572327110916376, 1.232519844052149e-05, 1.1701754374371376e-05, 1.2138558304286562e-05, 1.3110461623000447e-05, 1.3026562555751298e-05, 1.3334911272977479e-05, 1.3139894690539222e-05, 1.2821396012441255e-05, 1.2587227502081078e-05, 1.2413779586495366e-05, 1.2292553947190754e-05, 1.22022911455133e-05, 1.20440336104366e-05, 1.219086698256433e-05, 1.1934060239582323e-05, 1.1742068636522163e-05, 1.156266444013454e-05, 1.1518497558427043e-05, 1.1292428098386154e-05, 1.1004410225723404e-05, 1.0831129657162819e-05, 1.0687886970117688e-05, 1.0582018148852512e-05, 1.0512972949072719e-05, 1.0384117558714934e-05, 1.0295494575984776e-05, 1.0164731065742671e-05, 1.0053058758785482e-05, 1.0027722055383492e-05, 9.891422450891696e-06, 9.910669177770615e-06, 9.792306627787184e-06, 9.824316293816082e-06, 9.798569408303592e-06, 9.658028830017429e-06, 9.647646947996691e-06, 9.74411977949785e-06, 9.626914106775075e-06, 9.511609277979005e-06, 9.508869879937265e-06, 9.462401976634283e-06, 9.438466804567724e-06, 9.393075742991641e-06, 9.343383680970874e-06, 9.446589501749258e-06, 9.23061634239275e-06, 9.29436555452412e-06, 9.233424862031825e-06, 9.207268703903537e-06, 9.152043276117183e-06, 9.165341907646507e-06, 9.053303074324504e-06, 9.122236406255979e-06, 9.0129806267214e-06, 9.061314813152421e-06, 8.962049832916819e-06, 8.960560080595315e-06, 8.930980584409554e-06, 8.917116247175727e-06, 8.887721378414426e-06, 8.879558663466014e-06, 8.863632501743268e-06, 8.857811735651921e-06, 8.813579370325897e-06, 8.8033211795846e-06, 8.790227184363175e-06, 8.768450243223924e-06, 8.760513082961552e-06, 8.742273166717496e-06, 8.730197805562057e-06, 8.701261322130449e-06, 8.734020411793608e-06, 8.647201866551768e-06, 8.693178642715793e-06, 8.636448001198005e-06, 8.624379006505478e-06, 8.607737072452437e-06, 8.630730008007959e-06, 8.632504432171118e-06, 8.57383474794915e-06, 8.581450856581796e-06, 8.529562364856247e-06, 8.579815585108008e-06, 8.536690074834041e-06, 8.5086085164221e-06, 8.479314601572696e-06, 8.525753401045222e-06, 8.495704605593346e-06, 8.503717253915966e-06, 8.427365173702128e-06, 8.439657904091291e-06, 8.468674423056655e-06, 8.43341422296362e-06, 8.434794835920911e-06, 8.420983249379788e-06, 8.419997357123066e-06, 8.414819603785872e-06, 8.412554961978458e-06, 8.386617992073298e-06, 8.373134733119514e-06, 8.35879473015666e-06, 8.349634299520403e-06, 8.33470949146431e-06, 8.333633559232112e-06, 8.318403160956223e-06, 8.320133929373696e-06, 8.305747542181052e-06, 8.303990398417227e-06, 8.317522770084906e-06, 8.298466127598658e-06, 8.304056791530456e-06, 8.301980415126309e-06, 8.268410056189168e-06, 8.271983460872434e-06, 8.25532970338827e-06, 8.263488780357875e-06, 8.251770850620233e-06]\n",
      "Validation loss: [1.2190371307951864e-05, 1.2431038157956209e-05, 1.0471283530932851e-05, 1.1398310562071856e-05, 1.2934276128362399e-05, 1.0542828022153117e-05, 1.0480661330802832e-05, 1.0636180377332494e-05, 1.0530588951951358e-05, 1.1689751772792079e-05, 1.2931664969073609e-05, 1.3376677998166997e-05, 1.0898661457758863e-05, 9.685926670499612e-06, 1.0232626664219424e-05, 1.0043677320936695e-05, 9.465922630624846e-06, 9.2529571702471e-06, 9.257059900846798e-06, 9.533110642223619e-06, 9.656389011070132e-06, 9.981978109863121e-06, 1.029729173751548e-05, 1.0504574674996547e-05, 1.072774739441229e-05, 1.0927667062787805e-05, 1.0898857908614445e-05, 1.0916063729382586e-05, 1.098271877708612e-05, 1.0797704817377962e-05, 1.061701277649263e-05, 1.0376339560025372e-05, 9.915588634612504e-06, 1.017443082673708e-05, 1.0444121471664403e-05, 9.991256774810608e-06, 9.710818630992435e-06, 1.0289158126397524e-05, 9.784967005543876e-06, 1.0046708666777704e-05, 9.795477126317564e-06, 9.635140486352611e-06, 9.441071597393602e-06, 9.36985725275008e-06, 9.41132202569861e-06, 9.455054168938659e-06, 8.993046321847942e-06, 9.18987461773213e-06, 8.98373491509119e-06, 8.839832844387274e-06, 8.736353265703656e-06, 8.589459866925608e-06, 8.487635568599217e-06, 8.30681346997153e-06, 8.059187166509219e-06, 8.000233719940297e-06, 7.890685083111748e-06, 7.974899745022412e-06, 7.878647920733783e-06, 7.813438969606068e-06, 7.817152436473407e-06, 7.730327524768654e-06, 7.749843462079298e-06, 7.85946031101048e-06, 7.759858817735221e-06, 7.787295544403605e-06, 7.736638508504257e-06, 7.726964213361498e-06, 7.80363188823685e-06, 7.77614604885457e-06, 7.829685273463838e-06, 7.739681677776389e-06, 7.89087062003091e-06, 7.842533705115784e-06, 8.001088644959964e-06, 7.947575795697048e-06, 8.066333066381048e-06, 8.056068509176839e-06, 7.93435719970148e-06, 8.13550013845088e-06, 8.086637535598129e-06, 8.099005754047539e-06, 7.949261089379434e-06, 8.234402230300475e-06, 8.158555829140823e-06, 8.200043339456897e-06, 8.084661203611176e-06, 8.187986168195494e-06, 8.162644007825293e-06, 8.429150511801708e-06, 8.099155820673332e-06, 8.167180567397736e-06, 8.27480835141614e-06, 8.276243534055538e-06, 8.305720257339999e-06, 8.32449131848989e-06, 8.327699106303044e-06, 8.346763934241608e-06, 8.357981641893275e-06, 8.370046089112293e-06, 8.303939466713928e-06, 8.48415947984904e-06, 8.47041610541055e-06, 8.454883754893672e-06, 8.427911780017894e-06, 8.521319614374079e-06, 8.451526809949428e-06, 8.443619663012214e-06, 8.440089914074633e-06, 8.419203368248418e-06, 8.403959327552002e-06, 8.304595212393906e-06, 8.413647265115287e-06, 8.38053529150784e-06, 8.221841198974289e-06, 8.214892659452744e-06, 8.087114110821858e-06, 8.063529094215482e-06]\n",
      "Results for act1 = <keras.layers.activation.leaky_relu.LeakyReLU object at 0x7fa9873471c0>, act2 = linear:\n",
      "Training loss: [0.005252428352832794, 1.2279821930860635e-05, 1.1527316928550135e-05, 1.2700970728474203e-05, 1.2697912097792141e-05, 1.3610364476335235e-05, 1.3497804502549116e-05, 1.2902621165267192e-05, 1.24132839118829e-05, 1.2005521057290025e-05, 1.1675939276756253e-05, 1.131640328821959e-05, 1.118475483963266e-05, 1.107991465687519e-05, 1.0958875463984441e-05, 1.0916814062511548e-05, 1.0826734978763852e-05, 1.0605754141579382e-05, 1.0443148312333506e-05, 1.03471138572786e-05, 1.0275321983499452e-05, 1.0152318282052875e-05, 1.0036719686468132e-05, 9.93741923593916e-06, 9.84580401564017e-06, 9.846935427049175e-06, 9.7495312729734e-06, 9.719286026665941e-06, 9.669129212852567e-06, 9.629080523154698e-06, 9.635064088797662e-06, 9.57544853008585e-06, 9.579650395608041e-06, 9.468318239669316e-06, 9.456731277168728e-06, 9.419102752872277e-06, 9.347045306640211e-06, 9.337666597275529e-06, 9.352947017760016e-06, 9.314247108704876e-06, 9.28322879190091e-06, 9.264363143302035e-06, 9.211007636622526e-06, 9.189360753225628e-06, 9.134365427598823e-06, 9.125546966970433e-06, 9.08496804186143e-06, 9.080709787667729e-06, 9.049731488630641e-06, 9.037881682161242e-06, 9.011478141474072e-06, 8.994032214104664e-06, 8.961031198850833e-06, 8.947807145887055e-06, 8.932016498874873e-06, 8.908778909244575e-06, 8.880568202584982e-06, 8.878563676262274e-06, 8.845437150739599e-06, 8.844166586641222e-06, 8.808480743027758e-06, 8.812495252641384e-06, 8.778663868724834e-06, 8.761992830841336e-06, 8.736674317333382e-06, 8.739677468838636e-06, 8.724441613594536e-06, 8.69166888151085e-06, 8.684185559104662e-06, 8.65945276018465e-06, 8.649797564430628e-06, 8.63171590026468e-06, 8.616923878435045e-06, 8.60716409079032e-06, 8.577350854466204e-06, 8.595607141614892e-06, 8.574143976147752e-06, 8.561498361814301e-06, 8.537290341337211e-06, 8.542646355635952e-06, 8.53031815495342e-06, 8.52078665047884e-06, 8.507059646944981e-06, 8.472049557894934e-06, 8.473472007608507e-06, 8.45802242110949e-06, 8.451407666143496e-06, 8.437439646513667e-06, 8.424121006100904e-06, 8.421122402069159e-06, 8.414465810346883e-06, 8.405352673435118e-06, 8.384816283069085e-06, 8.395280019612983e-06, 8.373270247830078e-06, 8.355609679711051e-06, 8.342509318026714e-06, 8.33447393233655e-06, 8.328755029651802e-06, 8.322002031491138e-06, 8.315547347592656e-06, 8.29675900604343e-06, 8.288773642561864e-06, 8.286338015750516e-06, 8.272655577457044e-06, 8.262206392828375e-06, 8.252905900008045e-06, 8.236683243012521e-06, 8.23217851575464e-06, 8.238685040851124e-06, 8.229442755691707e-06, 8.238114787673112e-06, 8.220106792578008e-06, 8.129476555041037e-06, 8.09878565632971e-06, 8.03625243861461e-06, 8.015086677914951e-06, 7.965648364915978e-06]\n",
      "Validation loss: [1.9928444089600816e-05, 1.2671211152337492e-05, 1.111887650040444e-05, 1.349631929770112e-05, 1.087471900973469e-05, 1.0874788131332025e-05, 1.068904202838894e-05, 1.26713184727123e-05, 1.1244349479966331e-05, 1.0520146133785602e-05, 1.0209829270024784e-05, 1.0128997928404715e-05, 1.1376737347745802e-05, 1.0205214493907988e-05, 9.530461284157354e-06, 9.388670150656253e-06, 9.05990873434348e-06, 9.215900718118064e-06, 9.806758498598356e-06, 1.1138092304463498e-05, 1.11998569991556e-05, 1.0059945452667307e-05, 9.469302312936634e-06, 9.540999599266797e-06, 9.836467143031769e-06, 9.78582556854235e-06, 9.720702109916601e-06, 9.676203262642957e-06, 9.785236215975601e-06, 9.991937986342236e-06, 9.78843672783114e-06, 9.790734111447819e-06, 8.886159776011482e-06, 8.836266715661623e-06, 8.616355444246437e-06, 8.449238521279767e-06, 8.302727110276464e-06, 8.263831659860443e-06, 8.16742885945132e-06, 8.336381142726168e-06, 8.34990441944683e-06, 8.349710697075352e-06, 8.307641110150144e-06, 8.403362699027639e-06, 8.336606697412208e-06, 8.508846804033965e-06, 8.58744988363469e-06, 8.551860446459614e-06, 8.611388693680055e-06, 8.651853931951337e-06, 8.52796347317053e-06, 8.663349035487045e-06, 8.613702448201366e-06, 8.907498340704478e-06, 8.76918147696415e-06, 8.927778253564611e-06, 9.037601557793096e-06, 9.010032954392955e-06, 9.178059372061398e-06, 9.37120603339281e-06, 9.11453662411077e-06, 9.006360414787196e-06, 9.026450243254658e-06, 9.065647645911667e-06, 8.876044375938363e-06, 9.116389264818281e-06, 9.08962647372391e-06, 9.109605343837757e-06, 9.06348941498436e-06, 8.940909538068809e-06, 8.942918611865025e-06, 8.742011232243385e-06, 8.919855645217467e-06, 8.842966053634882e-06, 8.68506140250247e-06, 8.998189514386468e-06, 8.638293365947902e-06, 8.976360732049216e-06, 9.034106369654182e-06, 8.818031346891075e-06, 8.837264431349467e-06, 8.810081453702878e-06, 8.90220508154016e-06, 8.899925887817517e-06, 8.960493687482085e-06, 8.689517017046455e-06, 8.651591997477226e-06, 8.632670869701542e-06, 8.678666745254304e-06, 8.842737770464737e-06, 8.483981218887493e-06, 8.583056114730425e-06, 8.432805771008134e-06, 8.677053301653359e-06, 8.362685548490845e-06, 8.467458428640384e-06, 8.598804924986325e-06, 8.51295317261247e-06, 8.436889402219094e-06, 8.470111424685456e-06, 8.304255061375443e-06, 8.278128007077612e-06, 8.336697646882385e-06, 8.366267138626426e-06, 8.27644907985814e-06, 8.21506000647787e-06, 8.092740245047025e-06, 8.08075401437236e-06, 8.191179404093418e-06, 8.014238119358197e-06, 8.01227997726528e-06, 7.820053724572062e-06, 7.839004865672905e-06, 7.867851309129037e-06, 7.97287248133216e-06, 7.912133696663659e-06, 8.00164070824394e-06, 8.027168405533303e-06]\n",
      "Results for act1 = tanh, act2 = linear:\n",
      "Training loss: [0.00336018786765635, 2.6746622097562067e-05, 2.3633207092643715e-05, 2.4444161681458354e-05, 2.619889892230276e-05, 2.524086448829621e-05, 2.338000740564894e-05, 2.2615471607423387e-05, 2.137675073754508e-05, 2.0418379790498875e-05, 2.0014420442748815e-05, 1.896256799227558e-05, 1.8135608115699142e-05, 1.760701707098633e-05, 1.731953125272412e-05, 1.7185988326673396e-05, 1.6857016817084514e-05, 1.660228372202255e-05, 1.6429366951342672e-05, 1.6388687072321773e-05, 1.6298336049658246e-05, 1.6153375327121466e-05, 1.6058173059718683e-05, 1.593803426658269e-05, 1.5802806956344284e-05, 1.5716272173449397e-05, 1.5620316844433546e-05, 1.5483947208849713e-05, 1.533328031655401e-05, 1.5218598491628654e-05, 1.5150502804317512e-05, 1.5027126210043207e-05, 1.4988155271566939e-05, 1.4839300092717167e-05, 1.477210025768727e-05, 1.4731090232089628e-05, 1.4643764188804198e-05, 1.4556884707417339e-05, 1.450208219466731e-05, 1.4438530342886224e-05, 1.4359603483171668e-05, 1.4286048099165782e-05, 1.4245730199036188e-05, 1.418785541318357e-05, 1.4096959603193682e-05, 1.4040719179320149e-05, 1.3964468053018209e-05, 1.3862816558685154e-05, 1.3786626368528232e-05, 1.3658112038683612e-05, 1.3539925930672325e-05, 1.338746733381413e-05, 1.32426348500303e-05, 1.3079836207907647e-05, 1.2928483556606807e-05, 1.2796414011972956e-05, 1.2641577995964326e-05, 1.2526564205472823e-05, 1.2428551599441562e-05, 1.2328862794674933e-05, 1.2219446944072843e-05, 1.2164384315838106e-05, 1.2079442058166023e-05, 1.20472141134087e-05, 1.1977147551078815e-05, 1.1927228115382604e-05, 1.1873014045704622e-05, 1.1853747309942264e-05, 1.1808710951299872e-05, 1.1755527339118998e-05, 1.173814325738931e-05, 1.1699527021846734e-05, 1.164911373052746e-05, 1.1614947652560659e-05, 1.158027043857146e-05, 1.1548593647603411e-05, 1.1512831406434998e-05, 1.1482438821985852e-05, 1.1452341823314782e-05, 1.141348184319213e-05, 1.1388245184207335e-05, 1.1347214240231551e-05, 1.1335766430420335e-05, 1.1283673302386887e-05, 1.1272711162746418e-05, 1.1242667824262753e-05, 1.1219069165235851e-05, 1.1220493433938827e-05, 1.1186091796844266e-05, 1.1177310625498649e-05, 1.114760925702285e-05, 1.1141163668071385e-05, 1.1118730071757454e-05, 1.1106876627309248e-05, 1.1076373084506486e-05, 1.1069278116337955e-05, 1.105269529944053e-05, 1.1038946468033828e-05, 1.1030217137886211e-05, 1.106056970456848e-05, 1.1041815923817921e-05, 1.1037850526918191e-05, 1.1038366210414097e-05, 1.1017595170415007e-05, 1.1028628250642214e-05, 1.100643930840306e-05, 1.0994180229317863e-05, 1.1001769962604158e-05, 1.0989194379362743e-05, 1.099511246138718e-05, 1.0995303455274552e-05, 1.0991257113346364e-05, 1.0971801202686038e-05, 1.097297172236722e-05, 1.0957816812151577e-05, 1.0949054740194697e-05, 1.0959740393445827e-05, 1.0924522939603776e-05]\n",
      "Validation loss: [2.6822024665307254e-05, 2.1217449102550745e-05, 2.1355621356633492e-05, 2.7413718271418475e-05, 2.087401480821427e-05, 1.6543692254344933e-05, 1.6156915080500767e-05, 2.9087383154546842e-05, 2.8876724172732793e-05, 1.962773603736423e-05, 1.9459006580291316e-05, 1.8954509869217873e-05, 1.759738370310515e-05, 1.6556939954170957e-05, 1.571697612234857e-05, 1.6365145711461082e-05, 1.597098889760673e-05, 1.5780236935825087e-05, 1.5486775737372227e-05, 1.5186408745648805e-05, 1.5347985026892275e-05, 1.5419314877362922e-05, 1.532632450107485e-05, 1.5306513887480833e-05, 1.5287800124497153e-05, 1.5253726814989932e-05, 1.5192461432889104e-05, 1.4968514733482152e-05, 1.4687047951156273e-05, 1.4259870113164652e-05, 1.4055825886316597e-05, 1.3829425370204262e-05, 1.369922756566666e-05, 1.3619299352285452e-05, 1.3468255019688513e-05, 1.3383501027419697e-05, 1.3424460121314041e-05, 1.3342189049581066e-05, 1.3147981917427387e-05, 1.3238398423709441e-05, 1.3273362128529698e-05, 1.3099615898681805e-05, 1.315093322773464e-05, 1.311885625909781e-05, 1.3023290193814319e-05, 1.2897902706754394e-05, 1.2789052561856806e-05, 1.2675281141127925e-05, 1.2589061043399852e-05, 1.2388349205139093e-05, 1.2256428817636333e-05, 1.2206623978272546e-05, 1.2227325896674301e-05, 1.2229383173689712e-05, 1.2288905963941943e-05, 1.2290593986108433e-05, 1.2183293620182667e-05, 1.2063097528880462e-05, 1.1896603609784506e-05, 1.1765309864131268e-05, 1.1583798368519638e-05, 1.1492347766761668e-05, 1.139580945164198e-05, 1.1340958735672757e-05, 1.1182498383277562e-05, 1.1097601600340568e-05, 1.1069410902564414e-05, 1.100135523302015e-05, 1.0983025276800618e-05, 1.0974528777296655e-05, 1.1026603715436067e-05, 1.1081173397542443e-05, 1.1067706509493291e-05, 1.113835514843231e-05, 1.1236021236982197e-05, 1.1251539035583846e-05, 1.122532739827875e-05, 1.1342452125973068e-05, 1.1355743481544778e-05, 1.138269726652652e-05, 1.1465079296613112e-05, 1.1491092664073221e-05, 1.15615666800295e-05, 1.1605464351305272e-05, 1.1634228940238245e-05, 1.1686563084367663e-05, 1.1661827556963544e-05, 1.1747591088351328e-05, 1.1737674867617898e-05, 1.176359728560783e-05, 1.1808941962954123e-05, 1.1825245564978104e-05, 1.184135635412531e-05, 1.1842516869364772e-05, 1.1834917131636757e-05, 1.1848608664877247e-05, 1.1859960068250075e-05, 1.182961386803072e-05, 1.1849640031869058e-05, 1.190449529531179e-05, 1.1881526006618515e-05, 1.183415952255018e-05, 1.1706420991686173e-05, 1.1635333066806197e-05, 1.146907925431151e-05, 1.1401512892916799e-05, 1.1427390745666344e-05, 1.133383011620026e-05, 1.1247310794715304e-05, 1.1114562767033931e-05, 1.093898117687786e-05, 1.084053292288445e-05, 1.081317077478161e-05, 1.0782404388010036e-05, 1.0774149814096745e-05, 1.0773545909614768e-05, 1.0811570973601192e-05, 1.080263882613508e-05]\n"
     ]
    }
   ],
   "source": [
    "activations1 = ['relu', LeakyReLU(alpha=0.01), 'tanh']\n",
    "activations2 = ['linear']\n",
    "\n",
    "activation_results = {}\n",
    "for act1 in activations1:\n",
    "    # Initialize the dictionary for act1 if it doesn't exist\n",
    "    if act1 not in activation_results:\n",
    "        activation_results[act1] = {}\n",
    "\n",
    "\n",
    "for act1 in activations1:\n",
    "    for act2 in activations2:\n",
    "        # Get a new model with the current activation\n",
    "        model = create_model(act1, act2, train_input_shape)\n",
    "        # Fit the model and store the history results\n",
    "        model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss=keras.losses.MeanSquaredError(reduction=\"sum_over_batch_size\", name=\"mean_squared_error\"), metrics=['mse', 'mae'])\n",
    "        h_callback2 = model.fit(train_dataset, epochs=118, validation_data=test_dataset)\n",
    "        activation_results[act1][act2] = {\n",
    "            'history': h_callback2.history,\n",
    "            'loss': h_callback2.history['loss'],  # Training loss\n",
    "            'val_loss': h_callback2.history['val_loss'],  # Validation loss\n",
    "            'predictions': model.predict(test_x)\n",
    "        }\n",
    "\n",
    "for act1 in activations1:\n",
    "    for act2 in activations2:\n",
    "        print(f\"Results for act1 = {act1}, act2 = {act2}:\")\n",
    "        print(\"Training loss:\", activation_results[act1][act2]['loss'])\n",
    "        print(\"Validation loss:\", activation_results[act1][act2]['val_loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare a list to hold the results\n",
    "results_list = []\n",
    "\n",
    "# Iterate through the activation_results dictionary\n",
    "for act1 in activation_results:\n",
    "    for act2 in activation_results[act1]:\n",
    "        # Extract loss values\n",
    "        loss = activation_results[act1][act2]['loss']\n",
    "        val_loss = activation_results[act1][act2]['val_loss']\n",
    "        # Calculate mean loss\n",
    "        mean_training_loss = sum(loss) / len(loss)\n",
    "        mean_validation_loss = sum(val_loss) / len(val_loss)\n",
    "        # Create a dictionary for each epoch\n",
    "        for epoch in range(len(loss)):\n",
    "            result_dict = {\n",
    "                'Activation 1': act1,\n",
    "                'Activation 2': act2,\n",
    "                'Epoch': epoch + 1,\n",
    "                'Training Loss': loss[epoch],\n",
    "                'Validation Loss': val_loss[epoch],\n",
    "                'Mean Training Loss': mean_training_loss,\n",
    "                'Mean Validation Loss': mean_validation_loss\n",
    "            }\n",
    "            # Append the dictionary to the results list\n",
    "            results_list.append(result_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Activation 1 Activation 2  Epoch  Training Loss  Validation Loss  \\\n",
      "0           relu       linear      1       0.006572         0.000012   \n",
      "1           relu       linear      2       0.000012         0.000012   \n",
      "2           relu       linear      3       0.000012         0.000010   \n",
      "3           relu       linear      4       0.000012         0.000011   \n",
      "4           relu       linear      5       0.000013         0.000013   \n",
      "..           ...          ...    ...            ...              ...   \n",
      "349         tanh       linear    114       0.000011         0.000011   \n",
      "350         tanh       linear    115       0.000011         0.000011   \n",
      "351         tanh       linear    116       0.000011         0.000011   \n",
      "352         tanh       linear    117       0.000011         0.000011   \n",
      "353         tanh       linear    118       0.000011         0.000011   \n",
      "\n",
      "     Mean Training Loss  Mean Validation Loss Activation 1 string  \\\n",
      "0              0.000065              0.000009                relu   \n",
      "1              0.000065              0.000009                relu   \n",
      "2              0.000065              0.000009                relu   \n",
      "3              0.000065              0.000009                relu   \n",
      "4              0.000065              0.000009                relu   \n",
      "..                  ...                   ...                 ...   \n",
      "349            0.000042              0.000013                tanh   \n",
      "350            0.000042              0.000013                tanh   \n",
      "351            0.000042              0.000013                tanh   \n",
      "352            0.000042              0.000013                tanh   \n",
      "353            0.000042              0.000013                tanh   \n",
      "\n",
      "    combined_label  \n",
      "0    relu - linear  \n",
      "1    relu - linear  \n",
      "2    relu - linear  \n",
      "3    relu - linear  \n",
      "4    relu - linear  \n",
      "..             ...  \n",
      "349  tanh - linear  \n",
      "350  tanh - linear  \n",
      "351  tanh - linear  \n",
      "352  tanh - linear  \n",
      "353  tanh - linear  \n",
      "\n",
      "[354 rows x 9 columns]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_activation_name(activation):\n",
    "    if callable(activation):  # Check if it's a function or a callable object\n",
    "        return activation.__name__ if hasattr(activation, '__name__') else activation.__class__.__name__\n",
    "    return str(activation)  # Handle string names like 'linear', 'sigmoid'\n",
    "\n",
    "# Convert the list of dictionaries to a DataFrame\n",
    "results_df = pd.DataFrame(results_list)\n",
    "# Convert non-string types (e.g., functions or objects) to strings for concatenation\n",
    "results_df['Activation 1 string'] = results_df['Activation 1'].apply(get_activation_name)\n",
    "\n",
    "results_df['combined_label'] = results_df['Activation 1 string'].astype(str) + ' - ' + results_df['Activation 2']\n",
    "# Display the DataFrame\n",
    "print(results_df)\n",
    "# Manually define a list of colors (this list can be extended)\n",
    "colors_list = ['red', 'blue', 'green', 'orange', 'purple', 'cyan', 'brown', 'pink', 'gray', 'olive']\n",
    "\n",
    "# Get unique labels\n",
    "unique_labels = results_df['combined_label'].unique()\n",
    "\n",
    "# Create a color mapping: each label gets a color from the list\n",
    "color_dict = {label: colors_list[i % len(colors_list)] for i, label in enumerate(unique_labels)}\n",
    "\n",
    "# Initialize the plot\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Plot each group with its corresponding color\n",
    "for label, group in results_df.groupby('combined_label'):\n",
    "    ax.plot(group['Epoch'], group['Validation Loss'], linestyle='-', label=label, color=color_dict[label])\n",
    "\n",
    "# Add legend\n",
    "ax.legend(title=\"Activation Function Combinations\", fontsize='small', loc='upper right', bbox_to_anchor=(1, 1))\n",
    "plt.title(\"Activation Function Combinations for Variable 2\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Validation Loss\")\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.15 ('tensor')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.15"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "a25a6ef72695f59748d6e2ed74ef1b995e7ad4f65260be609df4d4311291f2d8"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}