Keras ANN_10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "06be79be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python ≥3.5 is required\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)\n",
    "\n",
    "# Scikit-Learn ≥0.20 is required\n",
    "import sklearn\n",
    "assert sklearn.__version__ >= \"0.20\"\n",
    "\n",
    "try:\n",
    "    # %tensorflow_version only exists in Colab.\n",
    "    %tensorflow_version 2.x\n",
    "except Exception:\n",
    "    pass\n",
    "\n",
    "# TensorFlow ≥2.0 is required\n",
    "import tensorflow as tf\n",
    "assert tf.__version__ >= \"2.0\"\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"ann\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce0078d9",
   "metadata": {},
   "source": [
    "# Perceptrons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "20dabb34",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asatullaev Ubaydullo\\AppData\\Local\\Temp\\ipykernel_13304\\2903736621.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y=(iris.target == 0).astype(np.int) # iris setosa?\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.linear_model import Perceptron\n",
    "\n",
    "iris = load_iris()\n",
    "X= iris.data[:,(2,3)] # petal length, petal width\n",
    "y=(iris.target == 0).astype(np.int) # iris setosa?\n",
    "per_clf = Perceptron()\n",
    "per_clf.fit(X,y)\n",
    "\n",
    "y_pred=per_clf.predict([[2,.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "065dccba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ee14f99a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asatullaev Ubaydullo\\AppData\\Local\\Temp\\ipykernel_13304\\222327901.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y=(iris.target == 0).astype(np.int) # iris setosa?\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.linear_model import Perceptron\n",
    "\n",
    "iris = load_iris()\n",
    "X= iris.data[:,(2,3)] # petal length, petal width\n",
    "y=(iris.target == 0).astype(np.int) # iris setosa?\n",
    "per_clf = Perceptron(max_iter=1000, tol=1e-3, random_state=42)\n",
    "per_clf.fit(X,y)\n",
    "\n",
    "y_pred=per_clf.predict([[2,.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ca2d72a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a3fb1d07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure perceptron_iris_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = -per_clf.coef_[0][0] / per_clf.coef_[0][1]\n",
    "b = -per_clf.intercept_ / per_clf.coef_[0][1]\n",
    "\n",
    "axes = [0, 5, 0, 2]\n",
    "\n",
    "x0, x1 = np.meshgrid(\n",
    "        np.linspace(axes[0], axes[1], 500).reshape(-1, 1),\n",
    "        np.linspace(axes[2], axes[3], 200).reshape(-1, 1),\n",
    "    )\n",
    "X_new = np.c_[x0.ravel(), x1.ravel()]\n",
    "y_predict = per_clf.predict(X_new)\n",
    "zz = y_predict.reshape(x0.shape)\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.plot(X[y==0, 0], X[y==0, 1], \"bs\", label=\"Not Iris-Setosa\")\n",
    "plt.plot(X[y==1, 0], X[y==1, 1], \"yo\", label=\"Iris-Setosa\")\n",
    "\n",
    "plt.plot([axes[0], axes[1]], [a * axes[0] + b, a * axes[1] + b], \"k-\", linewidth=3)\n",
    "from matplotlib.colors import ListedColormap\n",
    "custom_cmap = ListedColormap(['#9898ff', '#fafab0'])\n",
    "\n",
    "plt.contourf(x0, x1, zz, cmap=custom_cmap)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"lower right\", fontsize=14)\n",
    "plt.axis(axes)\n",
    "\n",
    "save_fig(\"perceptron_iris_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23135c74",
   "metadata": {},
   "source": [
    "#  Activation functions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3a26815e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(z):\n",
    "    return 1/(1+np.exp(-z))\n",
    "\n",
    "def relu(z):\n",
    "    return np.maximum(0,z)\n",
    "\n",
    "def derivative(f,z,eps=.000001):\n",
    "    return(f(z + eps)-f(z-eps))/(2*eps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "84acf808",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure activation_functions_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = np.linspace(-5, 5, 200)\n",
    "\n",
    "plt.figure(figsize=(11,4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(z, np.sign(z), \"r-\", linewidth=1, label=\"Step\")\n",
    "plt.plot(z, sigmoid(z), \"g--\", linewidth=2, label=\"Sigmoid\")\n",
    "plt.plot(z, np.tanh(z), \"b-\", linewidth=2, label=\"Tanh\")\n",
    "plt.plot(z, relu(z), \"m-.\", linewidth=2, label=\"ReLU\")\n",
    "plt.grid(True)\n",
    "plt.legend(loc=\"center right\", fontsize=14)\n",
    "plt.title(\"Activation functions\", fontsize=14)\n",
    "plt.axis([-5, 5, -1.2, 1.2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(z, derivative(np.sign, z), \"r-\", linewidth=1, label=\"Step\")\n",
    "plt.plot(0, 0, \"ro\", markersize=5)\n",
    "plt.plot(0, 0, \"rx\", markersize=10)\n",
    "plt.plot(z, derivative(sigmoid, z), \"g--\", linewidth=2, label=\"Sigmoid\")\n",
    "plt.plot(z, derivative(np.tanh, z), \"b-\", linewidth=2, label=\"Tanh\")\n",
    "plt.plot(z, derivative(relu, z), \"m-.\", linewidth=2, label=\"ReLU\")\n",
    "plt.grid(True)\n",
    "#plt.legend(loc=\"center right\", fontsize=14)\n",
    "plt.title(\"Derivatives\", fontsize=14)\n",
    "plt.axis([-5, 5, -0.2, 1.2])\n",
    "\n",
    "save_fig(\"activation_functions_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "458c9316",
   "metadata": {},
   "outputs": [],
   "source": [
    "def heaviside(z):\n",
    "    return(z>=0).astype(z.dtype)\n",
    "\n",
    "def mlp_xor(x1,x2, activation=heaviside):\n",
    "    return activation(-activation(x1 + x2 - 1.5)+activation(x1+x2-0.5)-0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0d64af1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1s = np.linspace(-0.2, 1.2, 100)\n",
    "x2s = np.linspace(-0.2, 1.2, 100)\n",
    "x1, x2 = np.meshgrid(x1s, x2s)\n",
    "\n",
    "z1 = mlp_xor(x1, x2, activation=heaviside)\n",
    "z2 = mlp_xor(x1, x2, activation=sigmoid)\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.contourf(x1, x2, z1)\n",
    "plt.plot([0, 1], [0, 1], \"gs\", markersize=20)\n",
    "plt.plot([0, 1], [1, 0], \"y^\", markersize=20)\n",
    "plt.title(\"Activation function: heaviside\", fontsize=14)\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.contourf(x1, x2, z2)\n",
    "plt.plot([0, 1], [0, 1], \"gs\", markersize=20)\n",
    "plt.plot([0, 1], [1, 0], \"y^\", markersize=20)\n",
    "plt.title(\"Activation function: sigmoid\", fontsize=14)\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44b3311b",
   "metadata": {},
   "source": [
    "# Building an Image Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2f358b0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "120ff68a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.10.0'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5a450199",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.10.0'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d93b8dea",
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_mnist=keras.datasets.fashion_mnist\n",
    "(X_train_full,y_train_full),(X_test, y_test) =fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9c92cc74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_full.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3d903dbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_full.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1def2208",
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's split full training set into validation set and a smaller training set. and also scale pixel intensity down to 0-1 range.\n",
    "X_valid,X_train=X_train_full[:5000] / 255., X_train_full[5000:]/255.\n",
    "y_valid,y_train=y_train_full[:5000],y_train_full[5000:]\n",
    "X_test=X_test/255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0a1dcf10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot image using Matplotlib's imshow() function with a 'binary' color map:\n",
    "plt.imshow(X_train[38],cmap='binary')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0d1b7a07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 0, 7, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c9971545",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\",\n",
    "               \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "db0e6bce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Coat'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_names[y_train[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ad160b00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 28, 28)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4d80ddea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d0cd8d19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure fashion_mnist_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x480 with 40 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_rows=4\n",
    "n_cols=10\n",
    "plt.figure(figsize=(n_cols*1.2, n_rows*1.2))\n",
    "for row in range (n_rows):\n",
    "    for col in range (n_cols):\n",
    "        index =n_cols * row+col\n",
    "        plt.subplot(n_rows,n_cols,index+1)\n",
    "        plt.imshow(X_train[index],cmap='binary',interpolation='nearest')\n",
    "        plt.axis('off')\n",
    "        plt.title(class_names[y_train[index]],fontsize=12)\n",
    "plt.subplots_adjust(wspace=.2,hspace=.5)\n",
    "save_fig('fashion_mnist_plot', tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ddceae5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sequential API\n",
    "model =keras.models.Sequential()\n",
    "model.add(keras.layers.Flatten(input_shape=[28,28]))\n",
    "model.add(keras.layers.Dense(300,activation='relu'))\n",
    "model.add(keras.layers.Dense(100,activation='relu'))\n",
    "model.add(keras.layers.Dense(10,activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "58d2dee1",
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f8bb37cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[28,28]),\n",
    "    keras.layers.Dense(300,activation='relu'),\n",
    "    keras.layers.Dense(100,activation='relu'),\n",
    "    keras.layers.Dense(10,activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d364499e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<keras.layers.reshaping.flatten.Flatten at 0x1713176a430>,\n",
       " <keras.layers.core.dense.Dense at 0x1713176a580>,\n",
       " <keras.layers.core.dense.Dense at 0x1713176a610>,\n",
       " <keras.layers.core.dense.Dense at 0x1713176a850>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "27315f0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " flatten (Flatten)           (None, 784)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 300)               235500    \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 100)               30100     \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 10)                1010      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 266,610\n",
      "Trainable params: 266,610\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8e729f91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pydot in c:\\users\\asatullaev ubaydullo\\anaconda3\\lib\\site-packages (1.4.2)\n",
      "Requirement already satisfied: pyparsing>=2.1.4 in c:\\users\\asatullaev ubaydullo\\anaconda3\\lib\\site-packages (from pydot) (3.0.9)\n",
      "Requirement already satisfied: pydotplus in c:\\users\\asatullaev ubaydullo\\anaconda3\\lib\\site-packages (2.0.2)\n",
      "Requirement already satisfied: pyparsing>=2.0.1 in c:\\users\\asatullaev ubaydullo\\anaconda3\\lib\\site-packages (from pydotplus) (3.0.9)\n",
      "Requirement already satisfied: graphviz in c:\\users\\asatullaev ubaydullo\\anaconda3\\lib\\site-packages (0.20.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install pydot\n",
    "!pip install pydotplus\n",
    "!pip install graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "49668618",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) for plot_model to work.\n"
     ]
    }
   ],
   "source": [
    "keras.utils.plot_model(model, \"my_fashion_mnist_model.png\", show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a222d2f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dense'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hidden1=model.layers[1]\n",
    "hidden1.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "175ac5aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_layer(hidden1.name) is hidden1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f8b94739",
   "metadata": {},
   "outputs": [],
   "source": [
    "weights, biases = hidden1.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "854c4306",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.03508732, -0.03484052, -0.01801683, ...,  0.00670526,\n",
       "        -0.01326476, -0.06454493],\n",
       "       [-0.06312148, -0.04891641,  0.03716235, ...,  0.03143676,\n",
       "        -0.03955733, -0.06551661],\n",
       "       [-0.06822289,  0.03238175,  0.04617196, ...,  0.03954784,\n",
       "         0.02731582, -0.03035832],\n",
       "       ...,\n",
       "       [ 0.02217428, -0.04234077,  0.05893373, ..., -0.00046748,\n",
       "         0.01238447,  0.0637909 ],\n",
       "       [-0.07072687,  0.06728442,  0.05071563, ...,  0.00315119,\n",
       "         0.04981714, -0.0558118 ],\n",
       "       [ 0.06909804,  0.00143059, -0.06124216, ...,  0.0386423 ,\n",
       "        -0.06320388,  0.01523597]], dtype=float32)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1450b6b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784, 300)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0db0bf46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5fa0eea6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biases.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "131cf09f",
   "metadata": {},
   "source": [
    "### Compiling the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e4758200",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='sparse_categorical_crossentropy',\n",
    "             optimizer='sgd',\n",
    "             metrics='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cea2215f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "1719/1719 [==============================] - 6s 3ms/step - loss: 0.7065 - accuracy: 0.7629 - val_loss: 0.5295 - val_accuracy: 0.8176\n",
      "Epoch 2/30\n",
      "1719/1719 [==============================] - 5s 3ms/step - loss: 0.4877 - accuracy: 0.8292 - val_loss: 0.4436 - val_accuracy: 0.8488\n",
      "Epoch 3/30\n",
      "1719/1719 [==============================] - 6s 4ms/step - loss: 0.4429 - accuracy: 0.8438 - val_loss: 0.5345 - val_accuracy: 0.8022\n",
      "Epoch 4/30\n",
      "1719/1719 [==============================] - 9s 5ms/step - loss: 0.4167 - accuracy: 0.8547 - val_loss: 0.3995 - val_accuracy: 0.8646\n",
      "Epoch 5/30\n",
      "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3985 - accuracy: 0.8606 - val_loss: 0.3819 - val_accuracy: 0.8668\n",
      "Epoch 6/30\n",
      "1719/1719 [==============================] - 8s 4ms/step - loss: 0.3801 - accuracy: 0.8656 - val_loss: 0.3779 - val_accuracy: 0.8704\n",
      "Epoch 7/30\n",
      "1719/1719 [==============================] - 13s 7ms/step - loss: 0.3678 - accuracy: 0.8696 - val_loss: 0.3742 - val_accuracy: 0.8710\n",
      "Epoch 8/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3560 - accuracy: 0.8732 - val_loss: 0.3906 - val_accuracy: 0.8580\n",
      "Epoch 9/30\n",
      "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3455 - accuracy: 0.8777 - val_loss: 0.3520 - val_accuracy: 0.8720\n",
      "Epoch 10/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3361 - accuracy: 0.8801 - val_loss: 0.3564 - val_accuracy: 0.8718\n",
      "Epoch 11/30\n",
      "1719/1719 [==============================] - 10s 6ms/step - loss: 0.3274 - accuracy: 0.8832 - val_loss: 0.3487 - val_accuracy: 0.8746\n",
      "Epoch 12/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3183 - accuracy: 0.8851 - val_loss: 0.3369 - val_accuracy: 0.8814\n",
      "Epoch 13/30\n",
      "1719/1719 [==============================] - 10s 6ms/step - loss: 0.3113 - accuracy: 0.8891 - val_loss: 0.3332 - val_accuracy: 0.8830\n",
      "Epoch 14/30\n",
      "1719/1719 [==============================] - 9s 6ms/step - loss: 0.3047 - accuracy: 0.8905 - val_loss: 0.3585 - val_accuracy: 0.8704\n",
      "Epoch 15/30\n",
      "1719/1719 [==============================] - 10s 6ms/step - loss: 0.2968 - accuracy: 0.8935 - val_loss: 0.3293 - val_accuracy: 0.8800\n",
      "Epoch 16/30\n",
      "1719/1719 [==============================] - 12s 7ms/step - loss: 0.2919 - accuracy: 0.8957 - val_loss: 0.3143 - val_accuracy: 0.8830\n",
      "Epoch 17/30\n",
      "1719/1719 [==============================] - 14s 8ms/step - loss: 0.2863 - accuracy: 0.8960 - val_loss: 0.3543 - val_accuracy: 0.8708\n",
      "Epoch 18/30\n",
      "1719/1719 [==============================] - 17s 10ms/step - loss: 0.2799 - accuracy: 0.8989 - val_loss: 0.3181 - val_accuracy: 0.8904\n",
      "Epoch 19/30\n",
      "1719/1719 [==============================] - 20s 12ms/step - loss: 0.2756 - accuracy: 0.9008 - val_loss: 0.3139 - val_accuracy: 0.8868\n",
      "Epoch 20/30\n",
      "1719/1719 [==============================] - 20s 11ms/step - loss: 0.2697 - accuracy: 0.9025 - val_loss: 0.3309 - val_accuracy: 0.8816\n",
      "Epoch 21/30\n",
      "1719/1719 [==============================] - 18s 10ms/step - loss: 0.2645 - accuracy: 0.9038 - val_loss: 0.3073 - val_accuracy: 0.8866\n",
      "Epoch 22/30\n",
      "1719/1719 [==============================] - 23s 13ms/step - loss: 0.2597 - accuracy: 0.9063 - val_loss: 0.2993 - val_accuracy: 0.8918\n",
      "Epoch 23/30\n",
      "1719/1719 [==============================] - 18s 10ms/step - loss: 0.2551 - accuracy: 0.9072 - val_loss: 0.3021 - val_accuracy: 0.8902\n",
      "Epoch 24/30\n",
      "1719/1719 [==============================] - 17s 10ms/step - loss: 0.2507 - accuracy: 0.9096 - val_loss: 0.3099 - val_accuracy: 0.8864\n",
      "Epoch 25/30\n",
      "1719/1719 [==============================] - 18s 11ms/step - loss: 0.2462 - accuracy: 0.9106 - val_loss: 0.2992 - val_accuracy: 0.8936\n",
      "Epoch 26/30\n",
      "1719/1719 [==============================] - 18s 10ms/step - loss: 0.2423 - accuracy: 0.9129 - val_loss: 0.3007 - val_accuracy: 0.8912\n",
      "Epoch 27/30\n",
      "1719/1719 [==============================] - 19s 11ms/step - loss: 0.2388 - accuracy: 0.9144 - val_loss: 0.2962 - val_accuracy: 0.8952\n",
      "Epoch 28/30\n",
      "1719/1719 [==============================] - 18s 11ms/step - loss: 0.2351 - accuracy: 0.9152 - val_loss: 0.3041 - val_accuracy: 0.8914\n",
      "Epoch 29/30\n",
      "1719/1719 [==============================] - 22s 13ms/step - loss: 0.2302 - accuracy: 0.9171 - val_loss: 0.3133 - val_accuracy: 0.8872\n",
      "Epoch 30/30\n",
      "1719/1719 [==============================] - 18s 11ms/step - loss: 0.2271 - accuracy: 0.9182 - val_loss: 0.3038 - val_accuracy: 0.8918\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs=30,\n",
    "                   validation_data=(X_valid, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "914375dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'verbose': 1, 'epochs': 30, 'steps': 1719}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8d3dc99b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]\n"
     ]
    }
   ],
   "source": [
    "print(history.epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "91a32031",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f9c064f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure keras_learning_curves_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "pd.DataFrame(history.history).plot(figsize=(8,5))\n",
    "plt.grid(True)\n",
    "plt.gca().set_ylim(0,1)\n",
    "save_fig('keras_learning_curves_plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dfddd40",
   "metadata": {},
   "source": [
    "### using the model to make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9dc2d4bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 251ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.  , 0.  , 0.  , 0.  , 0.  , 0.03, 0.  , 0.02, 0.  , 0.96],\n",
       "       [0.  , 0.  , 0.95, 0.  , 0.05, 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
       "       [0.  , 1.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
       "       [0.  , 1.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new=X_test[:4]\n",
    "y_proba = model.predict(X_new)\n",
    "y_proba.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e4edad73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 83ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([9, 2, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = np.argmax(model.predict(X_new),axis=1)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "49c9fb03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Ankle boot', 'Pullover', 'Trouser', 'Trouser'], dtype='<U11')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(class_names)[y_pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f8a7e7d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 2, 1, 1], dtype=uint8)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_new=y_test[:4]\n",
    "y_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "eef0a5c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure fashion_mnist_images_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x240 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7.2, 2.4))\n",
    "for index, image in enumerate(X_new):\n",
    "    plt.subplot(1,4, index+1)\n",
    "    plt.imshow(image, cmap='binary',interpolation='nearest')\n",
    "    plt.axis('off')\n",
    "    plt.title(class_names[y_test[index]],fontsize=12)\n",
    "plt.subplots_adjust(wspace=.1,hspace=.4)\n",
    "save_fig('fashion_mnist_images_plot',tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33c62a02",
   "metadata": {},
   "source": [
    "### Regression MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1cb0bc03",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "housing = fetch_california_housing()\n",
    "\n",
    "X_train_full, X_test, y_train_full, y_test = train_test_split(housing.data, housing.target, random_state=42)\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X_train_full, y_train_full, random_state=42)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_valid = scaler.transform(X_valid)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ea7b8ce0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "363/363 [==============================] - 3s 8ms/step - loss: 2.3793 - val_loss: 1.0202\n",
      "Epoch 2/20\n",
      "363/363 [==============================] - 2s 7ms/step - loss: 0.8518 - val_loss: 0.9886\n",
      "Epoch 3/20\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.7176 - val_loss: 0.8134\n",
      "Epoch 4/20\n",
      "363/363 [==============================] - 3s 8ms/step - loss: 0.6672 - val_loss: 0.6544\n",
      "Epoch 5/20\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.6306 - val_loss: 0.5820\n",
      "Epoch 6/20\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.5990 - val_loss: 0.5840\n",
      "Epoch 7/20\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.5730 - val_loss: 0.5791\n",
      "Epoch 8/20\n",
      "363/363 [==============================] - 2s 5ms/step - loss: 0.5509 - val_loss: 0.5026\n",
      "Epoch 9/20\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.5306 - val_loss: 0.4958\n",
      "Epoch 10/20\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.5136 - val_loss: 0.4875\n",
      "Epoch 11/20\n",
      "363/363 [==============================] - 2s 5ms/step - loss: 0.4992 - val_loss: 0.4598\n",
      "Epoch 12/20\n",
      "363/363 [==============================] - 2s 5ms/step - loss: 0.4861 - val_loss: 0.4536\n",
      "Epoch 13/20\n",
      "363/363 [==============================] - 2s 4ms/step - loss: 0.4757 - val_loss: 0.4377\n",
      "Epoch 14/20\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.4666 - val_loss: 0.4306\n",
      "Epoch 15/20\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.4590 - val_loss: 0.4248\n",
      "Epoch 16/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4526 - val_loss: 0.4186\n",
      "Epoch 17/20\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.4473 - val_loss: 0.4211\n",
      "Epoch 18/20\n",
      "363/363 [==============================] - 1s 4ms/step - loss: 0.4426 - val_loss: 0.4167\n",
      "Epoch 19/20\n",
      "363/363 [==============================] - 2s 5ms/step - loss: 0.4383 - val_loss: 0.4078\n",
      "Epoch 20/20\n",
      "363/363 [==============================] - 1s 4ms/step - loss: 0.4347 - val_loss: 0.4161\n",
      "162/162 [==============================] - 1s 3ms/step - loss: 0.4268\n",
      "1/1 [==============================] - 0s 81ms/step\n"
     ]
    }
   ],
   "source": [
    "model=keras.models.Sequential(\n",
    "    [keras.layers.Dense(30,activation='relu',input_shape=X_train.shape[1:]),\n",
    "     keras.layers.Dense(1)\n",
    "])\n",
    "model.compile(loss='mean_squared_error', optimizer=keras.optimizers.SGD(learning_rate=1e-3))\n",
    "history=model.fit(X_train,y_train,epochs=20, validation_data=(X_valid,y_valid))\n",
    "mse_test=model.evaluate(X_test, y_test)\n",
    "X_new=X_test[:4]\n",
    "y_pred=model.predict(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "70a5821a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(pd.DataFrame(history.history))\n",
    "plt.grid(True)\n",
    "plt.gca().set_ylim(0,1)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ab6ef8bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.52842855],\n",
       "       [1.6542633 ],\n",
       "       [3.4393773 ],\n",
       "       [2.464006  ]], dtype=float32)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28e2066e",
   "metadata": {},
   "source": [
    "### Functional API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ba38bda1",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d815894a",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_=keras.layers.Input(shape=X_train.shape[1:])\n",
    "hidden1=keras.layers.Dense(30,activation='relu')(input_)\n",
    "hidden2=keras.layers.Dense(30,activation='relu')(hidden1)\n",
    "concat=keras.layers.concatenate([input_,hidden2])\n",
    "output=keras.layers.Dense(1)(concat)\n",
    "model=keras.models.Model(inputs=[input_],outputs=[output])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6be8a198",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 8)]          0           []                               \n",
      "                                                                                                  \n",
      " dense_5 (Dense)                (None, 30)           270         ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " dense_6 (Dense)                (None, 30)           930         ['dense_5[0][0]']                \n",
      "                                                                                                  \n",
      " concatenate (Concatenate)      (None, 38)           0           ['input_1[0][0]',                \n",
      "                                                                  'dense_6[0][0]']                \n",
      "                                                                                                  \n",
      " dense_7 (Dense)                (None, 1)            39          ['concatenate[0][0]']            \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 1,239\n",
      "Trainable params: 1,239\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0251dfac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "363/363 [==============================] - 3s 6ms/step - loss: 1.6571 - val_loss: 1.1906\n",
      "Epoch 2/20\n",
      "363/363 [==============================] - 1s 4ms/step - loss: 0.7060 - val_loss: 0.6586\n",
      "Epoch 3/20\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.6425 - val_loss: 0.6050\n",
      "Epoch 4/20\n",
      "363/363 [==============================] - 2s 4ms/step - loss: 0.6036 - val_loss: 0.5848\n",
      "Epoch 5/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.5744 - val_loss: 0.5654\n",
      "Epoch 6/20\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.5489 - val_loss: 0.5190\n",
      "Epoch 7/20\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.5288 - val_loss: 0.4953\n",
      "Epoch 8/20\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.5117 - val_loss: 0.5098\n",
      "Epoch 9/20\n",
      "363/363 [==============================] - 2s 7ms/step - loss: 0.4958 - val_loss: 0.4713\n",
      "Epoch 10/20\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.4823 - val_loss: 0.4537\n",
      "Epoch 11/20\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.4710 - val_loss: 0.4918\n",
      "Epoch 12/20\n",
      "363/363 [==============================] - 2s 5ms/step - loss: 0.4604 - val_loss: 0.4363\n",
      "Epoch 13/20\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.4514 - val_loss: 0.4475\n",
      "Epoch 14/20\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.4431 - val_loss: 0.4361\n",
      "Epoch 15/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4358 - val_loss: 0.4447\n",
      "Epoch 16/20\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.4294 - val_loss: 0.4201\n",
      "Epoch 17/20\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.4241 - val_loss: 0.4473\n",
      "Epoch 18/20\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.4191 - val_loss: 0.4486\n",
      "Epoch 19/20\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.4143 - val_loss: 0.4135\n",
      "Epoch 20/20\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.4110 - val_loss: 0.4668\n",
      "162/162 [==============================] - 1s 3ms/step - loss: 0.4107\n",
      "1/1 [==============================] - 0s 179ms/step\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='mean_squared_error', optimizer=keras.optimizers.SGD(learning_rate=1e-3))\n",
    "history = model.fit(X_train, y_train, epochs=20,\n",
    "                    validation_data=(X_valid, y_valid))\n",
    "mse_test=model.evaluate(X_test, y_test)\n",
    "y_pred=model.predict(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2b07620b",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1f7c61e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_A=keras.layers.Input(shape=[5],name='wide_input')\n",
    "input_B=keras.layers.Input(shape=[6],name='deep_input')\n",
    "hidden1=keras.layers.Dense(30, activation='relu')(input_B)\n",
    "hidden2=keras.layers.Dense(30, activation='relu')(hidden1)\n",
    "concat=keras.layers.concatenate([input_A, hidden2])\n",
    "output=keras.layers.Dense(1,name='output')(concat)\n",
    "model=keras.models.Model(inputs=[input_A, input_B], outputs=[output])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "667657c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "363/363 [==============================] - 2s 4ms/step - loss: 2.3945 - val_loss: 1.8748\n",
      "Epoch 2/20\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.7644 - val_loss: 0.6490\n",
      "Epoch 3/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.6295 - val_loss: 0.5842\n",
      "Epoch 4/20\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.5768 - val_loss: 0.5412\n",
      "Epoch 5/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.5440 - val_loss: 0.5064\n",
      "Epoch 6/20\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.5180 - val_loss: 0.4854\n",
      "Epoch 7/20\n",
      "363/363 [==============================] - 3s 8ms/step - loss: 0.4989 - val_loss: 0.4701\n",
      "Epoch 8/20\n",
      "363/363 [==============================] - 2s 5ms/step - loss: 0.4831 - val_loss: 0.4501\n",
      "Epoch 9/20\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.4693 - val_loss: 0.4386\n",
      "Epoch 10/20\n",
      "363/363 [==============================] - 2s 5ms/step - loss: 0.4586 - val_loss: 0.4262\n",
      "Epoch 11/20\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.4505 - val_loss: 0.4288\n",
      "Epoch 12/20\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.4440 - val_loss: 0.4171\n",
      "Epoch 13/20\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.4389 - val_loss: 0.4215\n",
      "Epoch 14/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4347 - val_loss: 0.4133\n",
      "Epoch 15/20\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.4314 - val_loss: 0.4206\n",
      "Epoch 16/20\n",
      "363/363 [==============================] - 3s 9ms/step - loss: 0.4286 - val_loss: 0.4072\n",
      "Epoch 17/20\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.4267 - val_loss: 0.4136\n",
      "Epoch 18/20\n",
      "363/363 [==============================] - 4s 10ms/step - loss: 0.4242 - val_loss: 0.4096\n",
      "Epoch 19/20\n",
      "363/363 [==============================] - 6s 17ms/step - loss: 0.4218 - val_loss: 0.4017\n",
      "Epoch 20/20\n",
      "363/363 [==============================] - 4s 11ms/step - loss: 0.4206 - val_loss: 0.4261\n",
      "162/162 [==============================] - 1s 7ms/step - loss: 0.4162\n",
      "1/1 [==============================] - 0s 140ms/step\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=\"mse\", optimizer=keras.optimizers.SGD(learning_rate=1e-3))\n",
    "\n",
    "X_train_A, X_train_B = X_train[:, :5], X_train[:, 2:]\n",
    "X_valid_A, X_valid_B = X_valid[:, :5], X_valid[:, 2:]\n",
    "X_test_A, X_test_B = X_test[:, :5], X_test[:, 2:]\n",
    "X_new_A, X_new_B = X_test_A[:3], X_test_B[:3]\n",
    "\n",
    "history = model.fit((X_train_A, X_train_B), y_train, epochs=20,\n",
    "                    validation_data=((X_valid_A, X_valid_B), y_valid))\n",
    "mse_test = model.evaluate((X_test_A, X_test_B), y_test)\n",
    "y_pred = model.predict((X_new_A, X_new_B))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d913051d",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1363380f",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_A=keras.layers.Input(shape=[5],name='wide_input')\n",
    "input_B=keras.layers.Input(shape=[6],name='deep_input')\n",
    "hidden1=keras.layers.Dense(30, activation='relu')(input_B)\n",
    "hidden2=keras.layers.Dense(30, activation='relu')(hidden1)\n",
    "concat=keras.layers.concatenate([input_A, hidden2])\n",
    "output=keras.layers.Dense(1,name='output')(concat)\n",
    "aux_output = keras.layers.Dense(1,name='aux_output')(hidden2)\n",
    "model=keras.models.Model(inputs=[input_A, input_B], outputs=[output, aux_output])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7f3f5901",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=[\"mse\", 'mse'], loss_weights=[.9, .1], optimizer=keras.optimizers.SGD(learning_rate=1e-3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "287b46f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "363/363 [==============================] - 5s 11ms/step - loss: 2.0533 - output_loss: 1.7765 - aux_output_loss: 4.5443 - val_loss: 2.9588 - val_output_loss: 2.8086 - val_aux_output_loss: 4.3113\n",
      "Epoch 2/20\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.9491 - output_loss: 0.7617 - aux_output_loss: 2.6349 - val_loss: 1.4250 - val_output_loss: 1.2244 - val_aux_output_loss: 3.2310\n",
      "Epoch 3/20\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.7969 - output_loss: 0.6798 - aux_output_loss: 1.8508 - val_loss: 0.9083 - val_output_loss: 0.7077 - val_aux_output_loss: 2.7133\n",
      "Epoch 4/20\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.7252 - output_loss: 0.6361 - aux_output_loss: 1.5268 - val_loss: 0.8061 - val_output_loss: 0.6129 - val_aux_output_loss: 2.5449\n",
      "Epoch 5/20\n",
      "363/363 [==============================] - 2s 5ms/step - loss: 0.6810 - output_loss: 0.6033 - aux_output_loss: 1.3806 - val_loss: 0.7606 - val_output_loss: 0.5801 - val_aux_output_loss: 2.3845\n",
      "Epoch 6/20\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.6477 - output_loss: 0.5751 - aux_output_loss: 1.3008 - val_loss: 0.7407 - val_output_loss: 0.5761 - val_aux_output_loss: 2.2222\n",
      "Epoch 7/20\n",
      "363/363 [==============================] - 3s 9ms/step - loss: 0.6190 - output_loss: 0.5499 - aux_output_loss: 1.2409 - val_loss: 0.7276 - val_output_loss: 0.5797 - val_aux_output_loss: 2.0581\n",
      "Epoch 8/20\n",
      "363/363 [==============================] - 3s 8ms/step - loss: 0.5946 - output_loss: 0.5282 - aux_output_loss: 1.1927 - val_loss: 0.7089 - val_output_loss: 0.5718 - val_aux_output_loss: 1.9430\n",
      "Epoch 9/20\n",
      "363/363 [==============================] - 1s 4ms/step - loss: 0.5733 - output_loss: 0.5090 - aux_output_loss: 1.1519 - val_loss: 0.6866 - val_output_loss: 0.5581 - val_aux_output_loss: 1.8426\n",
      "Epoch 10/20\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.5553 - output_loss: 0.4932 - aux_output_loss: 1.1146 - val_loss: 0.6709 - val_output_loss: 0.5530 - val_aux_output_loss: 1.7313\n",
      "Epoch 11/20\n",
      "363/363 [==============================] - 3s 8ms/step - loss: 0.5408 - output_loss: 0.4808 - aux_output_loss: 1.0807 - val_loss: 0.6278 - val_output_loss: 0.5080 - val_aux_output_loss: 1.7059\n",
      "Epoch 12/20\n",
      "363/363 [==============================] - 2s 5ms/step - loss: 0.5282 - output_loss: 0.4702 - aux_output_loss: 1.0507 - val_loss: 0.6098 - val_output_loss: 0.4960 - val_aux_output_loss: 1.6340\n",
      "Epoch 13/20\n",
      "363/363 [==============================] - 3s 7ms/step - loss: 0.5177 - output_loss: 0.4616 - aux_output_loss: 1.0220 - val_loss: 0.5844 - val_output_loss: 0.4738 - val_aux_output_loss: 1.5796\n",
      "Epoch 14/20\n",
      "363/363 [==============================] - 3s 7ms/step - loss: 0.5086 - output_loss: 0.4546 - aux_output_loss: 0.9949 - val_loss: 0.5634 - val_output_loss: 0.4572 - val_aux_output_loss: 1.5197\n",
      "Epoch 15/20\n",
      "363/363 [==============================] - 3s 8ms/step - loss: 0.5009 - output_loss: 0.4487 - aux_output_loss: 0.9700 - val_loss: 0.5447 - val_output_loss: 0.4424 - val_aux_output_loss: 1.4651\n",
      "Epoch 16/20\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.4941 - output_loss: 0.4437 - aux_output_loss: 0.9476 - val_loss: 0.5297 - val_output_loss: 0.4343 - val_aux_output_loss: 1.3884\n",
      "Epoch 17/20\n",
      "363/363 [==============================] - 1s 4ms/step - loss: 0.4884 - output_loss: 0.4398 - aux_output_loss: 0.9253 - val_loss: 0.5150 - val_output_loss: 0.4241 - val_aux_output_loss: 1.3326\n",
      "Epoch 18/20\n",
      "363/363 [==============================] - 2s 5ms/step - loss: 0.4829 - output_loss: 0.4361 - aux_output_loss: 0.9045 - val_loss: 0.5015 - val_output_loss: 0.4156 - val_aux_output_loss: 1.2751\n",
      "Epoch 19/20\n",
      "363/363 [==============================] - 3s 9ms/step - loss: 0.4779 - output_loss: 0.4325 - aux_output_loss: 0.8860 - val_loss: 0.4911 - val_output_loss: 0.4116 - val_aux_output_loss: 1.2069\n",
      "Epoch 20/20\n",
      "363/363 [==============================] - 2s 7ms/step - loss: 0.4737 - output_loss: 0.4299 - aux_output_loss: 0.8670 - val_loss: 0.4784 - val_output_loss: 0.4017 - val_aux_output_loss: 1.1686\n"
     ]
    }
   ],
   "source": [
    "history = model.fit((X_train_A, X_train_B), [y_train,y_train], epochs=20,\n",
    "                    validation_data=((X_valid_A, X_valid_B), [y_valid,y_valid]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4b69b7ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "162/162 [==============================] - 1s 4ms/step - loss: 0.4629 - output_loss: 0.4218 - aux_output_loss: 0.8324\n",
      "WARNING:tensorflow:5 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x00000171332F51F0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "1/1 [==============================] - 0s 101ms/step\n"
     ]
    }
   ],
   "source": [
    "total_loss, main_loss, aux_loss =model.evaluate(\n",
    "    [X_test_A, X_test_B], [y_test, y_test])\n",
    "y_pred_main, y_pred_aux =model.predict([X_new_A, X_new_B])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c29e6ab",
   "metadata": {},
   "source": [
    "### Subclassing API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bc03ecdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "class WideAndDeepModel(keras.models.Model):\n",
    "    def __init__(self, units=30, activation=\"relu\", **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.hidden1 = keras.layers.Dense(units, activation=activation)\n",
    "        self.hidden2 = keras.layers.Dense(units, activation=activation)\n",
    "        self.main_output = keras.layers.Dense(1)\n",
    "        self.aux_output = keras.layers.Dense(1)\n",
    "        \n",
    "    def call(self, inputs):\n",
    "        input_A, input_B = inputs\n",
    "        hidden1 = self.hidden1(input_B)\n",
    "        hidden2 = self.hidden2(hidden1)\n",
    "        concat = keras.layers.concatenate([input_A, hidden2])\n",
    "        main_output = self.main_output(concat)\n",
    "        aux_output = self.aux_output(hidden2)\n",
    "        return main_output, aux_output\n",
    "\n",
    "model = WideAndDeepModel(30, activation=\"relu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ad437fd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "363/363 [==============================] - 3s 7ms/step - loss: 2.7341 - output_1_loss: 2.4161 - output_2_loss: 5.5958 - val_loss: 1.4364 - val_output_1_loss: 1.1095 - val_output_2_loss: 4.3776\n",
      "Epoch 2/10\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 1.1462 - output_1_loss: 0.8748 - output_2_loss: 3.5887 - val_loss: 1.0434 - val_output_1_loss: 0.8267 - val_output_2_loss: 2.9938\n",
      "Epoch 3/10\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.8941 - output_1_loss: 0.7188 - output_2_loss: 2.4715 - val_loss: 0.9006 - val_output_1_loss: 0.6760 - val_output_2_loss: 2.9221\n",
      "Epoch 4/10\n",
      "363/363 [==============================] - 1s 4ms/step - loss: 0.7716 - output_1_loss: 0.6424 - output_2_loss: 1.9340 - val_loss: 0.8295 - val_output_1_loss: 0.6077 - val_output_2_loss: 2.8261\n",
      "Epoch 5/10\n",
      "363/363 [==============================] - 3s 9ms/step - loss: 0.6975 - output_1_loss: 0.5916 - output_2_loss: 1.6513 - val_loss: 0.7733 - val_output_1_loss: 0.5634 - val_output_2_loss: 2.6630\n",
      "Epoch 6/10\n",
      "363/363 [==============================] - 2s 7ms/step - loss: 0.6481 - output_1_loss: 0.5548 - output_2_loss: 1.4873 - val_loss: 0.7261 - val_output_1_loss: 0.5348 - val_output_2_loss: 2.4477\n",
      "Epoch 7/10\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.6121 - output_1_loss: 0.5270 - output_2_loss: 1.3786 - val_loss: 0.6788 - val_output_1_loss: 0.5104 - val_output_2_loss: 2.1944\n",
      "Epoch 8/10\n",
      "363/363 [==============================] - 4s 11ms/step - loss: 0.5862 - output_1_loss: 0.5066 - output_2_loss: 1.3034 - val_loss: 0.6288 - val_output_1_loss: 0.4847 - val_output_2_loss: 1.9253\n",
      "Epoch 9/10\n",
      "363/363 [==============================] - 2s 5ms/step - loss: 0.5667 - output_1_loss: 0.4909 - output_2_loss: 1.2485 - val_loss: 0.5900 - val_output_1_loss: 0.4620 - val_output_2_loss: 1.7414\n",
      "Epoch 10/10\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.5518 - output_1_loss: 0.4791 - output_2_loss: 1.2062 - val_loss: 0.5602 - val_output_1_loss: 0.4463 - val_output_2_loss: 1.5848\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.5356 - output_1_loss: 0.4633 - output_2_loss: 1.1865\n",
      "WARNING:tensorflow:6 out of the last 7 calls to <function Model.make_predict_function.<locals>.predict_function at 0x00000171334A4C10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "1/1 [==============================] - 1s 535ms/step\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='mse',loss_weights=[.9,.1],optimizer=keras.optimizers.SGD(learning_rate=1e-3))\n",
    "history=model.fit((X_train_A, X_train_B), (y_train, y_train), epochs=10,\n",
    "                  validation_data=((X_valid_A, X_valid_B), (y_valid, y_valid)))\n",
    "total_loss, main_loss, aux_loss=model.evaluate((X_test_A, X_test_B), (y_test, y_test))\n",
    "y=pred_main, y_pred_aux=model.predict((X_new_A, X_new_B))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fe43787",
   "metadata": {},
   "source": [
    "### Saving and Restoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "79c88e4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4bd7afea",
   "metadata": {},
   "outputs": [],
   "source": [
    "model= keras.models.Sequential([\n",
    "    keras.layers.Dense(30, activation='relu', input_shape=[8]),\n",
    "    keras.layers.Dense(30,activation='relu'),\n",
    "    keras.layers.Dense(1)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2d264409",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "363/363 [==============================] - 5s 9ms/step - loss: 1.9136 - val_loss: 0.9832\n",
      "Epoch 2/10\n",
      "363/363 [==============================] - 4s 11ms/step - loss: 0.7599 - val_loss: 0.6470\n",
      "Epoch 3/10\n",
      "363/363 [==============================] - 1s 4ms/step - loss: 0.6434 - val_loss: 0.5798\n",
      "Epoch 4/10\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.5918 - val_loss: 0.5499\n",
      "Epoch 5/10\n",
      "363/363 [==============================] - 2s 4ms/step - loss: 0.5561 - val_loss: 0.5128\n",
      "Epoch 6/10\n",
      "363/363 [==============================] - 2s 5ms/step - loss: 0.5275 - val_loss: 0.4897\n",
      "Epoch 7/10\n",
      "363/363 [==============================] - 2s 7ms/step - loss: 0.5045 - val_loss: 0.4647\n",
      "Epoch 8/10\n",
      "363/363 [==============================] - 2s 5ms/step - loss: 0.4852 - val_loss: 0.4479\n",
      "Epoch 9/10\n",
      "363/363 [==============================] - 2s 7ms/step - loss: 0.4688 - val_loss: 0.4352\n",
      "Epoch 10/10\n",
      "363/363 [==============================] - 2s 7ms/step - loss: 0.4550 - val_loss: 0.4234\n",
      "162/162 [==============================] - 1s 7ms/step - loss: 0.4409\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='mse', optimizer=keras.optimizers.SGD(learning_rate=1e-3))\n",
    "hisoty=model.fit(X_train,y_train,epochs=10,validation_data=(X_valid,y_valid))\n",
    "mse_test=model.evaluate(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "67ef4c1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('my_keras_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a661efa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=keras.models.load_model('my_keras_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "3630f46d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 60ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.58485925],\n",
       "       [1.4712644 ],\n",
       "       [2.9469569 ],\n",
       "       [2.30846   ]], dtype=float32)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4f5de356",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_weights('my_keras_weights.ckpt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "643c1061",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.checkpoint.checkpoint.CheckpointLoadStatus at 0x1713a3c3370>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_weights('my_keras_weights.ckpt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b115406",
   "metadata": {},
   "source": [
    "### using Callbacks during Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "7d9929b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4ba817b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Dense(30, activation='relu', input_shape=[8]),\n",
    "    keras.layers.Dense(30,activation='relu'),\n",
    "    keras.layers.Dense(1)   \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "73b3a442",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "363/363 [==============================] - 3s 7ms/step - loss: 1.8463 - val_loss: 0.7597\n",
      "Epoch 2/10\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.7403 - val_loss: 0.7006\n",
      "Epoch 3/10\n",
      "363/363 [==============================] - 2s 5ms/step - loss: 0.6869 - val_loss: 0.7179\n",
      "Epoch 4/10\n",
      "363/363 [==============================] - 2s 5ms/step - loss: 0.6511 - val_loss: 0.6798\n",
      "Epoch 5/10\n",
      "363/363 [==============================] - 3s 8ms/step - loss: 0.6219 - val_loss: 0.6517\n",
      "Epoch 6/10\n",
      "363/363 [==============================] - 2s 5ms/step - loss: 0.5947 - val_loss: 0.6356\n",
      "Epoch 7/10\n",
      "363/363 [==============================] - 2s 5ms/step - loss: 0.5708 - val_loss: 0.6296\n",
      "Epoch 8/10\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.5484 - val_loss: 0.5881\n",
      "Epoch 9/10\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.5277 - val_loss: 0.5489\n",
      "Epoch 10/10\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.5089 - val_loss: 0.5218\n",
      "162/162 [==============================] - 1s 6ms/step - loss: 0.4850\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='mse', optimizer=keras.optimizers.SGD(learning_rate=1e-3))\n",
    "checkpoint_cb=keras.callbacks.ModelCheckpoint('my_keras_model.h5',save_best_only=True)\n",
    "hisoty=model.fit(X_train,y_train,epochs=10,\n",
    "                 validation_data=(X_valid,y_valid),\n",
    "                 callbacks=[checkpoint_cb])\n",
    "model=keras.models.load_model('my_keras_model.h5') # rollback to best model\n",
    "mse_test=model.evaluate(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e17d5b00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "363/363 [==============================] - 3s 8ms/step - loss: 0.4927 - val_loss: 0.4862\n",
      "Epoch 2/100\n",
      "363/363 [==============================] - 3s 7ms/step - loss: 0.4783 - val_loss: 0.4594\n",
      "Epoch 3/100\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.4658 - val_loss: 0.4390\n",
      "Epoch 4/100\n",
      "363/363 [==============================] - 3s 7ms/step - loss: 0.4546 - val_loss: 0.4237\n",
      "Epoch 5/100\n",
      "363/363 [==============================] - 1s 4ms/step - loss: 0.4456 - val_loss: 0.4138\n",
      "Epoch 6/100\n",
      "363/363 [==============================] - 1s 4ms/step - loss: 0.4376 - val_loss: 0.4092\n",
      "Epoch 7/100\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.4310 - val_loss: 0.4073\n",
      "Epoch 8/100\n",
      "363/363 [==============================] - 3s 9ms/step - loss: 0.4253 - val_loss: 0.4060\n",
      "Epoch 9/100\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.4203 - val_loss: 0.4099\n",
      "Epoch 10/100\n",
      "363/363 [==============================] - 3s 8ms/step - loss: 0.4157 - val_loss: 0.4142\n",
      "Epoch 11/100\n",
      "363/363 [==============================] - 3s 9ms/step - loss: 0.4118 - val_loss: 0.4197\n",
      "Epoch 12/100\n",
      "363/363 [==============================] - 4s 11ms/step - loss: 0.4081 - val_loss: 0.4266\n",
      "Epoch 13/100\n",
      "363/363 [==============================] - 3s 7ms/step - loss: 0.4048 - val_loss: 0.4386\n",
      "Epoch 14/100\n",
      "363/363 [==============================] - 2s 5ms/step - loss: 0.4020 - val_loss: 0.4378\n",
      "Epoch 15/100\n",
      "363/363 [==============================] - 3s 9ms/step - loss: 0.3995 - val_loss: 0.4402\n",
      "Epoch 16/100\n",
      "363/363 [==============================] - 2s 5ms/step - loss: 0.3973 - val_loss: 0.4408\n",
      "Epoch 17/100\n",
      "363/363 [==============================] - 2s 7ms/step - loss: 0.3953 - val_loss: 0.4448\n",
      "Epoch 18/100\n",
      "363/363 [==============================] - 2s 5ms/step - loss: 0.3933 - val_loss: 0.4477\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.4108\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='mse', optimizer=keras.optimizers.SGD(learning_rate=1e-3))\n",
    "early_stopping_cb=keras.callbacks.EarlyStopping(patience=10,\n",
    "                                              restore_best_weights=True)\n",
    "hisoty=model.fit(X_train,y_train,epochs=100,\n",
    "                 validation_data=(X_valid,y_valid),\n",
    "                 callbacks=[checkpoint_cb, early_stopping_cb])\n",
    "mse_test=model.evaluate(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e0b0081f",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PrintValRatioCallback(keras.callbacks.Callback):\n",
    "    def on_epoch_end(self, epoch, logs):\n",
    "        print('\\nval/train: {:.2f}'.format(logs[\"val_loss\"]/logs['loss']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "eb67bf2b",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'PrintValTrainRatioCallback' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_13304\\3968631515.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mval_train_ratio_cb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPrintValTrainRatioCallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m history = model.fit(X_train, y_train, epochs=1,\n\u001b[0;32m      3\u001b[0m                     \u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_valid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_valid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m                     callbacks=[val_train_ratio_cb])\n",
      "\u001b[1;31mNameError\u001b[0m: name 'PrintValTrainRatioCallback' is not defined"
     ]
    }
   ],
   "source": [
    "val_train_ratio_cb = PrintValTrainRatioCallback()\n",
    "history = model.fit(X_train, y_train, epochs=1,\n",
    "                    validation_data=(X_valid, y_valid),\n",
    "                    callbacks=[val_train_ratio_cb])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cde74ecc",
   "metadata": {},
   "source": [
    "### TensorBoard for Visualiztion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "11c3944f",
   "metadata": {},
   "outputs": [],
   "source": [
    "root_logdir=os.path.join(os.curdir,'my_logs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "907da429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'.\\\\my_logs\\\\run_2022_11_15-09_38_19'"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_run_logdir():\n",
    "    import time\n",
    "    run_id=time.strftime('run_%Y_%m_%d-%H_%M_%S')\n",
    "    return os.path.join(root_logdir,run_id)\n",
    "run_logdir=get_run_logdir()\n",
    "run_logdir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f436035b",
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "9f7924ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=keras.models.Sequential([\n",
    "    keras.layers.Dense(30,activation='relu',input_shape=[8]),\n",
    "    keras.layers.Dense(30,activation='relu'),\n",
    "    keras.layers.Dense(1)\n",
    "])\n",
    "model.compile(loss='mse', optimizer=keras.optimizers.SGD(learning_rate=1e-3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "d68ac9a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "363/363 [==============================] - 1s 1ms/step - loss: 2.1078 - val_loss: 1.0059\n",
      "Epoch 2/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.7717 - val_loss: 0.8456\n",
      "Epoch 3/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.6588 - val_loss: 0.6742\n",
      "Epoch 4/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.6093 - val_loss: 0.5654\n",
      "Epoch 5/30\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.5736 - val_loss: 0.5306\n",
      "Epoch 6/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.5436 - val_loss: 0.5060\n",
      "Epoch 7/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.5194 - val_loss: 0.4917\n",
      "Epoch 8/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.4992 - val_loss: 0.4636\n",
      "Epoch 9/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.4819 - val_loss: 0.4490\n",
      "Epoch 10/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.4678 - val_loss: 0.4354\n",
      "Epoch 11/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.4562 - val_loss: 0.4264\n",
      "Epoch 12/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.4464 - val_loss: 0.4176\n",
      "Epoch 13/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.4384 - val_loss: 0.4112\n",
      "Epoch 14/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.4316 - val_loss: 0.4036\n",
      "Epoch 15/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.4259 - val_loss: 0.4086\n",
      "Epoch 16/30\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4209 - val_loss: 0.3922\n",
      "Epoch 17/30\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4165 - val_loss: 0.4121\n",
      "Epoch 18/30\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4126 - val_loss: 0.4096\n",
      "Epoch 19/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.4086 - val_loss: 0.3903\n",
      "Epoch 20/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.4055 - val_loss: 0.4491\n",
      "Epoch 21/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.4028 - val_loss: 0.4125\n",
      "Epoch 22/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.3999 - val_loss: 0.4441\n",
      "Epoch 23/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.3976 - val_loss: 0.3774\n",
      "Epoch 24/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.3951 - val_loss: 0.3941\n",
      "Epoch 25/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.3927 - val_loss: 0.4366\n",
      "Epoch 26/30\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3911 - val_loss: 0.4141\n",
      "Epoch 27/30\n",
      "363/363 [==============================] - 1s 1ms/step - loss: 0.3896 - val_loss: 0.3668\n",
      "Epoch 28/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.3874 - val_loss: 0.4052\n",
      "Epoch 29/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.3861 - val_loss: 0.3790\n",
      "Epoch 30/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.3840 - val_loss: 0.4218\n"
     ]
    }
   ],
   "source": [
    "tensorboard_cb = keras.callbacks.TensorBoard(run_logdir)\n",
    "history = model.fit(X_train, y_train, epochs=30,\n",
    "                    validation_data=(X_valid, y_valid),\n",
    "                    callbacks=[checkpoint_cb, tensorboard_cb])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "8d1934df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The tensorboard extension is already loaded. To reload it, use:\n",
      "  %reload_ext tensorboard\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-8b7100f5b6ec26f5\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-8b7100f5b6ec26f5\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 6006;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# activation of TensorBoard server\n",
    "%load_ext tensorboard \n",
    "%tensorboard --logdir=./my_logs --port=6006 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "a3840ab0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'.\\\\my_logs\\\\run_2022_11_15-10_06_48'"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_logdir2 =get_run_logdir()\n",
    "run_logdir2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "ff773c6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "275ff6d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Dense(30, activation=\"relu\", input_shape=[8]),\n",
    "    keras.layers.Dense(30, activation=\"relu\"),\n",
    "    keras.layers.Dense(1)\n",
    "])    \n",
    "model.compile(loss=\"mse\", optimizer=keras.optimizers.SGD(learning_rate=0.05))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "07c280ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "363/363 [==============================] - 1s 1ms/step - loss: 0.5522 - val_loss: 53.2006\n",
      "Epoch 2/30\n",
      "363/363 [==============================] - 1s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 3/30\n",
      "363/363 [==============================] - 1s 2ms/step - loss: nan - val_loss: nan\n",
      "Epoch 4/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 5/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 6/30\n",
      "363/363 [==============================] - 1s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 7/30\n",
      "363/363 [==============================] - 1s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 8/30\n",
      "363/363 [==============================] - 1s 2ms/step - loss: nan - val_loss: nan\n",
      "Epoch 9/30\n",
      "363/363 [==============================] - 1s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 10/30\n",
      "363/363 [==============================] - 1s 2ms/step - loss: nan - val_loss: nan\n",
      "Epoch 11/30\n",
      "363/363 [==============================] - 1s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 12/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 13/30\n",
      "363/363 [==============================] - 1s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 14/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 15/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 16/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 17/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 18/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 19/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 20/30\n",
      "363/363 [==============================] - 1s 2ms/step - loss: nan - val_loss: nan\n",
      "Epoch 21/30\n",
      "363/363 [==============================] - 1s 2ms/step - loss: nan - val_loss: nan\n",
      "Epoch 22/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 23/30\n",
      "363/363 [==============================] - 1s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 24/30\n",
      "363/363 [==============================] - 1s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 25/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 26/30\n",
      "363/363 [==============================] - 1s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 27/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 28/30\n",
      "363/363 [==============================] - 0s 1ms/step - loss: nan - val_loss: nan\n",
      "Epoch 29/30\n",
      "363/363 [==============================] - 1s 2ms/step - loss: nan - val_loss: nan\n",
      "Epoch 30/30\n",
      "363/363 [==============================] - 1s 1ms/step - loss: nan - val_loss: nan\n"
     ]
    }
   ],
   "source": [
    "tensorboard_cb = keras.callbacks.TensorBoard(run_logdir2)\n",
    "history = model.fit(X_train, y_train, epochs=30,\n",
    "                    validation_data=(X_valid, y_valid),\n",
    "                    callbacks=[checkpoint_cb, tensorboard_cb])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "c8144fd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function __init__ in module keras.callbacks:\n",
      "\n",
      "__init__(self, log_dir='logs', histogram_freq=0, write_graph=True, write_images=False, write_steps_per_second=False, update_freq='epoch', profile_batch=0, embeddings_freq=0, embeddings_metadata=None, **kwargs)\n",
      "    Initialize self.  See help(type(self)) for accurate signature.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(keras.callbacks.TensorBoard.__init__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "f12291b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The tensorboard extension is already loaded. To reload it, use:\n",
      "  %reload_ext tensorboard\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Reusing TensorBoard on port 6006 (pid 1948), started 0:11:14 ago. (Use '!kill 1948' to kill it.)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-3cabad0e15f233c5\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-3cabad0e15f233c5\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 6006;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# activation of TensorBoard server\n",
    "%load_ext tensorboard \n",
    "%tensorboard --logdir=./my_logs --port=6006 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c778b1b7",
   "metadata": {},
   "source": [
    "### Fine-Tuning NN Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "acdd0d05",
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "700fc950",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(n_hidden=1, n_neurons=30, learning_rate=3e-3, input_shape=[8]):\n",
    "    model = keras.models.Sequential()\n",
    "    model.add(keras.layers.InputLayer(input_shape=input_shape))\n",
    "    for layer in range(n_hidden):\n",
    "        model.add(keras.layers.Dense(n_neurons, activation=\"relu\"))\n",
    "    model.add(keras.layers.Dense(1))\n",
    "    optimizer = keras.optimizers.SGD(learning_rate=learning_rate)\n",
    "    model.compile(loss=\"mse\", optimizer=optimizer)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "2ad52bef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asatullaev Ubaydullo\\AppData\\Local\\Temp\\ipykernel_13304\\4087176523.py:1: DeprecationWarning: KerasRegressor is deprecated, use Sci-Keras (https://github.com/adriangb/scikeras) instead. See https://www.adriangb.com/scikeras/stable/migration.html for help migrating.\n",
      "  keras_reg = keras.wrappers.scikit_learn.KerasRegressor(build_model)\n"
     ]
    }
   ],
   "source": [
    "keras_reg = keras.wrappers.scikit_learn.KerasRegressor(build_model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d63802bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "363/363 [==============================] - 1s 1ms/step - loss: 1.0928 - val_loss: 1.9245\n",
      "Epoch 2/100\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.5830 - val_loss: 2.5097\n",
      "Epoch 3/100\n",
      "363/363 [==============================] - 1s 1ms/step - loss: 0.5168 - val_loss: 1.9281\n",
      "Epoch 4/100\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4729 - val_loss: 3.0477\n",
      "Epoch 5/100\n",
      "363/363 [==============================] - 1s 1ms/step - loss: 0.4628 - val_loss: 5.2690\n",
      "Epoch 6/100\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.4765 - val_loss: 8.7776\n",
      "Epoch 7/100\n",
      "363/363 [==============================] - 0s 959us/step - loss: 0.4981 - val_loss: 3.0511\n",
      "Epoch 8/100\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.4375 - val_loss: 0.9346\n",
      "Epoch 9/100\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.4171 - val_loss: 0.3820\n",
      "Epoch 10/100\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.4089 - val_loss: 0.5427\n",
      "Epoch 11/100\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.4067 - val_loss: 0.4498\n",
      "Epoch 12/100\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.4023 - val_loss: 0.7808\n",
      "Epoch 13/100\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4022 - val_loss: 0.3713\n",
      "Epoch 14/100\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.3965 - val_loss: 0.3764\n",
      "Epoch 15/100\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.3950 - val_loss: 0.3702\n",
      "Epoch 16/100\n",
      "363/363 [==============================] - 1s 1ms/step - loss: 0.3925 - val_loss: 0.3666\n",
      "Epoch 17/100\n",
      "363/363 [==============================] - 1s 1ms/step - loss: 0.3901 - val_loss: 0.4569\n",
      "Epoch 18/100\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.3901 - val_loss: 0.3687\n",
      "Epoch 19/100\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.3875 - val_loss: 0.3919\n",
      "Epoch 20/100\n",
      "363/363 [==============================] - 1s 1ms/step - loss: 0.3860 - val_loss: 0.5305\n",
      "Epoch 21/100\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.3858 - val_loss: 0.3639\n",
      "Epoch 22/100\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3835 - val_loss: 0.6030\n",
      "Epoch 23/100\n",
      "363/363 [==============================] - 1s 1ms/step - loss: 0.3835 - val_loss: 0.6273\n",
      "Epoch 24/100\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.3820 - val_loss: 0.6790\n",
      "Epoch 25/100\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3824 - val_loss: 0.3771\n",
      "Epoch 26/100\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.3787 - val_loss: 0.5409\n",
      "Epoch 27/100\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.3801 - val_loss: 0.6734\n",
      "Epoch 28/100\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3789 - val_loss: 0.9760\n",
      "Epoch 29/100\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.3821 - val_loss: 0.5981\n",
      "Epoch 30/100\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.3780 - val_loss: 1.1081\n",
      "Epoch 31/100\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.3820 - val_loss: 0.5188\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1713297f1c0>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras_reg.fit(X_train, y_train, epochs=100,\n",
    "              validation_data=(X_valid, y_valid),\n",
    "              callbacks=[keras.callbacks.EarlyStopping(patience=10)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "5bcb483a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "162/162 [==============================] - 0s 778us/step - loss: 0.3749\n"
     ]
    }
   ],
   "source": [
    "mse_test = keras_reg.score(X_test, y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "d83959c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 42ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = keras_reg.predict(X_new)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "2a934afd",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "56f2b207",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 1s 1ms/step - loss: 1.2064 - val_loss: 52.4753\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.7248 - val_loss: 0.4359\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4522 - val_loss: 0.4063\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.4392 - val_loss: 0.3958\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.4353 - val_loss: 0.3992\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4358 - val_loss: 0.4003\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.4303 - val_loss: 0.4061\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.4298 - val_loss: 0.3914\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.4293 - val_loss: 0.4033\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4279 - val_loss: 0.3851\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.4240 - val_loss: 0.3862\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.4188 - val_loss: 0.3927\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.4174 - val_loss: 0.3811\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.4136 - val_loss: 0.3822\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4080 - val_loss: 0.3753\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4092 - val_loss: 0.3783\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.4047 - val_loss: 0.3711\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.4022 - val_loss: 0.3726\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.3990 - val_loss: 0.3727\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4004 - val_loss: 0.3703\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3993 - val_loss: 0.3776\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.3974 - val_loss: 0.3724\n",
      "Epoch 23/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3964 - val_loss: 0.3856\n",
      "Epoch 24/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.3995 - val_loss: 0.3811\n",
      "Epoch 25/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.3932 - val_loss: 0.3822\n",
      "Epoch 26/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3907 - val_loss: 0.3731\n",
      "Epoch 27/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4073 - val_loss: 0.3969\n",
      "Epoch 28/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3997 - val_loss: 0.3800\n",
      "Epoch 29/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3909 - val_loss: 0.3683\n",
      "Epoch 30/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3879 - val_loss: 0.3719\n",
      "Epoch 31/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3863 - val_loss: 0.3667\n",
      "Epoch 32/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3878 - val_loss: 0.3668\n",
      "Epoch 33/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.3845 - val_loss: 0.3695\n",
      "Epoch 34/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.3832 - val_loss: 0.3672\n",
      "Epoch 35/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.3829 - val_loss: 0.3668\n",
      "Epoch 36/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.3832 - val_loss: 0.3672\n",
      "Epoch 37/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.3856 - val_loss: 0.3609\n",
      "Epoch 38/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3835 - val_loss: 0.3668\n",
      "Epoch 39/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.3828 - val_loss: 0.3663\n",
      "Epoch 40/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.3839 - val_loss: 0.3626\n",
      "Epoch 41/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3840 - val_loss: 0.3660\n",
      "Epoch 42/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3799 - val_loss: 0.3596\n",
      "Epoch 43/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3796 - val_loss: 0.3618\n",
      "Epoch 44/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3791 - val_loss: 0.3592\n",
      "Epoch 45/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3788 - val_loss: 0.3576\n",
      "Epoch 46/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.3797 - val_loss: 0.3653\n",
      "Epoch 47/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3827 - val_loss: 0.3594\n",
      "Epoch 48/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3943 - val_loss: 0.3713\n",
      "Epoch 49/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.3807 - val_loss: 0.3575\n",
      "Epoch 50/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3790 - val_loss: 0.3570\n",
      "Epoch 51/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3782 - val_loss: 0.3762\n",
      "Epoch 52/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3989 - val_loss: 0.3599\n",
      "Epoch 53/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3797 - val_loss: 0.3568\n",
      "Epoch 54/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.3778 - val_loss: 0.3645\n",
      "Epoch 55/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3768 - val_loss: 0.3614\n",
      "Epoch 56/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.3774 - val_loss: 0.3551\n",
      "Epoch 57/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3781 - val_loss: 0.3607\n",
      "Epoch 58/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3893 - val_loss: 0.3635\n",
      "Epoch 59/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3783 - val_loss: 0.3604\n",
      "Epoch 60/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.3778 - val_loss: 0.3622\n",
      "Epoch 61/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3814 - val_loss: 0.3653\n",
      "Epoch 62/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3763 - val_loss: 0.3631\n",
      "Epoch 63/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3771 - val_loss: 0.3594\n",
      "Epoch 64/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.3776 - val_loss: 0.3637\n",
      "Epoch 65/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3762 - val_loss: 0.3565\n",
      "Epoch 66/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.3774 - val_loss: 0.3572\n",
      "121/121 [==============================] - 0s 1ms/step - loss: 0.3915\n",
      "[CV] END learning_rate=0.022174573948353458, n_hidden=1, n_neurons=4; total time=  27.1s\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.7105 - val_loss: 0.6321\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4555 - val_loss: 0.4392\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4271 - val_loss: 0.8212\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4322 - val_loss: 0.5185\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4263 - val_loss: 0.5924\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4222 - val_loss: 0.5447\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.4277 - val_loss: 0.5678\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4198 - val_loss: 0.4802\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4153 - val_loss: 0.5329\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4192 - val_loss: 0.3915\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4122 - val_loss: 0.3984\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4086 - val_loss: 0.3873\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4091 - val_loss: 0.3951\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4114 - val_loss: 0.4233\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4056 - val_loss: 0.3936\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4021 - val_loss: 0.5000\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.4029 - val_loss: 0.3957\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4020 - val_loss: 0.6533\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4003 - val_loss: 0.5949\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3990 - val_loss: 0.4709\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3994 - val_loss: 1.8618\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.3994 - val_loss: 0.6911\n",
      "121/121 [==============================] - 0s 896us/step - loss: 0.4159\n",
      "[CV] END learning_rate=0.022174573948353458, n_hidden=1, n_neurons=4; total time=  10.6s\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.9076 - val_loss: 0.5402\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.5210 - val_loss: 0.4381\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 0s 1ms/step - loss: 0.4586 - val_loss: 0.4035\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4373 - val_loss: 0.3960\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4351 - val_loss: 0.3937\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4265 - val_loss: 0.3898\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4259 - val_loss: 0.3852\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4253 - val_loss: 0.3850\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4208 - val_loss: 0.3802\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4313 - val_loss: 0.3778\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4158 - val_loss: 0.3767\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4165 - val_loss: 0.3838\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4152 - val_loss: 0.3785\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4146 - val_loss: 0.3772\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4091 - val_loss: 0.3782\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4425 - val_loss: 0.3736\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.4070 - val_loss: 0.3740\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4108 - val_loss: 0.3819\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4066 - val_loss: 0.3722\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4070 - val_loss: 0.3737\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4130 - val_loss: 0.3670\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4120 - val_loss: 0.3710\n",
      "Epoch 23/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4039 - val_loss: 0.3787\n",
      "Epoch 24/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4021 - val_loss: 0.3713\n",
      "Epoch 25/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4055 - val_loss: 0.3656\n",
      "Epoch 26/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4023 - val_loss: 0.3686\n",
      "Epoch 27/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4003 - val_loss: 0.3660\n",
      "Epoch 28/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4094 - val_loss: 0.4697\n",
      "Epoch 29/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.9800 - val_loss: 0.3963\n",
      "Epoch 30/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4347 - val_loss: 0.3833\n",
      "Epoch 31/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4219 - val_loss: 0.3829\n",
      "Epoch 32/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4169 - val_loss: 0.3726\n",
      "Epoch 33/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4128 - val_loss: 0.3745\n",
      "Epoch 34/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4090 - val_loss: 0.3797\n",
      "Epoch 35/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.4093 - val_loss: 0.3695\n",
      "121/121 [==============================] - 0s 2ms/step - loss: 0.3876\n",
      "[CV] END learning_rate=0.022174573948353458, n_hidden=1, n_neurons=4; total time=  17.0s\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 1.0251 - val_loss: 2.4173\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.5786 - val_loss: 8.4056\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.5391 - val_loss: 0.9269\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.4380 - val_loss: 0.4480\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.4073 - val_loss: 0.3765\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.3887 - val_loss: 0.3835\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3772 - val_loss: 0.3863\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3684 - val_loss: 0.4418\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3622 - val_loss: 0.3955\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3578 - val_loss: 0.4596\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.3541 - val_loss: 0.3669\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.3494 - val_loss: 0.3651\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3452 - val_loss: 0.4474\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3438 - val_loss: 0.4418\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3398 - val_loss: 0.3513\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.3374 - val_loss: 0.4393\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.3353 - val_loss: 0.3668\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3324 - val_loss: 0.5442\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3312 - val_loss: 0.3332\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3281 - val_loss: 0.4050\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.3273 - val_loss: 0.3868\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.3250 - val_loss: 0.4272\n",
      "Epoch 23/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3238 - val_loss: 0.3261\n",
      "Epoch 24/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.3225 - val_loss: 0.3597\n",
      "Epoch 25/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3193 - val_loss: 0.3868\n",
      "Epoch 26/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3192 - val_loss: 0.3214\n",
      "Epoch 27/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3191 - val_loss: 0.4208\n",
      "Epoch 28/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3180 - val_loss: 0.3438\n",
      "Epoch 29/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3145 - val_loss: 0.4466\n",
      "Epoch 30/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3141 - val_loss: 0.3186\n",
      "Epoch 31/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3133 - val_loss: 0.3997\n",
      "Epoch 32/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3114 - val_loss: 0.3088\n",
      "Epoch 33/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3098 - val_loss: 0.5110\n",
      "Epoch 34/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3103 - val_loss: 0.3095\n",
      "Epoch 35/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3077 - val_loss: 0.4679\n",
      "Epoch 36/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3071 - val_loss: 0.3093\n",
      "Epoch 37/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.3050 - val_loss: 0.4004\n",
      "Epoch 38/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.3049 - val_loss: 0.3081\n",
      "Epoch 39/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3030 - val_loss: 0.4636\n",
      "Epoch 40/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3031 - val_loss: 0.3033\n",
      "Epoch 41/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3011 - val_loss: 0.5711\n",
      "Epoch 42/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.3025 - val_loss: 0.3542\n",
      "Epoch 43/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3046 - val_loss: 0.3875\n",
      "Epoch 44/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2998 - val_loss: 0.3147\n",
      "Epoch 45/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2985 - val_loss: 0.3033\n",
      "Epoch 46/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.2963 - val_loss: 0.3222\n",
      "Epoch 47/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2953 - val_loss: 0.3448\n",
      "Epoch 48/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2948 - val_loss: 0.3069\n",
      "Epoch 49/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.2928 - val_loss: 0.3012\n",
      "Epoch 50/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.2926 - val_loss: 0.4019\n",
      "Epoch 51/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2916 - val_loss: 0.3163\n",
      "Epoch 52/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.2903 - val_loss: 0.4091\n",
      "Epoch 53/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2912 - val_loss: 0.2903\n",
      "Epoch 54/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2887 - val_loss: 0.3522\n",
      "Epoch 55/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2878 - val_loss: 0.2941\n",
      "Epoch 56/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.2875 - val_loss: 0.3944\n",
      "Epoch 57/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.2865 - val_loss: 0.2912\n",
      "Epoch 58/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.2861 - val_loss: 0.4138\n",
      "Epoch 59/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2855 - val_loss: 0.2915\n",
      "Epoch 60/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.2845 - val_loss: 0.3573\n",
      "Epoch 61/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2842 - val_loss: 0.3106\n",
      "Epoch 62/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.2818 - val_loss: 0.3853\n",
      "Epoch 63/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.2824 - val_loss: 0.3030\n",
      "121/121 [==============================] - 0s 1ms/step - loss: 0.3260\n",
      "[CV] END learning_rate=0.005432590230265343, n_hidden=2, n_neurons=94; total time=  47.3s\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.9081 - val_loss: 1.5093\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.5232 - val_loss: 0.4694\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4430 - val_loss: 0.4443\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.4110 - val_loss: 0.3825\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3926 - val_loss: 0.3722\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3800 - val_loss: 0.5571\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3735 - val_loss: 0.8420\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.3653 - val_loss: 0.9213\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3608 - val_loss: 1.0380\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3567 - val_loss: 0.7597\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3515 - val_loss: 0.7654\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.3480 - val_loss: 0.9281\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3450 - val_loss: 1.0836\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.3436 - val_loss: 0.9393\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.3405 - val_loss: 0.9458\n",
      "121/121 [==============================] - 0s 1ms/step - loss: 0.3566\n",
      "[CV] END learning_rate=0.005432590230265343, n_hidden=2, n_neurons=94; total time=  13.2s\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.9561 - val_loss: 11.0065\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.5917 - val_loss: 21.9693\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.6043 - val_loss: 0.7475\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4233 - val_loss: 0.4216\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.4021 - val_loss: 0.3701\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3890 - val_loss: 0.3937\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3820 - val_loss: 0.3610\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3744 - val_loss: 0.3569\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3684 - val_loss: 0.3942\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3683 - val_loss: 0.3694\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3660 - val_loss: 0.4084\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3590 - val_loss: 0.3412\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3539 - val_loss: 0.3390\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3521 - val_loss: 0.4164\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3500 - val_loss: 0.4215\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.3474 - val_loss: 0.4104\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3453 - val_loss: 0.3492\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3450 - val_loss: 0.3674\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3395 - val_loss: 0.3505\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3372 - val_loss: 0.3423\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3373 - val_loss: 0.3211\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3350 - val_loss: 0.3319\n",
      "Epoch 23/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.3320 - val_loss: 0.3290\n",
      "Epoch 24/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3302 - val_loss: 0.3412\n",
      "Epoch 25/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3281 - val_loss: 0.3326\n",
      "Epoch 26/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3266 - val_loss: 0.3260\n",
      "Epoch 27/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3246 - val_loss: 0.3375\n",
      "Epoch 28/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3244 - val_loss: 0.3978\n",
      "Epoch 29/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3272 - val_loss: 0.4804\n",
      "Epoch 30/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3238 - val_loss: 0.3105\n",
      "Epoch 31/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3200 - val_loss: 0.3324\n",
      "Epoch 32/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3181 - val_loss: 0.3184\n",
      "Epoch 33/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3180 - val_loss: 0.3328\n",
      "Epoch 34/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3163 - val_loss: 0.3104\n",
      "Epoch 35/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3144 - val_loss: 0.3118\n",
      "Epoch 36/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3155 - val_loss: 0.3166\n",
      "Epoch 37/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3122 - val_loss: 0.3680\n",
      "Epoch 38/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3127 - val_loss: 0.3849\n",
      "Epoch 39/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3117 - val_loss: 0.3045\n",
      "Epoch 40/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3103 - val_loss: 0.3385\n",
      "Epoch 41/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3087 - val_loss: 0.3070\n",
      "Epoch 42/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3073 - val_loss: 0.3830\n",
      "Epoch 43/100\n",
      "242/242 [==============================] - 0s 2ms/step - loss: 0.3112 - val_loss: 0.3619\n",
      "Epoch 44/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3064 - val_loss: 0.3853\n",
      "Epoch 45/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3073 - val_loss: 0.3199\n",
      "Epoch 46/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3048 - val_loss: 0.3716\n",
      "Epoch 47/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3024 - val_loss: 0.3308\n",
      "Epoch 48/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3025 - val_loss: 0.3001\n",
      "Epoch 49/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3003 - val_loss: 0.3374\n",
      "Epoch 50/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.2997 - val_loss: 0.3037\n",
      "Epoch 51/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.2986 - val_loss: 0.3359\n",
      "Epoch 52/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.2979 - val_loss: 0.3077\n",
      "Epoch 53/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.2978 - val_loss: 0.4116\n",
      "Epoch 54/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.2980 - val_loss: 0.4137\n",
      "Epoch 55/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2981 - val_loss: 0.3118\n",
      "Epoch 56/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2968 - val_loss: 0.3817\n",
      "Epoch 57/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2958 - val_loss: 0.3089\n",
      "Epoch 58/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.2937 - val_loss: 0.2946\n",
      "Epoch 59/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2916 - val_loss: 0.3085\n",
      "Epoch 60/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2926 - val_loss: 0.3038\n",
      "Epoch 61/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2924 - val_loss: 0.3361\n",
      "Epoch 62/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.2896 - val_loss: 0.2885\n",
      "Epoch 63/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.2894 - val_loss: 0.3262\n",
      "Epoch 64/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.2899 - val_loss: 0.2887\n",
      "Epoch 65/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.2872 - val_loss: 0.3080\n",
      "Epoch 66/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.2862 - val_loss: 0.3523\n",
      "Epoch 67/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.2866 - val_loss: 0.3212\n",
      "Epoch 68/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.2844 - val_loss: 0.3400\n",
      "Epoch 69/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.2844 - val_loss: 0.3002\n",
      "Epoch 70/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.2827 - val_loss: 0.3105\n",
      "Epoch 71/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.2845 - val_loss: 0.2842\n",
      "Epoch 72/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2839 - val_loss: 0.3000\n",
      "Epoch 73/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.2847 - val_loss: 0.2860\n",
      "Epoch 74/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2832 - val_loss: 0.2881\n",
      "Epoch 75/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2809 - val_loss: 0.3242\n",
      "Epoch 76/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2781 - val_loss: 0.4242\n",
      "Epoch 77/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2832 - val_loss: 0.3765\n",
      "Epoch 78/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2801 - val_loss: 0.3257\n",
      "Epoch 79/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2803 - val_loss: 0.3009\n",
      "Epoch 80/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2772 - val_loss: 0.2832\n",
      "Epoch 81/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.2778 - val_loss: 0.2843\n",
      "Epoch 82/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.2767 - val_loss: 0.3020\n",
      "Epoch 83/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.2757 - val_loss: 0.2843\n",
      "Epoch 84/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.2779 - val_loss: 0.3517\n",
      "Epoch 85/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.2810 - val_loss: 0.2924\n",
      "Epoch 86/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.2751 - val_loss: 0.4714\n",
      "Epoch 87/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.2861 - val_loss: 0.2817\n",
      "Epoch 88/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.2751 - val_loss: 0.3357\n",
      "Epoch 89/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2759 - val_loss: 0.3102\n",
      "Epoch 90/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2741 - val_loss: 0.3447\n",
      "Epoch 91/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.2758 - val_loss: 0.2957\n",
      "Epoch 92/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.2728 - val_loss: 0.2870\n",
      "Epoch 93/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.2709 - val_loss: 0.2837\n",
      "Epoch 94/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2707 - val_loss: 0.2916\n",
      "Epoch 95/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2698 - val_loss: 0.2978\n",
      "Epoch 96/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.2697 - val_loss: 0.3544\n",
      "Epoch 97/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.2809 - val_loss: 0.3040\n",
      "121/121 [==============================] - 0s 3ms/step - loss: 0.3149\n",
      "[CV] END learning_rate=0.005432590230265343, n_hidden=2, n_neurons=94; total time= 1.5min\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 2.6958 - val_loss: 2.2738\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 1.5570 - val_loss: 1.6344\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 1.1160 - val_loss: 1.1882\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.9275 - val_loss: 0.9267\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.8391 - val_loss: 0.8081\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.7919 - val_loss: 0.7672\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.7631 - val_loss: 0.7569\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.7428 - val_loss: 0.7485\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.7267 - val_loss: 0.7293\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.7123 - val_loss: 0.7216\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.6994 - val_loss: 0.7115\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.6875 - val_loss: 0.6903\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.6760 - val_loss: 0.6826\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.6654 - val_loss: 0.6680\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.6552 - val_loss: 0.6499\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.6453 - val_loss: 0.6389\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.6360 - val_loss: 0.6252\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.6269 - val_loss: 0.6170\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.6182 - val_loss: 0.6073\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.6100 - val_loss: 0.5987\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.6022 - val_loss: 0.5836\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.5945 - val_loss: 0.5758\n",
      "Epoch 23/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.5872 - val_loss: 0.5668\n",
      "Epoch 24/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.5800 - val_loss: 0.5608\n",
      "Epoch 25/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.5734 - val_loss: 0.5505\n",
      "Epoch 26/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.5667 - val_loss: 0.5413\n",
      "Epoch 27/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.5604 - val_loss: 0.5356\n",
      "Epoch 28/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.5543 - val_loss: 0.5264\n",
      "Epoch 29/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.5485 - val_loss: 0.5203\n",
      "Epoch 30/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.5429 - val_loss: 0.5149\n",
      "Epoch 31/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.5375 - val_loss: 0.5097\n",
      "Epoch 32/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.5324 - val_loss: 0.5033\n",
      "Epoch 33/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.5275 - val_loss: 0.4979\n",
      "Epoch 34/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.5227 - val_loss: 0.4925\n",
      "Epoch 35/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.5181 - val_loss: 0.4877\n",
      "Epoch 36/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.5137 - val_loss: 0.4834\n",
      "Epoch 37/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.5096 - val_loss: 0.4790\n",
      "Epoch 38/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.5055 - val_loss: 0.4750\n",
      "Epoch 39/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.5016 - val_loss: 0.4711\n",
      "Epoch 40/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.4978 - val_loss: 0.4677\n",
      "Epoch 41/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.4942 - val_loss: 0.4642\n",
      "Epoch 42/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.4907 - val_loss: 0.4609\n",
      "Epoch 43/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.4874 - val_loss: 0.4576\n",
      "Epoch 44/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.4842 - val_loss: 0.4549\n",
      "Epoch 45/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4812 - val_loss: 0.4518\n",
      "Epoch 46/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.4782 - val_loss: 0.4489\n",
      "Epoch 47/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.4754 - val_loss: 0.4469\n",
      "Epoch 48/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.4727 - val_loss: 0.4451\n",
      "Epoch 49/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.4701 - val_loss: 0.4426\n",
      "Epoch 50/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.4675 - val_loss: 0.4405\n",
      "Epoch 51/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.4651 - val_loss: 0.4390\n",
      "Epoch 52/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.4628 - val_loss: 0.4381\n",
      "Epoch 53/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.4605 - val_loss: 0.4372\n",
      "Epoch 54/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4584 - val_loss: 0.4350\n",
      "Epoch 55/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4563 - val_loss: 0.4343\n",
      "Epoch 56/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.4544 - val_loss: 0.4327\n",
      "Epoch 57/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.4525 - val_loss: 0.4315\n",
      "Epoch 58/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4506 - val_loss: 0.4319\n",
      "Epoch 59/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.4489 - val_loss: 0.4289\n",
      "Epoch 60/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4472 - val_loss: 0.4275\n",
      "Epoch 61/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.4457 - val_loss: 0.4274\n",
      "Epoch 62/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.4441 - val_loss: 0.4280\n",
      "Epoch 63/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.4426 - val_loss: 0.4267\n",
      "Epoch 64/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.4411 - val_loss: 0.4269\n",
      "Epoch 65/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4397 - val_loss: 0.4278\n",
      "Epoch 66/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4384 - val_loss: 0.4252\n",
      "Epoch 67/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4370 - val_loss: 0.4251\n",
      "Epoch 68/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4358 - val_loss: 0.4263\n",
      "Epoch 69/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.4346 - val_loss: 0.4260\n",
      "Epoch 70/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.4333 - val_loss: 0.4241\n",
      "Epoch 71/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4322 - val_loss: 0.4258\n",
      "Epoch 72/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4311 - val_loss: 0.4254\n",
      "Epoch 73/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.4301 - val_loss: 0.4219\n",
      "Epoch 74/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.4291 - val_loss: 0.4238\n",
      "Epoch 75/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.4281 - val_loss: 0.4241\n",
      "Epoch 76/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4271 - val_loss: 0.4241\n",
      "Epoch 77/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.4262 - val_loss: 0.4209\n",
      "Epoch 78/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.4253 - val_loss: 0.4235\n",
      "Epoch 79/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4244 - val_loss: 0.4230\n",
      "Epoch 80/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4236 - val_loss: 0.4222\n",
      "Epoch 81/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.4228 - val_loss: 0.4246\n",
      "Epoch 82/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4219 - val_loss: 0.4260\n",
      "Epoch 83/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.4212 - val_loss: 0.4233\n",
      "Epoch 84/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.4204 - val_loss: 0.4224\n",
      "Epoch 85/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.4197 - val_loss: 0.4205\n",
      "Epoch 86/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4188 - val_loss: 0.4242\n",
      "Epoch 87/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.4183 - val_loss: 0.4191\n",
      "Epoch 88/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.4176 - val_loss: 0.4165\n",
      "Epoch 89/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.4169 - val_loss: 0.4155\n",
      "Epoch 90/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.4162 - val_loss: 0.4187\n",
      "Epoch 91/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4155 - val_loss: 0.4155\n",
      "Epoch 92/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.4149 - val_loss: 0.4138\n",
      "Epoch 93/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.4142 - val_loss: 0.4163\n",
      "Epoch 94/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4137 - val_loss: 0.4149\n",
      "Epoch 95/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4131 - val_loss: 0.4143\n",
      "Epoch 96/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.4124 - val_loss: 0.4132\n",
      "Epoch 97/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.4119 - val_loss: 0.4110\n",
      "Epoch 98/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.4113 - val_loss: 0.4092\n",
      "Epoch 99/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.4107 - val_loss: 0.4160\n",
      "Epoch 100/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.4102 - val_loss: 0.4118\n",
      "121/121 [==============================] - 0s 2ms/step - loss: 0.4215\n",
      "[CV] END learning_rate=0.00037078874137762145, n_hidden=1, n_neurons=51; total time= 2.1min\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 3s 9ms/step - loss: 3.6255 - val_loss: 3.6193\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 1.7954 - val_loss: 5.6740\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 1.1976 - val_loss: 6.5399\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.9665 - val_loss: 6.2805\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.8599 - val_loss: 5.4385\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.8012 - val_loss: 4.5006\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.7632 - val_loss: 3.6299\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.7350 - val_loss: 2.8507\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.7122 - val_loss: 2.2166\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.6924 - val_loss: 1.7129\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.6747 - val_loss: 1.3224\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.6585 - val_loss: 1.0076\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 3s 14ms/step - loss: 0.6436 - val_loss: 0.7972\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.6297 - val_loss: 0.6639\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.6169 - val_loss: 0.5869\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.6049 - val_loss: 0.5643\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.5938 - val_loss: 0.5842\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.5835 - val_loss: 0.6429\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.5737 - val_loss: 0.7338\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.5647 - val_loss: 0.8438\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.5562 - val_loss: 0.9839\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.5483 - val_loss: 1.1290\n",
      "Epoch 23/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.5408 - val_loss: 1.2873\n",
      "Epoch 24/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.5339 - val_loss: 1.4336\n",
      "Epoch 25/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.5274 - val_loss: 1.5950\n",
      "Epoch 26/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.5212 - val_loss: 1.7520\n",
      "121/121 [==============================] - 1s 5ms/step - loss: 0.5584\n",
      "[CV] END learning_rate=0.00037078874137762145, n_hidden=1, n_neurons=51; total time=  47.8s\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 3s 9ms/step - loss: 4.3451 - val_loss: 3.6935\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 2.1915 - val_loss: 3.4272\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 1.4224 - val_loss: 2.5043\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 1.0749 - val_loss: 1.7496\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.9017 - val_loss: 1.1738\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.8105 - val_loss: 0.8892\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.7595 - val_loss: 0.7595\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.7289 - val_loss: 0.6907\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.7084 - val_loss: 0.6651\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.6926 - val_loss: 0.6483\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.6791 - val_loss: 0.6345\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.6673 - val_loss: 0.6232\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.6563 - val_loss: 0.6127\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.6459 - val_loss: 0.6029\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.6361 - val_loss: 0.5935\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.6266 - val_loss: 0.5848\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.6176 - val_loss: 0.5762\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.6089 - val_loss: 0.5680\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.6005 - val_loss: 0.5608\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.5925 - val_loss: 0.5527\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.5846 - val_loss: 0.5452\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.5770 - val_loss: 0.5382\n",
      "Epoch 23/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.5698 - val_loss: 0.5313\n",
      "Epoch 24/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.5628 - val_loss: 0.5252\n",
      "Epoch 25/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.5561 - val_loss: 0.5188\n",
      "Epoch 26/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.5498 - val_loss: 0.5132\n",
      "Epoch 27/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.5437 - val_loss: 0.5069\n",
      "Epoch 28/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.5379 - val_loss: 0.5015\n",
      "Epoch 29/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.5323 - val_loss: 0.4964\n",
      "Epoch 30/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.5271 - val_loss: 0.4914\n",
      "Epoch 31/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.5220 - val_loss: 0.4866\n",
      "Epoch 32/100\n",
      "242/242 [==============================] - 4s 18ms/step - loss: 0.5171 - val_loss: 0.4823\n",
      "Epoch 33/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.5125 - val_loss: 0.4780\n",
      "Epoch 34/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.5081 - val_loss: 0.4742\n",
      "Epoch 35/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.5039 - val_loss: 0.4702\n",
      "Epoch 36/100\n",
      "242/242 [==============================] - 3s 10ms/step - loss: 0.4998 - val_loss: 0.4674\n",
      "Epoch 37/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4960 - val_loss: 0.4651\n",
      "Epoch 38/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4923 - val_loss: 0.4609\n",
      "Epoch 39/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.4888 - val_loss: 0.4579\n",
      "Epoch 40/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.4855 - val_loss: 0.4549\n",
      "Epoch 41/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.4822 - val_loss: 0.4531\n",
      "Epoch 42/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.4792 - val_loss: 0.4510\n",
      "Epoch 43/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.4763 - val_loss: 0.4481\n",
      "Epoch 44/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.4735 - val_loss: 0.4457\n",
      "Epoch 45/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.4708 - val_loss: 0.4428\n",
      "Epoch 46/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4683 - val_loss: 0.4405\n",
      "Epoch 47/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4658 - val_loss: 0.4397\n",
      "Epoch 48/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.4635 - val_loss: 0.4373\n",
      "Epoch 49/100\n",
      "242/242 [==============================] - 3s 14ms/step - loss: 0.4612 - val_loss: 0.4365\n",
      "Epoch 50/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4590 - val_loss: 0.4351\n",
      "Epoch 51/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4569 - val_loss: 0.4334\n",
      "Epoch 52/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4549 - val_loss: 0.4325\n",
      "Epoch 53/100\n",
      "242/242 [==============================] - 3s 14ms/step - loss: 0.4530 - val_loss: 0.4311\n",
      "Epoch 54/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.4511 - val_loss: 0.4299\n",
      "Epoch 55/100\n",
      "242/242 [==============================] - 5s 19ms/step - loss: 0.4493 - val_loss: 0.4290\n",
      "Epoch 56/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.4476 - val_loss: 0.4292\n",
      "Epoch 57/100\n",
      "242/242 [==============================] - 4s 17ms/step - loss: 0.4460 - val_loss: 0.4277\n",
      "Epoch 58/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4444 - val_loss: 0.4262\n",
      "Epoch 59/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4428 - val_loss: 0.4249\n",
      "Epoch 60/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.4414 - val_loss: 0.4223\n",
      "Epoch 61/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4399 - val_loss: 0.4239\n",
      "Epoch 62/100\n",
      "242/242 [==============================] - 3s 10ms/step - loss: 0.4385 - val_loss: 0.4240\n",
      "Epoch 63/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4373 - val_loss: 0.4226\n",
      "Epoch 64/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.4360 - val_loss: 0.4208\n",
      "Epoch 65/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.4348 - val_loss: 0.4200\n",
      "Epoch 66/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.4336 - val_loss: 0.4213\n",
      "Epoch 67/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4325 - val_loss: 0.4174\n",
      "Epoch 68/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.4314 - val_loss: 0.4175\n",
      "Epoch 69/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.4303 - val_loss: 0.4174\n",
      "Epoch 70/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.4293 - val_loss: 0.4161\n",
      "Epoch 71/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4282 - val_loss: 0.4149\n",
      "Epoch 72/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4273 - val_loss: 0.4149\n",
      "Epoch 73/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4263 - val_loss: 0.4132\n",
      "Epoch 74/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.4254 - val_loss: 0.4139\n",
      "Epoch 75/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.4245 - val_loss: 0.4148\n",
      "Epoch 76/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4236 - val_loss: 0.4157\n",
      "Epoch 77/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.4227 - val_loss: 0.4141\n",
      "Epoch 78/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.4219 - val_loss: 0.4134\n",
      "Epoch 79/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4211 - val_loss: 0.4126\n",
      "Epoch 80/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4203 - val_loss: 0.4143\n",
      "Epoch 81/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.4196 - val_loss: 0.4137\n",
      "Epoch 82/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4189 - val_loss: 0.4123\n",
      "Epoch 83/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.4181 - val_loss: 0.4119\n",
      "Epoch 84/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 0.4175 - val_loss: 0.4123\n",
      "Epoch 85/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.4167 - val_loss: 0.4096\n",
      "Epoch 86/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4161 - val_loss: 0.4096\n",
      "Epoch 87/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.4154 - val_loss: 0.4117\n",
      "Epoch 88/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.4147 - val_loss: 0.4104\n",
      "Epoch 89/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4141 - val_loss: 0.4118\n",
      "Epoch 90/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.4135 - val_loss: 0.4096\n",
      "Epoch 91/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4129 - val_loss: 0.4080\n",
      "Epoch 92/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.4123 - val_loss: 0.4061\n",
      "Epoch 93/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4117 - val_loss: 0.4072\n",
      "Epoch 94/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.4111 - val_loss: 0.4065\n",
      "Epoch 95/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.4105 - val_loss: 0.4068\n",
      "Epoch 96/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.4100 - val_loss: 0.4072\n",
      "Epoch 97/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4095 - val_loss: 0.4091\n",
      "Epoch 98/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4089 - val_loss: 0.4053\n",
      "Epoch 99/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.4084 - val_loss: 0.4064\n",
      "Epoch 100/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4079 - val_loss: 0.4078\n",
      "121/121 [==============================] - 1s 4ms/step - loss: 0.4121\n",
      "[CV] END learning_rate=0.00037078874137762145, n_hidden=1, n_neurons=51; total time= 3.3min\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 3s 10ms/step - loss: 1.6742 - val_loss: 6.0984\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.7393 - val_loss: 1.5735\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.5992 - val_loss: 0.5281\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.5404 - val_loss: 0.4938\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.5023 - val_loss: 0.4556\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.4731 - val_loss: 0.4689\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4512 - val_loss: 0.4178\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4346 - val_loss: 0.4403\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.4227 - val_loss: 0.4203\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4134 - val_loss: 0.4199\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.4061 - val_loss: 0.3894\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3995 - val_loss: 0.3776\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3940 - val_loss: 0.4360\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3897 - val_loss: 0.4297\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3857 - val_loss: 0.3874\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3817 - val_loss: 0.4067\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.3783 - val_loss: 0.4275\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.3756 - val_loss: 0.3822\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3719 - val_loss: 0.4225\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3703 - val_loss: 0.3954\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3677 - val_loss: 0.3813\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3652 - val_loss: 0.3940\n",
      "121/121 [==============================] - 0s 3ms/step - loss: 0.3805\n",
      "[CV] END learning_rate=0.0016535051383872363, n_hidden=2, n_neurons=70; total time=  39.1s\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 1.7703 - val_loss: 12.3046\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.7244 - val_loss: 5.2493\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.6422 - val_loss: 2.0715\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.5898 - val_loss: 0.7933\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 3s 14ms/step - loss: 0.5477 - val_loss: 0.5040\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.5149 - val_loss: 0.5419\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.4884 - val_loss: 0.6574\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 4s 17ms/step - loss: 0.4670 - val_loss: 0.7712\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.4493 - val_loss: 0.7634\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4358 - val_loss: 0.7691\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4242 - val_loss: 0.7451\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4148 - val_loss: 0.7351\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.4073 - val_loss: 0.6530\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.4013 - val_loss: 0.5047\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3956 - val_loss: 0.4510\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3907 - val_loss: 0.3955\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3864 - val_loss: 0.3714\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3832 - val_loss: 0.3629\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3792 - val_loss: 0.3600\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3767 - val_loss: 0.3645\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3745 - val_loss: 0.3734\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3720 - val_loss: 0.3884\n",
      "Epoch 23/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3698 - val_loss: 0.4272\n",
      "Epoch 24/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3678 - val_loss: 0.4371\n",
      "Epoch 25/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3661 - val_loss: 0.4664\n",
      "Epoch 26/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3643 - val_loss: 0.4970\n",
      "Epoch 27/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3624 - val_loss: 0.5279\n",
      "Epoch 28/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3612 - val_loss: 0.5806\n",
      "Epoch 29/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3593 - val_loss: 0.6475\n",
      "121/121 [==============================] - 1s 11ms/step - loss: 0.3762\n",
      "[CV] END learning_rate=0.0016535051383872363, n_hidden=2, n_neurons=70; total time= 1.1min\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 1.7551 - val_loss: 1.1622\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.7254 - val_loss: 0.7158\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.6284 - val_loss: 0.5758\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.5782 - val_loss: 0.5391\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.5385 - val_loss: 0.5540\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.5075 - val_loss: 0.4863\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.4815 - val_loss: 0.4491\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4617 - val_loss: 0.4267\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4461 - val_loss: 0.4791\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.4345 - val_loss: 0.4099\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.4247 - val_loss: 0.4614\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4167 - val_loss: 0.4191\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 4s 15ms/step - loss: 0.4095 - val_loss: 0.3887\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4050 - val_loss: 0.4699\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.4009 - val_loss: 0.3795\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3960 - val_loss: 0.4480\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3932 - val_loss: 0.3663\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3900 - val_loss: 0.3990\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3875 - val_loss: 0.4284\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3839 - val_loss: 0.3866\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3821 - val_loss: 0.3824\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3803 - val_loss: 0.3592\n",
      "Epoch 23/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3778 - val_loss: 0.3759\n",
      "Epoch 24/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3752 - val_loss: 0.4064\n",
      "Epoch 25/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3745 - val_loss: 0.3493\n",
      "Epoch 26/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3720 - val_loss: 0.3570\n",
      "Epoch 27/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3701 - val_loss: 0.4542\n",
      "Epoch 28/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3704 - val_loss: 0.3566\n",
      "Epoch 29/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3691 - val_loss: 0.4883\n",
      "Epoch 30/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3687 - val_loss: 0.3540\n",
      "Epoch 31/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3648 - val_loss: 0.3666\n",
      "Epoch 32/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3635 - val_loss: 0.4166\n",
      "Epoch 33/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3620 - val_loss: 0.3753\n",
      "Epoch 34/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3612 - val_loss: 0.4393\n",
      "Epoch 35/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3610 - val_loss: 0.3478\n",
      "Epoch 36/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3584 - val_loss: 0.3587\n",
      "Epoch 37/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3582 - val_loss: 0.3378\n",
      "Epoch 38/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.3567 - val_loss: 0.4742\n",
      "Epoch 39/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3566 - val_loss: 0.3352\n",
      "Epoch 40/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.3546 - val_loss: 0.4118\n",
      "Epoch 41/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3536 - val_loss: 0.4016\n",
      "Epoch 42/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3529 - val_loss: 0.3914\n",
      "Epoch 43/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3522 - val_loss: 0.3382\n",
      "Epoch 44/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3509 - val_loss: 0.4212\n",
      "Epoch 45/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.3510 - val_loss: 0.3443\n",
      "Epoch 46/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3494 - val_loss: 0.3930\n",
      "Epoch 47/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3484 - val_loss: 0.3452\n",
      "Epoch 48/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3481 - val_loss: 0.3473\n",
      "Epoch 49/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3463 - val_loss: 0.4167\n",
      "121/121 [==============================] - 1s 3ms/step - loss: 0.3472\n",
      "[CV] END learning_rate=0.0016535051383872363, n_hidden=2, n_neurons=70; total time= 1.8min\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 1.1388 - val_loss: 96.0509\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.7388 - val_loss: 63.4535\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 1.9148 - val_loss: 801.1329\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 2.1792 - val_loss: 1753.2214\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 10.1188 - val_loss: 10832.8105\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 392.7352 - val_loss: 47162.3242\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 497.1987 - val_loss: 215150.2812\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 7127.0986 - val_loss: 959133.5000\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 14947.7568 - val_loss: 4373523.0000\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 188175.9688 - val_loss: 21375172.0000\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 332161.6250 - val_loss: 94521576.0000\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 244361.8594 - val_loss: 426292928.0000\n",
      "121/121 [==============================] - 1s 4ms/step - loss: 1117739.7500\n",
      "[CV] END learning_rate=0.01824796188192035, n_hidden=0, n_neurons=40; total time=  20.5s\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 3s 9ms/step - loss: 1.0258 - val_loss: 6.6763\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.5521 - val_loss: 20.2037\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.5160 - val_loss: 24.0783\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.5140 - val_loss: 22.1572\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.5117 - val_loss: 21.6589\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.5098 - val_loss: 21.0805\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.5117 - val_loss: 19.7592\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.5105 - val_loss: 22.3853\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.5070 - val_loss: 20.0325\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.5088 - val_loss: 10.6910\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.5083 - val_loss: 19.6994\n",
      "121/121 [==============================] - 0s 2ms/step - loss: 0.9642\n",
      "[CV] END learning_rate=0.01824796188192035, n_hidden=0, n_neurons=40; total time=  21.4s\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 1.1148 - val_loss: 359.6145\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.9848 - val_loss: 97.4337\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 1s 2ms/step - loss: 1.4506 - val_loss: 880.5694\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 38.7322 - val_loss: 773.6395\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 1.3678 - val_loss: 2819.5352\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 9.7907 - val_loss: 1537.1517\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 5.0389 - val_loss: 1628.4519\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 44.3241 - val_loss: 1471.5590\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 11.4475 - val_loss: 1399.6675\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 1.5266 - val_loss: 239.8857\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 15.1723 - val_loss: 140.9613\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.6547 - val_loss: 4.3432\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.7531 - val_loss: 821.7935\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 9.6569 - val_loss: 489.9930\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 2.2796 - val_loss: 1103.3295\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 38.4582 - val_loss: 888.2972\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 9.1430 - val_loss: 1151.7919\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 3.9201 - val_loss: 513.3096\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 25.5212 - val_loss: 319.6289\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 3.7829 - val_loss: 368.5360\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 4.3826 - val_loss: 575.1464\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 4.6872 - val_loss: 405.8124\n",
      "121/121 [==============================] - 1s 4ms/step - loss: 0.6302\n",
      "[CV] END learning_rate=0.01824796188192035, n_hidden=0, n_neurons=40; total time=  41.8s\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 4s 17ms/step - loss: 1.4601 - val_loss: 3.6844\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.5742 - val_loss: 0.5005\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.4701 - val_loss: 0.4257\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.4365 - val_loss: 0.4017\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.4180 - val_loss: 0.3991\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4039 - val_loss: 0.3979\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3933 - val_loss: 0.3866\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3846 - val_loss: 0.3873\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3787 - val_loss: 0.3852\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3741 - val_loss: 0.3761\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3701 - val_loss: 0.3736\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 4s 16ms/step - loss: 0.3655 - val_loss: 0.3803\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 3s 10ms/step - loss: 0.3620 - val_loss: 0.3664\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3603 - val_loss: 0.3720\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3571 - val_loss: 0.3638\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3551 - val_loss: 0.3672\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3526 - val_loss: 0.3655\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3509 - val_loss: 0.3592\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3479 - val_loss: 0.3572\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3463 - val_loss: 0.3539\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3449 - val_loss: 0.3663\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3431 - val_loss: 0.3564\n",
      "Epoch 23/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3411 - val_loss: 0.3534\n",
      "Epoch 24/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3401 - val_loss: 0.3470\n",
      "Epoch 25/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3372 - val_loss: 0.3575\n",
      "Epoch 26/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3364 - val_loss: 0.3368\n",
      "Epoch 27/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3354 - val_loss: 0.3441\n",
      "Epoch 28/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3343 - val_loss: 0.3560\n",
      "Epoch 29/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3321 - val_loss: 0.3603\n",
      "Epoch 30/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3303 - val_loss: 0.3283\n",
      "Epoch 31/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3296 - val_loss: 0.3234\n",
      "Epoch 32/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3287 - val_loss: 0.3195\n",
      "Epoch 33/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3268 - val_loss: 0.3286\n",
      "Epoch 34/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3254 - val_loss: 0.3278\n",
      "Epoch 35/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3247 - val_loss: 0.3161\n",
      "Epoch 36/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.3231 - val_loss: 0.3350\n",
      "Epoch 37/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3227 - val_loss: 0.3171\n",
      "Epoch 38/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3210 - val_loss: 0.3365\n",
      "Epoch 39/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3201 - val_loss: 0.3140\n",
      "Epoch 40/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3197 - val_loss: 0.3276\n",
      "Epoch 41/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3183 - val_loss: 0.3229\n",
      "Epoch 42/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3167 - val_loss: 0.3092\n",
      "Epoch 43/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3168 - val_loss: 0.3158\n",
      "Epoch 44/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3145 - val_loss: 0.3244\n",
      "Epoch 45/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3145 - val_loss: 0.3100\n",
      "Epoch 46/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3125 - val_loss: 0.3300\n",
      "Epoch 47/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3120 - val_loss: 0.3238\n",
      "Epoch 48/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3117 - val_loss: 0.3130\n",
      "Epoch 49/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.3096 - val_loss: 0.3067\n",
      "Epoch 50/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3092 - val_loss: 0.3297\n",
      "Epoch 51/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3087 - val_loss: 0.3347\n",
      "Epoch 52/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3076 - val_loss: 0.3380\n",
      "Epoch 53/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3071 - val_loss: 0.3174\n",
      "Epoch 54/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.3055 - val_loss: 0.3169\n",
      "Epoch 55/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3040 - val_loss: 0.3287\n",
      "Epoch 56/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3046 - val_loss: 0.3035\n",
      "Epoch 57/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3029 - val_loss: 0.3335\n",
      "Epoch 58/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3020 - val_loss: 0.3357\n",
      "Epoch 59/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3020 - val_loss: 0.2991\n",
      "Epoch 60/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.3014 - val_loss: 0.3219\n",
      "Epoch 61/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3009 - val_loss: 0.3325\n",
      "Epoch 62/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.2991 - val_loss: 0.3526\n",
      "Epoch 63/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.2992 - val_loss: 0.3353\n",
      "Epoch 64/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.2976 - val_loss: 0.3615\n",
      "Epoch 65/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.2985 - val_loss: 0.3021\n",
      "Epoch 66/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.2978 - val_loss: 0.3252\n",
      "Epoch 67/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.2966 - val_loss: 0.3407\n",
      "Epoch 68/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.2957 - val_loss: 0.3253\n",
      "Epoch 69/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.2948 - val_loss: 0.3187\n",
      "121/121 [==============================] - 0s 2ms/step - loss: 0.3312\n",
      "[CV] END learning_rate=0.0045455096956331, n_hidden=3, n_neurons=30; total time= 2.3min\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 1.2309 - val_loss: 2.8773\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.5650 - val_loss: 0.5020\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4827 - val_loss: 0.7349\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4478 - val_loss: 1.0724\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4248 - val_loss: 1.0879\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.4093 - val_loss: 0.8086\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3990 - val_loss: 0.5907\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3893 - val_loss: 0.4739\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3802 - val_loss: 0.3718\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3744 - val_loss: 0.3628\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3678 - val_loss: 0.4467\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3630 - val_loss: 0.4464\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3596 - val_loss: 0.5476\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3584 - val_loss: 0.7529\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3540 - val_loss: 0.6840\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3507 - val_loss: 0.7743\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3488 - val_loss: 0.7795\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3464 - val_loss: 1.0397\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3435 - val_loss: 0.8678\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.3410 - val_loss: 0.7270\n",
      "121/121 [==============================] - 0s 3ms/step - loss: 0.3635\n",
      "[CV] END learning_rate=0.0045455096956331, n_hidden=3, n_neurons=30; total time=  39.3s\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 3s 9ms/step - loss: 0.9754 - val_loss: 0.8340\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.5384 - val_loss: 0.7678\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4795 - val_loss: 0.8098\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.4481 - val_loss: 1.5087\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.4293 - val_loss: 2.1847\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4257 - val_loss: 0.8566\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4068 - val_loss: 0.4681\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3928 - val_loss: 0.3809\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3829 - val_loss: 0.4330\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3795 - val_loss: 0.3550\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3735 - val_loss: 0.5010\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3716 - val_loss: 0.3517\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3657 - val_loss: 0.3545\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3623 - val_loss: 0.4029\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3615 - val_loss: 0.3467\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3581 - val_loss: 0.3886\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3570 - val_loss: 0.3433\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3529 - val_loss: 0.3925\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3517 - val_loss: 0.4241\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3488 - val_loss: 0.3565\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3474 - val_loss: 0.4056\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3511 - val_loss: 0.3515\n",
      "Epoch 23/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3487 - val_loss: 0.3318\n",
      "Epoch 24/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3452 - val_loss: 0.4333\n",
      "Epoch 25/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.3455 - val_loss: 0.3794\n",
      "Epoch 26/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3424 - val_loss: 0.4637\n",
      "Epoch 27/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3393 - val_loss: 0.3296\n",
      "Epoch 28/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3370 - val_loss: 0.3660\n",
      "Epoch 29/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3393 - val_loss: 0.7073\n",
      "Epoch 30/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3391 - val_loss: 0.3253\n",
      "Epoch 31/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3333 - val_loss: 0.3576\n",
      "Epoch 32/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3318 - val_loss: 0.4386\n",
      "Epoch 33/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.3319 - val_loss: 0.3314\n",
      "Epoch 34/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3282 - val_loss: 0.4947\n",
      "Epoch 35/100\n",
      "242/242 [==============================] - 3s 10ms/step - loss: 0.3333 - val_loss: 0.3207\n",
      "Epoch 36/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3287 - val_loss: 0.3998\n",
      "Epoch 37/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3290 - val_loss: 0.3178\n",
      "Epoch 38/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3263 - val_loss: 0.5715\n",
      "Epoch 39/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3266 - val_loss: 0.3271\n",
      "Epoch 40/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3238 - val_loss: 0.7325\n",
      "Epoch 41/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3267 - val_loss: 0.3248\n",
      "Epoch 42/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3229 - val_loss: 0.3815\n",
      "Epoch 43/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3208 - val_loss: 0.3128\n",
      "Epoch 44/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3196 - val_loss: 0.3648\n",
      "Epoch 45/100\n",
      "242/242 [==============================] - 4s 15ms/step - loss: 0.3207 - val_loss: 0.3366\n",
      "Epoch 46/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.3181 - val_loss: 0.3440\n",
      "Epoch 47/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3169 - val_loss: 0.3234\n",
      "Epoch 48/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3174 - val_loss: 0.3083\n",
      "Epoch 49/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3156 - val_loss: 0.3959\n",
      "Epoch 50/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3157 - val_loss: 0.3999\n",
      "Epoch 51/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3143 - val_loss: 0.4352\n",
      "Epoch 52/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3141 - val_loss: 0.3702\n",
      "Epoch 53/100\n",
      "242/242 [==============================] - 4s 15ms/step - loss: 0.3152 - val_loss: 0.3163\n",
      "Epoch 54/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3141 - val_loss: 0.3389\n",
      "Epoch 55/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3121 - val_loss: 0.3164\n",
      "Epoch 56/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3093 - val_loss: 0.3637\n",
      "Epoch 57/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3107 - val_loss: 0.4732\n",
      "Epoch 58/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3117 - val_loss: 0.3115\n",
      "121/121 [==============================] - 2s 12ms/step - loss: 0.3192\n",
      "[CV] END learning_rate=0.0045455096956331, n_hidden=3, n_neurons=30; total time= 2.1min\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 1.9448 - val_loss: 0.7311\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.6639 - val_loss: 0.7039\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.5911 - val_loss: 0.5551\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.5521 - val_loss: 0.6733\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.5179 - val_loss: 0.5167\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4953 - val_loss: 0.5452\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4741 - val_loss: 0.5280\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.4636 - val_loss: 0.6552\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 4s 17ms/step - loss: 0.4513 - val_loss: 0.4189\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4421 - val_loss: 0.4828\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.4349 - val_loss: 0.4614\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4300 - val_loss: 0.5033\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4244 - val_loss: 0.4161\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.4198 - val_loss: 0.5685\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4166 - val_loss: 0.3950\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4116 - val_loss: 0.4747\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.4088 - val_loss: 0.3841\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.4051 - val_loss: 0.4232\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4022 - val_loss: 0.3940\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3996 - val_loss: 0.4078\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3974 - val_loss: 0.3909\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3949 - val_loss: 0.3997\n",
      "Epoch 23/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3929 - val_loss: 0.3896\n",
      "Epoch 24/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3911 - val_loss: 0.3782\n",
      "Epoch 25/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3889 - val_loss: 0.5110\n",
      "Epoch 26/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3889 - val_loss: 0.4920\n",
      "Epoch 27/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.3874 - val_loss: 0.8698\n",
      "Epoch 28/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3900 - val_loss: 0.5592\n",
      "Epoch 29/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3847 - val_loss: 1.0511\n",
      "Epoch 30/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3889 - val_loss: 0.9833\n",
      "Epoch 31/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3867 - val_loss: 1.0876\n",
      "Epoch 32/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.3863 - val_loss: 1.2803\n",
      "Epoch 33/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3888 - val_loss: 2.0508\n",
      "Epoch 34/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3983 - val_loss: 1.3729\n",
      "121/121 [==============================] - 0s 2ms/step - loss: 0.3894\n",
      "[CV] END learning_rate=0.0020587676114196545, n_hidden=1, n_neurons=49; total time= 1.2min\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 1.4869 - val_loss: 12.8439\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.7094 - val_loss: 5.3180\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.6373 - val_loss: 2.1120\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.5892 - val_loss: 0.8069\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.5524 - val_loss: 0.5118\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.5248 - val_loss: 0.5567\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.5036 - val_loss: 0.6750\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.4861 - val_loss: 0.8354\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.4722 - val_loss: 0.8133\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.4621 - val_loss: 0.8597\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4529 - val_loss: 0.8248\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4451 - val_loss: 0.9107\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4394 - val_loss: 0.8766\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.4342 - val_loss: 0.7172\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.4292 - val_loss: 0.6986\n",
      "121/121 [==============================] - 0s 3ms/step - loss: 0.4341\n",
      "[CV] END learning_rate=0.0020587676114196545, n_hidden=1, n_neurons=49; total time=  32.8s\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 3s 9ms/step - loss: 2.1696 - val_loss: 42.1422\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 1.1120 - val_loss: 13.2473\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.7720 - val_loss: 0.9727\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.6217 - val_loss: 0.5579\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.5841 - val_loss: 0.5348\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.5574 - val_loss: 0.5062\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.5338 - val_loss: 0.4865\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.5138 - val_loss: 0.4922\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 5s 20ms/step - loss: 0.4972 - val_loss: 0.4582\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 4s 18ms/step - loss: 0.4831 - val_loss: 0.4385\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.4700 - val_loss: 0.4303\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4594 - val_loss: 0.4189\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4509 - val_loss: 0.4110\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4433 - val_loss: 0.4168\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.4369 - val_loss: 0.3981\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.4311 - val_loss: 0.4128\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.4265 - val_loss: 0.3906\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4226 - val_loss: 0.3863\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.4188 - val_loss: 0.3914\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.4157 - val_loss: 0.3837\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4130 - val_loss: 0.3799\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4102 - val_loss: 0.3761\n",
      "Epoch 23/100\n",
      "242/242 [==============================] - 3s 14ms/step - loss: 0.4077 - val_loss: 0.3755\n",
      "Epoch 24/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.4052 - val_loss: 0.3909\n",
      "Epoch 25/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4035 - val_loss: 0.3851\n",
      "Epoch 26/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4007 - val_loss: 0.3722\n",
      "Epoch 27/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3984 - val_loss: 0.4052\n",
      "Epoch 28/100\n",
      "242/242 [==============================] - 4s 17ms/step - loss: 0.3975 - val_loss: 0.3769\n",
      "Epoch 29/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3947 - val_loss: 0.4152\n",
      "Epoch 30/100\n",
      "242/242 [==============================] - 5s 20ms/step - loss: 0.3946 - val_loss: 0.3635\n",
      "Epoch 31/100\n",
      "242/242 [==============================] - 5s 20ms/step - loss: 0.3925 - val_loss: 0.3644\n",
      "Epoch 32/100\n",
      "242/242 [==============================] - 4s 15ms/step - loss: 0.3909 - val_loss: 0.3871\n",
      "Epoch 33/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3901 - val_loss: 0.3697\n",
      "Epoch 34/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3884 - val_loss: 0.3895\n",
      "Epoch 35/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3883 - val_loss: 0.3591\n",
      "Epoch 36/100\n",
      "242/242 [==============================] - 4s 15ms/step - loss: 0.3863 - val_loss: 0.3579\n",
      "Epoch 37/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3858 - val_loss: 0.3822\n",
      "Epoch 38/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.3843 - val_loss: 0.4142\n",
      "Epoch 39/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3848 - val_loss: 0.3687\n",
      "Epoch 40/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3825 - val_loss: 0.3673\n",
      "Epoch 41/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3815 - val_loss: 0.3856\n",
      "Epoch 42/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.3812 - val_loss: 0.3607\n",
      "Epoch 43/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3801 - val_loss: 0.3887\n",
      "Epoch 44/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3793 - val_loss: 0.3829\n",
      "Epoch 45/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3790 - val_loss: 0.3542\n",
      "Epoch 46/100\n",
      "242/242 [==============================] - 4s 17ms/step - loss: 0.3777 - val_loss: 0.3669\n",
      "Epoch 47/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3771 - val_loss: 0.4079\n",
      "Epoch 48/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3767 - val_loss: 0.3530\n",
      "Epoch 49/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3752 - val_loss: 0.3737\n",
      "Epoch 50/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3751 - val_loss: 0.3586\n",
      "Epoch 51/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.3739 - val_loss: 0.3665\n",
      "Epoch 52/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3737 - val_loss: 0.3606\n",
      "Epoch 53/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3734 - val_loss: 0.3620\n",
      "Epoch 54/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3723 - val_loss: 0.3612\n",
      "Epoch 55/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3715 - val_loss: 0.3529\n",
      "Epoch 56/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3707 - val_loss: 0.3475\n",
      "Epoch 57/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.3701 - val_loss: 0.3630\n",
      "Epoch 58/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3701 - val_loss: 0.3493\n",
      "Epoch 59/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.3691 - val_loss: 0.3496\n",
      "Epoch 60/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3687 - val_loss: 0.3497\n",
      "Epoch 61/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3680 - val_loss: 0.3455\n",
      "Epoch 62/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.3669 - val_loss: 0.3745\n",
      "Epoch 63/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3676 - val_loss: 0.3794\n",
      "Epoch 64/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3664 - val_loss: 0.3441\n",
      "Epoch 65/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3660 - val_loss: 0.3828\n",
      "Epoch 66/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3651 - val_loss: 0.3675\n",
      "Epoch 67/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3654 - val_loss: 0.3840\n",
      "Epoch 68/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3643 - val_loss: 0.4003\n",
      "Epoch 69/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3647 - val_loss: 0.4384\n",
      "Epoch 70/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.3635 - val_loss: 0.4382\n",
      "Epoch 71/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3644 - val_loss: 0.4074\n",
      "Epoch 72/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.3625 - val_loss: 0.3898\n",
      "Epoch 73/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3626 - val_loss: 0.4023\n",
      "Epoch 74/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.3618 - val_loss: 0.3408\n",
      "Epoch 75/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3611 - val_loss: 0.3412\n",
      "Epoch 76/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3601 - val_loss: 0.3712\n",
      "Epoch 77/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3609 - val_loss: 0.3424\n",
      "Epoch 78/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3596 - val_loss: 0.3405\n",
      "Epoch 79/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3594 - val_loss: 0.3611\n",
      "Epoch 80/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3589 - val_loss: 0.3399\n",
      "Epoch 81/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3589 - val_loss: 0.3433\n",
      "Epoch 82/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3582 - val_loss: 0.3546\n",
      "Epoch 83/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3584 - val_loss: 0.3501\n",
      "Epoch 84/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3574 - val_loss: 0.3944\n",
      "Epoch 85/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3581 - val_loss: 0.4586\n",
      "Epoch 86/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3576 - val_loss: 0.3592\n",
      "Epoch 87/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3568 - val_loss: 0.3541\n",
      "Epoch 88/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.3568 - val_loss: 0.3385\n",
      "Epoch 89/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3559 - val_loss: 0.3564\n",
      "Epoch 90/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3561 - val_loss: 0.3715\n",
      "Epoch 91/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3549 - val_loss: 0.3988\n",
      "Epoch 92/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3566 - val_loss: 0.4844\n",
      "Epoch 93/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3558 - val_loss: 0.5300\n",
      "Epoch 94/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3574 - val_loss: 0.4124\n",
      "Epoch 95/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3542 - val_loss: 0.4017\n",
      "Epoch 96/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3549 - val_loss: 0.3350\n",
      "Epoch 97/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.3534 - val_loss: 0.3870\n",
      "Epoch 98/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3542 - val_loss: 0.4703\n",
      "Epoch 99/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3533 - val_loss: 0.5565\n",
      "Epoch 100/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.3565 - val_loss: 0.3479\n",
      "121/121 [==============================] - 1s 4ms/step - loss: 0.3538\n",
      "[CV] END learning_rate=0.0020587676114196545, n_hidden=1, n_neurons=49; total time= 3.8min\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 4s 13ms/step - loss: 1.0427 - val_loss: 2.9065\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 4s 15ms/step - loss: 0.5574 - val_loss: 1.7862\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.4588 - val_loss: 0.5445\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4064 - val_loss: 0.4086\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3812 - val_loss: 0.3591\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3670 - val_loss: 0.4108\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.3588 - val_loss: 0.3473\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3506 - val_loss: 0.4023\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.3451 - val_loss: 0.3607\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3405 - val_loss: 0.4042\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3379 - val_loss: 0.3292\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 3s 10ms/step - loss: 0.3329 - val_loss: 0.3477\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3285 - val_loss: 0.3614\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.3269 - val_loss: 0.3431\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3234 - val_loss: 0.3442\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3211 - val_loss: 0.3264\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3180 - val_loss: 0.3703\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3172 - val_loss: 0.3146\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3129 - val_loss: 0.4094\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3135 - val_loss: 0.3171\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3098 - val_loss: 0.4948\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3116 - val_loss: 0.4033\n",
      "Epoch 23/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3076 - val_loss: 0.7532\n",
      "Epoch 24/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3147 - val_loss: 1.0185\n",
      "Epoch 25/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.3126 - val_loss: 0.6888\n",
      "Epoch 26/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3048 - val_loss: 0.3128\n",
      "Epoch 27/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.3010 - val_loss: 0.3345\n",
      "Epoch 28/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.2997 - val_loss: 0.3259\n",
      "Epoch 29/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.2970 - val_loss: 0.3265\n",
      "Epoch 30/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.2949 - val_loss: 0.2960\n",
      "Epoch 31/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.2942 - val_loss: 0.3021\n",
      "Epoch 32/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.2929 - val_loss: 0.2896\n",
      "Epoch 33/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.2906 - val_loss: 0.3174\n",
      "Epoch 34/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.2895 - val_loss: 0.3137\n",
      "Epoch 35/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.2883 - val_loss: 0.2994\n",
      "Epoch 36/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.2873 - val_loss: 0.3664\n",
      "Epoch 37/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.2868 - val_loss: 0.3133\n",
      "Epoch 38/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.2852 - val_loss: 0.3802\n",
      "Epoch 39/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.2848 - val_loss: 0.3128\n",
      "Epoch 40/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.2836 - val_loss: 0.3539\n",
      "Epoch 41/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.2821 - val_loss: 0.2902\n",
      "Epoch 42/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.2805 - val_loss: 0.2935\n",
      "121/121 [==============================] - 1s 4ms/step - loss: 0.3228\n",
      "[CV] END learning_rate=0.005803602934201024, n_hidden=3, n_neurons=74; total time= 1.5min\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 4s 14ms/step - loss: 0.9177 - val_loss: 0.5562\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.5108 - val_loss: 0.7574\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.4333 - val_loss: 0.6081\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4009 - val_loss: 0.4654\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.3827 - val_loss: 0.3767\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3713 - val_loss: 0.3746\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3635 - val_loss: 0.5734\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3571 - val_loss: 0.6707\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3503 - val_loss: 0.8474\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3465 - val_loss: 0.6807\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3406 - val_loss: 0.8238\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3365 - val_loss: 0.8254\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3335 - val_loss: 1.0662\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3315 - val_loss: 0.9752\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3288 - val_loss: 0.9476\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3245 - val_loss: 0.9826\n",
      "121/121 [==============================] - 1s 6ms/step - loss: 0.3514\n",
      "[CV] END learning_rate=0.005803602934201024, n_hidden=3, n_neurons=74; total time=  37.0s\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.9632 - val_loss: 2.4288\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 3s 14ms/step - loss: 0.4794 - val_loss: 0.7758\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.4145 - val_loss: 0.4465\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3916 - val_loss: 0.6534\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3830 - val_loss: 0.4336\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3706 - val_loss: 0.4457\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3645 - val_loss: 0.3433\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3580 - val_loss: 0.3499\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 3s 10ms/step - loss: 0.3525 - val_loss: 0.4071\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3519 - val_loss: 0.3477\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.3482 - val_loss: 0.4806\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.3437 - val_loss: 0.3257\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3384 - val_loss: 0.3353\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3352 - val_loss: 0.4094\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3339 - val_loss: 0.3337\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.3300 - val_loss: 0.3945\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3305 - val_loss: 0.3633\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3259 - val_loss: 0.3905\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3243 - val_loss: 0.3305\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3211 - val_loss: 0.3802\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3211 - val_loss: 0.3170\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3182 - val_loss: 0.3226\n",
      "Epoch 23/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3154 - val_loss: 0.3686\n",
      "Epoch 24/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3156 - val_loss: 0.3405\n",
      "Epoch 25/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3143 - val_loss: 0.4179\n",
      "Epoch 26/100\n",
      "242/242 [==============================] - 4s 15ms/step - loss: 0.3115 - val_loss: 0.3612\n",
      "Epoch 27/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3085 - val_loss: 0.3207\n",
      "Epoch 28/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3087 - val_loss: 0.3372\n",
      "Epoch 29/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.3135 - val_loss: 0.4846\n",
      "Epoch 30/100\n",
      "242/242 [==============================] - 3s 14ms/step - loss: 0.3083 - val_loss: 0.3199\n",
      "Epoch 31/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3031 - val_loss: 0.3183\n",
      "121/121 [==============================] - 0s 3ms/step - loss: 0.3080\n",
      "[CV] END learning_rate=0.005803602934201024, n_hidden=3, n_neurons=74; total time= 1.3min\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 1.1280 - val_loss: 1.3668\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.5643 - val_loss: 3.9403\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.4758 - val_loss: 0.6458\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.4028 - val_loss: 0.4426\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3793 - val_loss: 0.4744\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3652 - val_loss: 0.4602\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.3551 - val_loss: 0.3401\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3456 - val_loss: 0.4227\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3414 - val_loss: 0.3413\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3364 - val_loss: 0.3806\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3341 - val_loss: 0.3247\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3286 - val_loss: 0.3400\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.3239 - val_loss: 0.3720\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3237 - val_loss: 0.3164\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3184 - val_loss: 0.4322\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3188 - val_loss: 0.3102\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3142 - val_loss: 0.5458\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3181 - val_loss: 0.4167\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3124 - val_loss: 0.4961\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.3110 - val_loss: 0.2993\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.3070 - val_loss: 0.3516\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3059 - val_loss: 0.3247\n",
      "Epoch 23/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3032 - val_loss: 0.3694\n",
      "Epoch 24/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3059 - val_loss: 0.4233\n",
      "Epoch 25/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3024 - val_loss: 0.3270\n",
      "Epoch 26/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.2980 - val_loss: 0.3028\n",
      "Epoch 27/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.2976 - val_loss: 0.3348\n",
      "Epoch 28/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.2962 - val_loss: 0.3214\n",
      "Epoch 29/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.2940 - val_loss: 0.3153\n",
      "Epoch 30/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.2916 - val_loss: 0.3040\n",
      "121/121 [==============================] - 0s 2ms/step - loss: 0.3267\n",
      "[CV] END learning_rate=0.0059640580092043885, n_hidden=3, n_neurons=80; total time= 1.1min\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 4s 16ms/step - loss: 0.8925 - val_loss: 0.5188\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 4s 16ms/step - loss: 0.4643 - val_loss: 0.7456\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.4123 - val_loss: 0.4618\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3921 - val_loss: 0.3700\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3776 - val_loss: 0.3684\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3676 - val_loss: 0.4659\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.3609 - val_loss: 0.6835\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3545 - val_loss: 0.6935\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3483 - val_loss: 0.7996\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3450 - val_loss: 0.6674\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 4s 15ms/step - loss: 0.3392 - val_loss: 0.7075\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3357 - val_loss: 0.6762\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3328 - val_loss: 0.7522\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3313 - val_loss: 0.6900\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3278 - val_loss: 0.6394\n",
      "121/121 [==============================] - 1s 5ms/step - loss: 0.3430\n",
      "[CV] END learning_rate=0.0059640580092043885, n_hidden=3, n_neurons=80; total time=  44.1s\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 4s 13ms/step - loss: 0.9063 - val_loss: 0.7589\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 4s 15ms/step - loss: 0.5042 - val_loss: 2.5707\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.4590 - val_loss: 7.1565\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.5086 - val_loss: 1.5402\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.4018 - val_loss: 0.5350\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 4s 15ms/step - loss: 0.3815 - val_loss: 0.5311\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3742 - val_loss: 0.3465\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 3s 14ms/step - loss: 0.3666 - val_loss: 0.3831\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3597 - val_loss: 0.3825\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.3579 - val_loss: 0.3517\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 4s 15ms/step - loss: 0.3509 - val_loss: 0.5189\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3484 - val_loss: 0.3301\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 3s 14ms/step - loss: 0.3431 - val_loss: 0.3343\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3396 - val_loss: 0.5207\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3395 - val_loss: 0.3552\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.3346 - val_loss: 0.4558\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3326 - val_loss: 0.3233\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 4s 15ms/step - loss: 0.3287 - val_loss: 0.3456\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3266 - val_loss: 0.3569\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.3251 - val_loss: 0.3506\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3226 - val_loss: 0.3323\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 4s 15ms/step - loss: 0.3208 - val_loss: 0.3086\n",
      "Epoch 23/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3176 - val_loss: 0.3446\n",
      "Epoch 24/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3156 - val_loss: 0.3574\n",
      "Epoch 25/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3138 - val_loss: 0.3072\n",
      "Epoch 26/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3109 - val_loss: 0.3238\n",
      "Epoch 27/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3084 - val_loss: 0.3421\n",
      "Epoch 28/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3084 - val_loss: 0.3149\n",
      "Epoch 29/100\n",
      "242/242 [==============================] - 4s 16ms/step - loss: 0.3070 - val_loss: 0.4420\n",
      "Epoch 30/100\n",
      "242/242 [==============================] - 4s 18ms/step - loss: 0.3067 - val_loss: 0.3001\n",
      "Epoch 31/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3027 - val_loss: 0.3510\n",
      "Epoch 32/100\n",
      "242/242 [==============================] - 3s 14ms/step - loss: 0.3019 - val_loss: 0.2937\n",
      "Epoch 33/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3009 - val_loss: 0.4259\n",
      "Epoch 34/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3000 - val_loss: 0.2977\n",
      "Epoch 35/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.2965 - val_loss: 0.3295\n",
      "Epoch 36/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.2944 - val_loss: 0.2987\n",
      "Epoch 37/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.2929 - val_loss: 0.3132\n",
      "Epoch 38/100\n",
      "242/242 [==============================] - 3s 14ms/step - loss: 0.2932 - val_loss: 0.5153\n",
      "Epoch 39/100\n",
      "242/242 [==============================] - 3s 14ms/step - loss: 0.2955 - val_loss: 0.4537\n",
      "Epoch 40/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.2945 - val_loss: 0.8146\n",
      "Epoch 41/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.2960 - val_loss: 0.3314\n",
      "Epoch 42/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.2891 - val_loss: 0.7497\n",
      "121/121 [==============================] - 1s 9ms/step - loss: 0.3044\n",
      "[CV] END learning_rate=0.0059640580092043885, n_hidden=3, n_neurons=80; total time= 2.0min\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 4s 13ms/step - loss: 1.2510 - val_loss: 3.9644\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.6111 - val_loss: 1.0932\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.5036 - val_loss: 0.5091\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.4557 - val_loss: 0.4384\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 3s 14ms/step - loss: 0.4283 - val_loss: 0.4173\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.4103 - val_loss: 0.4449\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3974 - val_loss: 0.4098\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3869 - val_loss: 0.4428\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3793 - val_loss: 0.4306\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3726 - val_loss: 0.4178\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3673 - val_loss: 0.3822\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3620 - val_loss: 0.3747\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3578 - val_loss: 0.4041\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3551 - val_loss: 0.4419\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.3517 - val_loss: 0.3760\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3491 - val_loss: 0.4203\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3466 - val_loss: 0.4179\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3446 - val_loss: 0.3833\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.3417 - val_loss: 0.4121\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3408 - val_loss: 0.4005\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3386 - val_loss: 0.4128\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3374 - val_loss: 0.3904\n",
      "121/121 [==============================] - 1s 6ms/step - loss: 0.3613\n",
      "[CV] END learning_rate=0.004591455636549438, n_hidden=2, n_neurons=59; total time=  49.9s\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 2s 5ms/step - loss: 1.0973 - val_loss: 0.6710\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.5562 - val_loss: 0.5969\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4770 - val_loss: 0.7528\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.4404 - val_loss: 0.7686\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.4187 - val_loss: 0.6764\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.4058 - val_loss: 0.4761\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3978 - val_loss: 0.3824\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3882 - val_loss: 0.3632\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3806 - val_loss: 0.4186\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3763 - val_loss: 0.4398\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3704 - val_loss: 0.5476\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3657 - val_loss: 0.5758\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3631 - val_loss: 0.6739\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3613 - val_loss: 0.8367\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3575 - val_loss: 0.8594\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3546 - val_loss: 0.9828\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3525 - val_loss: 0.9695\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3503 - val_loss: 1.1329\n",
      "121/121 [==============================] - 1s 11ms/step - loss: 0.3852\n",
      "[CV] END learning_rate=0.004591455636549438, n_hidden=2, n_neurons=59; total time=  41.9s\n",
      "Epoch 1/100\n",
      "242/242 [==============================] - 4s 13ms/step - loss: 1.1544 - val_loss: 3.1484\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.5940 - val_loss: 4.8718\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.5221 - val_loss: 3.5098\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 1s 3ms/step - loss: 0.4960 - val_loss: 0.4342\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.4236 - val_loss: 0.4076\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.4071 - val_loss: 0.4314\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3951 - val_loss: 0.3989\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3873 - val_loss: 0.3942\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3806 - val_loss: 0.4138\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 3s 10ms/step - loss: 0.3755 - val_loss: 0.3588\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3721 - val_loss: 0.4190\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.3668 - val_loss: 0.3779\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3623 - val_loss: 0.3550\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3607 - val_loss: 0.4124\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3580 - val_loss: 0.3474\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3546 - val_loss: 0.3811\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3527 - val_loss: 0.3407\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3501 - val_loss: 0.3615\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3487 - val_loss: 0.3806\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.3464 - val_loss: 0.3489\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 1s 5ms/step - loss: 0.3453 - val_loss: 0.3450\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3446 - val_loss: 0.3296\n",
      "Epoch 23/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3424 - val_loss: 0.3427\n",
      "Epoch 24/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3408 - val_loss: 0.3651\n",
      "Epoch 25/100\n",
      "242/242 [==============================] - 2s 6ms/step - loss: 0.3399 - val_loss: 0.3221\n",
      "Epoch 26/100\n",
      "242/242 [==============================] - 3s 14ms/step - loss: 0.3373 - val_loss: 0.3348\n",
      "Epoch 27/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3360 - val_loss: 0.3813\n",
      "Epoch 28/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3360 - val_loss: 0.3244\n",
      "Epoch 29/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3381 - val_loss: 0.3788\n",
      "Epoch 30/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3360 - val_loss: 0.3498\n",
      "Epoch 31/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3317 - val_loss: 0.3359\n",
      "Epoch 32/100\n",
      "242/242 [==============================] - 4s 17ms/step - loss: 0.3304 - val_loss: 0.3671\n",
      "Epoch 33/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3299 - val_loss: 0.3462\n",
      "Epoch 34/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3285 - val_loss: 0.3506\n",
      "Epoch 35/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3294 - val_loss: 0.3203\n",
      "Epoch 36/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3263 - val_loss: 0.3446\n",
      "Epoch 37/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3263 - val_loss: 0.3215\n",
      "Epoch 38/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3262 - val_loss: 0.4012\n",
      "Epoch 39/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3245 - val_loss: 0.3121\n",
      "Epoch 40/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3235 - val_loss: 0.3581\n",
      "Epoch 41/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3217 - val_loss: 0.3507\n",
      "Epoch 42/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3211 - val_loss: 0.3496\n",
      "Epoch 43/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3214 - val_loss: 0.3116\n",
      "Epoch 44/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3195 - val_loss: 0.3441\n",
      "Epoch 45/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3217 - val_loss: 0.3108\n",
      "Epoch 46/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3196 - val_loss: 0.3616\n",
      "Epoch 47/100\n",
      "242/242 [==============================] - 3s 14ms/step - loss: 0.3168 - val_loss: 0.3073\n",
      "Epoch 48/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3175 - val_loss: 0.3128\n",
      "Epoch 49/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3149 - val_loss: 0.3821\n",
      "Epoch 50/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3157 - val_loss: 0.3094\n",
      "Epoch 51/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3136 - val_loss: 0.3730\n",
      "Epoch 52/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3136 - val_loss: 0.3223\n",
      "Epoch 53/100\n",
      "242/242 [==============================] - 3s 10ms/step - loss: 0.3128 - val_loss: 0.3051\n",
      "Epoch 54/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3122 - val_loss: 0.3090\n",
      "Epoch 55/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3128 - val_loss: 0.3051\n",
      "Epoch 56/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3109 - val_loss: 0.3394\n",
      "Epoch 57/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.3106 - val_loss: 0.3370\n",
      "Epoch 58/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3108 - val_loss: 0.3115\n",
      "Epoch 59/100\n",
      "242/242 [==============================] - 4s 16ms/step - loss: 0.3082 - val_loss: 0.3532\n",
      "Epoch 60/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3100 - val_loss: 0.3012\n",
      "Epoch 61/100\n",
      "242/242 [==============================] - 3s 14ms/step - loss: 0.3080 - val_loss: 0.3375\n",
      "Epoch 62/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3059 - val_loss: 0.3369\n",
      "Epoch 63/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3075 - val_loss: 0.3000\n",
      "Epoch 64/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3065 - val_loss: 0.3422\n",
      "Epoch 65/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.3047 - val_loss: 0.3065\n",
      "Epoch 66/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3042 - val_loss: 0.4081\n",
      "Epoch 67/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3049 - val_loss: 0.3456\n",
      "Epoch 68/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.3029 - val_loss: 0.4125\n",
      "Epoch 69/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3026 - val_loss: 0.3253\n",
      "Epoch 70/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.3014 - val_loss: 0.4496\n",
      "Epoch 71/100\n",
      "242/242 [==============================] - 3s 14ms/step - loss: 0.3027 - val_loss: 0.3151\n",
      "Epoch 72/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3021 - val_loss: 0.3546\n",
      "Epoch 73/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.3013 - val_loss: 0.2986\n",
      "Epoch 74/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.3013 - val_loss: 0.3231\n",
      "Epoch 75/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.3000 - val_loss: 0.3149\n",
      "Epoch 76/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.2966 - val_loss: 0.4797\n",
      "Epoch 77/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.3005 - val_loss: 0.3079\n",
      "Epoch 78/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.2972 - val_loss: 0.3509\n",
      "Epoch 79/100\n",
      "242/242 [==============================] - 3s 14ms/step - loss: 0.2969 - val_loss: 0.3126\n",
      "Epoch 80/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.2950 - val_loss: 0.3453\n",
      "Epoch 81/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.2956 - val_loss: 0.3178\n",
      "Epoch 82/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.2960 - val_loss: 0.3791\n",
      "Epoch 83/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.2946 - val_loss: 0.2925\n",
      "Epoch 84/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.2953 - val_loss: 0.3611\n",
      "Epoch 85/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.2991 - val_loss: 0.3097\n",
      "Epoch 86/100\n",
      "242/242 [==============================] - 1s 4ms/step - loss: 0.2942 - val_loss: 0.3952\n",
      "Epoch 87/100\n",
      "242/242 [==============================] - 2s 9ms/step - loss: 0.3025 - val_loss: 0.3107\n",
      "Epoch 88/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.2968 - val_loss: 0.2872\n",
      "Epoch 89/100\n",
      "242/242 [==============================] - 2s 8ms/step - loss: 0.2946 - val_loss: 0.4096\n",
      "Epoch 90/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.2923 - val_loss: 0.3039\n",
      "Epoch 91/100\n",
      "242/242 [==============================] - 3s 11ms/step - loss: 0.2940 - val_loss: 0.5187\n",
      "Epoch 92/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.2940 - val_loss: 0.4224\n",
      "Epoch 93/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.2926 - val_loss: 0.5642\n",
      "Epoch 94/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.2922 - val_loss: 0.2901\n",
      "Epoch 95/100\n",
      "242/242 [==============================] - 3s 13ms/step - loss: 0.2885 - val_loss: 0.3489\n",
      "Epoch 96/100\n",
      "242/242 [==============================] - 2s 7ms/step - loss: 0.2889 - val_loss: 0.3208\n",
      "Epoch 97/100\n",
      "242/242 [==============================] - 1s 6ms/step - loss: 0.2956 - val_loss: 0.3187\n",
      "Epoch 98/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.2895 - val_loss: 0.2868\n",
      "Epoch 99/100\n",
      "242/242 [==============================] - 3s 12ms/step - loss: 0.2873 - val_loss: 0.3595\n",
      "Epoch 100/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.2882 - val_loss: 0.3206\n",
      "121/121 [==============================] - 1s 7ms/step - loss: 0.2996\n",
      "[CV] END learning_rate=0.004591455636549438, n_hidden=2, n_neurons=59; total time= 3.9min\n",
      "Epoch 1/100\n",
      "363/363 [==============================] - 5s 13ms/step - loss: 0.7246 - val_loss: 1.4682\n",
      "Epoch 2/100\n",
      "363/363 [==============================] - 3s 8ms/step - loss: 0.4361 - val_loss: 1.4198\n",
      "Epoch 3/100\n",
      "363/363 [==============================] - 3s 9ms/step - loss: 0.4000 - val_loss: 0.3654\n",
      "Epoch 4/100\n",
      "363/363 [==============================] - 3s 9ms/step - loss: 0.3735 - val_loss: 0.4364\n",
      "Epoch 5/100\n",
      "363/363 [==============================] - 3s 9ms/step - loss: 0.3627 - val_loss: 0.3614\n",
      "Epoch 6/100\n",
      "363/363 [==============================] - 5s 13ms/step - loss: 0.3528 - val_loss: 0.6328\n",
      "Epoch 7/100\n",
      "363/363 [==============================] - 3s 9ms/step - loss: 0.3522 - val_loss: 0.3884\n",
      "Epoch 8/100\n",
      "363/363 [==============================] - 2s 6ms/step - loss: 0.3426 - val_loss: 0.4310\n",
      "Epoch 9/100\n",
      "363/363 [==============================] - 4s 12ms/step - loss: 0.3362 - val_loss: 0.3162\n",
      "Epoch 10/100\n",
      "363/363 [==============================] - 4s 10ms/step - loss: 0.3312 - val_loss: 0.4683\n",
      "Epoch 11/100\n",
      "363/363 [==============================] - 3s 9ms/step - loss: 0.3291 - val_loss: 0.4040\n",
      "Epoch 12/100\n",
      "363/363 [==============================] - 3s 7ms/step - loss: 0.3262 - val_loss: 0.6304\n",
      "Epoch 13/100\n",
      "363/363 [==============================] - 3s 10ms/step - loss: 0.3221 - val_loss: 0.3030\n",
      "Epoch 14/100\n",
      "363/363 [==============================] - 4s 10ms/step - loss: 0.3169 - val_loss: 0.3102\n",
      "Epoch 15/100\n",
      "363/363 [==============================] - 3s 8ms/step - loss: 0.3143 - val_loss: 0.3054\n",
      "Epoch 16/100\n",
      "363/363 [==============================] - 3s 8ms/step - loss: 0.3113 - val_loss: 0.2987\n",
      "Epoch 17/100\n",
      "363/363 [==============================] - 3s 9ms/step - loss: 0.3087 - val_loss: 0.5529\n",
      "Epoch 18/100\n",
      "363/363 [==============================] - 4s 10ms/step - loss: 0.3071 - val_loss: 0.2923\n",
      "Epoch 19/100\n",
      "363/363 [==============================] - 3s 8ms/step - loss: 0.3044 - val_loss: 0.3427\n",
      "Epoch 20/100\n",
      "363/363 [==============================] - 3s 8ms/step - loss: 0.3024 - val_loss: 0.3905\n",
      "Epoch 21/100\n",
      "363/363 [==============================] - 3s 7ms/step - loss: 0.2998 - val_loss: 0.3078\n",
      "Epoch 22/100\n",
      "363/363 [==============================] - 3s 9ms/step - loss: 0.2970 - val_loss: 0.6607\n",
      "Epoch 23/100\n",
      "363/363 [==============================] - 4s 10ms/step - loss: 0.2974 - val_loss: 0.6065\n",
      "Epoch 24/100\n",
      "363/363 [==============================] - 3s 8ms/step - loss: 0.2976 - val_loss: 0.3541\n",
      "Epoch 25/100\n",
      "363/363 [==============================] - 3s 9ms/step - loss: 0.2927 - val_loss: 0.4099\n",
      "Epoch 26/100\n",
      "363/363 [==============================] - 4s 11ms/step - loss: 0.2908 - val_loss: 0.4086\n",
      "Epoch 27/100\n",
      "363/363 [==============================] - 3s 8ms/step - loss: 0.2893 - val_loss: 0.3668\n",
      "Epoch 28/100\n",
      "363/363 [==============================] - 4s 10ms/step - loss: 0.2894 - val_loss: 0.4014\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=3,\n",
       "                   estimator=&lt;keras.wrappers.scikit_learn.KerasRegressor object at 0x0000017131CAE250&gt;,\n",
       "                   param_distributions={&#x27;learning_rate&#x27;: [0.001683454924600351,\n",
       "                                                          0.02390836445593178,\n",
       "                                                          0.008731907739399206,\n",
       "                                                          0.004725396149933917,\n",
       "                                                          0.0006154014789262348,\n",
       "                                                          0.0006153331256530192,\n",
       "                                                          0.0003920021771415983,\n",
       "                                                          0.01619845322936229,\n",
       "                                                          0.004779156784872302,\n",
       "                                                          0.007821074275112...\n",
       "                                                          0.005021425736625637,\n",
       "                                                          0.0005703073595961105,\n",
       "                                                          0.001151888789941251,\n",
       "                                                          0.001621231156394198,\n",
       "                                                          0.0024505367684280487,\n",
       "                                                          0.011155092541719619,\n",
       "                                                          0.0007524347058135697,\n",
       "                                                          0.0032032448128444043,\n",
       "                                                          0.004591455636549438,\n",
       "                                                          0.0003715541189658278, ...],\n",
       "                                        &#x27;n_hidden&#x27;: [0, 1, 2, 3],\n",
       "                                        &#x27;n_neurons&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
       "                                                      10, 11, 12, 13, 14, 15,\n",
       "                                                      16, 17, 18, 19, 20, 21,\n",
       "                                                      22, 23, 24, 25, 26, 27,\n",
       "                                                      28, 29, 30, ...]},\n",
       "                   verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=3,\n",
       "                   estimator=&lt;keras.wrappers.scikit_learn.KerasRegressor object at 0x0000017131CAE250&gt;,\n",
       "                   param_distributions={&#x27;learning_rate&#x27;: [0.001683454924600351,\n",
       "                                                          0.02390836445593178,\n",
       "                                                          0.008731907739399206,\n",
       "                                                          0.004725396149933917,\n",
       "                                                          0.0006154014789262348,\n",
       "                                                          0.0006153331256530192,\n",
       "                                                          0.0003920021771415983,\n",
       "                                                          0.01619845322936229,\n",
       "                                                          0.004779156784872302,\n",
       "                                                          0.007821074275112...\n",
       "                                                          0.005021425736625637,\n",
       "                                                          0.0005703073595961105,\n",
       "                                                          0.001151888789941251,\n",
       "                                                          0.001621231156394198,\n",
       "                                                          0.0024505367684280487,\n",
       "                                                          0.011155092541719619,\n",
       "                                                          0.0007524347058135697,\n",
       "                                                          0.0032032448128444043,\n",
       "                                                          0.004591455636549438,\n",
       "                                                          0.0003715541189658278, ...],\n",
       "                                        &#x27;n_hidden&#x27;: [0, 1, 2, 3],\n",
       "                                        &#x27;n_neurons&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
       "                                                      10, 11, 12, 13, 14, 15,\n",
       "                                                      16, 17, 18, 19, 20, 21,\n",
       "                                                      22, 23, 24, 25, 26, 27,\n",
       "                                                      28, 29, 30, ...]},\n",
       "                   verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KerasRegressor</label><div class=\"sk-toggleable__content\"><pre>&lt;keras.wrappers.scikit_learn.KerasRegressor object at 0x0000017131CAE250&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KerasRegressor</label><div class=\"sk-toggleable__content\"><pre>&lt;keras.wrappers.scikit_learn.KerasRegressor object at 0x0000017131CAE250&gt;</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=3,\n",
       "                   estimator=<keras.wrappers.scikit_learn.KerasRegressor object at 0x0000017131CAE250>,\n",
       "                   param_distributions={'learning_rate': [0.001683454924600351,\n",
       "                                                          0.02390836445593178,\n",
       "                                                          0.008731907739399206,\n",
       "                                                          0.004725396149933917,\n",
       "                                                          0.0006154014789262348,\n",
       "                                                          0.0006153331256530192,\n",
       "                                                          0.0003920021771415983,\n",
       "                                                          0.01619845322936229,\n",
       "                                                          0.004779156784872302,\n",
       "                                                          0.007821074275112...\n",
       "                                                          0.005021425736625637,\n",
       "                                                          0.0005703073595961105,\n",
       "                                                          0.001151888789941251,\n",
       "                                                          0.001621231156394198,\n",
       "                                                          0.0024505367684280487,\n",
       "                                                          0.011155092541719619,\n",
       "                                                          0.0007524347058135697,\n",
       "                                                          0.0032032448128444043,\n",
       "                                                          0.004591455636549438,\n",
       "                                                          0.0003715541189658278, ...],\n",
       "                                        'n_hidden': [0, 1, 2, 3],\n",
       "                                        'n_neurons': [1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
       "                                                      10, 11, 12, 13, 14, 15,\n",
       "                                                      16, 17, 18, 19, 20, 21,\n",
       "                                                      22, 23, 24, 25, 26, 27,\n",
       "                                                      28, 29, 30, ...]},\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import reciprocal\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "param_distribs = {\n",
    "    \"n_hidden\": [0, 1, 2, 3],\n",
    "    \"n_neurons\": np.arange(1, 100)               .tolist(),\n",
    "    \"learning_rate\": reciprocal(3e-4, 3e-2)      .rvs(1000).tolist(),\n",
    "}\n",
    "\n",
    "rnd_search_cv = RandomizedSearchCV(keras_reg, param_distribs, n_iter=10, cv=3, verbose=2)\n",
    "rnd_search_cv.fit(X_train, y_train, epochs=100,\n",
    "                  validation_data=(X_valid, y_valid),\n",
    "                  callbacks=[keras.callbacks.EarlyStopping(patience=10)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "76cff1c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neurons': 80, 'n_hidden': 3, 'learning_rate': 0.0059640580092043885}"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "94a4bdaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3247098426024119"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "2cdd20e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.wrappers.scikit_learn.KerasRegressor at 0x17131b0c610>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "6207718e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "162/162 [==============================] - 1s 5ms/step - loss: 0.2969\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-0.2969483733177185"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "fb006eb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.engine.sequential.Sequential at 0x1712f3d2e80>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = rnd_search_cv.best_estimator_.model\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "db75c6fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "162/162 [==============================] - 1s 4ms/step - loss: 0.2969\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.2969483733177185"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcca10b3",
   "metadata": {},
   "source": [
    "### Practices & Exercises"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "8d57da6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "11490434/11490434 [==============================] - 2s 0us/step\n"
     ]
    }
   ],
   "source": [
    "# 10\n",
    "(X_train_full, y_train_full), (X_test, y_test) = keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "0e136b50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_full.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "329d9932",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_full.dtype\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "b1af3701",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's split the full training set into a validation set and a (smaller) training set. We also scale the pixel intensities down to the 0-1 range and convert them to floats, by dividing by 255, just like we did for Fashion\n",
    "\n",
    "X_valid, X_train = X_train_full[:5000] / 255., X_train_full[5000:] / 255.\n",
    "y_valid, y_train = y_train_full[:5000], y_train_full[5000:]\n",
    "X_test = X_test / 255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "5912eb43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot an image using Matplotlib's imshow() function, with a 'binary' color map:\n",
    "plt.imshow(X_train[1], cmap=\"binary\")\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "45074bd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 3, 4, ..., 5, 6, 8], dtype=uint8)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "573fb9e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 28, 28)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "0865877e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "d205384c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x480 with 40 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's take a look at a sample of the images in the dataset:\n",
    "n_rows = 4\n",
    "n_cols = 10\n",
    "plt.figure(figsize=(n_cols * 1.2, n_rows * 1.2))\n",
    "for row in range(n_rows):\n",
    "    for col in range(n_cols):\n",
    "        index = n_cols * row + col\n",
    "        plt.subplot(n_rows, n_cols, index + 1)\n",
    "        plt.imshow(X_train[index], cmap=\"binary\", interpolation=\"nearest\")\n",
    "        plt.axis('off')\n",
    "        plt.title(y_train[index], fontsize=12)\n",
    "plt.subplots_adjust(wspace=0.2, hspace=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "cd7185f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's build a simple dense network and find the optimal learning rate. We will need a callback to grow the learning rate at each iteration. It will also record the learning rate and the loss at each iteration:\n",
    "\n",
    "K = keras.backend\n",
    "\n",
    "class ExponentialLearningRate(keras.callbacks.Callback):\n",
    "    def __init__(self, factor):\n",
    "        self.factor = factor\n",
    "        self.rates = []\n",
    "        self.losses = []\n",
    "    def on_batch_end(self, batch, logs):\n",
    "        self.rates.append(K.get_value(self.model.optimizer.learning_rate))\n",
    "        self.losses.append(logs[\"loss\"])\n",
    "        K.set_value(self.model.optimizer.learning_rate, self.model.optimizer.learning_rate * self.factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "9fa4c266",
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "4fc6c582",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[28, 28]),\n",
    "    keras.layers.Dense(300, activation=\"relu\"),\n",
    "    keras.layers.Dense(100, activation=\"relu\"),\n",
    "    keras.layers.Dense(10, activation=\"softmax\")\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "f544c8d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We will start with a small learning rate of 1e-3, and grow it by 0.5% at each iteration:\n",
    "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
    "              optimizer=keras.optimizers.SGD(learning_rate=1e-3),\n",
    "              metrics=[\"accuracy\"])\n",
    "expon_lr = ExponentialLearningRate(factor=1.005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "8a850c50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1719/1719 [==============================] - 25s 14ms/step - loss: nan - accuracy: 0.5742 - val_loss: nan - val_accuracy: 0.0958\n"
     ]
    }
   ],
   "source": [
    "#Now let's train the model for just 1 epoch:\n",
    "history = model.fit(X_train, y_train, epochs=1,\n",
    "                    validation_data=(X_valid, y_valid),\n",
    "                    callbacks=[expon_lr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "cd4d4c48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Loss')"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can now plot the loss as a function of the learning rate:\n",
    "\n",
    "plt.plot(expon_lr.rates, expon_lr.losses)\n",
    "plt.gca().set_xscale('log')\n",
    "plt.hlines(min(expon_lr.losses), min(expon_lr.rates), max(expon_lr.rates))\n",
    "plt.axis([min(expon_lr.rates), max(expon_lr.rates), 0, expon_lr.losses[0]])\n",
    "plt.grid()\n",
    "plt.xlabel(\"Learning rate\")\n",
    "plt.ylabel(\"Loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "771a1b41",
   "metadata": {},
   "outputs": [],
   "source": [
    "#The loss starts shooting back up violently when the learning rate goes over 6e-1, so let's try using half of that, at 3e-1:\n",
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "80952ebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[28, 28]),\n",
    "    keras.layers.Dense(300, activation=\"relu\"),\n",
    "    keras.layers.Dense(100, activation=\"relu\"),\n",
    "    keras.layers.Dense(10, activation=\"softmax\")\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "98b3fe13",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
    "              optimizer=keras.optimizers.SGD(learning_rate=3e-1),\n",
    "              metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "487d0c91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'.\\\\my_mnist_logs\\\\run_001'"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_index = 1 # increment this at every run\n",
    "run_logdir = os.path.join(os.curdir, \"my_mnist_logs\", \"run_{:03d}\".format(run_index))\n",
    "run_logdir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "d11e4704",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1719/1719 [==============================] - 26s 15ms/step - loss: 0.2343 - accuracy: 0.9263 - val_loss: 0.0976 - val_accuracy: 0.9714\n",
      "Epoch 2/100\n",
      "1719/1719 [==============================] - 29s 17ms/step - loss: 0.0935 - accuracy: 0.9708 - val_loss: 0.1002 - val_accuracy: 0.9716\n",
      "Epoch 3/100\n",
      "1719/1719 [==============================] - 32s 19ms/step - loss: 0.0661 - accuracy: 0.9793 - val_loss: 0.0778 - val_accuracy: 0.9770\n",
      "Epoch 4/100\n",
      "1719/1719 [==============================] - 38s 22ms/step - loss: 0.0480 - accuracy: 0.9848 - val_loss: 0.0824 - val_accuracy: 0.9768\n",
      "Epoch 5/100\n",
      "1719/1719 [==============================] - 42s 25ms/step - loss: 0.0367 - accuracy: 0.9880 - val_loss: 0.0848 - val_accuracy: 0.9778\n",
      "Epoch 6/100\n",
      "1719/1719 [==============================] - 52s 30ms/step - loss: 0.0286 - accuracy: 0.9908 - val_loss: 0.0699 - val_accuracy: 0.9810\n",
      "Epoch 7/100\n",
      "1719/1719 [==============================] - 68s 40ms/step - loss: 0.0233 - accuracy: 0.9925 - val_loss: 0.0842 - val_accuracy: 0.9792\n",
      "Epoch 8/100\n",
      "1719/1719 [==============================] - 92s 54ms/step - loss: 0.0194 - accuracy: 0.9936 - val_loss: 0.0886 - val_accuracy: 0.9780\n",
      "Epoch 9/100\n",
      "1719/1719 [==============================] - 69s 40ms/step - loss: 0.0152 - accuracy: 0.9949 - val_loss: 0.0919 - val_accuracy: 0.9816\n",
      "Epoch 10/100\n",
      "1719/1719 [==============================] - 74s 43ms/step - loss: 0.0123 - accuracy: 0.9959 - val_loss: 0.0810 - val_accuracy: 0.9828\n",
      "Epoch 11/100\n",
      "1719/1719 [==============================] - 77s 45ms/step - loss: 0.0144 - accuracy: 0.9950 - val_loss: 0.0990 - val_accuracy: 0.9822\n",
      "Epoch 12/100\n",
      "1719/1719 [==============================] - 52s 30ms/step - loss: 0.0126 - accuracy: 0.9959 - val_loss: 0.1336 - val_accuracy: 0.9754\n",
      "Epoch 13/100\n",
      "1719/1719 [==============================] - 41s 24ms/step - loss: 0.0091 - accuracy: 0.9969 - val_loss: 0.1139 - val_accuracy: 0.9796\n",
      "Epoch 14/100\n",
      "1719/1719 [==============================] - 41s 24ms/step - loss: 0.0056 - accuracy: 0.9985 - val_loss: 0.1108 - val_accuracy: 0.9796\n",
      "Epoch 15/100\n",
      "1719/1719 [==============================] - 44s 26ms/step - loss: 0.0057 - accuracy: 0.9984 - val_loss: 0.1051 - val_accuracy: 0.9802\n",
      "Epoch 16/100\n",
      "1719/1719 [==============================] - 38s 22ms/step - loss: 0.0039 - accuracy: 0.9988 - val_loss: 0.1047 - val_accuracy: 0.9832\n",
      "Epoch 17/100\n",
      "1719/1719 [==============================] - 37s 22ms/step - loss: 0.0016 - accuracy: 0.9996 - val_loss: 0.0969 - val_accuracy: 0.9836\n",
      "Epoch 18/100\n",
      "1719/1719 [==============================] - 39s 23ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.1082 - val_accuracy: 0.9848\n",
      "Epoch 19/100\n",
      "1719/1719 [==============================] - 38s 22ms/step - loss: 6.4433e-04 - accuracy: 0.9999 - val_loss: 0.1026 - val_accuracy: 0.9844\n",
      "Epoch 20/100\n",
      "1719/1719 [==============================] - 43s 25ms/step - loss: 1.0506e-04 - accuracy: 1.0000 - val_loss: 0.1032 - val_accuracy: 0.9848\n",
      "Epoch 21/100\n",
      "1719/1719 [==============================] - 38s 22ms/step - loss: 6.9679e-05 - accuracy: 1.0000 - val_loss: 0.1036 - val_accuracy: 0.9844\n",
      "Epoch 22/100\n",
      "1719/1719 [==============================] - 38s 22ms/step - loss: 5.7532e-05 - accuracy: 1.0000 - val_loss: 0.1043 - val_accuracy: 0.9844\n",
      "Epoch 23/100\n",
      "1719/1719 [==============================] - 36s 21ms/step - loss: 5.0297e-05 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9844\n",
      "Epoch 24/100\n",
      "1719/1719 [==============================] - 40s 23ms/step - loss: 4.4876e-05 - accuracy: 1.0000 - val_loss: 0.1055 - val_accuracy: 0.9846\n",
      "Epoch 25/100\n",
      "1719/1719 [==============================] - 39s 23ms/step - loss: 4.0852e-05 - accuracy: 1.0000 - val_loss: 0.1060 - val_accuracy: 0.9842\n",
      "Epoch 26/100\n",
      "1719/1719 [==============================] - 53s 31ms/step - loss: 3.7560e-05 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9844\n"
     ]
    }
   ],
   "source": [
    "early_stopping_cb = keras.callbacks.EarlyStopping(patience=20)\n",
    "checkpoint_cb = keras.callbacks.ModelCheckpoint(\"my_mnist_model.h5\", save_best_only=True)\n",
    "tensorboard_cb = keras.callbacks.TensorBoard(run_logdir)\n",
    "\n",
    "history = model.fit(X_train, y_train, epochs=100,\n",
    "                    validation_data=(X_valid, y_valid),\n",
    "                    callbacks=[checkpoint_cb, early_stopping_cb, tensorboard_cb])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "63308057",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 6s 19ms/step - loss: 0.0724 - accuracy: 0.9811\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.07236559689044952, 0.9811000227928162]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.models.load_model(\"my_mnist_model.h5\") # rollback to best model\n",
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "88b93101",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-dc908db62883b19\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-dc908db62883b19\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 6006;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%tensorboard --logdir=./my_mnist_logs --port=6006"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fe331b0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "HML",
   "language": "python",
   "name": "hml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}