10_neural_nets_with_keras.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chapter 10 – Introduction to Artificial Neural Networks with Keras**\n",
    "\n",
    "_This notebook contains all the sample code and solutions to the exercises in chapter 10._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0-preview."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
    "# TensorFlow ≥2.0-preview 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)\n",
    "\n",
    "# Ignore useless warnings (see SciPy issue #5998)\n",
    "import warnings\n",
    "warnings.filterwarnings(action=\"ignore\", message=\"^internal gelsd\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perceptrons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: we set `max_iter` and `tol` explicitly to avoid warnings about the fact that their default value will change in future versions of Scikit-Learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "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)\n",
    "\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, 0.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
   "metadata": {},
   "source": [
    "# Activation functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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=0.000001):\n",
    "    return (f(z + eps) - f(z - eps))/(2 * eps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 7,
   "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": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4e/Yd3Lzi89y/ajE7n3dA3uWIFILGsGTKFtQgCmt5WnLgk5mHtaoENaD0QS3kITh6zGwq0ekXJpvZ1GZt6Wb2CqKjn58en+dNSuDmFZ9n819vV1CTytIYlg0FtWQU1JIrc1ALOZP2UaJzrl0AvCn+/0fNbJGZDZjZoni5C4lOhfHP8fUDZvaDgHVIStYsv5bNf72dx08/OO9SRNKgMSwjCmrJ9PVmu0+jglrr0gpqwUKau1/k7tZwucjdH3b3me7+cLzcy929J76udnlNqDokXWuWX8vJb/uFgppUjsawbJ0x6+7ShbUiBDXNqCWXRVALHdZ0gnVpW62hQEFNRDqloNa+PIJaFmFt22LLpKEgbSGDmkKaJKLOTxEJpYxBLe+wps7P5MrU+amQJomp81NEQilbUIP8Z9XU+dmZMgQ1hTTpmDo/RSSE2ZN35F1C2/IOaqD91DqRxdefnVBIkyDU+SkiIWhGLRkFteSKHNQU0iQYdX6KSAgKaskoqCVX1KCmkCZBqfNTRELQITqSyTqojfaOqvMzRQppEpw6P0UklDIGtbzDmjo/k8ui87MdCmmSilrn5+CCXjUUiEhHyhbUIP9ZNXV+dqYoQU0hTVK1dL8n1PkpIh1TUEtGQS25IgQ1hTRJnTo/RSQEBbVkFNSSyzuoKaRJJtT5KSIhKKglo6CWXJ5BTSFNMqPOTxEJQZ2fyeicn8nlFdQU0iRT6vwUkVDKGNTyDmvq/Ewuj87Pnky3ViGjwyP09vwDo8MjTOqZnHc5pXL27Ds4e8UdnMr7WfTPw0y797G8S6q8n5x1NUPTJz7lzg/7gXdMvL7eHdN5xdfP7bwwyc3I8AhTJl/GyPAIk0s8hp0x626+tfXovMtoy8kL1nLrxmW5bb8W1NY/Oi+zbQ4uGqLv4d7E9183+nFG2Dbhcu9ZDxw08fp6RvZh+eMXJaply9I+9l03mOi+7dJMWkI7+7cyyR5kZ//WvEspLZ3zMzutBLQ81yfZG+gfwOxBBvoH8i6lY2WbUYPu/fozqVYCWjuGJ3e2vqxm1BTSEhgdHmFoYDtmztDAdkaHR/IuqbTU+SmSvZHhEXZs24GZs2PbDkYqMIYpqCVTpqBWNFkENYW0BHb2bwWPf3A0m9YhdX6KZGugf2CPMawKs2mgoJaUglpyaQc1hbQ21WbR6mk2rXPq/BTJRm0WrV5VZtNAnZ9JVbXzMwtpBjWFtDbtMYtWo9m0INT5KZK+PWbRaio0m1ZTxqCWd1ircudn2tLq/FRIa0OzWbQazaaFUTvn5/2rFquhQCSwZrNoNVWaTaspW1CD/GfVqnzOzyyEDmpBQ5qZnWdmvzKzQTO7boJlP2BmG81sq5ldY2b5nyRrAk1n0Wo0mxaUOj8la1Ufv2CMWbSaCs6mgYJaUgpqyYUMaqFn0h4HPglcM95CZvYq4ALglcBi4DnAJwLXEtR4s2g1mk0LS52fkrHKjl8w/ixaTRVn00BBLSkFteRCBbWgIc3db3L3W4CnJlj0zcDV7n6fu/cDfwO8JWQtoY07i1aj2bTg1PkpWany+AUTzKLVVHQ2DRTUklJQSy5EUMvrjANHAt+u+/nXwHwz29/d9xggzWwlsBJg3ry5PL72Y9lV+YwtTJ1yMdbCGScGtw6x5al3AbNSr6qZ3bvm8/jaVblsu5kQ9bwSOOrVM9h67EuY0t/ZUZ5nL5jBaauO62gdIWVVzw/7w68zq+fxh+/LZDPtSDx+rV97YXZV7mELvT1/09IYtn3Lbvqfeg95jGGDuxawfu0Fqa3/2Pjf/pHpLS3fN7QfSx9ZkVo9rVgKbN09FYA5o9M5c8dR2RcxBwaHmseF+ZP7+NDMwM1ez43+mTT0p3mk8x4IuwmAt77ywHg7E316Se6uqzu7f14hbSawpe7n2v/3oeFTrLtfBVwFcOhhi3zhstWZFFhv+5P9DG3b3dKyZrvZd/9PM2Pe7JSrau7xtavI4zkaS6h6FgJr+l/MTf/7hRy+ekPi9Zy26jhuWn1nx/WEklk9LZzqqV1Feh4zlmj8OuywRb5k2SWZFNhoy5Nb2LG19TFs9v6fZt95+6Zc1d7Wr72ALJ6jJdDSqaSWPrKCdQfdkno9LXvwDVw//Z58tj29+WmkPjRzMZcOJB+TJ9LJqaQmctkf/nRKwn02pBfUOpFXd+cAe35Mq/0/7HkfAmhlX7RG2jctHer8lIIozfgFre2L1qiq+6bVK+PXn7Om7Mp1+1Xu/Ez75OxJ5RXS7gOW1/28HNjU+FVBEbS0L1oj7ZuWKnV+Ss5KM35Bi/uiNarwvmn1yhjUtJ9aeooY1EIfgqPHzKYCk4HJZjbVzJp9pfpV4Fwze66Z7Qd8FLguZC0hJJlFq9FsWrrU+SmhVW38gmSzaDXdMJsGCmpJ5XHg2ywULaiFnkn7KLCTqD39TfH/P2pmi8xswMwWAbj7D4HVwE+Bh4ENwMcD19KxRLNoNZpNS506PyWwSo1fkHAWraZLZtNAQS0pBbX0BW0ccPeLgIvGuHlmw7KfAz4XcvshdTKLVjM0sJ1ps2cxqWdyoKqk0dmz74C3wa0cz8IbH8q7HCmxKo1f0NksWs2ObTuYOXsmk7tgDKsFtVYaCori5AVruXXjslxr6OsdznX7aakFtbwbCnRaqDF0NItWo9m0TOicnyJ762gWraaLZtNqyjar1q3n/MxK3rNqCmlNhJhFq9G+adlQ56fIn4SYRavpln3T6pUtqEH+X3/m0fmZlTyDmkJaE0Fm0Wo0m5YpdX6KBJpFq+nC2TRQUEtKQS0shbQmRgbDtvuGXp+MT52fe+vd0doR1vNan4Q1tCvsmBN6fWUxe3KY2cgsVTWoTZo0c+KF2jCZfdq+Tx5BLa8zDhTarAPnt7Rc0Y7uL3+yZvm1rFn0YjUUxF7x9XNbWq5oZ2SQZOYdtPeR4ZvJ6gj/ZXbGrLtL1UwAxWgoWHLgk03PUJDUQYs+OuEyfQ/38t5DDtjjTAKhbVtsmTYTaCZNKqvWUKAZNRHpxBmz7i7d159VnVEbz+CiIUZ7R1PfzrbFltmsmkKaVJo6P0UklDIGtbzDWh77qFXpDAUKaVJ59Z2fPm1K3uWISImVLahB/rNqOudncgpp0jVuXvF5hp81qs5PEemIgloyCmrtU0iTrnLwtKfU+SkiHVNQS0ZBrT0KadJ1dM5PEQlBQS0ZBbXWKaRJV1Lnp4iEoM7PZPIIalmEtdCdnwpp0rXU+SkioZQxqOUd1tT5OTGFNOlqOueniIRStqAG+c+qqfNzfAppIuicnyIShoJaMlUNap1SSBOJ6ZyfkqZhDbddQ0EtGQW1vWnUEKmjzk9JU9nOASnJKaglo6C2J4U0kQbq/JQ0Kah1D3V+JlPVzs8kFNJEmlDnp6RJQa27lDGo5R3Wqtz52Q6FNJExqPNT0qSg1l3KFtQg/1m1Knd+tkohTWQC6vyUtCiodRcFtWS6OagFDWlmNsfMbjaz7Wa2wczOHGO5PjP7kpltMrPNZvZdM9M7oBSWOj+rL6/x61tbj1ZY6yIKasl0a1ALPZN2BTAEzAfOAq40syObLPc+4M+A5wMLgX7g8sC1iASlzs/Ky3X8UlDrHgpqyXRjUAsW0sxsBnA6cKG7D7j77cB3gLObLH4w8CN33+Tuu4BvAM0GQ5FCUednNRVl/FJQ6x7q/Eym2zo/zd3DrMjsBcDP3X163XXnAy9z91Malj0W+AfgvwBPA18BnnD39zdZ70pgJcC8eXOP+aevfSxIvSHs3jWfKVM35V3GHopWU1XreWpkBk9vm0Hfxs7+eGcvmEH/xu0d1xNK0eoBWPm+c+5y92PT3EYW49fceXOPufyrn26pntmTdyR8JO0Z3LWAvqkbM9lWK7q1nv6R6RMvBPQN7cdg79MpV9OarbunAjBndDqbJ2Xzeq03ONTT9Pr5k/vYNDKYyjYnDbU/r3XemX/V0fjV/FEmMxPY2nDdFmCfJsv+HngEeAwYAX4LnNdspe5+FXAVwKGHLfKFy1aHqrdjj69dRZHqgeLVVNV6Fsb/nnrL+1n0z8NMu/exROs5bdVx3LT6zo7rCaVo9WQo9fHrOYct8XUH3dJyQVnMsqxfewFLll2S+nZa1a31LKG1WdSlj6ygnddQ2m7duIwzdxzF9dPvyX7jca5d/+i8Pa7+0MzFXDqwIbXN9j3cm9q6mwm5T9oAMKvhulnAtibLXgH0AfsDM4CbgB8ErEUkE+r8rIzCjV/66rO7lO2rT+jerz+zFDKkPQD0mNmhddctB+5rsuxRwHXuvtndB4l2un2hmc0NWI9IJtT5WQmFHL/U+dldyhjUZk3ZlXcJlQ5qwUKau28n+kR5sZnNMLMTgNcDa5osfidwjpnta2ZTgPcAj7v7H0PVI5IldX6WW9HHLwW17lHGoKYZtfSEPgTHe4BpwBPADcC73f0+MzvRzAbqljsf2EW0b8eTwGuBUwPXIpIpdX6WXqHHLwW17qHOz2Sq2PkZsnEAd98MrGhy/W1EO+bWfn6K6DhEIpVy9uw74G1w09IXcvjq9HZelfDKMH59a+vRpXvzluTOmHV3qcJ5LajdunFZbjX09Q5nvs3BRUOpNRTotFAigemcn5KmMr1pS+fKGMrznlWr0jk/FdJEUqLOT0mLglp3UVBLpgpBTSFNJEXq/JS0qPOzuyioJVP2oKaQJpIydX5KmhTUukdWZ6IISUGtMwppIhlQ56ekSUGte6jzM5mydn4qpIlkpBbU7l+1OO9SpIIU1LpLGYNa3mEt66AWgkKaSIbU+SlpUlDrLmULapD/rFoenZ+dUEgTyUGt89OnTcm7FKkYBbXuoqCWTFmCmkKaSE7WLL+W4WeNaj81CU6dn91FQS2ZMgQ1hTSRHB087Sk1FEhqFNS6h4JaMkUPaqUKaYMj+mpIqkedn5ImBbXuoc7PZIoc1EoV0ibtHuXUW97Pmv4X512KSFDq/JQ0Kah1lzIGtbzDWlGDWqlCGsDhqzdw6zXHK6hJ5ajzU9KkoNZdyhbUIP9ZtSJ2fpYupAEsvPEhbr3meM7+9VvzLkUkOJ3zU9KioNZdFNSSKVJQK2VIgyiozblihoKaVJLO+SlpUednd1FQS6YoQa20IQ1g2r2PMeeKGZx6y/vzLkUkOJ3zU9KkoNY9FNSSKUJQK3VIgyioHb56g4KaVJI6PyVN/SPT8y5BMqLOz2TyDmqlD2k1taCmhgKpGnV+Spo0o9ZdyhjU8g5reQa1yoQ0UOenVJc6PyVNCmrdpWxBDfKfVcur87NSIQ3U+SnVps5PSYuCWndRUEsm66BWuZAG6vyUalPnp6RFnZ/dRUEtmSyDWtCQZmZzzOxmM9tuZhvM7Mxxlj3azP7NzAbMbJOZvS9kLer8lCpT52d4RRq/8qag1j0U1JLJKqiFnkm7AhgC5gNnAVea2ZGNC5nZXOCHwJeB/YFDgB8HrkWdn1Jp6vwMrlDjV94U1LqHOj+TySKoBQtpZjYDOB240N0H3P124DvA2U0W/yDwI3f/ursPuvs2d/9dqFoaqfNTqkqdn2FkMX6NePn2LlFQ6y5lDGp5h7W0g1rIUeMwYNjdH6i77tfAXp9EgeOBzWZ2h5k9YWbfNbNFAWvZizo/parU+RlEJuPXrRuXBSg1Wwpq3aVsQQ3yn1VLs/PT3D3MisxOBL7p7gvqrnsHcJa7n9Sw7APAs4CTgd8Cq4Fj3P2EJutdCawEmDt37jGfuvBzHdW5e3YfzBrm4GlPdbQegN275jNl6qaO1xNS0WpSPeMLXc9DO/en54lJ2M7die4/e8EM+jduD1ZPCCvfd85d7n5smtvIZPyaN/eYv73675+5bdaUXeEfSJv6hvZjsPfplpadPXlHytXA4K4F9E3dmPp2WtXN9bR6oON2XkNp27p7KnNGp7N5Uvqv1bEMDvXs8fN7z3hjR+NXz8SLtGwAmNVw3SxgW5NldwI3u/udAGb2CeCPZravu2+pX9DdrwKuAliy6GC/afWdHRe683kHsPmvt7Nm+bUdrefxtatYuGx1x/WEVLSaVM/4QtezEDj712/F/mU2C298qO37n7bqOEL8jZVQ6uPX4kOf49dPv2ePFeU9A7D0kRWsO+iWtu6T5kzL+rUXsGTZJamtv13dXM+XhkxWAAAb4ElEQVQSWptFTfIaStWDb6Dx7yxT02H9o/OCrS7k150PAD1mdmjddcuB+5os+xugfgovzHRei9T5KVWmzs9Echm/9PWnFFkZv/oswgx1yK8+g4U0d98O3ARcbGYzzOwE4PXAmiaLXwucamZHmdkU4ELg9sZPoWlS56dUmTo/25Pn+KWgJkWmzs9kQgW10O1G7wGmAU8ANwDvdvf7zOxEMxuoLeTuPwE+Anw/XvYQYMxjEqVJnZ9SVer8bFtu45eCmhRdGYNa3mEtRFALGtLcfbO7r3D3Ge6+yN2vj6+/zd1nNix7pbsf4O6z3f0Ud38kZC3tUOenVJU6P1uX9/iloCZFV7agBsWYVetE+Q7ckxKd81OqTOf8LIdbNy4rXVhTUOsuCmrZUkiro3N+SpXVzvmpoFZ8ZQxqCmvdQ0EtOwppDdT5KVW2Zvm1HPuZu9VQUAJlC2qgWbVuoqCWDYW0JtT5KVWmzs/yUFCTIjtj1t2ZHOQ4pLIFNYW0cajzU6pKnZ/loaAmRVe2WbUidH62SiFtAur8lKpS52d5KKhJ0ZUtqEE5ZtUU0lqgzk+pMnV+loM6P6XoFNTCU0hrkTo/pcpqnZ+7Z/flXYpMoIxBTWGteyiohaWQ1gZ1fkqVrVl+LbPmDqihoATKFtRAs2rdREEtHIW0NqnzU6ps/8nb1flZEgpqUmQ652cYCmkJHb56A+uefpYaCqRy1PlZHgpqUnRlDGpFCmsKaR3o2zikzk+pJHV+loeCmhRd2YIaFGdWTSGtQ+r8lCpT52c5qPNTik5BLRmFtADU+SlVpnN+lkcZg5rCWvdQUGufQlog6vyUKtM5P8ujbEENNKvWTRTU2qOQFpA6P6XKdM7P8lBQkyJT52frFNJSoHN+SlWp87M8yhjU+kem512CZKiMQS3rsKaQlhKd81OqSp2f5VHGoKYZte5StqAG2c6qKaSlSJ2fUmXq/CwHdX5K0SmojU0hLWXq/JQqU+dneZQxqCmsdQ8FteYU0jKgzk+pMnV+tsZH866gfEENNKvWTRTU9hY0pJnZHDO72cy2m9kGMztzguV7zex3ZvZoyDqKSJ2fUmVV6PzMYvxa/+i8zgvtkIKaFJk6P/cUeibtCmAImA+cBVxpZkeOs/yHgScD11Bo6vyUqqpA52cm45eCWjIKat2ljEEtjbAWLKSZ2QzgdOBCdx9w99uB7wBnj7H8wcCbgE+HqqEs1PkpVVXWzs+sx6/1j87LPawpqEnRlS2oQfhZtZAzaYcBw+7+QN11vwbG+iR6OfARYGfAGkpDnZ9SZSXs/Mxl/Mo7qG3dPbV0YU1Brbt0e1Azdw+zIrMTgW+6+4K6694BnOXuJzUseyqw0t1fY2YnAV9z9wPHWO9KYCXA3Llzj/nUhZ8LUm8IsxfMoH/j9o7W4dOmMPysUQ6e9lSQmnbvms+UqZuCrCsE1TO+qtfz0M796XliErZzd+J1rHzfOXe5+7HBimoik/Fr3txjPnbl5U2339c7HOJhtG3O6HQ2T9oBwKwpu3KpoV7f0H4M9j7d8vKzJ+9IsRoY3LWAvqkbU91GO4pWD2RXU6sHOm73NZSmrbun8s6/PLuj8asnYD0DwKyG62YB2+qviL9WWA28tpWVuvtVwFUASxYd7DetvrPzSgM5bdVxhKhn5/MO4OHX9nDzis93vK7H165i4bLVHa8nFNUzvqrXsxBY0/9ibr3meBbe+FCw9aYg9fFr0dLn+KUDG8ZcdsmB2e+ee+aOo7h++j3P/Jz3yaSXPrKCdQfd0tZ90pxpWb/2ApYsuyS19beraPVAdjUtobVZ1CSvoSIL+XXnA0CPmR1ad91y4L6G5Q4ler5vM7ONwE3As81so5ktCVhPaajzU6qsJJ2fuY9feX/1CdpPTYqtjJ2fnQoW0tx9O9GAdbGZzTCzE4DXA2saFr0XOAg4Kr68HdgU//+RUPWUkTo/paqK3vlZlPFLQS0ZBbXu0k1BLfQhON4DTAOeAG4A3u3u95nZiWY2AODuw+6+sXYBNgOj8c8jgespHXV+SlWVoPOzEOOXOj+TUVDrLt0S1IKGNHff7O4r3H2Guy9y9+vj629z95lj3OdnY+10263U+SlVVtTOz6KNX0UIamULawpq3aUbgppOC1VQOuenVJnO+dmavIMalG9WTef87C5VD2oKaQWmc35Klemcn61RUEtGQa17VDmoKaQVnDo/pcpK0vmZOwW1ZBTUukdVOz8V0kpCnZ9SVUXv/CwKBbVkFNS6S9oHOM6aQlqJqPNTqqoEnZ+FoM7PZBTUukuVZtQU0kpGnZ9SZUXt/CyaIgS1soU1BbXuUpWgppBWQur8lCpT52dr8g5qUL5ZNXV+dpcqBDWFtJJS56dUmTo/W6OgloyCWvcoe1BTSCsxdX5KldUaCmR8CmrJKKh1jzJ3fiqkVYA6P6Wqzp59R94llIKCWjIKat2ljEFNIa0iap2fT43MyLsUEcmBOj+TUVDrLmULagppFbLwxofY+seZaigQ6WJFCGplC2sKat2lTEFNIa1ipvQPqvNTpMvlHdSgfLNq6vzsLmUJagppFaTOT5ECcst0cwpqySiodY8yBDWFtIpS56dI8fQ93Jvp9hTUklFQ6x5F7/xUSKs4dX6KFIuCWjkoqHWXogY1hbQuoHN+ihRLHkEt77BWxqDWPzI97xIkQ0UMagppXULn/BQplqyDGuQ/q6bOTym6ogU1hbQuonN+ihRL38O9+vqzBNT52V2KFNQU0rqMOj9FikdBrRwU1LpHUYKaQloXUuenSPEoqJWDglr3KELnp0JaF1Pnp0ixZB3UBod6Mt1eMwpqUnR5BrWgIc3M5pjZzWa23cw2mNmZYyz3YTO718y2mdlDZvbhkHVI69T5KRIpyvilzs9yUFDrLnkFtdAzaVcAQ8B84CzgSjM7sslyBpwDzAZeDZxnZm8IXIu0SJ2fIkCBxq9u7fzcuntqrjW0S0Gtu+QR1IKFNDObAZwOXOjuA+5+O/Ad4OzGZd19tbvf7e7D7n4/8G3ghFC1SPvU+SndrIjjlzo/y0Gdn90l66Bm7h5mRWYvAH7u7tPrrjsfeJm7nzLO/Qy4G/iyu3+pye0rgZUAc+fOPeZTF34uSL0hzF4wg/6N2/MuYw+d1uTTpjC0r7F0vyeC1LN713ymTN0UZF0hqJ7xFa0egNe+6r13ufuxaW4jm/Fr3jEXXfaFRPWN9o4mut9E5k/uY9PI4F7X9/UOp7K9icwZnc7mSTsAmDVlVy411Osb2o/B3qdbXn725B0pVgODuxbQN3VjqttoV9FqyqqeVg90/MbXvKOj8SvkXqMzga0N120B9pngfhcRzehd2+xGd78KuApgyaKD/abVd3ZWZUCnrTqOItUD4Wq6f9Vibl7x+Y7X8/jaVSxctrrj9YSiesZXtHoylPr4teg5S/2yPzyWuMDBRUOJ7zuWD81czKUDG5retuTAJ4NvbyJn7jiK66ff88zPJy9Ym3kN9ZY+soJ1B93S1n3SnGlZv/YCliy7JLX1J1G0mrKqZ0n8b9qzqCH3SRsAZjVcNwvYNtYdzOw8on07Xufue3+ck9yo81O6TOHHL331WQ766rO7pP31Z8iQ9gDQY2aH1l23HLiv2cJm9jbgAuCV7v5owDokEHV+Shcpxfilzs9yUFDrLmkGtWAhzd23AzcBF5vZDDM7AXg9sKZxWTM7C/gUcLK7PxiqBglPnZ/SDco0fnVr52fZwpqCWndJK6iFPgTHe4BpwBPADcC73f0+MzvRzAbqlvsksD9wp5kNxJe9drqVYlDnp3SJ0oxf6vwsB3V+dpc0glrQkObum919hbvPcPdF7n59fP1t7j6zbrmD3X2Ku8+su7wrZC0Sls75KVVXxvFLQa0cFNS6R+igptNCSct0zk+R4lFQKwcFte4R8pyfCmnSNnV+ihSLglo5KKh1lxBBTSFNElHnp0ixqPOzHBTUpB0KaZKYOj9FikWdn+WgoCatUkiTjqjzU6Q15rDPhjCn4RuPOj/LQUFNWqGQJh1T56dI67IIaqD91MpAh+iQiSikSRDq/BRpnYJaesoW1ECzajI2hTQJSp2fIq1RUEuPgppUhUKaBKfOT5HWVDmo5R3WFNSkChTSJBW1zs+Hdu6fdykihVbVoAb5z6qp81PKTiFNUrPwxofoeWKSOj9FJrDPBlfnZ4oU1KSsFNIkVbZztzo/RVpU1Vk1BbX2qfNTQCFNMqDOT5HWKailp2xBDTSr1u0U0iQz6vwUaU1WQW3SULZvAQpqySioda+evAsoop+cdTVD03dMuNwP+4F3TLy+3h3TecXXz+28sAo4fPUGbl13PLwNzp59R97lSJ3R4RF6e/6B0eERJvVMzrucrrfPBmfbYkt033WjH2eEbRMud94Dra1v0qSZHLToo4lqaVQLaksOfDLI+pK4deMyTl6wNrftJ/GtrUdzbN5FFNzI8AhTJl/GyPAIkysyhmkmrYlWAlqe6ys7nfOzmHb2b2WSPcjO/q15lyKxpDNqrQS0doyODgRdH+Q/q1bGzs/+kel5l1BoA/0DmD3IQH/412teFNIkFzrnZ7GMDo8wNLAdM2doYDujwyN5lySxrDo/85B3UIPyff2prz6bGxkeYce2HZg5O7btYKQiY5hCmuRG5/wsjp39W6GWAxzNphWQglp6yhjUFNb2NNA/sMcYVpXZNIU0yZU6P/NXm0Wrp9m0YlJQS0/ZghpoVq2mNotWryqzaQppUgjq/MzPHrNoNZpNKywFtfQoqJXTHrNoNRWZTVNIk8LQOT+z12wWrUazacVV1aA2ONSTe1hTUCuXZrNoNVWYTQsa0sxsjpndbGbbzWyDmZ05xnJmZp8xs6fiy2fMLFmvuVSKOj+z1XQWrabLZtPKNn5VNahB/rNqZez87Nag1nQWraYCs2mhZ9KuAIaA+cBZwJVmdmST5VYCK4DlwPOBU4B3Bq5FSkqdn9kYbxatpstm00o3fmXZ+dmNZyjYuntq3iW0pduC2nizaDVln00LFtLMbAZwOnChuw+4++3Ad4Czmyz+ZuBSd3/U3R8DLgXeEqoWKT91fqZv3Fm0mi6ZTSv7+KWglp4yzqh1S1gbdxatpuSzaeYe5o/bzF4A/Nzdp9dddz7wMnc/pWHZLcBfuPv/iX8+Fvipu+/TZL0riT65Mnfu3GM+deHngtQ7npX95wRf51Wzvxp8nc3MXjCD/o3jz45kKUQ9gwt6WbrfE0Hq2b1rPlOmbgqyrhDyq2cLU6dcjNnuCZd0n8Ku3R8HZqVfVhOvfdV773L3VA+2ntX4dfFnv5Dio4DR3uhb1/Me+Kvg6/7CYd+o285o8PUDzJ/cx6aRwb2u7+sdTmV7E5kzOp3Nk6KZmllTduVSQ72+of0Y7H265eVnT07/QOqDuxbQN3Vj6tvZ2xZ6e/6m5TFsaPhj5DGGvfov3tfR+BXytFAzgcaP3FuAvQaueNktDcvNNDPzhtTo7lcBVwEsWXSw37T6znAVj6WFUz21K5O6gdNWHZfZtloRqp77Vy3mtJf9suNTST2+dhULl63uuJ5Q8qpn+5P9DG2beHADMNvNvvt/mhnzZqdcVa5SH78WL36OX33bY+EqHkPSU0lN5LI/7Fn74KKh4Nv40MzFXDqwoelteZxG6swdR3H99Hue+TnvU0ktfWQF6w66pa37nDHr7pSqiaxfewFLll2S6jaa2fLkFnZsbX0Mm73/p9l33r4pVxVeyH3SBtg7ps6CpucnaVx2FjDQOMCJ1KjzM5xW9kVr1AX7pmUyfu27bu9ZotCq/NVn3l9/lu2rT6jmfmqt7IvWqKz7poUMaQ8APWZ2aN11y4H7mix7X3zbRMuJPEOdn2G0tC9ao+rvm5bZ+LXvusFMwloWsg5qkP9+aur8zF9L+6I1Kum+acFCmrtvB24CLjazGWZ2AvB6YE2Txb8KfNDMDjCzhcCHgOtC1SLVpc7PziSZRaup8mxaHuNXlYKaGgqKrypBLcksWk0ZZ9NCH4LjPcA04AngBuDd7n6fmZ1oZvUR9svAd4HfAvcC34+vE5mQOj+TSzSLVlP92bTMx6+qBDVQ52cZVKHzM9EsWk0JZ9OChjR33+zuK9x9hrsvcvfr4+tvc/eZdcu5u69y9znxZZX2R5N26Jyf7etkFq2m4rNpuYxfCmrJKaglU9ag1sksWk3ZZtN0WigpNZ3zs3UdzaLVVH82LRcKaskpqCVTxqDW0SxaTclm0xTSpPTU+TmxELNoNVWeTcuTglpy6vxMpkxBLcQsWk2ZZtMU0qQS1Pk5viCzaDWaTUuNOj87U4SgVrawVpagFmQWraZEs2kKaU307pg+8UI5rk+aU+fn2EYGwx54NPT6ZE+dBrWekWbH4E1uctNj+k5MnZ/lUIagNrQr7JgTen1pCXnGgcp4xdfPbWm5oh3dX2qdnwdw6mvfz80rPp93OYUx68D5LS1XtDMydLN91w2yZWlfovsuf/yilpY798QD+PzDjyfaRjv6Hu5N5QwFY1n/6LxczlBQ79aNy3I/Q0E7akEt7TMUJDXvoNbCd15nQEiLZtKkctT5KVWhMxQkpxm1ZMowq9ZNFNKkstT5KVWgoJacgloyCmrFoZAmlabOT6kCBbXk1PmZjIJaMSikSeWp81OqIIvOz6oGNch/Vk2dn5KEQpp0hVrn50M798+7FJGOZBHUsghr6vwsBwW1fCmkSdeYdu9j9DwxSQ0FUnr6+jM5BbX2VeGcn2WlkCZdxXbuVuenVIKCWnIKaskoqGVPIU26kjo/pQoU1JJTUEtGQS1bCmnStdT5KVVQpaA2aSjbtyR1fiajoJYdhTTpaur8lCpQ52dnihDUyhbWFNSyoZAmXU/n/JQsmKcfctT5mVzeQQ3KN6umoJY+hTQRauf8nKGGAklV79pH6V37aKrbqNLXnwpqxdc/Ml1hLUUKaSIxnfNTsqKg1joFtXJQUEuHQppIA3V+ShYU1FqnoFYOCmrhKaSJNKHOT8mCglrr8ghqg0M9mW6zkYKaKKSJjEGdn5KFLIKaOj+Ty3tWTZ2f3S1ISDOzOWZ2s5ltN7MNZnbmOMt+2MzuNbNtZvaQmX04RA0iaVDnZ3fIewxLO6iBOj87kXdQg/LNqimohRFqJu0KYAiYD5wFXGlmR46xrAHnALOBVwPnmdkbAtUhEpw6P7tC7mOYOj/b041BbevuqXmX0Bad87NzHYc0M5sBnA5c6O4D7n478B3g7GbLu/tqd7/b3Yfd/X7g28AJndYhkiZ1flZX0cYwBbXWdWNQK9uMGmhWrRPmHR5g0cxeAPzc3afXXXc+8DJ3P2WC+xpwN/Bld//SGMusBFbGPz4PuLejgsOaC/wx7yIaFK0m1TM+1TOxw919n7RWnuYYVvDxC4r3+1Y94ytaPVC8mopWT0fjV4jWlZnA1obrtgCtFHUR0WzetWMt4O5XAVcBmNmv3P3YZGWGV7R6oHg1qZ7xqZ6JmdmvUt5EamNYkccvKF5Nqmd8RasHildTEevp5P4Tft1pZj8zMx/jcjswAMxquNssYNsE6z2PaL+O17l7+vPvItKVNIaJSFlNOJPm7ieNd3u8P0ePmR3q7r+Pr14O3DfOfd4GXAC81N3Tb2sSka6lMUxEyqrjxgF33w7cBFxsZjPM7ATg9cCaZsub2VnAp4CT3f3BNjd3VUfFhle0eqB4Name8ameiaVaU4ZjWNc9twmonvEVrR4oXk2VqqfjxgGIjjEEXAOcDDwFXODu18e3nQj8wN1nxj8/BBwI1H898DV3f1fHhYiIJKAxTESKKEhIExEREZGwdFooERERkQJSSBMREREpoEKHtLzPp9dODRb5jJk9FV8+Ex/oMqg26snkHKnt/I7i5XvN7HdmlkpHXJuvmaPN7N/MbMDMNpnZ+/Kqx8z6zOxLcR2bzey7ZnZACvWcZ2a/MrNBM7tugmU/YGYbzWyrmV1jZn2h62mnJjN7s5ndFdfzqJmtNrMQx3pMhcavjurJ7BzPGsPC1NOtY1ja41ehQxoFOJ9eGzWsBFYQte4/HzgFeGeA7SetJ6tzpLbzOwL4MPBkCnW0VY+ZzQV+CHwZ2B84BPhxXvUA7wP+jOi1sxDoBy5PoZ7HgU8S7SQ/JjN7FdEhJl4JLAaeA3wihXpargmYDryf6IjiL4prOz+lmkLQ+JW8nizP8awxLEA9dO8Ylu745e6FvAAziF4Yh9Vdtwa4pMX7XwZcnlUNwB3AyrqfzwV+UZTnJMTz0Wk9wMHA74DXAI/m+ZohOoTCmtA1dFDPlcDqup9fB9yfYm2fBK4b5/brgU/V/fxKYGPKz9e4NTVZ/oPAd9OsKYvf/Rj31/gV+PkIUZPGMI1hSetpsnxL41eRZ9IOA4bd/YG6634NjPcJB3jmfHonMs7BKFOo4cj4tomWy6qeZwR8Pjqt53LgI8DOwHUkqed4YLOZ3WFmT8RT84tyrOdq4AQzW2hm04k+sf4gcD3taPZ6nm9m++dUTzMvJfxrOhSNX53V84wUx68kNWkM0xgWSkvjV5FDWqrnBE2hhpnxbfXLzQy8X0fS5+Qiwjwfiesxs1OBye5+c+AaEtVDdJyrNxNN0S8CHgJuyLGe3wOPAI/F9zkCuDhwPe1o9nqG1v7+UmfREf+PBT6bdy1j0PjVWT31LiKd8autmjSGTViPxrAWtTN+5RbSrBzn02unhsZlZwEDHs9rBtL2cxL4+UhUj0Wn3VkNvDfw9hPVE9sJ3Ozud7r7LqJ9FV5sZvvmVM8VQB/RviUziI6An+en0GavZ5jg7y8LZrYC+DTwGnf/Y041aPxKtx4gk/OjagwLV4/GsBa0O37lFtLc/SR3tzEuLwEeID6fXt3dWj2f3is9zPn02qnhvvi2lmrNoJ40no+k9RwKLAFuM7ONRH+8z467bpbkUA/Ab4D6N6A0jurcTj1HEe3PsDl+M7oceGG8c3Aemr2eN7n7UznVA4CZvRr4R+AUd/9tXnVo/Eq9nizGr3Zq0hg2cT0awyaQaPxKaye6QDvi/U+i6dsZwAlE05VHjrHsWcBG4Ig8agDeRbRD6QFEnS33Ae/K6zlJ6/lIUg/QAyyou5xG1BGzgOjrgzyen1cQdR8dBUwB/h64Lcff17XAjcC+cT0fAR5LoZ4eYCrRJ7k18f97miz36vj181xgP+AntLjTe4o1vYLolE0vTfM1nfXvPl5W41cGz0fSmjSGaQwLUE+i8SvVF3+ABz8HuAXYDjwMnFl324lE0/G1nx8CdhNNcdYuX0qrhibbN6Lp8M3xZTXxabeyeE6yej6S1tNwn5NIoTOq3XqAdxPtP9EPfBc4KMff1/7A14EngKeB24EXplDPRUSfuOsvFxHt0zIALKpb9oPAJqL9S64F+lL6nbVUE/BTYLjhNf2DNGpK83c/xu9f41cO41c7NTXc5yQ0hmkMa6MeEo5fOneniIiISAEVubtTREREpGsppImIiIgUkEKaiIiISAEppImIiIgUkEKaiIiISAEppImIiIgUkEKaiIiISAEppImIiIgU0P8Hut8DXs9ag3AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
   "metadata": {},
   "source": [
    "# Building an Image Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's import TensorFlow and Keras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.13.0-dev20190117'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.4-tf'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by loading the fashion MNIST dataset. Keras has a number of functions to load popular datasets in `keras.datasets`. The dataset is already split for you between a training set and a test set, but it can be useful to split the training set further to have a validation set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": "markdown",
   "metadata": {},
   "source": [
    "The training set contains 60,000 grayscale images, each 28x28 pixels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_full.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each pixel intensity is represented as a byte (0 to 255):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_full.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": "markdown",
   "metadata": {},
   "source": [
    "You can plot an image using Matplotlib's `imshow()` function, with a `'binary'`\n",
    " color map:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_train[0], cmap=\"binary\")\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The labels are the class IDs (represented as uint8), from 0 to 9:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 0, 7, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the corresponding class names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\",\n",
    "               \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the first image in the training set is a coat:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Coat'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_names[y_train[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The validation set contains 5,000 images, and the test set contains 10,000 images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 28, 28)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at a sample of the images in the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure fashion_mnist_diagram\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x345.6 with 40 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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=0.2, hspace=0.5)\n",
    "save_fig('fashion_mnist_diagram', tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/ageron/.virtualenvs/tf2/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py:655: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "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": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tensorflow.python.keras.layers.core.Flatten at 0x12ae25160>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x12ae79be0>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x12ae899e8>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x12ae89160>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten_1 (Flatten)          (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 300)               235500    \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 100)               30100     \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 10)                1010      \n",
      "=================================================================\n",
      "Total params: 266,610\n",
      "Trainable params: 266,610\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: the following 2 cells do not work yet due to [TensorFlow issue 24622](https://github.com/tensorflow/tensorflow/issues/24622) (you are using a preview version of TensorFlow, hence there are still a few issues).\n",
    "You can work around this issue by applying [PR 24626](https://github.com/tensorflow/tensorflow/pull/24625/files) to your copy of `tensorflow/python/keras/utils/vis_utils.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#keras.utils.plot_model(model, \"my_mnist_model.png\", show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"my_mnist_model.png\" />\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<img src=\"my_mnist_model.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: the following cell does not work yet due to [TensorFlow issue 24622](https://github.com/tensorflow/tensorflow/issues/24622) and [TensorFlow issue 24639](https://github.com/tensorflow/tensorflow/issues/24639).\n",
    "You can work around issue 24639 by writing `from tensorflow.keras.utils.vis_utils import model_to_dot`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import SVG\n",
    "#SVG(keras.utils.model_to_dot(model, show_shapes=True).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dense_3'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hidden1 = model.layers[1]\n",
    "hidden1.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dense_3'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_layer(hidden1.name).name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights, biases = hidden1.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.02448617, -0.00877795, -0.02189048, ..., -0.02766046,\n",
       "         0.03859074, -0.06889391],\n",
       "       [ 0.00476504, -0.03105379, -0.0586676 , ...,  0.00602964,\n",
       "        -0.02763776, -0.04165364],\n",
       "       [-0.06189284, -0.06901957,  0.07102345, ..., -0.04238207,\n",
       "         0.07121518, -0.07331658],\n",
       "       ...,\n",
       "       [-0.03048757,  0.02155137, -0.05400612, ..., -0.00113463,\n",
       "         0.00228987,  0.05581069],\n",
       "       [ 0.07061854, -0.06960931,  0.07038955, ..., -0.00384101,\n",
       "         0.00034875,  0.02878492],\n",
       "       [-0.06022581,  0.01577859, -0.02585464, ..., -0.00527829,\n",
       "         0.00272203, -0.06793761]], dtype=float32)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784, 300)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300,)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biases.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
    "              optimizer=\"sgd\",\n",
    "              metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is equivalent to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=keras.losses.sparse_categorical_crossentropy,\n",
    "              optimizer=keras.optimizers.SGD(),\n",
    "              metrics=[keras.metrics.sparse_categorical_accuracy])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 55000 samples, validate on 5000 samples\n",
      "Epoch 1/50\n",
      "55000/55000==============================] - 3s 60us/sample - loss: 1.4948 - sparse_categorical_accuracy: 0.5757 - val_loss: 1.0042 - val_sparse_categorical_accuracy: 0.7168\n",
      "Epoch 2/50\n",
      "55000/55000==============================] - 3s 51us/sample - loss: 0.8690 - sparse_categorical_accuracy: 0.7319 - val_loss: 0.7549 - val_sparse_categorical_accuracy: 0.7616\n",
      "Epoch 3/50\n",
      "55000/55000==============================] - 3s 51us/sample - loss: 0.7176 - sparse_categorical_accuracy: 0.7667 - val_loss: 0.6611 - val_sparse_categorical_accuracy: 0.7890\n",
      "Epoch 4/50\n",
      "55000/55000==============================] - 3s 53us/sample - loss: 0.6459 - sparse_categorical_accuracy: 0.7891 - val_loss: 0.6049 - val_sparse_categorical_accuracy: 0.8094\n",
      "Epoch 5/50\n",
      "55000/55000==============================] - 3s 53us/sample - loss: 0.5995 - sparse_categorical_accuracy: 0.8039 - val_loss: 0.5691 - val_sparse_categorical_accuracy: 0.8186\n",
      "Epoch 6/50\n",
      "55000/55000==============================] - 3s 53us/sample - loss: 0.5673 - sparse_categorical_accuracy: 0.8131 - val_loss: 0.5432 - val_sparse_categorical_accuracy: 0.8240\n",
      "Epoch 7/50\n",
      "55000/55000==============================] - 3s 53us/sample - loss: 0.5433 - sparse_categorical_accuracy: 0.8196 - val_loss: 0.5230 - val_sparse_categorical_accuracy: 0.8322\n",
      "Epoch 8/50\n",
      "55000/55000==============================] - 3s 56us/sample - loss: 0.5245 - sparse_categorical_accuracy: 0.8249 - val_loss: 0.5057 - val_sparse_categorical_accuracy: 0.8358\n",
      "Epoch 9/50\n",
      "55000/55000==============================] - 3s 48us/sample - loss: 0.5094 - sparse_categorical_accuracy: 0.8293 - val_loss: 0.4926 - val_sparse_categorical_accuracy: 0.8360\n",
      "Epoch 10/50\n",
      "55000/55000==============================] - 3s 50us/sample - loss: 0.4970 - sparse_categorical_accuracy: 0.8323 - val_loss: 0.4841 - val_sparse_categorical_accuracy: 0.8368\n",
      "Epoch 11/50\n",
      "55000/55000==============================] - 3s 51us/sample - loss: 0.4865 - sparse_categorical_accuracy: 0.8359 - val_loss: 0.4710 - val_sparse_categorical_accuracy: 0.8422\n",
      "Epoch 12/50\n",
      "55000/55000==============================] - 3s 61us/sample - loss: 0.4774 - sparse_categorical_accuracy: 0.8382 - val_loss: 0.4673 - val_sparse_categorical_accuracy: 0.8436\n",
      "Epoch 13/50\n",
      "55000/55000==============================] - 3s 51us/sample - loss: 0.4694 - sparse_categorical_accuracy: 0.8412 - val_loss: 0.4611 - val_sparse_categorical_accuracy: 0.8456\n",
      "Epoch 14/50\n",
      "55000/55000==============================] - 3s 47us/sample - loss: 0.4628 - sparse_categorical_accuracy: 0.8428 - val_loss: 0.4522 - val_sparse_categorical_accuracy: 0.8508\n",
      "Epoch 15/50\n",
      "55000/55000==============================] - 3s 52us/sample - loss: 0.4564 - sparse_categorical_accuracy: 0.8436 - val_loss: 0.4465 - val_sparse_categorical_accuracy: 0.8516\n",
      "Epoch 16/50\n",
      "55000/55000==============================] - 3s 52us/sample - loss: 0.4506 - sparse_categorical_accuracy: 0.8457 - val_loss: 0.4418 - val_sparse_categorical_accuracy: 0.8494\n",
      "Epoch 17/50\n",
      "55000/55000==============================] - 3s 48us/sample - loss: 0.4453 - sparse_categorical_accuracy: 0.8474 - val_loss: 0.4389 - val_sparse_categorical_accuracy: 0.8510\n",
      "Epoch 18/50\n",
      "55000/55000==============================] - 3s 53us/sample - loss: 0.4409 - sparse_categorical_accuracy: 0.8485 - val_loss: 0.4353 - val_sparse_categorical_accuracy: 0.8518\n",
      "Epoch 19/50\n",
      "55000/55000==============================] - 3s 55us/sample - loss: 0.4362 - sparse_categorical_accuracy: 0.8501 - val_loss: 0.4293 - val_sparse_categorical_accuracy: 0.8546\n",
      "Epoch 20/50\n",
      "55000/55000==============================] - 3s 55us/sample - loss: 0.4319 - sparse_categorical_accuracy: 0.8512 - val_loss: 0.4283 - val_sparse_categorical_accuracy: 0.8560\n",
      "Epoch 21/50\n",
      "55000/55000==============================] - 3s 55us/sample - loss: 0.4283 - sparse_categorical_accuracy: 0.8518 - val_loss: 0.4214 - val_sparse_categorical_accuracy: 0.8620\n",
      "Epoch 22/50\n",
      "55000/55000==============================] - 3s 54us/sample - loss: 0.4245 - sparse_categorical_accuracy: 0.8544 - val_loss: 0.4201 - val_sparse_categorical_accuracy: 0.8578\n",
      "Epoch 23/50\n",
      "55000/55000==============================] - 3s 52us/sample - loss: 0.4209 - sparse_categorical_accuracy: 0.8553 - val_loss: 0.4163 - val_sparse_categorical_accuracy: 0.8594\n",
      "Epoch 24/50\n",
      "55000/55000==============================] - 3s 50us/sample - loss: 0.4177 - sparse_categorical_accuracy: 0.8560 - val_loss: 0.4164 - val_sparse_categorical_accuracy: 0.8596\n",
      "Epoch 25/50\n",
      "55000/55000==============================] - 3s 52us/sample - loss: 0.4144 - sparse_categorical_accuracy: 0.8574 - val_loss: 0.4112 - val_sparse_categorical_accuracy: 0.8618\n",
      "Epoch 26/50\n",
      "55000/55000==============================] - 3s 50us/sample - loss: 0.4115 - sparse_categorical_accuracy: 0.8581 - val_loss: 0.4149 - val_sparse_categorical_accuracy: 0.8594\n",
      "Epoch 27/50\n",
      "55000/55000==============================] - 3s 50us/sample - loss: 0.4085 - sparse_categorical_accuracy: 0.8598 - val_loss: 0.4088 - val_sparse_categorical_accuracy: 0.8640\n",
      "Epoch 28/50\n",
      "55000/55000==============================] - 3s 47us/sample - loss: 0.4053 - sparse_categorical_accuracy: 0.8605 - val_loss: 0.4054 - val_sparse_categorical_accuracy: 0.8634\n",
      "Epoch 29/50\n",
      "55000/55000==============================] - 3s 48us/sample - loss: 0.4029 - sparse_categorical_accuracy: 0.8614 - val_loss: 0.4018 - val_sparse_categorical_accuracy: 0.8650\n",
      "Epoch 30/50\n",
      "55000/55000==============================] - 3s 59us/sample - loss: 0.4004 - sparse_categorical_accuracy: 0.8621 - val_loss: 0.4010 - val_sparse_categorical_accuracy: 0.8656\n",
      "Epoch 31/50\n",
      "55000/55000==============================] - 3s 53us/sample - loss: 0.3980 - sparse_categorical_accuracy: 0.8626 - val_loss: 0.3964 - val_sparse_categorical_accuracy: 0.8664\n",
      "Epoch 32/50\n",
      "55000/55000==============================] - 3s 54us/sample - loss: 0.3956 - sparse_categorical_accuracy: 0.8631 - val_loss: 0.4007 - val_sparse_categorical_accuracy: 0.8664\n",
      "Epoch 33/50\n",
      "55000/55000==============================] - 3s 61us/sample - loss: 0.3933 - sparse_categorical_accuracy: 0.8652 - val_loss: 0.3934 - val_sparse_categorical_accuracy: 0.8678\n",
      "Epoch 34/50\n",
      "55000/55000==============================] - 4s 64us/sample - loss: 0.3910 - sparse_categorical_accuracy: 0.8641 - val_loss: 0.3965 - val_sparse_categorical_accuracy: 0.8636\n",
      "Epoch 35/50\n",
      "55000/55000==============================] - 3s 63us/sample - loss: 0.3883 - sparse_categorical_accuracy: 0.8654 - val_loss: 0.3891 - val_sparse_categorical_accuracy: 0.8678\n",
      "Epoch 36/50\n",
      "55000/55000==============================] - 3s 56us/sample - loss: 0.3866 - sparse_categorical_accuracy: 0.8658 - val_loss: 0.3915 - val_sparse_categorical_accuracy: 0.8668\n",
      "Epoch 37/50\n",
      "55000/55000==============================] - 3s 57us/sample - loss: 0.3845 - sparse_categorical_accuracy: 0.8672 - val_loss: 0.3868 - val_sparse_categorical_accuracy: 0.8682\n",
      "Epoch 38/50\n",
      "55000/55000==============================] - 3s 61us/sample - loss: 0.3820 - sparse_categorical_accuracy: 0.8673 - val_loss: 0.3846 - val_sparse_categorical_accuracy: 0.8704\n",
      "Epoch 39/50\n",
      "55000/55000==============================] - 3s 56us/sample - loss: 0.3800 - sparse_categorical_accuracy: 0.8686 - val_loss: 0.3866 - val_sparse_categorical_accuracy: 0.8688\n",
      "Epoch 40/50\n",
      "55000/55000==============================] - 3s 53us/sample - loss: 0.3782 - sparse_categorical_accuracy: 0.8686 - val_loss: 0.3863 - val_sparse_categorical_accuracy: 0.8700\n",
      "Epoch 41/50\n",
      "55000/55000==============================] - 3s 53us/sample - loss: 0.3764 - sparse_categorical_accuracy: 0.8691 - val_loss: 0.3846 - val_sparse_categorical_accuracy: 0.8670\n",
      "Epoch 42/50\n",
      "55000/55000==============================] - 3s 57us/sample - loss: 0.3745 - sparse_categorical_accuracy: 0.8697 - val_loss: 0.3784 - val_sparse_categorical_accuracy: 0.8720\n",
      "Epoch 43/50\n",
      "55000/55000==============================] - 3s 55us/sample - loss: 0.3725 - sparse_categorical_accuracy: 0.8710 - val_loss: 0.3778 - val_sparse_categorical_accuracy: 0.8702\n",
      "Epoch 44/50\n",
      "55000/55000==============================] - 3s 55us/sample - loss: 0.3709 - sparse_categorical_accuracy: 0.8709 - val_loss: 0.3847 - val_sparse_categorical_accuracy: 0.8672\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 45/50\n",
      "55000/55000==============================] - 3s 55us/sample - loss: 0.3691 - sparse_categorical_accuracy: 0.8711 - val_loss: 0.3771 - val_sparse_categorical_accuracy: 0.8694\n",
      "Epoch 46/50\n",
      "55000/55000==============================] - 3s 51us/sample - loss: 0.3675 - sparse_categorical_accuracy: 0.8720 - val_loss: 0.3756 - val_sparse_categorical_accuracy: 0.8704\n",
      "Epoch 47/50\n",
      "55000/55000==============================] - 3s 51us/sample - loss: 0.3659 - sparse_categorical_accuracy: 0.8727 - val_loss: 0.3731 - val_sparse_categorical_accuracy: 0.8716\n",
      "Epoch 48/50\n",
      "55000/55000==============================] - 3s 52us/sample - loss: 0.3643 - sparse_categorical_accuracy: 0.8731 - val_loss: 0.3733 - val_sparse_categorical_accuracy: 0.8698\n",
      "Epoch 49/50\n",
      "55000/55000==============================] - 3s 50us/sample - loss: 0.3623 - sparse_categorical_accuracy: 0.8738 - val_loss: 0.3724 - val_sparse_categorical_accuracy: 0.8710\n",
      "Epoch 50/50\n",
      "55000/55000==============================] - 3s 50us/sample - loss: 0.3607 - sparse_categorical_accuracy: 0.8752 - val_loss: 0.3707 - val_sparse_categorical_accuracy: 0.8728\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs=50,\n",
    "                    validation_data=(X_valid, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'batch_size': 32,\n",
       " 'epochs': 50,\n",
       " 'steps': None,\n",
       " 'samples': 55000,\n",
       " 'verbose': 0,\n",
       " 'do_validation': True,\n",
       " 'metrics': ['loss', 'acc', 'val_loss', 'val_acc']}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]\n"
     ]
    }
   ],
   "source": [
    "print(history.epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'acc', 'val_loss', 'val_acc'])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure keras_learning_curves_graph\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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_graph\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000==============================] - 0s 34us/sample - loss: 0.4074 - acc: 0.8541\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.4073760202884674, 0.8541]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.  , 0.  , 0.  , 0.  , 0.  , 0.09, 0.  , 0.12, 0.  , 0.79],\n",
       "       [0.  , 0.  , 0.94, 0.  , 0.02, 0.  , 0.04, 0.  , 0.  , 0.  ],\n",
       "       [0.  , 1.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new = X_test[:3]\n",
    "y_proba = model.predict(X_new)\n",
    "y_proba.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 2, 1])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict_classes(X_new)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Ankle boot', 'Pullover', 'Trouser'], dtype='<U11')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(class_names)[y_pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 2, 1], dtype=uint8)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_new = y_test[:3]\n",
    "y_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression MLP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load, split and scale the California housing dataset (the original one, not the modified one as in chapter 2):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11610 samples, validate on 3870 samples\n",
      "Epoch 1/20\n",
      "11610/11610==============================] - 1s 44us/sample - loss: 1.6343 - val_loss: 0.9361\n",
      "Epoch 2/20\n",
      "11610/11610==============================] - 0s 32us/sample - loss: 0.7053 - val_loss: 0.6556\n",
      "Epoch 3/20\n",
      "11610/11610==============================] - 0s 33us/sample - loss: 0.6343 - val_loss: 0.6028\n",
      "Epoch 4/20\n",
      "11610/11610==============================] - 0s 33us/sample - loss: 0.5974 - val_loss: 0.5631\n",
      "Epoch 5/20\n",
      "11610/11610==============================] - 0s 32us/sample - loss: 0.5701 - val_loss: 0.5375\n",
      "Epoch 6/20\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.5474 - val_loss: 0.5165\n",
      "Epoch 7/20\n",
      "11610/11610==============================] - 0s 37us/sample - loss: 0.5283 - val_loss: 0.5072\n",
      "Epoch 8/20\n",
      "11610/11610==============================] - 0s 33us/sample - loss: 0.5130 - val_loss: 0.4806\n",
      "Epoch 9/20\n",
      "11610/11610==============================] - 0s 32us/sample - loss: 0.4989 - val_loss: 0.4712\n",
      "Epoch 10/20\n",
      "11610/11610==============================] - 0s 30us/sample - loss: 0.4874 - val_loss: 0.4651\n",
      "Epoch 11/20\n",
      "11610/11610==============================] - 0s 31us/sample - loss: 0.4780 - val_loss: 0.4457\n",
      "Epoch 12/20\n",
      "11610/11610==============================] - 0s 33us/sample - loss: 0.4690 - val_loss: 0.4364\n",
      "Epoch 13/20\n",
      "11610/11610==============================] - 0s 33us/sample - loss: 0.4614 - val_loss: 0.4299\n",
      "Epoch 14/20\n",
      "11610/11610==============================] - 0s 31us/sample - loss: 0.4550 - val_loss: 0.4245\n",
      "Epoch 15/20\n",
      "11610/11610==============================] - 0s 30us/sample - loss: 0.4490 - val_loss: 0.4175\n",
      "Epoch 16/20\n",
      "11610/11610==============================] - 0s 31us/sample - loss: 0.4437 - val_loss: 0.4121\n",
      "Epoch 17/20\n",
      "11610/11610==============================] - 0s 31us/sample - loss: 0.4390 - val_loss: 0.4099\n",
      "Epoch 18/20\n",
      "11610/11610==============================] - 0s 33us/sample - loss: 0.4347 - val_loss: 0.4037\n",
      "Epoch 19/20\n",
      "11610/11610==============================] - 0s 33us/sample - loss: 0.4309 - val_loss: 0.3997\n",
      "Epoch 20/20\n",
      "11610/11610==============================] - 0s 32us/sample - loss: 0.4275 - val_loss: 0.3971\n",
      "5160/5160==============================] - 0s 14us/sample - loss: 0.4213\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=\"sgd\")\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[:3]\n",
    "y_pred = model.predict(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CYEOKsi8DyX1B2TWIfOrp3myYm/4MD38f/vAJFhbMhmn/AdPP7vVlEwrCXHn2DK5YMp3X3q7jrud3c99Lu1n56l7KoiHef/JELlk4mXkTCzGzEfyDRMQP+g1651yDmd0NXG1mVwAnAxcBZ6UofjNwl5n9N15DcBWwxtd78z2ZwZz3wuwLYP0KQn++Cm69EGadB+d9DypO6uOlxryJRcybWMS33nc8j72xn7te2MXta3dw85PbOb6igEsWTuaikycyoTDc6/uIiCRLZ48e4PPATUAlUAV8zjm3wcyWACudczEA59yjZvZt4EEgAqwBeh1z72s5ATjlMp6tHsfZ4TfgiZ/BdUvgpA/Du/4FSqb1+fJgIIfzTijnvBPKqW1s5U/r93DXC7v5t//dyI9WbuTs48ZzycLJnH9COeFgYGT+JhEZk9IKeudcNfCBFOufAGI91l0LXDsktfOBzkAeLP5H74SrJ38Oa6+FDffAqVfA2V+D6Lh+36M4EuLyM6dx+ZnT2FwZ5+4XdnHPi7v50h0vUpCXyznHT+D8E8pZNmc8BeHgCPxVIjKWpLtHL4OVX+x13Zy2HFb/CJ69Hl78HZz1JVj0CSioSOttZk2I8X/eczxfvWAOa7dWce+Lu3nk9UruX7+HYMA4Y0YZFyR+CRxTlD+sf5KIjA0K+pFWONGbN+fML8IjV8Pqf4fH/q83gdqJH4IT3u/Nr9OPQI6xeNY4Fs8aR0en44UdNTz02j4eem0fV923gavu28CJkwo5f24F559QztxjCnQgVyRLKehHS9eQzP2b4NU/wit/gD/9Izz4Ve/A7Ukf8g7qhqL9vlUgxzh1WimnTivlW+89ni374zz0WiUPvbaXnz+yif98eBOTS/I5b245F5xQzqnTSzVkUySLKOhH2/jj4Jxvw7JvwZ4X4dW7vMsYbloJwQjMeZ8X+jPP9aZf6IeZMWtCAbMmFPC5ZTPZX9/CIxv38fDGfdzx7A5ueWo7heFD/fpLjxs/An+kiIwmBX2mMINJC73l/B/Ajqe8vfzX7vP2+MPFXrfOSR+GYxd7o3rSML4gj0tPm8qlp02lsbWdJ948wEOv7ePR1yu57yWvX396ofFS+ybOmjmOk6cUE8rV3r6InyjoM1FOjtdnP+2d8N6fwNZV8Mof4ZW74IXfQqwCTvyg16c/aaHXSKQhEsrl3fMqePe8iu5+/Yc37uOvL27nvx55k58//Cb5wQCnTi9l8cwyzpo5jhMmFhLIUd++yFimoM90uSE47t3e0trodem8chc89xtY+/+88fjHvdfr15+2GILpjbRJ7tc/M38fp5y2mLXbqnhq8wGe2lLFj1a+DkBRfpAzZpSyeNY4zppZxszxMR3UFRljFPRjSSgCJ17iLU013pWuXrsX1t0Ez1wLufner4DZ53vBXzYz7bcuigS79/YBKuuaeXprFU9uPsCTm6v4y4Z9AEwoyOOsmWWclQj+ySWRvt5WRDKAgn6syi+BhZd7S2sjvPUkvPkQbH4IVj7klSmZfij0py3xGoo0TSgMc9HJk7jo5EkA7Khq5KktB3hySxVrNh/g3pe8a+dOLY2wcGox8ycXs2CKN32DztQVySwKej8IRbxAn32+97hqC2x+xAv9F26DZ2+AQJ7XtTPrPJh1vne1rKPogplaFmFqmXdQ1znHpn1xntpygLVbq1i7tbo7+AM5xnHlBZw8pYj5k4uZP7mI48oLNJxTZBQp6P2obKa3nL4c2pq9vf3ND3t7/H/5trcUT/UCf+a7yG+s9coF05sozcyYU1HAnIoCPrV4OgD76ppZv7OWl3cdZP2uWv73lb3c8exOAPJyc5g3sZAFU4pZkAj/aWVRcnSQV2REKOj9LhiGWed6y3t+BDXbvdDf/AisXwHrbuR0gGe/AAXHeA1A8bFQcuzh9wsnQaD3eXTKC8NcMK+CCxJ9/M45dlQ38lIi/F/eVcuKZ3dy85PbASgI5zJ/srfXf+LEIuZNLGRqaUThLzIMFPTZpmSaN6HaqVdAewvseYmNT/0vcysiULsDat+CHWu9sfuu89DrLOCFffHURCOQaAhKZ3hDPHs0AmbGsWVRji2Ldvfzt3d0snl/nPU7a1mfCP9fP76V9k7vsgWxvFxOOKaQEyYWMm+idzt7QsFIbRkR31LQZ7PcPJh6Ovu2NjF32bLDn+tog7rdUPPWoQagdof3eMujUP/2obKRcd7Zu/M/AhN7H9efG8jh+IpCjq8o5O9O9da1tHewaW+c194+yIY9dWzYU8ed63bS2OpdND0UyOGYKJx+YH1irv5C5h5TSDRPX12RdOl/i6QWCHp7/73Nm9/WDAd3QeUGb9qGdTfDM9dB2SyY/3feGbyl0/v9mLzcACdNLuKkyYcuudjR6dhe1ZAI/oOseXU7D2+s5M51uwCvHZleFmVuYs//uAkFzJwQY0pJPrk66CtyBAW9DEwwDONmecsJF0FTLWy8H9b/Hlb9m7dMOcPby593MURK037rQI4xc3yMmeNjvH/BRFbn72Pp0qXsq2thw56D3Q3A+p21PPjyoV8WwYAxrSzqvXZCtPs9ZoyPap5+yWoKehka+cXexVUWfhxqd3rz9Lz8e3jwK7DyG96lFed/BI57T9qje5KZGRVFYSqKwpw7t7x7/cGmNrbuj7NlfwNb9sfZUhnnzcp6Ht64r7vvH6C8MK87+GdNiHU3BhWFYZ3pK76noJehVzwFlnwF3vnPsPcVL/Bf+SO88SDkFcG8i7zunalnefP6DEJRfpBTppZwytTD5/Bv6+hkR3UjmyvjiQbAawjufWk39c3t3eUioQBTSiJMKc1nckmEySX5TCmNMKUkwuTSfAr1S0B8QEEvw8cMjpnvLedfDdseg5fvPDQ5W+FkOOkSqJh/aBhndPxRncjVm2Agp3sPPplzjv3xlu7g37I/zs7qJnbVNPL0lioaEgeBuxTlB73wT2oMkhuFSEj/hSTz6VsqIyMnADPf5S1/8zN4Y6W3p//UL8ElhWtuftIQTm8c/7j9cdhT5DUG+SWDagjMjAkFYSYUhDlzZtlhzznnqG1sY2dNI7tqmthZnbitaeTNynpWvVFJS3vnYa8ZFwtREGjnzt3PM6k4n0nFXiMwqSSfSSX6RSCZQUEvIy8U9YZjnvQhaG04NGyzaxhnzXbv/s5noPkgJwJs+LH32rzCI0/qio73LrIeHe8N9YyUQeDov9pmRkk0REk0xPzJxUc83/Vr4LBGoLqRV7bu5vW363lk45ENQUE4NxH+iQag2GsAum7LoiEdI5Bhp6CX0RWKwoS53pJKUy3rHr6bd8waf3hjUL3Vm6e/rTH168LFhxqASNnhDUH3uvEQm5B2d1Hyr4GFSccEVq+uZtmyZTjnOBBvZXet1xW0u6aJ3bVN7K5pYldNE2u3VhNvaT/sPcPBHCoKw5R3L3nd9yuKwpQXhJlQmKeJ4mRQFPSS2fKLiRfMgLnLjnzOOWishob93tJ4ABoOQGOVd9uw37tftcX7ddBYdfjZvl2Kp8L0pTBjGUw/2wv/ATAzxhfkMb4gj5OnpP5FUNfUzq7aQ43Arpom9tU1U1nXwks7a9lX13zErwKA4kiQ8oIw5UVhygvyqCgKM6EwTEVhmHGxEONieYyL5ZEfUoMgR1LQy9hlBtEyb+H4/st3dnrz+Hc3CAfg4G5v0reN98OLt3nlJsyDGUu98J+2GPKGZhoGM6MoEqQo4k3nnIpzjoNNbeyra2FfXTN765qprGtmX11L9/039taxv76FpNGj3SKhAGWJ4C+L5jEuFqKhqpVtwW2UxfK6G4WyaIiSSEhzC2UJBb1kj5ycQw3D+DmH1p/5eejsgLfXw9bV3uigdTd5V/DKyYVJiw7t8U8+Na2LtA+UmVEcCVEcCTGnovcGpqPTURX3wr8q3sr+eAtV8Vaq4i0ciLdQ1eB1Ia3fVUtVvI0/bX3tiPfIMSiNeqFfGg1RGgsdup+0lEXzKI2GKIkEdebxGKWgFwFvVFDXxdmXfMWb4mHnM17ob10NT/wUHr8GghGYeqa3xz9jGZSfNCrVDeQYEwq97pv+PLpqFaectpgD8RYOxFupamjhQL3XGBxINBDVDa1sfLuO6oZWahvben2v4kiQ0khSIxDzGqWSSJDi/BBFkSAlkRDFkSDF+UGKIkHyctWdNNoU9CKpBMOJMF8K537Hm+Jh+5pE8D8GD33HK5dfwinBcqia32NY6FQomtLn1M4jJSdpNNHs8v7Lt3d0UtPYRnWD1yhUN3gNQVeD0LW8VdXICztqqW1sPews5J4ioQAlkRBF+UGKEw1BUaIhKImE2LOrjeZX36YwHKQwP0hRfpDCcJCCcK66loaIgl4kHfnFMPdvvQWgbg9sexy2r8FtfbGXqZ1zoGDikQ1A1xTPhZMGNAx0uOUGcroPKkP/xyecczS0dlDb6P0aqG1so7aplZrGNg4m1tU0tnGwybv/+t46DjZ55boaiJtffeGI9zXzpq4uDCfCPz/5fleDkEtBolHouu1qJGLhXF3ZLCHzvmUiY0HhRFhwKSy4lJdWr2bZsmWJqZ33HJrSOfn8gG1PeNM+k7Tn2zXHf0E5RCdAbHxiSGiK++HiITljeDiYGbG8XGJ5uUwu6b98F+cc8ZZ2/rrqCeYueAd1zW0cbGqjrqmNuub2pPuJ26Z2dlQ3dq/veRZzKuFgzmENgdcw5FKQd2hdLJxLLC9ALC9INC9AQTiXaF4u0ZBXtq9fK2OFgl5kqASC3p57ybGpn29vhbpdPU4Q2wENld5JYrue7X0IaE4wMe4/RWMQKYP8Uu+s4UjiNlyckb8WkpkZBeEgZfk5nDCx8Khf39bRSX1zO/XNbdQ3t1OXuE1e133bcmj92webu9c3ptFYAOQ9upJYntcAdDVqse4GIUAklEs0L0B+KEA0lEsksS6Sl/z40LpIMDCiB7Yz+5sg4ie5Ie+KXKUzei/T2ZE4N6AS4pWJ8wFS3K/c6J0n0NHa+3uFiyC/hIXtQdg5NdEI9GgQ8ku9BiNW4Z1IljN2DpwGAzndB4UHqr2jk4bWDuIt7TQkGoOGlnbiiaWhpZ1XXn+T8ROnEO9+roN4Sxv761vYdqCBhpZ2mlo7aGhtTznktTeh3ByioQCfXjydL507e8B/QzoU9CKZJCeQCN7xUD6v77LOQfNBaKpOnB9Qk3Q/cdtUTfuuzd796i3ebfPB1O9nOYfOFo5VQKzcu19QkbQu8TgUHfq/fRTkBnIoys+hKL/3g+ar295i2bJeztxO4pyjpb2TxtYOL/zbvNvG1o7E0k5Di3ebvG52+fBfLlNBLzJWmXkHifOPPAs32ctdxxC6dLRDc22iQajyfiHE9x1a6hO3+zZ4ty5F90YolmgIyr1fB12/Fo741VB66NdDBoxAGk5mRjgYIBwMDOpXxnBQ0Itkm0BuYu6fcUA/XQadnd6vhPq9qRuD+D5violdz3m/Ijp7H4NPqAAiJUc0CDP2VoF7yruGcSDPu80NJ5ZQ4jaxLtDjcW7Y66LK8OMRoy2trWNmpcCNwAXAAeBbzrn/6aN8CFgPFDjnJg9FRUVkFOTkJDUKJ/Zd1jlojSd1HVUndSElreu6X7MdmmqY1NIAO/s41pCO5EnsouMOTV7XdbC6e4K7gc9uOpal+9f+CmgFyoGTgQfNbL1zbkMv5b8O7CedQbgi4g9m3rxAeQXeeQJpemL1apYtXeodWG5vSSzN3m1H0v32Zm/kUvJzbc1ew5E8qd2BzdC4tvcRTJDoWko0BOGiQ/XOK4C8mDcdduJxcc1W2FVweJlQNGOHu6bSb9CbWRS4BDjRORcH1pjZ/cDlwDdTlJ8O/D3wFeDXQ1tdEfEls0R3TN7QvWdnh3dGc3cjsD9pdtPE/YYD3pDXlnpoiXu3HS2Hvc3J4PVPHFbfHK8rKi/mTYsRDHsXzQmGvce5YQjmJ257Pp9/6H5uPoyb7S3DyJzrezyQmZ0CPOmciySt+xqw1Dl3YYryD+B189QAv+ut68bMlgPLAcrLyxetWLFiQH9APB4nFov1X3CUZHr9IPPrqPoNjup3dKyzjdz2JgIdTQQ6GmmrryIWgtz2RgIdTYnbxkSZRgIdLeR0tpLT2Zp0v6XH41ZyUh3UBnZM+SDHNgQEAAAK+0lEQVRbZ35iQHU955xznnfOvaPfgs65PhdgCbC3x7orgdUpyl4MrEzcXwbs6u/9nXMsWrTIDdSqVasG/NqRkOn1cy7z66j6DY7qNzhDVr/2VueaDjpXv8+56u3OVb7u3O4XnKvZMeC3BNa5NDI2nT76ONDztLVCoD55RaKL5xrgfWm8p4hIdgkEE0NMj/4s4MFKJ+g3AblmNts592Zi3QKg54HY2cA04InENTBDQJGZ7QXOcM5tH5Iai4jIUek36J1zDWZ2N3C1mV2Bd2ziIuCsHkVfBaYkPT4L+CWwEG8EjoiIjIJ0Z9X5PJAPVAJ3AJ9zzm0wsyVmFgdwzrU75/Z2LUA10Jl4nN7MQSIiMuTSGkfvnKsGPpBi/RNAysPlzrnVgE6WEhEZZZqVX0TE5xT0IiI+p6AXEfE5Bb2IiM8p6EVEfE5BLyLicwp6ERGfU9CLiPicgl5ExOcU9CIiPqegFxHxOQW9iIjPKehFRHxOQS8i4nMKehERn1PQi4j4nIJeRMTnFPQiIj6noBcR8TkFvYiIzynoRUR8TkEvIuJzCnoREZ9T0IuI+JyCXkTE5xT0IiI+p6AXEfE5Bb2IiM8p6EVEfE5BLyLicwp6ERGfU9CLiPicgl5ExOfSCnozKzWze8yswczeMrOP9VLu62b2qpnVm9k2M/v60FZXRESOVm6a5X4FtALlwMnAg2a23jm3oUc5Az4OvAzMBP5qZjudcyuGqsIiInJ0+t2jN7MocAlwlXMu7pxbA9wPXN6zrHPuGufcC865dufcG8B9wOKhrrSIiKTPnHN9FzA7BXjSORdJWvc1YKlz7sI+XmfAC8D1zrnrUjy/HFgOUF5evmjFioHt9MfjcWKx2IBeOxIyvX6Q+XVU/QZH9RucTK7fOeec87xz7h39FnTO9bkAS4C9PdZdCazu53XfB9YDef19xqJFi9xArVq1asCvHQmZXj/nMr+Oqt/gqH6Dk8n1A9a5fvLVOZdWH30cKOyxrhCo7+0FZvZFvL76Jc65ljQ+Q0REhkk6o242AblmNjtp3QKg54FYAMzs08A3gXOdc7sGX0URERmMfoPeOdcA3A1cbWZRM1sMXATc1rOsmV0G/DtwvnNu61BXVkREjl66J0x9HsgHKoE7gM855zaY2RIziyeV+yFQBjxnZvHEcsSBWBERGTlpjaN3zlUDH0ix/gkglvR4+tBVTUREhoKmQBAR8TkFvYiIzynoRUR8TkEvIuJzCnoREZ9T0IuI+JyCXkTE5xT0IiI+p6AXEfE5Bb2IiM8p6EVEfE5BLyLicwp6ERGfU9CLiPicgl5ExOcU9CIiPqegFxHxOQW9iIjPKehFRHxOQS8i4nMKehERn1PQi4j4nIJeRMTnFPQiIj6noBcR8TkFvYiIzynoRUR8TkEvIuJzCnoREZ9T0IuI+JyCXkTE5xT0IiI+p6AXEfE5Bb2IiM+lFfRmVmpm95hZg5m9ZWYf66WcmdmPzawqsfzYzGxoqywiIkcjN81yvwJagXLgZOBBM1vvnNvQo9xy4APAAsABDwHbgOuGproiInK0+t2jN7MocAlwlXMu7pxbA9wPXJ6i+CeAnznndjnndgM/Az45hPUVEZGjlM4e/XFAu3NuU9K69cDSFGXnJZ5LLjcv1Zua2XK8XwAAcTN7I426pDIOODDA146ETK8fZH4dVb/BUf0GJ5Prd2w6hdIJ+hhQ12PdQaCgl7IHe5SLmZk551xyQefcDcAN6VSyL2a2zjn3jsG+z3DJ9PpB5tdR9Rsc1W9wMr1+6UjnYGwcKOyxrhCoT6NsIRDvGfIiIjJy0gn6TUCumc1OWrcA6HkglsS6BWmUExGREdJv0DvnGoC7gavNLGpmi4GLgNtSFP8t8BUzm2RmE4GvArcMYX1TGXT3zzDL9PpB5tdR9Rsc1W9wMr1+/bJ0elXMrBS4CTgfqAK+6Zz7HzNbAqx0zsUS5Qz4MXBF4qW/Ab6hrhsRkdGTVtCLiMjYpSkQRER8TkEvIuJzYyLoM3muHTPLM7MbE/WqN7OXzOy9vZT9pJl1mFk8aVk2nPVLfO5qM2tO+syUJ6eN0vaL91g6zOwXvZQdke1nZl80s3Vm1mJmt/R47lwze93MGs1slZn1esKKmU1LlGlMvOa84ayfmZ1hZg+ZWbWZ7TezP5jZMX28T1rfiyGs3zQzcz3+/a7q431Gevtd1qNujYn6LurlfYZl+w2HMRH0HD7XzmXAtWaW6ozb5Ll25gMXAp8d5rrlAjvxzhQuAv4VuNPMpvVS/mnnXCxpWT3M9evyxaTPnNNLmRHffsnbAqgAmoA/9PGSkdh+e4Af4g1A6GZm4/BGoF0FlALrgN/38T53AC8CZcC/AH80s/HDVT+gBG+EyDS8MybrgZv7ea90vhdDVb8uxUmf+YM+3mdEt59z7vYe38fPA1uBF/p4r+HYfkMu44PeMnyuHedcg3Pue8657c65TufcA3gTuaXcC8hwoz1X0SVAJfDECH7mEZxzdzvn7sUbYZbsg8AG59wfnHPNwPeABWZ2fM/3MLPjgIXAd51zTc65u4BX8P7GYamfc25lom51zrlG4JfA4sF+3lDV72iMxvZL4RPAb/0wajDjg57e59pJtUef9lw7w8XMyvHq3NuJYqeY2QEz22RmV5lZujOIDtaPEp/7ZB/dHaO9/dL5jzVa2w96bJ/EOSZb6P27uNU5l3wG+Uhvz7Pp/4TFdL4XQ+0tM9tlZjcnfiWlMqrbL9EldzbeuUF9GY3td9TGQtAPyVw7w1S3w5hZELgduNU593qKIo8DJwIT8PZMPgp8fQSq9g1gBjAJ76f9n8xsZopyo7b9Ev+xlgK39lFstLZfl57bB9L/LvZVdsiZ2XzgO/S9fdL9XgyVA8CpeN1Ki/C2xe29lB3V7Qd8HHjCObetjzIjvf0GbCwE/ZiYa8fMcvDOFm4FvpiqjHNuq3NuW6KL5xXgauBDw10359wzzrl651yLc+5W4EngfSmKjuZcRZcDa/r6jzVa2y/JYL6LfZUdUmY2C1gJfNk512s32FF8L4ZEout1nXOu3Tm3D+//yQVmliq8R237JXycvnc6Rnz7DcZYCPqMn2snscd7I97B4kucc21pvtQBo3EFrt4+dzTnKur3P1YKI739Dts+ieNHM+n9uzijR4gN+/ZM/DJ6GPiBcy7VNCV9Gent2bUDkSqHRmX7AZg3zctE4I9H+dLR+v/cr4wP+jEw1w7AtcBc4ELnXFNvhczsvYk+fBIH8K4C7hvOiplZsZm928zCZpZrZpfh9T3+OUXxUdl+ZnYW3s/fvkbbjNj2S2ynMBAAAl3bDrgHONHMLkk8/x3g5VTddIljSi8B3028/mK8kUx3DVf9zGwS8CjwS+dcn1d1O8rvxVDV73Qzm2NmOWZWBvw3sNo517OLZlS2X1KRTwB39Tg+0PM9hm37DQvnXMYveEPZ7gUagB3AxxLrl+B1LXSVM+AaoDqxXENimodhrNuxeC15M97Pza7lMmBq4v7URNmfAvsSf8dWvK6H4DDXbzzwHN5P3lpgLXB+pmy/xOdeD9yWYv2obD+80TSux/K9xHPnAa/jDQNdDUxLet11wHVJj6clyjQBbwDnDWf9gO8m7id/D5P/fb+NNzdVn9+LYazfR/FGpDUAb+PtWFRkyvZLPBdObI9zU7xuRLbfcCya60ZExOcyvutGREQGR0EvIuJzCnoREZ9T0IuI+JyCXkTE5xT0IiI+p6AXEfE5Bb2IiM/9fxa1TMRPJlw0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.38745382],\n",
       "       [1.6820507 ],\n",
       "       [3.0960472 ]], dtype=float32)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functional API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not all neural network models are simply sequential. Some may have complex topologies. Some may have multiple inputs and/or multiple outputs. For example, a Wide & Deep neural network (see [paper](https://ai.google/research/pubs/pub45413)) connects all or part of the inputs directly to the output layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 8)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_8 (Dense)                 (None, 30)           270         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_9 (Dense)                 (None, 30)           930         dense_8[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 38)           0           input_1[0][0]                    \n",
      "                                                                 dense_9[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_10 (Dense)                (None, 1)            39          concatenate[0][0]                \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": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11610 samples, validate on 3870 samples\n",
      "Epoch 1/20\n",
      "11610/11610==============================] - 1s 50us/sample - loss: 1.2544 - val_loss: 2.9415\n",
      "Epoch 2/20\n",
      "11610/11610==============================] - 0s 32us/sample - loss: 0.6531 - val_loss: 0.8754\n",
      "Epoch 3/20\n",
      "11610/11610==============================] - 0s 32us/sample - loss: 0.5873 - val_loss: 0.5529\n",
      "Epoch 4/20\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.5574 - val_loss: 0.5205\n",
      "Epoch 5/20\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.5343 - val_loss: 0.5399\n",
      "Epoch 6/20\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.5160 - val_loss: 0.4793\n",
      "Epoch 7/20\n",
      "11610/11610==============================] - 0s 32us/sample - loss: 0.5004 - val_loss: 0.4655\n",
      "Epoch 8/20\n",
      "11610/11610==============================] - 0s 33us/sample - loss: 0.4873 - val_loss: 0.4820\n",
      "Epoch 9/20\n",
      "11610/11610==============================] - 0s 32us/sample - loss: 0.4761 - val_loss: 0.4421\n",
      "Epoch 10/20\n",
      "11610/11610==============================] - 0s 32us/sample - loss: 0.4658 - val_loss: 0.4321\n",
      "Epoch 11/20\n",
      "11610/11610==============================] - 0s 32us/sample - loss: 0.4577 - val_loss: 0.4526\n",
      "Epoch 12/20\n",
      "11610/11610==============================] - 0s 32us/sample - loss: 0.4499 - val_loss: 0.4178\n",
      "Epoch 13/20\n",
      "11610/11610==============================] - 0s 32us/sample - loss: 0.4428 - val_loss: 0.4162\n",
      "Epoch 14/20\n",
      "11610/11610==============================] - 0s 33us/sample - loss: 0.4367 - val_loss: 0.4185\n",
      "Epoch 15/20\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.4311 - val_loss: 0.3991\n",
      "Epoch 16/20\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.4259 - val_loss: 0.3998\n",
      "Epoch 17/20\n",
      "11610/11610==============================] - 0s 33us/sample - loss: 0.4210 - val_loss: 0.3896\n",
      "Epoch 18/20\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.4165 - val_loss: 0.3864\n",
      "Epoch 19/20\n",
      "11610/11610==============================] - 0s 33us/sample - loss: 0.4123 - val_loss: 0.3819\n",
      "Epoch 20/20\n",
      "11610/11610==============================] - 0s 32us/sample - loss: 0.4087 - val_loss: 0.3878\n",
      "5160/5160==============================] - 0s 14us/sample - loss: 0.4029\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=\"mean_squared_error\", optimizer=\"sgd\")\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": "markdown",
   "metadata": {},
   "source": [
    "What if you want to send different subsets of input features through the wide or deep paths? We will send 5 features (features 0 to 4), and 6 through the deep path (features 2 to 7). Note that 3 features will go through both (features 2, 3 and 4)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_A = keras.layers.Input(shape=[5])\n",
    "input_B = keras.layers.Input(shape=[6])\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)(concat)\n",
    "model = keras.models.Model(inputs=[input_A, input_B], outputs=[output])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11610 samples, validate on 3870 samples\n",
      "Epoch 1/20\n",
      "11610/11610==============================] - 1s 53us/sample - loss: 1.8070 - val_loss: 0.8422\n",
      "Epoch 2/20\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.6752 - val_loss: 0.6426\n",
      "Epoch 3/20\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.5969 - val_loss: 0.5829\n",
      "Epoch 4/20\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.5580 - val_loss: 0.5259\n",
      "Epoch 5/20\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.5317 - val_loss: 0.5005\n",
      "Epoch 6/20\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.5125 - val_loss: 0.4792\n",
      "Epoch 7/20\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.4963 - val_loss: 0.4786\n",
      "Epoch 8/20\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.4840 - val_loss: 0.4562\n",
      "Epoch 9/20\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.4733 - val_loss: 0.4407\n",
      "Epoch 10/20\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.4640 - val_loss: 0.4320\n",
      "Epoch 11/20\n",
      "11610/11610==============================] - 0s 38us/sample - loss: 0.4576 - val_loss: 0.4286\n",
      "Epoch 12/20\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.4516 - val_loss: 0.4226\n",
      "Epoch 13/20\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.4463 - val_loss: 0.4263\n",
      "Epoch 14/20\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.4428 - val_loss: 0.4102\n",
      "Epoch 15/20\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.4392 - val_loss: 0.4039\n",
      "Epoch 16/20\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.4352 - val_loss: 0.4021\n",
      "Epoch 17/20\n",
      "11610/11610==============================] - 0s 37us/sample - loss: 0.4329 - val_loss: 0.4035\n",
      "Epoch 18/20\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.4307 - val_loss: 0.3951\n",
      "Epoch 19/20\n",
      "11610/11610==============================] - 0s 37us/sample - loss: 0.4275 - val_loss: 0.3932\n",
      "Epoch 20/20\n",
      "11610/11610==============================] - 0s 37us/sample - loss: 0.4264 - val_loss: 0.4000\n",
      "5160/5160==============================] - 0s 15us/sample - loss: 0.4204\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=\"mse\", optimizer=\"sgd\")\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": "markdown",
   "metadata": {},
   "source": [
    "Adding an auxiliary output for regularization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_A = keras.layers.Input(shape=[5])\n",
    "input_B = keras.layers.Input(shape=[6])\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)(concat)\n",
    "aux_output = keras.layers.Dense(1)(hidden2)\n",
    "model = keras.models.Model(inputs=[input_A, input_B],\n",
    "                           outputs=[output, aux_output])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=[\"mse\", \"mse\"], loss_weights=[0.9, 0.1], optimizer=\"sgd\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11610 samples, validate on 3870 samples\n",
      "Epoch 1/20\n",
      "11610/11610==============================] - 1s 83us/sample - loss: 2.1325 - dense_16_loss: 1.9139 - dense_17_loss: 4.0950 - val_loss: 1.6650 - val_dense_16_loss: 0.8892 - val_dense_17_loss: 8.6440\n",
      "Epoch 2/20\n",
      "11610/11610==============================] - 1s 47us/sample - loss: 0.8870 - dense_16_loss: 0.6930 - dense_17_loss: 2.6303 - val_loss: 1.4991 - val_dense_16_loss: 0.6627 - val_dense_17_loss: 9.0235\n",
      "Epoch 3/20\n",
      "11610/11610==============================] - 1s 49us/sample - loss: 0.7421 - dense_16_loss: 0.6079 - dense_17_loss: 1.9515 - val_loss: 1.4340 - val_dense_16_loss: 0.5748 - val_dense_17_loss: 9.1628\n",
      "Epoch 4/20\n",
      "11610/11610==============================] - 1s 44us/sample - loss: 0.6766 - dense_16_loss: 0.5683 - dense_17_loss: 1.6515 - val_loss: 1.3260 - val_dense_16_loss: 0.5579 - val_dense_17_loss: 8.2343\n",
      "Epoch 5/20\n",
      "11610/11610==============================] - 1s 45us/sample - loss: 0.6366 - dense_16_loss: 0.5422 - dense_17_loss: 1.4873 - val_loss: 1.2081 - val_dense_16_loss: 0.5184 - val_dense_17_loss: 7.4115\n",
      "Epoch 6/20\n",
      "11610/11610==============================] - 1s 48us/sample - loss: 0.6082 - dense_16_loss: 0.5209 - dense_17_loss: 1.3933 - val_loss: 1.0861 - val_dense_16_loss: 0.5113 - val_dense_17_loss: 6.2558\n",
      "Epoch 7/20\n",
      "11610/11610==============================] - 1s 48us/sample - loss: 0.5849 - dense_16_loss: 0.5034 - dense_17_loss: 1.3185 - val_loss: 0.9914 - val_dense_16_loss: 0.5227 - val_dense_17_loss: 5.2070\n",
      "Epoch 8/20\n",
      "11610/11610==============================] - 0s 43us/sample - loss: 0.5664 - dense_16_loss: 0.4897 - dense_17_loss: 1.2556 - val_loss: 0.8656 - val_dense_16_loss: 0.4650 - val_dense_17_loss: 4.4682\n",
      "Epoch 9/20\n",
      "11610/11610==============================] - 1s 44us/sample - loss: 0.5506 - dense_16_loss: 0.4770 - dense_17_loss: 1.2125 - val_loss: 0.7778 - val_dense_16_loss: 0.4563 - val_dense_17_loss: 3.6704\n",
      "Epoch 10/20\n",
      "11610/11610==============================] - 0s 43us/sample - loss: 0.5371 - dense_16_loss: 0.4668 - dense_17_loss: 1.1711 - val_loss: 0.7089 - val_dense_16_loss: 0.4513 - val_dense_17_loss: 3.0250\n",
      "Epoch 11/20\n",
      "11610/11610==============================] - 1s 44us/sample - loss: 0.5268 - dense_16_loss: 0.4593 - dense_17_loss: 1.1340 - val_loss: 0.6476 - val_dense_16_loss: 0.4292 - val_dense_17_loss: 2.6140\n",
      "Epoch 12/20\n",
      "11610/11610==============================] - 1s 44us/sample - loss: 0.5176 - dense_16_loss: 0.4524 - dense_17_loss: 1.1035 - val_loss: 0.6025 - val_dense_16_loss: 0.4200 - val_dense_17_loss: 2.2451\n",
      "Epoch 13/20\n",
      "11610/11610==============================] - 1s 44us/sample - loss: 0.5095 - dense_16_loss: 0.4463 - dense_17_loss: 1.0766 - val_loss: 0.5692 - val_dense_16_loss: 0.4158 - val_dense_17_loss: 1.9492\n",
      "Epoch 14/20\n",
      "11610/11610==============================] - 1s 48us/sample - loss: 0.5031 - dense_16_loss: 0.4420 - dense_17_loss: 1.0529 - val_loss: 0.5362 - val_dense_16_loss: 0.4092 - val_dense_17_loss: 1.6791\n",
      "Epoch 15/20\n",
      "11610/11610==============================] - 1s 48us/sample - loss: 0.4972 - dense_16_loss: 0.4381 - dense_17_loss: 1.0284 - val_loss: 0.5120 - val_dense_16_loss: 0.4053 - val_dense_17_loss: 1.4733\n",
      "Epoch 16/20\n",
      "11610/11610==============================] - 1s 43us/sample - loss: 0.4915 - dense_16_loss: 0.4341 - dense_17_loss: 1.0078 - val_loss: 0.4935 - val_dense_16_loss: 0.4013 - val_dense_17_loss: 1.3245\n",
      "Epoch 17/20\n",
      "11610/11610==============================] - 0s 43us/sample - loss: 0.4871 - dense_16_loss: 0.4314 - dense_17_loss: 0.9878 - val_loss: 0.4797 - val_dense_16_loss: 0.3979 - val_dense_17_loss: 1.2147\n",
      "Epoch 18/20\n",
      "11610/11610==============================] - 0s 43us/sample - loss: 0.4830 - dense_16_loss: 0.4290 - dense_17_loss: 0.9694 - val_loss: 0.4669 - val_dense_16_loss: 0.3954 - val_dense_17_loss: 1.1119\n",
      "Epoch 19/20\n",
      "11610/11610==============================] - 0s 43us/sample - loss: 0.4787 - dense_16_loss: 0.4262 - dense_17_loss: 0.9513 - val_loss: 0.4577 - val_dense_16_loss: 0.3930 - val_dense_17_loss: 1.0410\n",
      "Epoch 20/20\n",
      "11610/11610==============================] - 1s 44us/sample - loss: 0.4758 - dense_16_loss: 0.4249 - dense_17_loss: 0.9342 - val_loss: 0.4558 - val_dense_16_loss: 0.3952 - val_dense_17_loss: 1.0002\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": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5160/5160==============================] - 0s 22us/sample - loss: 0.4661 - dense_16_loss: 0.4167 - dense_17_loss: 0.9138\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",
   "metadata": {},
   "source": [
    "# The subclassing API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11610 samples, validate on 3870 samples\n",
      "Epoch 1/10\n",
      "11610/11610==============================] - 1s 81us/sample - loss: 2.2914 - output_1_loss: 2.1790 - output_2_loss: 3.2954 - val_loss: 2.7823 - val_output_1_loss: 2.0246 - val_output_2_loss: 9.5935\n",
      "Epoch 2/10\n",
      "11610/11610==============================] - 1s 44us/sample - loss: 0.9834 - output_1_loss: 0.8678 - output_2_loss: 2.0207 - val_loss: 1.6646 - val_output_1_loss: 0.7720 - val_output_2_loss: 9.6934\n",
      "Epoch 3/10\n",
      "11610/11610==============================] - 1s 44us/sample - loss: 0.8264 - output_1_loss: 0.7295 - output_2_loss: 1.6967 - val_loss: 1.4471 - val_output_1_loss: 0.6947 - val_output_2_loss: 8.2170\n",
      "Epoch 4/10\n",
      "11610/11610==============================] - 1s 44us/sample - loss: 0.7616 - output_1_loss: 0.6750 - output_2_loss: 1.5418 - val_loss: 1.2658 - val_output_1_loss: 0.6379 - val_output_2_loss: 6.9130\n",
      "Epoch 5/10\n",
      "11610/11610==============================] - 1s 43us/sample - loss: 0.7203 - output_1_loss: 0.6400 - output_2_loss: 1.4425 - val_loss: 1.1078 - val_output_1_loss: 0.6015 - val_output_2_loss: 5.6652\n",
      "Epoch 6/10\n",
      "11610/11610==============================] - 1s 45us/sample - loss: 0.6889 - output_1_loss: 0.6124 - output_2_loss: 1.3768 - val_loss: 0.9780 - val_output_1_loss: 0.5691 - val_output_2_loss: 4.8158\n",
      "Epoch 7/10\n",
      "11610/11610==============================] - 0s 43us/sample - loss: 0.6620 - output_1_loss: 0.5885 - output_2_loss: 1.3240 - val_loss: 0.8896 - val_output_1_loss: 0.5852 - val_output_2_loss: 3.6278\n",
      "Epoch 8/10\n",
      "11610/11610==============================] - 1s 44us/sample - loss: 0.6402 - output_1_loss: 0.5697 - output_2_loss: 1.2749 - val_loss: 0.7913 - val_output_1_loss: 0.5294 - val_output_2_loss: 3.1492\n",
      "Epoch 9/10\n",
      "11610/11610==============================] - 1s 46us/sample - loss: 0.6200 - output_1_loss: 0.5513 - output_2_loss: 1.2377 - val_loss: 0.7284 - val_output_1_loss: 0.5119 - val_output_2_loss: 2.6768\n",
      "Epoch 10/10\n",
      "11610/11610==============================] - 1s 46us/sample - loss: 0.6020 - output_1_loss: 0.5353 - output_2_loss: 1.2002 - val_loss: 0.6832 - val_output_1_loss: 0.4927 - val_output_2_loss: 2.3969\n",
      "5160/5160==============================] - 0s 21us/sample - loss: 0.5847 - output_1_loss: 0.5204 - output_2_loss: 1.1756\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=\"mse\", loss_weights=[0.9, 0.1], optimizer=\"sgd\")\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": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = WideAndDeepModel(30, activation=\"relu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving and Restoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11610 samples, validate on 3870 samples\n",
      "Epoch 1/10\n",
      "11610/11610==============================] - 1s 64us/sample - loss: 1.8807 - val_loss: 0.7701\n",
      "Epoch 2/10\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.6570 - val_loss: 0.7268\n",
      "Epoch 3/10\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.5932 - val_loss: 0.6569\n",
      "Epoch 4/10\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.5547 - val_loss: 0.5244\n",
      "Epoch 5/10\n",
      "11610/11610==============================] - 0s 33us/sample - loss: 0.5269 - val_loss: 0.5068\n",
      "Epoch 6/10\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.5035 - val_loss: 0.5028\n",
      "Epoch 7/10\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.4852 - val_loss: 0.4924\n",
      "Epoch 8/10\n",
      "11610/11610==============================] - 0s 37us/sample - loss: 0.4709 - val_loss: 0.4534\n",
      "Epoch 9/10\n",
      "11610/11610==============================] - 0s 33us/sample - loss: 0.4578 - val_loss: 0.4403\n",
      "Epoch 10/10\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.4473 - val_loss: 0.4496\n",
      "5160/5160==============================] - 0s 15us/sample - loss: 0.4379\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=\"mse\", optimizer=\"sgd\")\n",
    "history = 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": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"my_keras_model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.load_model(\"my_keras_model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.54925185],\n",
       "       [1.6747888 ],\n",
       "       [3.0483947 ]], dtype=float32)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_weights(\"my_keras_weights.ckpt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.training.checkpointable.util.CheckpointLoadStatus at 0x13a73f860>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_weights(\"my_keras_weights.ckpt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Callbacks during Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11610 samples, validate on 3870 samples\n",
      "Epoch 1/10\n",
      "11610/11610==============================] - 1s 71us/sample - loss: 1.8807 - val_loss: 0.7701\n",
      "Epoch 2/10\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.6570 - val_loss: 0.7268\n",
      "Epoch 3/10\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.5932 - val_loss: 0.6569\n",
      "Epoch 4/10\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.5547 - val_loss: 0.5244\n",
      "Epoch 5/10\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.5269 - val_loss: 0.5068\n",
      "Epoch 6/10\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.5035 - val_loss: 0.5028\n",
      "Epoch 7/10\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.4852 - val_loss: 0.4924\n",
      "Epoch 8/10\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.4709 - val_loss: 0.4534\n",
      "Epoch 9/10\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.4578 - val_loss: 0.4403\n",
      "Epoch 10/10\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.4473 - val_loss: 0.4496\n",
      "5160/5160==============================] - 0s 45us/sample - loss: 0.4468\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=\"mse\", optimizer=\"sgd\")\n",
    "checkpoint_cb = keras.callbacks.ModelCheckpoint(\"my_keras_model.h5\", save_best_only=True)\n",
    "history = 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": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11610 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "11610/11610==============================] - 1s 72us/sample - loss: 0.4483 - val_loss: 0.4280\n",
      "Epoch 2/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.4393 - val_loss: 0.4106\n",
      "Epoch 3/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.4319 - val_loss: 0.4078\n",
      "Epoch 4/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.4262 - val_loss: 0.4037\n",
      "Epoch 5/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.4205 - val_loss: 0.3956\n",
      "Epoch 6/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.4160 - val_loss: 0.3894\n",
      "Epoch 7/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.4116 - val_loss: 0.3891\n",
      "Epoch 8/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.4077 - val_loss: 0.3817\n",
      "Epoch 9/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.4041 - val_loss: 0.3783\n",
      "Epoch 10/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.4010 - val_loss: 0.3757\n",
      "Epoch 11/100\n",
      "11610/11610==============================] - 0s 37us/sample - loss: 0.3980 - val_loss: 0.3731\n",
      "Epoch 12/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3951 - val_loss: 0.3742\n",
      "Epoch 13/100\n",
      "11610/11610==============================] - 0s 39us/sample - loss: 0.3924 - val_loss: 0.3685\n",
      "Epoch 14/100\n",
      "11610/11610==============================] - 0s 40us/sample - loss: 0.3898 - val_loss: 0.3677\n",
      "Epoch 15/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3873 - val_loss: 0.3626\n",
      "Epoch 16/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3850 - val_loss: 0.3612\n",
      "Epoch 17/100\n",
      "11610/11610==============================] - 0s 38us/sample - loss: 0.3831 - val_loss: 0.3583\n",
      "Epoch 18/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3807 - val_loss: 0.3597\n",
      "Epoch 19/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3785 - val_loss: 0.3699\n",
      "Epoch 20/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3768 - val_loss: 0.3526\n",
      "Epoch 21/100\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.3748 - val_loss: 0.3534\n",
      "Epoch 22/100\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.3730 - val_loss: 0.3541\n",
      "Epoch 23/100\n",
      "11610/11610==============================] - 0s 38us/sample - loss: 0.3715 - val_loss: 0.3506\n",
      "Epoch 24/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3699 - val_loss: 0.3482\n",
      "Epoch 25/100\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.3684 - val_loss: 0.3541\n",
      "Epoch 26/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3669 - val_loss: 0.3452\n",
      "Epoch 27/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3658 - val_loss: 0.3488\n",
      "Epoch 28/100\n",
      "11610/11610==============================] - 0s 37us/sample - loss: 0.3645 - val_loss: 0.3439\n",
      "Epoch 29/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3631 - val_loss: 0.3451\n",
      "Epoch 30/100\n",
      "11610/11610==============================] - 0s 40us/sample - loss: 0.3623 - val_loss: 0.3466\n",
      "Epoch 31/100\n",
      "11610/11610==============================] - 0s 39us/sample - loss: 0.3610 - val_loss: 0.3490\n",
      "Epoch 32/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3596 - val_loss: 0.3543\n",
      "Epoch 33/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3590 - val_loss: 0.3437\n",
      "Epoch 34/100\n",
      "11610/11610==============================] - 0s 39us/sample - loss: 0.3577 - val_loss: 0.3536\n",
      "Epoch 35/100\n",
      "11610/11610==============================] - 0s 37us/sample - loss: 0.3570 - val_loss: 0.3640\n",
      "Epoch 36/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3562 - val_loss: 0.3382\n",
      "Epoch 37/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3551 - val_loss: 0.3772\n",
      "Epoch 38/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3545 - val_loss: 0.3350\n",
      "Epoch 39/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3534 - val_loss: 0.3379\n",
      "Epoch 40/100\n",
      "11610/11610==============================] - 0s 37us/sample - loss: 0.3527 - val_loss: 0.3561\n",
      "Epoch 41/100\n",
      "11610/11610==============================] - 0s 39us/sample - loss: 0.3523 - val_loss: 0.3320\n",
      "Epoch 42/100\n",
      "11610/11610==============================] - 0s 37us/sample - loss: 0.3513 - val_loss: 0.3832\n",
      "Epoch 43/100\n",
      "11610/11610==============================] - 0s 40us/sample - loss: 0.3509 - val_loss: 0.3367\n",
      "Epoch 44/100\n",
      "11610/11610==============================] - 0s 40us/sample - loss: 0.3499 - val_loss: 0.3489\n",
      "Epoch 45/100\n",
      "11610/11610==============================] - 0s 37us/sample - loss: 0.3493 - val_loss: 0.3410\n",
      "Epoch 46/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3486 - val_loss: 0.3290\n",
      "Epoch 47/100\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.3480 - val_loss: 0.3921\n",
      "Epoch 48/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3474 - val_loss: 0.3278\n",
      "Epoch 49/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3467 - val_loss: 0.3842\n",
      "Epoch 50/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3464 - val_loss: 0.3370\n",
      "Epoch 51/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3458 - val_loss: 0.3441\n",
      "Epoch 52/100\n",
      "11610/11610==============================] - 0s 38us/sample - loss: 0.3451 - val_loss: 0.3427\n",
      "Epoch 53/100\n",
      "11610/11610==============================] - 0s 39us/sample - loss: 0.3445 - val_loss: 0.3459\n",
      "Epoch 54/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3441 - val_loss: 0.3379\n",
      "Epoch 55/100\n",
      "11610/11610==============================] - 0s 38us/sample - loss: 0.3436 - val_loss: 0.3432\n",
      "Epoch 56/100\n",
      "11610/11610==============================] - 0s 38us/sample - loss: 0.3433 - val_loss: 0.3414\n",
      "Epoch 57/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3427 - val_loss: 0.3347\n",
      "Epoch 58/100\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.3421 - val_loss: 0.3485\n",
      "5160/5160==============================] - 0s 15us/sample - loss: 0.3466\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=\"mse\", optimizer=\"sgd\")\n",
    "early_stopping_cb = keras.callbacks.EarlyStopping(patience=10,\n",
    "                                                  restore_best_weights=True)\n",
    "history = 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": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PrintValTrainRatioCallback(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": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11610 samples, validate on 3870 samples\n",
      "11168/11610===========================>..] - ETA: 0s - loss: 0.3464\n",
      "val/train: 0.98\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3467 - val_loss: 0.3384\n"
     ]
    }
   ],
   "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",
   "metadata": {},
   "source": [
    "# TensorBoard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "root_logdir = os.path.join(os.curdir, \"my_logs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./my_logs/run_2019_01_16-23_13_22'"
      ]
     },
     "execution_count": 85,
     "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",
    "\n",
    "run_logdir = get_run_logdir()\n",
    "run_logdir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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=\"sgd\")\n",
    "# or try another learning rate:\n",
    "model.compile(loss=\"mse\", optimizer=keras.optimizers.SGD(lr=0.05))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11610 samples, validate on 3870 samples\n",
      "Epoch 1/30\n",
      "11610/11610==============================] - 1s 79us/sample - loss: 0.5259 - val_loss: 0.4834\n",
      "Epoch 2/30\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.4069 - val_loss: 0.3912\n",
      "Epoch 3/30\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3744 - val_loss: 7.5516\n",
      "Epoch 4/30\n",
      "11610/11610==============================] - 0s 38us/sample - loss: 0.3656 - val_loss: 1.1554\n",
      "Epoch 5/30\n",
      "11610/11610==============================] - 0s 38us/sample - loss: 0.3526 - val_loss: 0.4557\n",
      "Epoch 6/30\n",
      "11610/11610==============================] - 0s 37us/sample - loss: 0.3459 - val_loss: 0.3200\n",
      "Epoch 7/30\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3354 - val_loss: 0.3228\n",
      "Epoch 8/30\n",
      "11610/11610==============================] - 0s 38us/sample - loss: 0.3328 - val_loss: 0.3086\n",
      "Epoch 9/30\n",
      "11610/11610==============================] - 0s 37us/sample - loss: 0.3219 - val_loss: 0.3210\n",
      "Epoch 10/30\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3210 - val_loss: 0.4311\n",
      "Epoch 11/30\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3164 - val_loss: 0.3045\n",
      "Epoch 12/30\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3132 - val_loss: 0.3113\n",
      "Epoch 13/30\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3118 - val_loss: 0.3037\n",
      "Epoch 14/30\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3087 - val_loss: 0.3260\n",
      "Epoch 15/30\n",
      "11610/11610==============================] - 0s 40us/sample - loss: 0.3039 - val_loss: 0.2957\n",
      "Epoch 16/30\n",
      "11610/11610==============================] - 0s 38us/sample - loss: 0.3021 - val_loss: 0.2845\n",
      "Epoch 17/30\n",
      "11610/11610==============================] - 0s 38us/sample - loss: 0.3015 - val_loss: 0.3471\n",
      "Epoch 18/30\n",
      "11610/11610==============================] - 0s 38us/sample - loss: 0.3000 - val_loss: 0.2993\n",
      "Epoch 19/30\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.2953 - val_loss: 0.2841\n",
      "Epoch 20/30\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.2957 - val_loss: 0.3537\n",
      "Epoch 21/30\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.2952 - val_loss: 0.2773\n",
      "Epoch 22/30\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.2939 - val_loss: 0.2755\n",
      "Epoch 23/30\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.2911 - val_loss: 0.2789\n",
      "Epoch 24/30\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.2915 - val_loss: 0.2758\n",
      "Epoch 25/30\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.2890 - val_loss: 0.3176\n",
      "Epoch 26/30\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.2901 - val_loss: 0.2784\n",
      "Epoch 27/30\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.2858 - val_loss: 0.2988\n",
      "Epoch 28/30\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.2895 - val_loss: 0.3054\n",
      "Epoch 29/30\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.2848 - val_loss: 0.3335\n",
      "Epoch 30/30\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.2896 - val_loss: 2.8334\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": "markdown",
   "metadata": {},
   "source": [
    "To start the TensorBoard server, one option is to open a terminal, if needed activate the virtualenv where you installed TensorBoard, then type:\n",
    "\n",
    "```bash\n",
    "$ tensorboard --logdir=./my_logs --port=6006\n",
    "```\n",
    "\n",
    "You can then open your web browser to [localhost:6006](http://localhost:6006) and use TensorBoard. Once you are done, press Ctrl-C in the terminal window, this will shutdown the TensorBoard server.\n",
    "\n",
    "Alternatively, you can create a Jupyter cell with this code:\n",
    "\n",
    "```bash\n",
    "%%bash\n",
    "tensorboard --logdir={run_logdir} --port=6006\n",
    "```\n",
    "\n",
    "When you run this cell, the TensorBoard server will start and you can use it at [localhost:6006](http://localhost:6006), but Jupyter will be blocked until you interrupt this cell, which will shutdown the server.\n",
    "\n",
    "Lastly, you can use the following `tb()` function that starts the TensorBoard server in a way that does not block Jupyter, and directly opens a new browser tab for you. It returns a handle on the server's process, so you can call `server.kill()` when you want to shutdown the server. Note that interrupting this notebook will shutdown all TensorBoard servers that you started this way.\n",
    "\n",
    "You may also want to install the jupyter-tensorboard extension which integrates nicely into Jupyter to start/stop TensorBoard servers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tb(logdir=root_logdir, port=6006, open_tab=True, sleep=3):\n",
    "    import subprocess\n",
    "    proc = subprocess.Popen(\n",
    "        \"tensorboard --logdir={0} --port={1}\".format(logdir, port), shell=True)\n",
    "    if open_tab:\n",
    "        import time\n",
    "        print(\"Waiting a few seconds for the TensorBoard Server to start...\")\n",
    "        time.sleep(sleep)\n",
    "        import webbrowser\n",
    "        webbrowser.open(\"http://127.0.0.1:{}/\".format(port))\n",
    "    return proc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Waiting a few seconds for the TensorBoard Server to start...\n"
     ]
    }
   ],
   "source": [
    "server = tb()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./my_logs/run_2019_01_16-23_13_38'"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_logdir2 = get_run_logdir()\n",
    "run_logdir2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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(lr=0.015))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11610 samples, validate on 3870 samples\n",
      "Epoch 1/10\n",
      "11610/11610==============================] - 1s 80us/sample - loss: 0.5997 - val_loss: 0.4971\n",
      "Epoch 2/10\n",
      "11610/11610==============================] - 0s 41us/sample - loss: 0.4173 - val_loss: 1.5356\n",
      "Epoch 3/10\n",
      "11610/11610==============================] - 0s 41us/sample - loss: 0.3927 - val_loss: 28.6786\n",
      "Epoch 4/10\n",
      "11610/11610==============================] - 0s 39us/sample - loss: 0.4054 - val_loss: 7.2650\n",
      "Epoch 5/10\n",
      "11610/11610==============================] - 0s 40us/sample - loss: 0.4046 - val_loss: 0.6207\n",
      "Epoch 6/10\n",
      "11610/11610==============================] - 0s 40us/sample - loss: 0.3645 - val_loss: 0.3646\n",
      "Epoch 7/10\n",
      "11610/11610==============================] - 0s 39us/sample - loss: 0.3516 - val_loss: 0.3539\n",
      "Epoch 8/10\n",
      "11610/11610==============================] - 0s 38us/sample - loss: 0.3480 - val_loss: 0.3315\n",
      "Epoch 9/10\n",
      "11610/11610==============================] - 0s 39us/sample - loss: 0.3464 - val_loss: 0.3483\n",
      "Epoch 10/10\n",
      "11610/11610==============================] - 0s 39us/sample - loss: 0.3394 - val_loss: 0.3602\n"
     ]
    }
   ],
   "source": [
    "tensorboard_cb = keras.callbacks.TensorBoard(run_logdir2)\n",
    "history = model.fit(X_train, y_train, epochs=10,\n",
    "                    validation_data=(X_valid, y_valid),\n",
    "                    callbacks=[checkpoint_cb, tensorboard_cb])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how TensorBoard now sees two runs, and you can compare the learning curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function __init__ in module tensorflow.python.keras.callbacks:\n",
      "\n",
      "__init__(self, log_dir='./logs', histogram_freq=0, batch_size=32, write_graph=True, write_grads=False, write_images=False, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None, embeddings_data=None, update_freq='epoch')\n",
      "    Initialize self.  See help(type(self)) for accurate signature.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(keras.callbacks.TensorBoard.__init__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "#server.kill() # uncomment and run this to stop the TensorBoard server"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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",
    "    options = {\"input_shape\": input_shape}\n",
    "    for layer in range(n_hidden):\n",
    "        model.add(keras.layers.Dense(n_neurons, activation=\"relu\", **options))\n",
    "        options = {}\n",
    "    model.add(keras.layers.Dense(1, **options))\n",
    "    optimizer = keras.optimizers.SGD(learning_rate)\n",
    "    model.compile(loss=\"mse\", optimizer=optimizer)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras_reg = keras.wrappers.scikit_learn.KerasRegressor(build_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11610 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "11610/11610==============================] - 1s 79us/sample - loss: 1.0910 - val_loss: 21.1892\n",
      "Epoch 2/100\n",
      "11610/11610==============================] - 0s 38us/sample - loss: 0.7607 - val_loss: 4.8909\n",
      "Epoch 3/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.5401 - val_loss: 0.5721\n",
      "Epoch 4/100\n",
      "11610/11610==============================] - 0s 42us/sample - loss: 0.4752 - val_loss: 0.4433\n",
      "Epoch 5/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.4499 - val_loss: 0.4270\n",
      "Epoch 6/100\n",
      "11610/11610==============================] - 0s 37us/sample - loss: 0.4348 - val_loss: 0.4165\n",
      "Epoch 7/100\n",
      "11610/11610==============================] - 0s 41us/sample - loss: 0.4240 - val_loss: 0.4060\n",
      "Epoch 8/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.4166 - val_loss: 0.4030\n",
      "Epoch 9/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.4109 - val_loss: 0.4069\n",
      "Epoch 10/100\n",
      "11610/11610==============================] - 0s 37us/sample - loss: 0.4055 - val_loss: 0.4220\n",
      "Epoch 11/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.4021 - val_loss: 0.4191\n",
      "Epoch 12/100\n",
      "11610/11610==============================] - 0s 39us/sample - loss: 0.3982 - val_loss: 0.3941\n",
      "Epoch 13/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3950 - val_loss: 0.4145\n",
      "Epoch 14/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3923 - val_loss: 0.4155\n",
      "Epoch 15/100\n",
      "11610/11610==============================] - 0s 37us/sample - loss: 0.3897 - val_loss: 0.3774\n",
      "Epoch 16/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3873 - val_loss: 0.3828\n",
      "Epoch 17/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3847 - val_loss: 0.4104\n",
      "Epoch 18/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3828 - val_loss: 0.3771\n",
      "Epoch 19/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3806 - val_loss: 0.3754\n",
      "Epoch 20/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3793 - val_loss: 0.4093\n",
      "Epoch 21/100\n",
      "11610/11610==============================] - 0s 34us/sample - loss: 0.3778 - val_loss: 0.3568\n",
      "Epoch 22/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3755 - val_loss: 0.4230\n",
      "Epoch 23/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3742 - val_loss: 0.3569\n",
      "Epoch 24/100\n",
      "11610/11610==============================] - 0s 37us/sample - loss: 0.3728 - val_loss: 0.3613\n",
      "Epoch 25/100\n",
      "11610/11610==============================] - 0s 37us/sample - loss: 0.3711 - val_loss: 0.3606\n",
      "Epoch 26/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3696 - val_loss: 0.3813\n",
      "Epoch 27/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3686 - val_loss: 0.3866\n",
      "Epoch 28/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3678 - val_loss: 0.3745\n",
      "Epoch 29/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3661 - val_loss: 0.4055\n",
      "Epoch 30/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3660 - val_loss: 0.3458\n",
      "Epoch 31/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3646 - val_loss: 0.3594\n",
      "Epoch 32/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3636 - val_loss: 0.4055\n",
      "Epoch 33/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3631 - val_loss: 0.3813\n",
      "Epoch 34/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3623 - val_loss: 0.3423\n",
      "Epoch 35/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3611 - val_loss: 0.3705\n",
      "Epoch 36/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3604 - val_loss: 0.3405\n",
      "Epoch 37/100\n",
      "11610/11610==============================] - 0s 37us/sample - loss: 0.3599 - val_loss: 0.3479\n",
      "Epoch 38/100\n",
      "11610/11610==============================] - 0s 38us/sample - loss: 0.3590 - val_loss: 0.3376\n",
      "Epoch 39/100\n",
      "11610/11610==============================] - 0s 37us/sample - loss: 0.3579 - val_loss: 0.3381\n",
      "Epoch 40/100\n",
      "11610/11610==============================] - 0s 38us/sample - loss: 0.3576 - val_loss: 0.3400\n",
      "Epoch 41/100\n",
      "11610/11610==============================] - 0s 40us/sample - loss: 0.3566 - val_loss: 0.3436\n",
      "Epoch 42/100\n",
      "11610/11610==============================] - 0s 38us/sample - loss: 0.3553 - val_loss: 0.3677\n",
      "Epoch 43/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3553 - val_loss: 0.3358\n",
      "Epoch 44/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3544 - val_loss: 0.3434\n",
      "Epoch 45/100\n",
      "11610/11610==============================] - 0s 37us/sample - loss: 0.3539 - val_loss: 0.4162\n",
      "Epoch 46/100\n",
      "11610/11610==============================] - 0s 42us/sample - loss: 0.3540 - val_loss: 0.3336\n",
      "Epoch 47/100\n",
      "11610/11610==============================] - 0s 38us/sample - loss: 0.3526 - val_loss: 0.3837\n",
      "Epoch 48/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3526 - val_loss: 0.3353\n",
      "Epoch 49/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3519 - val_loss: 0.3319\n",
      "Epoch 50/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3510 - val_loss: 0.3781\n",
      "Epoch 51/100\n",
      "11610/11610==============================] - 0s 35us/sample - loss: 0.3512 - val_loss: 0.3411\n",
      "Epoch 52/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3502 - val_loss: 0.4134\n",
      "Epoch 53/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3501 - val_loss: 0.3335\n",
      "Epoch 54/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3490 - val_loss: 0.3411\n",
      "Epoch 55/100\n",
      "11610/11610==============================] - 0s 36us/sample - loss: 0.3485 - val_loss: 0.3699\n",
      "Epoch 56/100\n",
      "11610/11610==============================] - 0s 39us/sample - loss: 0.3483 - val_loss: 0.3790\n",
      "Epoch 57/100\n",
      "11610/11610==============================] - 1s 44us/sample - loss: 0.3479 - val_loss: 0.4730\n",
      "Epoch 58/100\n",
      "11610/11610==============================] - 0s 43us/sample - loss: 0.3480 - val_loss: 0.4227\n",
      "Epoch 59/100\n",
      "11610/11610==============================] - 0s 40us/sample - loss: 0.3470 - val_loss: 0.6592\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x13bbabda0>"
      ]
     },
     "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,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5160/5160==============================] - 0s 16us/sample - loss: 0.3504\n"
     ]
    }
   ],
   "source": [
    "mse_test = keras_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = keras_reg.predict(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] learning_rate=0.001683454924600351, n_hidden=0, n_neurons=15 ....\n",
      "Train on 7740 samples, validate on 3870 samples\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 104us/sample - loss: 3.5539 - val_loss: 1.8522\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 1.3322 - val_loss: 1.0094\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.8593 - val_loss: 0.7356\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 39us/sample - loss: 0.7338 - val_loss: 0.6880\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.6915 - val_loss: 0.8475\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 37us/sample - loss: 0.6710 - val_loss: 0.6480\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.6507 - val_loss: 0.8315\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 37us/sample - loss: 0.6384 - val_loss: 0.8030\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.6268 - val_loss: 0.6048\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.6152 - val_loss: 0.6129\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.6038 - val_loss: 0.8304\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5992 - val_loss: 0.5584\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5896 - val_loss: 0.6129\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5826 - val_loss: 0.6516\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5775 - val_loss: 0.5914\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5692 - val_loss: 0.8666\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5695 - val_loss: 0.6169\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5634 - val_loss: 0.5636\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5593 - val_loss: 0.5333\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5539 - val_loss: 0.7167\n",
      "Epoch 21/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5542 - val_loss: 0.5827\n",
      "Epoch 22/100\n",
      "7740/7740==============================] - 0s 37us/sample - loss: 0.5480 - val_loss: 0.8048\n",
      "Epoch 23/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5467 - val_loss: 0.9102\n",
      "Epoch 24/100\n",
      "7740/7740==============================] - 0s 37us/sample - loss: 0.5462 - val_loss: 0.8832\n",
      "Epoch 25/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5466 - val_loss: 0.5515\n",
      "Epoch 26/100\n",
      "7740/7740==============================] - 0s 37us/sample - loss: 0.5426 - val_loss: 0.5441\n",
      "Epoch 27/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5410 - val_loss: 0.5190\n",
      "Epoch 28/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5391 - val_loss: 0.5654\n",
      "Epoch 29/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5365 - val_loss: 0.7380\n",
      "Epoch 30/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5384 - val_loss: 0.6237\n",
      "Epoch 31/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5365 - val_loss: 0.5883\n",
      "Epoch 32/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5358 - val_loss: 0.5474\n",
      "Epoch 33/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5343 - val_loss: 0.5918\n",
      "Epoch 34/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5339 - val_loss: 0.5825\n",
      "Epoch 35/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5303 - val_loss: 0.8564\n",
      "Epoch 36/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5335 - val_loss: 0.7790\n",
      "Epoch 37/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5319 - val_loss: 0.8012\n",
      "3870/3870==============================] - 0s 16us/sample - loss: 0.5406\n",
      "7740/7740==============================] - 0s 15us/sample - loss: 0.5274\n",
      "[CV]  learning_rate=0.001683454924600351, n_hidden=0, n_neurons=15, total=  11.1s\n",
      "[CV] learning_rate=0.001683454924600351, n_hidden=0, n_neurons=15 ....\n",
      "Train on 7740 samples, validate on 3870 samples\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   11.2s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 104us/sample - loss: 3.5558 - val_loss: 23.2739\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 1.4750 - val_loss: 10.9332\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 38us/sample - loss: 1.0160 - val_loss: 4.5177\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 37us/sample - loss: 0.8740 - val_loss: 1.5648\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.8032 - val_loss: 0.7202\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.7547 - val_loss: 1.1957\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 38us/sample - loss: 0.7165 - val_loss: 2.4106\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.6853 - val_loss: 4.0210\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.6593 - val_loss: 5.8110\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 37us/sample - loss: 0.6375 - val_loss: 7.7606\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 37us/sample - loss: 0.6189 - val_loss: 9.6135\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 37us/sample - loss: 0.6032 - val_loss: 11.3376\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5898 - val_loss: 12.9419\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 37us/sample - loss: 0.5782 - val_loss: 14.4217\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5683 - val_loss: 15.5480\n",
      "3870/3870==============================] - 0s 16us/sample - loss: 0.9233\n",
      "7740/7740==============================] - 0s 15us/sample - loss: 0.5630\n",
      "[CV]  learning_rate=0.001683454924600351, n_hidden=0, n_neurons=15, total=   5.2s\n",
      "[CV] learning_rate=0.001683454924600351, n_hidden=0, n_neurons=15 ....\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 113us/sample - loss: 3.2441 - val_loss: 1.3335\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 38us/sample - loss: 0.9595 - val_loss: 0.9037\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.6181 - val_loss: 0.5278\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5463 - val_loss: 0.7142\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5372 - val_loss: 0.5743\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5336 - val_loss: 0.5108\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5322 - val_loss: 0.4960\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5287 - val_loss: 0.7813\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5314 - val_loss: 0.7797\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5335 - val_loss: 0.5988\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5297 - val_loss: 0.7315\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5310 - val_loss: 0.7327\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5331 - val_loss: 0.4971\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5305 - val_loss: 0.4958\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5293 - val_loss: 0.6525\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5308 - val_loss: 0.6313\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5287 - val_loss: 0.7933\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5325 - val_loss: 0.5582\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5295 - val_loss: 0.6371\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 37us/sample - loss: 0.5316 - val_loss: 0.5046\n",
      "Epoch 21/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5292 - val_loss: 0.6195\n",
      "Epoch 22/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5300 - val_loss: 0.6234\n",
      "Epoch 23/100\n",
      "7740/7740==============================] - 0s 38us/sample - loss: 0.5312 - val_loss: 0.5003\n",
      "Epoch 24/100\n",
      "7740/7740==============================] - 0s 37us/sample - loss: 0.5291 - val_loss: 0.5936\n",
      "3870/3870==============================] - 0s 16us/sample - loss: 0.5312\n",
      "7740/7740==============================] - 0s 15us/sample - loss: 0.5259\n",
      "[CV]  learning_rate=0.001683454924600351, n_hidden=0, n_neurons=15, total=   7.6s\n",
      "[CV] learning_rate=0.008731907739399206, n_hidden=0, n_neurons=21 ....\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 108us/sample - loss: 1.4129 - val_loss: 90.6689\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 1.1356 - val_loss: 270.7661\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 8.0200 - val_loss: 951.4204\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 4.6734 - val_loss: 2949.4876\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 14.2591 - val_loss: 13335.6799\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 184.5611 - val_loss: 50551.7115\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 870.4205 - val_loss: 197200.0089\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 2498.2361 - val_loss: 771487.3197\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 6016.3268 - val_loss: 3026833.1600\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 16044.3830 - val_loss: 12779260.2184\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 187473.2956 - val_loss: 49345292.3600\n",
      "3870/3870==============================] - 0s 16us/sample - loss: 129395.1324\n",
      "7740/7740==============================] - 0s 16us/sample - loss: 673904.4341\n",
      "[CV]  learning_rate=0.008731907739399206, n_hidden=0, n_neurons=21, total=   4.1s\n",
      "[CV] learning_rate=0.008731907739399206, n_hidden=0, n_neurons=21 ....\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 114us/sample - loss: 1.1501 - val_loss: 25.5322\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5347 - val_loss: 23.6567\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 37us/sample - loss: 0.5239 - val_loss: 23.2485\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5153 - val_loss: 19.4312\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5094 - val_loss: 19.9520\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 37us/sample - loss: 0.5085 - val_loss: 19.8248\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5075 - val_loss: 18.0130\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5047 - val_loss: 17.9394\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5046 - val_loss: 18.3538\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5049 - val_loss: 20.7601\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5041 - val_loss: 20.0979\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5021 - val_loss: 17.5124\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5025 - val_loss: 18.4272\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5059 - val_loss: 16.8474\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5027 - val_loss: 20.9784\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.5014 - val_loss: 21.5029\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5042 - val_loss: 20.6266\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5027 - val_loss: 17.7721\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 37us/sample - loss: 0.5040 - val_loss: 20.4231\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5033 - val_loss: 20.1321\n",
      "Epoch 21/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 0.5027 - val_loss: 20.9063\n",
      "Epoch 22/100\n",
      "7740/7740==============================] - 0s 38us/sample - loss: 0.5027 - val_loss: 18.9330\n",
      "Epoch 23/100\n",
      "7740/7740==============================] - 0s 38us/sample - loss: 0.5033 - val_loss: 18.4228\n",
      "Epoch 24/100\n",
      "7740/7740==============================] - 0s 39us/sample - loss: 0.5034 - val_loss: 18.7782\n",
      "3870/3870==============================] - 0s 18us/sample - loss: 0.9522\n",
      "7740/7740==============================] - 0s 17us/sample - loss: 0.4982\n",
      "[CV]  learning_rate=0.008731907739399206, n_hidden=0, n_neurons=21, total=   7.7s\n",
      "[CV] learning_rate=0.008731907739399206, n_hidden=0, n_neurons=21 ....\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 123us/sample - loss: 1.3241 - val_loss: 4.3479\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 37us/sample - loss: 0.7604 - val_loss: 35.8365\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 39us/sample - loss: 0.6136 - val_loss: 4.3261\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.7222 - val_loss: 50.4618\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 0.6805 - val_loss: 68.3690\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 1.6583 - val_loss: 191.9560\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 1.1449 - val_loss: 244.7069\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 35us/sample - loss: 3.0920 - val_loss: 620.3677\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 4.1613 - val_loss: 937.5814\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 7.5112 - val_loss: 2228.7911\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 6.3457 - val_loss: 3502.0332\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 104.7046 - val_loss: 6564.0357\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 36us/sample - loss: 67.7997 - val_loss: 12287.0979\n",
      "3870/3870==============================] - 0s 16us/sample - loss: 12.0379\n",
      "7740/7740==============================] - 0s 16us/sample - loss: 176.6801\n",
      "[CV]  learning_rate=0.008731907739399206, n_hidden=0, n_neurons=21, total=   4.7s\n",
      "[CV] learning_rate=0.0006154014789262348, n_hidden=2, n_neurons=87 ...\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 130us/sample - loss: 2.5240 - val_loss: 3.2356\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 1.0654 - val_loss: 1.2841\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.8010 - val_loss: 0.7762\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.7186 - val_loss: 0.6757\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.6782 - val_loss: 0.6430\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.6495 - val_loss: 0.6225\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.6258 - val_loss: 0.5980\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.6049 - val_loss: 0.5846\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.5864 - val_loss: 0.5681\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.5697 - val_loss: 0.5444\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.5544 - val_loss: 0.5306\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.5404 - val_loss: 0.5141\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.5275 - val_loss: 0.5008\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.5156 - val_loss: 0.4896\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.5046 - val_loss: 0.4791\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4946 - val_loss: 0.4710\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4852 - val_loss: 0.4635\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4769 - val_loss: 0.4525\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.4689 - val_loss: 0.4454\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.4616 - val_loss: 0.4388\n",
      "Epoch 21/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4550 - val_loss: 0.4329\n",
      "Epoch 22/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4491 - val_loss: 0.4304\n",
      "Epoch 23/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4436 - val_loss: 0.4248\n",
      "Epoch 24/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4385 - val_loss: 0.4210\n",
      "Epoch 25/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4336 - val_loss: 0.4197\n",
      "Epoch 26/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4292 - val_loss: 0.4164\n",
      "Epoch 27/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4253 - val_loss: 0.4116\n",
      "Epoch 28/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4215 - val_loss: 0.4054\n",
      "Epoch 29/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.4179 - val_loss: 0.4038\n",
      "Epoch 30/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4148 - val_loss: 0.4049\n",
      "Epoch 31/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4117 - val_loss: 0.3993\n",
      "Epoch 32/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4089 - val_loss: 0.4014\n",
      "Epoch 33/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4060 - val_loss: 0.4019\n",
      "Epoch 34/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4035 - val_loss: 0.4051\n",
      "Epoch 35/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4012 - val_loss: 0.3930\n",
      "Epoch 36/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3987 - val_loss: 0.3866\n",
      "Epoch 37/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3966 - val_loss: 0.3937\n",
      "Epoch 38/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3945 - val_loss: 0.4001\n",
      "Epoch 39/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3926 - val_loss: 0.3935\n",
      "Epoch 40/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3907 - val_loss: 0.4043\n",
      "Epoch 41/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3889 - val_loss: 0.3905\n",
      "Epoch 42/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3872 - val_loss: 0.4048\n",
      "Epoch 43/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3857 - val_loss: 0.3898\n",
      "Epoch 44/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3840 - val_loss: 0.3958\n",
      "Epoch 45/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3824 - val_loss: 0.3986\n",
      "Epoch 46/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3812 - val_loss: 0.3928\n",
      "3870/3870==============================] - 0s 17us/sample - loss: 0.3931\n",
      "7740/7740==============================] - 0s 17us/sample - loss: 0.3797\n",
      "[CV]  learning_rate=0.0006154014789262348, n_hidden=2, n_neurons=87, total=  15.8s\n",
      "[CV] learning_rate=0.0006154014789262348, n_hidden=2, n_neurons=87 ...\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 131us/sample - loss: 2.7707 - val_loss: 17.9496\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 1.1097 - val_loss: 14.9042\n",
      "Epoch 3/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7740/7740==============================] - 0s 43us/sample - loss: 0.8034 - val_loss: 10.8992\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.7041 - val_loss: 7.9535\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.6575 - val_loss: 6.1155\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.6261 - val_loss: 4.7621\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.6009 - val_loss: 3.6578\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.5790 - val_loss: 2.8729\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.5602 - val_loss: 2.2465\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.5433 - val_loss: 1.7917\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.5285 - val_loss: 1.4892\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.5153 - val_loss: 1.1888\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.5037 - val_loss: 0.9932\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.4932 - val_loss: 0.8323\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4838 - val_loss: 0.7294\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.4756 - val_loss: 0.6345\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4681 - val_loss: 0.5681\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4612 - val_loss: 0.5266\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4550 - val_loss: 0.4952\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4495 - val_loss: 0.4741\n",
      "Epoch 21/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4442 - val_loss: 0.4622\n",
      "Epoch 22/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4397 - val_loss: 0.4462\n",
      "Epoch 23/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4353 - val_loss: 0.4368\n",
      "Epoch 24/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4313 - val_loss: 0.4354\n",
      "Epoch 25/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.4276 - val_loss: 0.4338\n",
      "Epoch 26/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4241 - val_loss: 0.4215\n",
      "Epoch 27/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4210 - val_loss: 0.4225\n",
      "Epoch 28/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4181 - val_loss: 0.4258\n",
      "Epoch 29/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4152 - val_loss: 0.4315\n",
      "Epoch 30/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4125 - val_loss: 0.4296\n",
      "Epoch 31/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4098 - val_loss: 0.4313\n",
      "Epoch 32/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4075 - val_loss: 0.4523\n",
      "Epoch 33/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4053 - val_loss: 0.4587\n",
      "Epoch 34/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4031 - val_loss: 0.4665\n",
      "Epoch 35/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4010 - val_loss: 0.4737\n",
      "Epoch 36/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3988 - val_loss: 0.4903\n",
      "3870/3870==============================] - 0s 17us/sample - loss: 0.4126\n",
      "7740/7740==============================] - 0s 17us/sample - loss: 0.3975\n",
      "[CV]  learning_rate=0.0006154014789262348, n_hidden=2, n_neurons=87, total=  12.7s\n",
      "[CV] learning_rate=0.0006154014789262348, n_hidden=2, n_neurons=87 ...\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 141us/sample - loss: 2.8159 - val_loss: 3.8883\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 1.1382 - val_loss: 1.2675\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.8434 - val_loss: 0.8051\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.7618 - val_loss: 0.7409\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.7209 - val_loss: 0.6828\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.6920 - val_loss: 0.6731\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.6677 - val_loss: 0.6357\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.6461 - val_loss: 0.6117\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.6267 - val_loss: 0.6125\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.6082 - val_loss: 0.5875\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.5911 - val_loss: 0.5776\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.5751 - val_loss: 0.5521\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.5602 - val_loss: 0.5334\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.5469 - val_loss: 0.5310\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.5339 - val_loss: 0.5091\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.5227 - val_loss: 0.5137\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.5120 - val_loss: 0.4996\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.5021 - val_loss: 0.4838\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4929 - val_loss: 0.4808\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4848 - val_loss: 0.4846\n",
      "Epoch 21/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4770 - val_loss: 0.4964\n",
      "Epoch 22/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4702 - val_loss: 0.4778\n",
      "Epoch 23/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4635 - val_loss: 0.4665\n",
      "Epoch 24/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4575 - val_loss: 0.4466\n",
      "Epoch 25/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4522 - val_loss: 0.4698\n",
      "Epoch 26/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4470 - val_loss: 0.4529\n",
      "Epoch 27/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4422 - val_loss: 0.4512\n",
      "Epoch 28/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4378 - val_loss: 0.4387\n",
      "Epoch 29/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4337 - val_loss: 0.4415\n",
      "Epoch 30/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4299 - val_loss: 0.4256\n",
      "Epoch 31/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4265 - val_loss: 0.4401\n",
      "Epoch 32/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4233 - val_loss: 0.4429\n",
      "Epoch 33/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4202 - val_loss: 0.4408\n",
      "Epoch 34/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4173 - val_loss: 0.4212\n",
      "Epoch 35/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4147 - val_loss: 0.4266\n",
      "Epoch 36/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4122 - val_loss: 0.4134\n",
      "Epoch 37/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4099 - val_loss: 0.4203\n",
      "Epoch 38/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4076 - val_loss: 0.4307\n",
      "Epoch 39/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4055 - val_loss: 0.4345\n",
      "Epoch 40/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4036 - val_loss: 0.4155\n",
      "Epoch 41/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4016 - val_loss: 0.4294\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 42/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3998 - val_loss: 0.4492\n",
      "Epoch 43/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3982 - val_loss: 0.4254\n",
      "Epoch 44/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3966 - val_loss: 0.4334\n",
      "Epoch 45/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3951 - val_loss: 0.4365\n",
      "Epoch 46/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3936 - val_loss: 0.4153\n",
      "3870/3870==============================] - 0s 17us/sample - loss: 0.3925\n",
      "7740/7740==============================] - 0s 16us/sample - loss: 0.3921\n",
      "[CV]  learning_rate=0.0006154014789262348, n_hidden=2, n_neurons=87, total=  16.7s\n",
      "[CV] learning_rate=0.0003920021771415983, n_hidden=3, n_neurons=24 ...\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 140us/sample - loss: 2.3862 - val_loss: 13.9845\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 1.2058 - val_loss: 5.5593\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.8959 - val_loss: 4.5457\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.8120 - val_loss: 3.3820\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.7697 - val_loss: 2.6813\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.7419 - val_loss: 2.3451\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.7197 - val_loss: 2.0632\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.7008 - val_loss: 1.8155\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.6839 - val_loss: 1.6386\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.6683 - val_loss: 1.4779\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.6539 - val_loss: 1.3511\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.6403 - val_loss: 1.2563\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.6273 - val_loss: 1.1679\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.6150 - val_loss: 1.0896\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.6033 - val_loss: 1.0179\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.5920 - val_loss: 0.9522\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.5810 - val_loss: 0.8950\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.5708 - val_loss: 0.8479\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.5609 - val_loss: 0.8090\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.5514 - val_loss: 0.7714\n",
      "Epoch 21/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.5424 - val_loss: 0.7420\n",
      "Epoch 22/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.5336 - val_loss: 0.7130\n",
      "Epoch 23/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.5253 - val_loss: 0.6878\n",
      "Epoch 24/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.5173 - val_loss: 0.6637\n",
      "Epoch 25/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.5095 - val_loss: 0.6434\n",
      "Epoch 26/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.5023 - val_loss: 0.6227\n",
      "Epoch 27/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4952 - val_loss: 0.6046\n",
      "Epoch 28/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4888 - val_loss: 0.5920\n",
      "Epoch 29/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4822 - val_loss: 0.5776\n",
      "Epoch 30/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4767 - val_loss: 0.5682\n",
      "Epoch 31/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4711 - val_loss: 0.5589\n",
      "Epoch 32/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4657 - val_loss: 0.5486\n",
      "Epoch 33/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4607 - val_loss: 0.5425\n",
      "Epoch 34/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4560 - val_loss: 0.5326\n",
      "Epoch 35/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4514 - val_loss: 0.5223\n",
      "Epoch 36/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.4475 - val_loss: 0.5164\n",
      "Epoch 37/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4436 - val_loss: 0.5121\n",
      "Epoch 38/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4398 - val_loss: 0.5051\n",
      "Epoch 39/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4364 - val_loss: 0.5014\n",
      "Epoch 40/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4331 - val_loss: 0.4988\n",
      "Epoch 41/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.4301 - val_loss: 0.4946\n",
      "Epoch 42/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4271 - val_loss: 0.4891\n",
      "Epoch 43/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4245 - val_loss: 0.4877\n",
      "Epoch 44/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.4219 - val_loss: 0.4829\n",
      "Epoch 45/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.4195 - val_loss: 0.4829\n",
      "Epoch 46/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4172 - val_loss: 0.4812\n",
      "Epoch 47/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4150 - val_loss: 0.4769\n",
      "Epoch 48/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4129 - val_loss: 0.4749\n",
      "Epoch 49/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4110 - val_loss: 0.4748\n",
      "Epoch 50/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4090 - val_loss: 0.4745\n",
      "Epoch 51/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4073 - val_loss: 0.4716\n",
      "Epoch 52/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4056 - val_loss: 0.4712\n",
      "Epoch 53/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4037 - val_loss: 0.4693\n",
      "Epoch 54/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4024 - val_loss: 0.4699\n",
      "Epoch 55/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4008 - val_loss: 0.4697\n",
      "Epoch 56/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3994 - val_loss: 0.4691\n",
      "Epoch 57/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3980 - val_loss: 0.4677\n",
      "Epoch 58/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3966 - val_loss: 0.4693\n",
      "Epoch 59/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3955 - val_loss: 0.4687\n",
      "Epoch 60/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3942 - val_loss: 0.4683\n",
      "Epoch 61/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3930 - val_loss: 0.4688\n",
      "Epoch 62/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3919 - val_loss: 0.4700\n",
      "Epoch 63/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3908 - val_loss: 0.4699\n",
      "Epoch 64/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3898 - val_loss: 0.4691\n",
      "Epoch 65/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3888 - val_loss: 0.4687\n",
      "Epoch 66/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3878 - val_loss: 0.4692\n",
      "Epoch 67/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.3868 - val_loss: 0.4683\n",
      "3870/3870==============================] - 0s 18us/sample - loss: 0.4021\n",
      "7740/7740==============================] - 0s 16us/sample - loss: 0.3859\n",
      "[CV]  learning_rate=0.0003920021771415983, n_hidden=3, n_neurons=24, total=  23.0s\n",
      "[CV] learning_rate=0.0003920021771415983, n_hidden=3, n_neurons=24 ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 148us/sample - loss: 3.7474 - val_loss: 29.0692\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 2.0463 - val_loss: 43.0949\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 1.6110 - val_loss: 36.5078\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 1.3588 - val_loss: 28.6080\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 1.1672 - val_loss: 20.6365\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 1.0247 - val_loss: 14.8384\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.9255 - val_loss: 10.2883\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.8588 - val_loss: 7.4387\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.8133 - val_loss: 5.3757\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.7808 - val_loss: 3.8511\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.7557 - val_loss: 2.8283\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.7348 - val_loss: 2.0967\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.7166 - val_loss: 1.5848\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.7003 - val_loss: 1.2574\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.6853 - val_loss: 1.0125\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.6714 - val_loss: 0.8604\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.6583 - val_loss: 0.7284\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.6460 - val_loss: 0.6511\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 50us/sample - loss: 0.6341 - val_loss: 0.6091\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.6227 - val_loss: 0.5766\n",
      "Epoch 21/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.6119 - val_loss: 0.5827\n",
      "Epoch 22/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.6014 - val_loss: 0.6034\n",
      "Epoch 23/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.5913 - val_loss: 0.6142\n",
      "Epoch 24/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.5817 - val_loss: 0.6558\n",
      "Epoch 25/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.5728 - val_loss: 0.7344\n",
      "Epoch 26/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.5640 - val_loss: 0.8111\n",
      "Epoch 27/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.5556 - val_loss: 0.9001\n",
      "Epoch 28/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.5478 - val_loss: 0.8953\n",
      "Epoch 29/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.5405 - val_loss: 0.9494\n",
      "Epoch 30/100\n",
      "7740/7740==============================] - 0s 51us/sample - loss: 0.5332 - val_loss: 0.9521\n",
      "3870/3870==============================] - 0s 21us/sample - loss: 0.5482\n",
      "7740/7740==============================] - 0s 20us/sample - loss: 0.5291\n",
      "[CV]  learning_rate=0.0003920021771415983, n_hidden=3, n_neurons=24, total=  11.7s\n",
      "[CV] learning_rate=0.0003920021771415983, n_hidden=3, n_neurons=24 ...\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 148us/sample - loss: 2.9140 - val_loss: 4.2983\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 1.2803 - val_loss: 2.8777\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.9716 - val_loss: 1.8147\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.8516 - val_loss: 1.2949\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.7865 - val_loss: 0.9642\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.7446 - val_loss: 0.7976\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.7144 - val_loss: 0.7115\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.6902 - val_loss: 0.6654\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.6700 - val_loss: 0.6360\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.6525 - val_loss: 0.6160\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.6365 - val_loss: 0.5998\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.6218 - val_loss: 0.5858\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.6084 - val_loss: 0.5733\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.5958 - val_loss: 0.5610\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.5846 - val_loss: 0.5503\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.5740 - val_loss: 0.5402\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.5641 - val_loss: 0.5305\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.5548 - val_loss: 0.5209\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.5462 - val_loss: 0.5134\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.5382 - val_loss: 0.5063\n",
      "Epoch 21/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.5305 - val_loss: 0.5005\n",
      "Epoch 22/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.5235 - val_loss: 0.4932\n",
      "Epoch 23/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.5169 - val_loss: 0.4865\n",
      "Epoch 24/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.5107 - val_loss: 0.4816\n",
      "Epoch 25/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.5050 - val_loss: 0.4763\n",
      "Epoch 26/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4996 - val_loss: 0.4756\n",
      "Epoch 27/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4945 - val_loss: 0.4690\n",
      "Epoch 28/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4897 - val_loss: 0.4658\n",
      "Epoch 29/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4853 - val_loss: 0.4648\n",
      "Epoch 30/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4808 - val_loss: 0.4612\n",
      "Epoch 31/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4770 - val_loss: 0.4572\n",
      "Epoch 32/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4733 - val_loss: 0.4557\n",
      "Epoch 33/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4698 - val_loss: 0.4603\n",
      "Epoch 34/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4664 - val_loss: 0.4562\n",
      "Epoch 35/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4633 - val_loss: 0.4539\n",
      "Epoch 36/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4603 - val_loss: 0.4586\n",
      "Epoch 37/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4575 - val_loss: 0.4563\n",
      "Epoch 38/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.4550 - val_loss: 0.4522\n",
      "Epoch 39/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4525 - val_loss: 0.4505\n",
      "Epoch 40/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4502 - val_loss: 0.4559\n",
      "Epoch 41/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4480 - val_loss: 0.4529\n",
      "Epoch 42/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.4458 - val_loss: 0.4525\n",
      "Epoch 43/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4438 - val_loss: 0.4479\n",
      "Epoch 44/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4420 - val_loss: 0.4480\n",
      "Epoch 45/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4400 - val_loss: 0.4570\n",
      "Epoch 46/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4383 - val_loss: 0.4465\n",
      "Epoch 47/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4366 - val_loss: 0.4532\n",
      "Epoch 48/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4352 - val_loss: 0.4507\n",
      "Epoch 49/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4336 - val_loss: 0.4486\n",
      "Epoch 50/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4322 - val_loss: 0.4484\n",
      "Epoch 51/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.4307 - val_loss: 0.4520\n",
      "Epoch 52/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4294 - val_loss: 0.4432\n",
      "Epoch 53/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4281 - val_loss: 0.4536\n",
      "Epoch 54/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4268 - val_loss: 0.4497\n",
      "Epoch 55/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4254 - val_loss: 0.4599\n",
      "Epoch 56/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4244 - val_loss: 0.4474\n",
      "Epoch 57/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4232 - val_loss: 0.4450\n",
      "Epoch 58/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4219 - val_loss: 0.4586\n",
      "Epoch 59/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4211 - val_loss: 0.4512\n",
      "Epoch 60/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4199 - val_loss: 0.4459\n",
      "Epoch 61/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4189 - val_loss: 0.4471\n",
      "Epoch 62/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4180 - val_loss: 0.4457\n",
      "3870/3870==============================] - 0s 18us/sample - loss: 0.4166\n",
      "7740/7740==============================] - 0s 17us/sample - loss: 0.4167\n",
      "[CV]  learning_rate=0.0003920021771415983, n_hidden=3, n_neurons=24, total=  22.2s\n",
      "[CV] learning_rate=0.006010328378268217, n_hidden=0, n_neurons=2 .....\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 145us/sample - loss: 2.1078 - val_loss: 46.3930\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 38us/sample - loss: 1.4085 - val_loss: 154.3806\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 1.7590 - val_loss: 250.1559\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 39us/sample - loss: 5.1266 - val_loss: 536.2075\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 39us/sample - loss: 7.7256 - val_loss: 1357.6809\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 39us/sample - loss: 10.2720 - val_loss: 3606.7003\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 39.2440 - val_loss: 7979.6003\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 62.8579 - val_loss: 19083.1640\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 224.9636 - val_loss: 45243.5991\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 39us/sample - loss: 725.3053 - val_loss: 102597.4937\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 37us/sample - loss: 1082.2094 - val_loss: 234243.2275\n",
      "3870/3870==============================] - 0s 17us/sample - loss: 615.9416\n",
      "7740/7740==============================] - 0s 17us/sample - loss: 3221.4655\n",
      "[CV]  learning_rate=0.006010328378268217, n_hidden=0, n_neurons=2, total=   4.7s\n",
      "[CV] learning_rate=0.006010328378268217, n_hidden=0, n_neurons=2 .....\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 147us/sample - loss: 1.4712 - val_loss: 13.6461\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 37us/sample - loss: 0.5771 - val_loss: 14.8648\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 37us/sample - loss: 0.5523 - val_loss: 16.7720\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 38us/sample - loss: 0.5375 - val_loss: 19.1827\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 37us/sample - loss: 0.5266 - val_loss: 20.6951\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 38us/sample - loss: 0.5197 - val_loss: 20.7977\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 39us/sample - loss: 0.5147 - val_loss: 19.7069\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 38us/sample - loss: 0.5120 - val_loss: 19.8623\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 38us/sample - loss: 0.5070 - val_loss: 20.1563\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 38us/sample - loss: 0.5069 - val_loss: 19.5303\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 39us/sample - loss: 0.5061 - val_loss: 18.1264\n",
      "3870/3870==============================] - 0s 16us/sample - loss: 0.9305\n",
      "7740/7740==============================] - 0s 17us/sample - loss: 0.5022\n",
      "[CV]  learning_rate=0.006010328378268217, n_hidden=0, n_neurons=2, total=   4.5s\n",
      "[CV] learning_rate=0.006010328378268217, n_hidden=0, n_neurons=2 .....\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 150us/sample - loss: 1.8909 - val_loss: 16.1078\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 38us/sample - loss: 0.6519 - val_loss: 19.7876\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 38us/sample - loss: 1.0094 - val_loss: 13.6433\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.5960 - val_loss: 15.2432\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 39us/sample - loss: 0.8916 - val_loss: 18.7778\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 38us/sample - loss: 0.6047 - val_loss: 7.9147\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 37us/sample - loss: 0.7356 - val_loss: 4.0041\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.5434 - val_loss: 1.6372\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 39us/sample - loss: 0.5942 - val_loss: 0.8274\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 39us/sample - loss: 0.5437 - val_loss: 0.5222\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 38us/sample - loss: 0.5654 - val_loss: 1.8387\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 38us/sample - loss: 0.5365 - val_loss: 1.8525\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 38us/sample - loss: 0.6035 - val_loss: 4.7480\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 39us/sample - loss: 0.5480 - val_loss: 0.5232\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 39us/sample - loss: 0.5609 - val_loss: 15.6846\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 38us/sample - loss: 0.5747 - val_loss: 9.1065\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 39us/sample - loss: 0.7113 - val_loss: 18.7340\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 38us/sample - loss: 0.6000 - val_loss: 11.9635\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 39us/sample - loss: 0.8235 - val_loss: 11.7582\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 39us/sample - loss: 0.5695 - val_loss: 1.6298\n",
      "3870/3870==============================] - 0s 18us/sample - loss: 0.5199\n",
      "7740/7740==============================] - 0s 18us/sample - loss: 0.5300\n",
      "[CV]  learning_rate=0.006010328378268217, n_hidden=0, n_neurons=2, total=   7.3s\n",
      "[CV] learning_rate=0.008339092654580042, n_hidden=1, n_neurons=38 ....\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 161us/sample - loss: 1.1897 - val_loss: 144.7378\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 39us/sample - loss: 2.5051 - val_loss: 6.1038\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.5315 - val_loss: 14.2632\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.5829 - val_loss: 0.7138\n",
      "Epoch 5/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4283 - val_loss: 0.4454\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.4102 - val_loss: 0.4449\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 39us/sample - loss: 0.4004 - val_loss: 0.3889\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 39us/sample - loss: 0.3985 - val_loss: 0.3901\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3868 - val_loss: 0.3757\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3847 - val_loss: 0.3859\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3865 - val_loss: 0.3712\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.3841 - val_loss: 0.3654\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 50us/sample - loss: 0.3735 - val_loss: 0.3740\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3727 - val_loss: 0.3642\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.3710 - val_loss: 0.3625\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.3677 - val_loss: 0.3682\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3677 - val_loss: 0.3691\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.3657 - val_loss: 0.3593\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3652 - val_loss: 0.3594\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3643 - val_loss: 0.3559\n",
      "Epoch 21/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.3606 - val_loss: 0.3636\n",
      "Epoch 22/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3571 - val_loss: 0.3566\n",
      "Epoch 23/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3627 - val_loss: 0.3459\n",
      "Epoch 24/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.3556 - val_loss: 0.3521\n",
      "Epoch 25/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3528 - val_loss: 0.3659\n",
      "Epoch 26/100\n",
      "7740/7740==============================] - 0s 50us/sample - loss: 0.3523 - val_loss: 0.3762\n",
      "Epoch 27/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4103 - val_loss: 0.3484\n",
      "Epoch 28/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3567 - val_loss: 0.3556\n",
      "Epoch 29/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3536 - val_loss: 0.3620\n",
      "Epoch 30/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3588 - val_loss: 0.3444\n",
      "Epoch 31/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3500 - val_loss: 0.3556\n",
      "Epoch 32/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3739 - val_loss: 0.3397\n",
      "Epoch 33/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.3560 - val_loss: 0.3438\n",
      "Epoch 34/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3490 - val_loss: 0.3426\n",
      "Epoch 35/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.3421 - val_loss: 0.3458\n",
      "Epoch 36/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.3408 - val_loss: 0.3576\n",
      "Epoch 37/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3557 - val_loss: 0.3513\n",
      "Epoch 38/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.3441 - val_loss: 0.3946\n",
      "Epoch 39/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4083 - val_loss: 0.3425\n",
      "Epoch 40/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3403 - val_loss: 0.3472\n",
      "Epoch 41/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3400 - val_loss: 0.3491\n",
      "Epoch 42/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3406 - val_loss: 0.3441\n",
      "3870/3870==============================] - 0s 18us/sample - loss: 0.3765\n",
      "7740/7740==============================] - 0s 17us/sample - loss: 0.3373\n",
      "[CV]  learning_rate=0.008339092654580042, n_hidden=1, n_neurons=38, total=  15.3s\n",
      "[CV] learning_rate=0.008339092654580042, n_hidden=1, n_neurons=38 ....\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 165us/sample - loss: 0.8861 - val_loss: 2.5461\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.5227 - val_loss: 0.4614\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4617 - val_loss: 0.4899\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4348 - val_loss: 0.4041\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4207 - val_loss: 0.4473\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4114 - val_loss: 0.5539\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4055 - val_loss: 0.8075\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4008 - val_loss: 0.7600\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3959 - val_loss: 1.0842\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3921 - val_loss: 1.0654\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3878 - val_loss: 0.9827\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3850 - val_loss: 0.8621\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3823 - val_loss: 1.0495\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3795 - val_loss: 1.1396\n",
      "3870/3870==============================] - 0s 20us/sample - loss: 0.4055\n",
      "7740/7740==============================] - 0s 18us/sample - loss: 0.3821\n",
      "[CV]  learning_rate=0.008339092654580042, n_hidden=1, n_neurons=38, total=   6.0s\n",
      "[CV] learning_rate=0.008339092654580042, n_hidden=1, n_neurons=38 ....\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 176us/sample - loss: 1.1067 - val_loss: 14.2226\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.5861 - val_loss: 47.5998\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.7327 - val_loss: 13.5957\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.7681 - val_loss: 2.7430\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4636 - val_loss: 0.3968\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4239 - val_loss: 0.3962\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4135 - val_loss: 0.3999\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4067 - val_loss: 0.4062\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4008 - val_loss: 0.4145\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3940 - val_loss: 0.4088\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3897 - val_loss: 0.4213\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3878 - val_loss: 0.4143\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3839 - val_loss: 0.3978\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3816 - val_loss: 0.3809\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3832 - val_loss: 0.3859\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3891 - val_loss: 0.3944\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3791 - val_loss: 0.3976\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3764 - val_loss: 0.3646\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3726 - val_loss: 0.3891\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3686 - val_loss: 0.3755\n",
      "Epoch 21/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3664 - val_loss: 0.3869\n",
      "Epoch 22/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3653 - val_loss: 0.3998\n",
      "Epoch 23/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3637 - val_loss: 0.4089\n",
      "Epoch 24/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3628 - val_loss: 0.3601\n",
      "Epoch 25/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3706 - val_loss: 0.4110\n",
      "Epoch 26/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3894 - val_loss: 0.4125\n",
      "Epoch 27/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3609 - val_loss: 0.3811\n",
      "Epoch 28/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.3573 - val_loss: 0.3606\n",
      "Epoch 29/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3614 - val_loss: 0.3583\n",
      "Epoch 30/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3537 - val_loss: 0.3800\n",
      "Epoch 31/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3525 - val_loss: 0.3602\n",
      "Epoch 32/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3511 - val_loss: 0.3524\n",
      "Epoch 33/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3496 - val_loss: 0.3670\n",
      "Epoch 34/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3483 - val_loss: 0.4263\n",
      "Epoch 35/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3519 - val_loss: 0.3332\n",
      "Epoch 36/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.3474 - val_loss: 0.3853\n",
      "Epoch 37/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3459 - val_loss: 0.3860\n",
      "Epoch 38/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3765 - val_loss: 0.3252\n",
      "Epoch 39/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3437 - val_loss: 0.3651\n",
      "Epoch 40/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3465 - val_loss: 0.3671\n",
      "Epoch 41/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3435 - val_loss: 0.3393\n",
      "Epoch 42/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3417 - val_loss: 0.3879\n",
      "Epoch 43/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3469 - val_loss: 0.3227\n",
      "Epoch 44/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3413 - val_loss: 0.3516\n",
      "Epoch 45/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.3485 - val_loss: 0.3818\n",
      "Epoch 46/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3394 - val_loss: 0.3399\n",
      "Epoch 47/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3400 - val_loss: 0.3632\n",
      "Epoch 48/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3399 - val_loss: 0.3516\n",
      "Epoch 49/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3391 - val_loss: 0.3215\n",
      "Epoch 50/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3459 - val_loss: 0.3520\n",
      "Epoch 51/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.3345 - val_loss: 0.3744\n",
      "Epoch 52/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3380 - val_loss: 0.3210\n",
      "Epoch 53/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3339 - val_loss: 0.3559\n",
      "Epoch 54/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3314 - val_loss: 0.3459\n",
      "Epoch 55/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.3325 - val_loss: 0.4225\n",
      "Epoch 56/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3322 - val_loss: 0.3153\n",
      "Epoch 57/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3321 - val_loss: 0.3412\n",
      "Epoch 58/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3330 - val_loss: 0.4236\n",
      "Epoch 59/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3311 - val_loss: 0.3260\n",
      "Epoch 60/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3312 - val_loss: 0.3301\n",
      "Epoch 61/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.3302 - val_loss: 0.3150\n",
      "Epoch 62/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3375 - val_loss: 0.3231\n",
      "Epoch 63/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.3448 - val_loss: 0.3116\n",
      "Epoch 64/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3285 - val_loss: 0.3585\n",
      "Epoch 65/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3517 - val_loss: 0.3785\n",
      "Epoch 66/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3335 - val_loss: 0.3653\n",
      "Epoch 67/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3266 - val_loss: 0.3151\n",
      "Epoch 68/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3270 - val_loss: 0.3768\n",
      "Epoch 69/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3280 - val_loss: 0.3104\n",
      "Epoch 70/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3250 - val_loss: 0.3425\n",
      "Epoch 71/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3249 - val_loss: 0.3399\n",
      "Epoch 72/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3237 - val_loss: 0.3250\n",
      "Epoch 73/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3230 - val_loss: 0.3237\n",
      "Epoch 74/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.3274 - val_loss: 0.3088\n",
      "Epoch 75/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.3212 - val_loss: 0.3456\n",
      "Epoch 76/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.3219 - val_loss: 0.3458\n",
      "Epoch 77/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3218 - val_loss: 0.3332\n",
      "Epoch 78/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3284 - val_loss: 0.3099\n",
      "Epoch 79/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3228 - val_loss: 0.3143\n",
      "Epoch 80/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.3234 - val_loss: 0.3401\n",
      "Epoch 81/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3211 - val_loss: 0.3050\n",
      "Epoch 82/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3213 - val_loss: 0.3384\n",
      "Epoch 83/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3218 - val_loss: 0.3252\n",
      "Epoch 84/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3175 - val_loss: 0.4177\n",
      "Epoch 85/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3564 - val_loss: 0.3064\n",
      "Epoch 86/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3188 - val_loss: 0.3613\n",
      "Epoch 87/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.3446 - val_loss: 0.3307\n",
      "Epoch 88/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3186 - val_loss: 0.3951\n",
      "Epoch 89/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.3253 - val_loss: 0.3162\n",
      "Epoch 90/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.3196 - val_loss: 0.3265\n",
      "Epoch 91/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3188 - val_loss: 0.3278\n",
      "3870/3870==============================] - 0s 17us/sample - loss: 0.3156\n",
      "7740/7740==============================] - 0s 18us/sample - loss: 0.3115\n",
      "[CV]  learning_rate=0.008339092654580042, n_hidden=1, n_neurons=38, total=  31.6s\n",
      "[CV] learning_rate=0.00030107783636342726, n_hidden=3, n_neurons=21 ..\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 174us/sample - loss: 4.0347 - val_loss: 7.4753\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 2.3077 - val_loss: 7.4152\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 1.6237 - val_loss: 6.0030\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 1.3410 - val_loss: 3.7805\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 49us/sample - loss: 1.1814 - val_loss: 2.6328\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 1.0672 - val_loss: 2.1427\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.9879 - val_loss: 1.7334\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.9232 - val_loss: 1.4483\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.8700 - val_loss: 1.2320\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.8256 - val_loss: 1.0651\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.7885 - val_loss: 0.9278\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.7575 - val_loss: 0.8346\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.7318 - val_loss: 0.7631\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.7103 - val_loss: 0.7128\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.6922 - val_loss: 0.6770\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.6767 - val_loss: 0.6525\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.6634 - val_loss: 0.6373\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.6520 - val_loss: 0.6272\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.6419 - val_loss: 0.6207\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.6328 - val_loss: 0.6176\n",
      "Epoch 21/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.6244 - val_loss: 0.6164\n",
      "Epoch 22/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.6169 - val_loss: 0.6119\n",
      "Epoch 23/100\n",
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      "Epoch 24/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.6029 - val_loss: 0.6089\n",
      "Epoch 25/100\n",
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      "Epoch 26/100\n",
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      "Epoch 27/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.5843 - val_loss: 0.5998\n",
      "Epoch 28/100\n",
      "7740/7740==============================] - 0s 49us/sample - loss: 0.5787 - val_loss: 0.5931\n",
      "Epoch 29/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.5730 - val_loss: 0.5880\n",
      "Epoch 30/100\n",
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      "Epoch 31/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.5623 - val_loss: 0.5746\n",
      "Epoch 32/100\n",
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      "Epoch 33/100\n",
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      "Epoch 34/100\n",
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      "Epoch 35/100\n",
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      "Epoch 36/100\n",
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      "Epoch 37/100\n",
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      "Epoch 38/100\n",
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      "Epoch 39/100\n",
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      "Epoch 40/100\n",
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      "Epoch 41/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.5159 - val_loss: 0.5250\n",
      "Epoch 42/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.5116 - val_loss: 0.5236\n",
      "Epoch 43/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.5078 - val_loss: 0.5174\n",
      "Epoch 44/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.5040 - val_loss: 0.5124\n",
      "Epoch 45/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.5002 - val_loss: 0.5088\n",
      "Epoch 46/100\n",
      "7740/7740==============================] - 0s 51us/sample - loss: 0.4966 - val_loss: 0.5012\n",
      "Epoch 47/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4929 - val_loss: 0.4977\n",
      "Epoch 48/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4893 - val_loss: 0.4934\n",
      "Epoch 49/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4856 - val_loss: 0.4943\n",
      "Epoch 50/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4825 - val_loss: 0.4870\n",
      "Epoch 51/100\n",
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      "Epoch 52/100\n",
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      "Epoch 53/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4726 - val_loss: 0.4738\n",
      "Epoch 54/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4695 - val_loss: 0.4695\n",
      "Epoch 55/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4664 - val_loss: 0.4667\n",
      "Epoch 56/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.4634 - val_loss: 0.4612\n",
      "Epoch 57/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4604 - val_loss: 0.4559\n",
      "Epoch 58/100\n",
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      "Epoch 59/100\n",
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      "Epoch 60/100\n",
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      "Epoch 61/100\n",
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      "Epoch 62/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4471 - val_loss: 0.4411\n",
      "Epoch 63/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4446 - val_loss: 0.4373\n",
      "Epoch 64/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4421 - val_loss: 0.4354\n",
      "Epoch 65/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4399 - val_loss: 0.4327\n",
      "Epoch 66/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4375 - val_loss: 0.4280\n",
      "Epoch 67/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4353 - val_loss: 0.4259\n",
      "Epoch 68/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4332 - val_loss: 0.4241\n",
      "Epoch 69/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4310 - val_loss: 0.4211\n",
      "Epoch 70/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4289 - val_loss: 0.4198\n",
      "Epoch 71/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4269 - val_loss: 0.4169\n",
      "Epoch 72/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4249 - val_loss: 0.4144\n",
      "Epoch 73/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4231 - val_loss: 0.4122\n",
      "Epoch 74/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.4211 - val_loss: 0.4104\n",
      "Epoch 75/100\n",
      "7740/7740==============================] - 0s 50us/sample - loss: 0.4194 - val_loss: 0.4083\n",
      "Epoch 76/100\n",
      "7740/7740==============================] - 0s 49us/sample - loss: 0.4177 - val_loss: 0.4067\n",
      "Epoch 77/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.4160 - val_loss: 0.4050\n",
      "Epoch 78/100\n",
      "7740/7740==============================] - 0s 51us/sample - loss: 0.4144 - val_loss: 0.4035\n",
      "Epoch 79/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4128 - val_loss: 0.4019\n",
      "Epoch 80/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.4112 - val_loss: 0.4005\n",
      "Epoch 81/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.4097 - val_loss: 0.3991\n",
      "Epoch 82/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4082 - val_loss: 0.3973\n",
      "Epoch 83/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4068 - val_loss: 0.3960\n",
      "Epoch 84/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4054 - val_loss: 0.3951\n",
      "Epoch 85/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4040 - val_loss: 0.3935\n",
      "Epoch 86/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.4026 - val_loss: 0.3931\n",
      "Epoch 87/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4015 - val_loss: 0.3913\n",
      "Epoch 88/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4002 - val_loss: 0.3901\n",
      "Epoch 89/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.3989 - val_loss: 0.3895\n",
      "Epoch 90/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.3978 - val_loss: 0.3882\n",
      "Epoch 91/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3967 - val_loss: 0.3869\n",
      "Epoch 92/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.3955 - val_loss: 0.3865\n",
      "Epoch 93/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3944 - val_loss: 0.3854\n",
      "Epoch 94/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3934 - val_loss: 0.3840\n",
      "Epoch 95/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3924 - val_loss: 0.3841\n",
      "Epoch 96/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.3915 - val_loss: 0.3827\n",
      "Epoch 97/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3905 - val_loss: 0.3822\n",
      "Epoch 98/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3896 - val_loss: 0.3810\n",
      "Epoch 99/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3887 - val_loss: 0.3811\n",
      "Epoch 100/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.3878 - val_loss: 0.3810\n",
      "3870/3870==============================] - 0s 20us/sample - loss: 0.3994\n",
      "7740/7740==============================] - 0s 19us/sample - loss: 0.3871\n",
      "[CV]  learning_rate=0.00030107783636342726, n_hidden=3, n_neurons=21, total=  36.7s\n",
      "[CV] learning_rate=0.00030107783636342726, n_hidden=3, n_neurons=21 ..\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 181us/sample - loss: 5.0772 - val_loss: 2.9801\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 2.1542 - val_loss: 5.9047\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 1.2817 - val_loss: 10.7273\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 1.0915 - val_loss: 11.3587\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 1.0055 - val_loss: 10.1313\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 52us/sample - loss: 0.9448 - val_loss: 8.1891\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.8982 - val_loss: 6.4288\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 55us/sample - loss: 0.8614 - val_loss: 5.0080\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 49us/sample - loss: 0.8313 - val_loss: 3.7324\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.8065 - val_loss: 2.7158\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.7849 - val_loss: 2.0442\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.7663 - val_loss: 1.5017\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.7496 - val_loss: 1.1548\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.7344 - val_loss: 0.8854\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.7205 - val_loss: 0.7283\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.7075 - val_loss: 0.6645\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.6955 - val_loss: 0.6726\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.6840 - val_loss: 0.7382\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.6732 - val_loss: 0.8638\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.6629 - val_loss: 1.0252\n",
      "Epoch 21/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.6534 - val_loss: 1.2662\n",
      "Epoch 22/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.6439 - val_loss: 1.4988\n",
      "Epoch 23/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.6353 - val_loss: 1.7841\n",
      "Epoch 24/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.6271 - val_loss: 2.1034\n",
      "Epoch 25/100\n",
      "7740/7740==============================] - 0s 52us/sample - loss: 0.6192 - val_loss: 2.4662\n",
      "Epoch 26/100\n",
      "7740/7740==============================] - 0s 49us/sample - loss: 0.6118 - val_loss: 2.7818\n",
      "3870/3870==============================] - 0s 19us/sample - loss: 0.6752\n",
      "7740/7740==============================] - 0s 18us/sample - loss: 0.6075\n",
      "[CV]  learning_rate=0.00030107783636342726, n_hidden=3, n_neurons=21, total=  11.4s\n",
      "[CV] learning_rate=0.00030107783636342726, n_hidden=3, n_neurons=21 ..\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 180us/sample - loss: 4.4065 - val_loss: 3.5601\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 2.5663 - val_loss: 2.8733\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 1.4020 - val_loss: 2.5297\n",
      "Epoch 4/100\n",
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      "Epoch 5/100\n",
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      "Epoch 6/100\n",
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      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.7278 - val_loss: 0.8273\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 50us/sample - loss: 0.6999 - val_loss: 0.7465\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.6804 - val_loss: 0.7005\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.6654 - val_loss: 0.6677\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 49us/sample - loss: 0.6531 - val_loss: 0.6424\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.6422 - val_loss: 0.6253\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.6326 - val_loss: 0.6092\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.6236 - val_loss: 0.5959\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.6153 - val_loss: 0.5859\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.6072 - val_loss: 0.5764\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.5994 - val_loss: 0.5675\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.5917 - val_loss: 0.5594\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.5841 - val_loss: 0.5524\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.5769 - val_loss: 0.5449\n",
      "Epoch 21/100\n",
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      "Epoch 22/100\n",
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      "Epoch 23/100\n",
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      "Epoch 24/100\n",
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      "Epoch 25/100\n",
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      "Epoch 26/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.5361 - val_loss: 0.5050\n",
      "Epoch 27/100\n",
      "7740/7740==============================] - 0s 49us/sample - loss: 0.5297 - val_loss: 0.4989\n",
      "Epoch 28/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.5236 - val_loss: 0.4932\n",
      "Epoch 29/100\n",
      "7740/7740==============================] - 0s 49us/sample - loss: 0.5175 - val_loss: 0.4879\n",
      "Epoch 30/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.5117 - val_loss: 0.4822\n",
      "Epoch 31/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.5059 - val_loss: 0.4775\n",
      "Epoch 32/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.5005 - val_loss: 0.4728\n",
      "Epoch 33/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4952 - val_loss: 0.4686\n",
      "Epoch 34/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.4903 - val_loss: 0.4641\n",
      "Epoch 35/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4853 - val_loss: 0.4602\n",
      "Epoch 36/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4811 - val_loss: 0.4573\n",
      "Epoch 37/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.4767 - val_loss: 0.4547\n",
      "Epoch 38/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.4727 - val_loss: 0.4523\n",
      "Epoch 39/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4688 - val_loss: 0.4498\n",
      "Epoch 40/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.4651 - val_loss: 0.4479\n",
      "Epoch 41/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4617 - val_loss: 0.4467\n",
      "Epoch 42/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.4584 - val_loss: 0.4452\n",
      "Epoch 43/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4553 - val_loss: 0.4447\n",
      "Epoch 44/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4523 - val_loss: 0.4445\n",
      "Epoch 45/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4496 - val_loss: 0.4444\n",
      "Epoch 46/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4469 - val_loss: 0.4432\n",
      "Epoch 47/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.4444 - val_loss: 0.4426\n",
      "Epoch 48/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4420 - val_loss: 0.4426\n",
      "Epoch 49/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4397 - val_loss: 0.4440\n",
      "Epoch 50/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.4376 - val_loss: 0.4440\n",
      "Epoch 51/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4354 - val_loss: 0.4451\n",
      "Epoch 52/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4334 - val_loss: 0.4447\n",
      "Epoch 53/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.4315 - val_loss: 0.4452\n",
      "Epoch 54/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4296 - val_loss: 0.4458\n",
      "Epoch 55/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.4280 - val_loss: 0.4472\n",
      "Epoch 56/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4262 - val_loss: 0.4470\n",
      "Epoch 57/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4246 - val_loss: 0.4475\n",
      "Epoch 58/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4231 - val_loss: 0.4504\n",
      "3870/3870==============================] - 0s 20us/sample - loss: 0.4237\n",
      "7740/7740==============================] - 0s 21us/sample - loss: 0.4220\n",
      "[CV]  learning_rate=0.00030107783636342726, n_hidden=3, n_neurons=21, total=  22.4s\n",
      "[CV] learning_rate=0.005153286333701512, n_hidden=1, n_neurons=22 ....\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 178us/sample - loss: 1.4089 - val_loss: 38.2711\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 1.0477 - val_loss: 0.5353\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.5289 - val_loss: 0.4757\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4892 - val_loss: 0.4437\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4639 - val_loss: 0.4270\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4468 - val_loss: 0.4088\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4344 - val_loss: 0.4013\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4266 - val_loss: 0.3946\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4192 - val_loss: 0.3895\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4136 - val_loss: 0.3849\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4102 - val_loss: 0.3837\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4071 - val_loss: 0.3792\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.4040 - val_loss: 0.3771\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4015 - val_loss: 0.3774\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3995 - val_loss: 0.3726\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3967 - val_loss: 0.3695\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3952 - val_loss: 0.3754\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3938 - val_loss: 0.3671\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3926 - val_loss: 0.3662\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3912 - val_loss: 0.3667\n",
      "Epoch 21/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3895 - val_loss: 0.3651\n",
      "Epoch 22/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3872 - val_loss: 0.3658\n",
      "Epoch 23/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3864 - val_loss: 0.3686\n",
      "Epoch 24/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3860 - val_loss: 0.3604\n",
      "Epoch 25/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3844 - val_loss: 0.3661\n",
      "Epoch 26/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3840 - val_loss: 0.3603\n",
      "Epoch 27/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3827 - val_loss: 0.3633\n",
      "Epoch 28/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3815 - val_loss: 0.3571\n",
      "Epoch 29/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3803 - val_loss: 0.3591\n",
      "Epoch 30/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3792 - val_loss: 0.3572\n",
      "Epoch 31/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3796 - val_loss: 0.3577\n",
      "Epoch 32/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3782 - val_loss: 0.3553\n",
      "Epoch 33/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3766 - val_loss: 0.3532\n",
      "Epoch 34/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3758 - val_loss: 0.3552\n",
      "Epoch 35/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3744 - val_loss: 0.3524\n",
      "Epoch 36/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3757 - val_loss: 0.3547\n",
      "Epoch 37/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3733 - val_loss: 0.3519\n",
      "Epoch 38/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3729 - val_loss: 0.3506\n",
      "Epoch 39/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3730 - val_loss: 0.3498\n",
      "Epoch 40/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3708 - val_loss: 0.3493\n",
      "Epoch 41/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3712 - val_loss: 0.3495\n",
      "Epoch 42/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3700 - val_loss: 0.3495\n",
      "Epoch 43/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3689 - val_loss: 0.3503\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 44/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3689 - val_loss: 0.3486\n",
      "Epoch 45/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3681 - val_loss: 0.3488\n",
      "Epoch 46/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3688 - val_loss: 0.3505\n",
      "Epoch 47/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3677 - val_loss: 0.3480\n",
      "Epoch 48/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3664 - val_loss: 0.3467\n",
      "Epoch 49/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3659 - val_loss: 0.3462\n",
      "Epoch 50/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3653 - val_loss: 0.3479\n",
      "Epoch 51/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3648 - val_loss: 0.3456\n",
      "Epoch 52/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.3648 - val_loss: 0.3457\n",
      "Epoch 53/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3639 - val_loss: 0.3447\n",
      "Epoch 54/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3632 - val_loss: 0.3441\n",
      "Epoch 55/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3634 - val_loss: 0.3456\n",
      "Epoch 56/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3614 - val_loss: 0.3427\n",
      "Epoch 57/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3613 - val_loss: 0.3438\n",
      "Epoch 58/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3610 - val_loss: 0.3454\n",
      "Epoch 59/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3608 - val_loss: 0.3413\n",
      "Epoch 60/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3607 - val_loss: 0.3436\n",
      "Epoch 61/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3607 - val_loss: 0.3423\n",
      "Epoch 62/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3594 - val_loss: 0.3407\n",
      "Epoch 63/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3585 - val_loss: 0.3413\n",
      "Epoch 64/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3582 - val_loss: 0.3431\n",
      "Epoch 65/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3596 - val_loss: 0.3413\n",
      "Epoch 66/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3574 - val_loss: 0.3417\n",
      "Epoch 67/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3566 - val_loss: 0.3400\n",
      "Epoch 68/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3564 - val_loss: 0.3396\n",
      "Epoch 69/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3561 - val_loss: 0.3388\n",
      "Epoch 70/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3557 - val_loss: 0.3383\n",
      "Epoch 71/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3555 - val_loss: 0.3384\n",
      "Epoch 72/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3544 - val_loss: 0.3380\n",
      "Epoch 73/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3556 - val_loss: 0.3412\n",
      "Epoch 74/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3541 - val_loss: 0.3403\n",
      "Epoch 75/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3531 - val_loss: 0.3372\n",
      "Epoch 76/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3534 - val_loss: 0.3422\n",
      "Epoch 77/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3566 - val_loss: 0.3381\n",
      "Epoch 78/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3532 - val_loss: 0.3445\n",
      "Epoch 79/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3525 - val_loss: 0.3387\n",
      "Epoch 80/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3520 - val_loss: 0.3480\n",
      "Epoch 81/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3507 - val_loss: 0.3548\n",
      "Epoch 82/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3502 - val_loss: 0.3570\n",
      "Epoch 83/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3501 - val_loss: 0.3580\n",
      "Epoch 84/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3497 - val_loss: 0.3559\n",
      "Epoch 85/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3500 - val_loss: 0.3613\n",
      "3870/3870==============================] - 0s 18us/sample - loss: 0.3693\n",
      "7740/7740==============================] - 0s 18us/sample - loss: 0.3486\n",
      "[CV]  learning_rate=0.005153286333701512, n_hidden=1, n_neurons=22, total=  29.4s\n",
      "[CV] learning_rate=0.005153286333701512, n_hidden=1, n_neurons=22 ....\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 179us/sample - loss: 1.2423 - val_loss: 0.6564\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.5761 - val_loss: 0.8749\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.5144 - val_loss: 1.1742\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4811 - val_loss: 1.1996\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4589 - val_loss: 0.9221\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4424 - val_loss: 0.6963\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4317 - val_loss: 0.5350\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4245 - val_loss: 0.4103\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4151 - val_loss: 0.4021\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4095 - val_loss: 0.3959\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4060 - val_loss: 0.4136\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4009 - val_loss: 0.4592\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3968 - val_loss: 0.4996\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3934 - val_loss: 0.4935\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3911 - val_loss: 0.6229\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3899 - val_loss: 0.5392\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3878 - val_loss: 0.6591\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3839 - val_loss: 0.6856\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3835 - val_loss: 0.7006\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.3807 - val_loss: 0.7215\n",
      "3870/3870==============================] - 0s 19us/sample - loss: 0.3952\n",
      "7740/7740==============================] - 0s 19us/sample - loss: 0.3778\n",
      "[CV]  learning_rate=0.005153286333701512, n_hidden=1, n_neurons=22, total=   8.2s\n",
      "[CV] learning_rate=0.005153286333701512, n_hidden=1, n_neurons=22 ....\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 184us/sample - loss: 1.6166 - val_loss: 50.5385\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 1.0057 - val_loss: 0.6152\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.5774 - val_loss: 0.5348\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.5251 - val_loss: 0.4869\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4920 - val_loss: 0.4565\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4713 - val_loss: 0.4381\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4567 - val_loss: 0.4263\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4471 - val_loss: 0.4193\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4382 - val_loss: 0.4153\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4330 - val_loss: 0.4148\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4289 - val_loss: 0.4117\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4251 - val_loss: 0.4127\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4215 - val_loss: 0.4155\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.4187 - val_loss: 0.4214\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4160 - val_loss: 0.4226\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4141 - val_loss: 0.4234\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4124 - val_loss: 0.4180\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4102 - val_loss: 0.4144\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4086 - val_loss: 0.4166\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.4063 - val_loss: 0.4148\n",
      "Epoch 21/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4052 - val_loss: 0.4188\n",
      "3870/3870==============================] - 0s 20us/sample - loss: 0.4018\n",
      "7740/7740==============================] - 0s 18us/sample - loss: 0.4019\n",
      "[CV]  learning_rate=0.005153286333701512, n_hidden=1, n_neurons=22, total=   8.8s\n",
      "[CV] learning_rate=0.0003099230412972121, n_hidden=0, n_neurons=49 ...\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 187us/sample - loss: 7.7177 - val_loss: 43.1516\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 5.5435 - val_loss: 27.5088\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 4.0704 - val_loss: 17.4154\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 3.0652 - val_loss: 11.0862\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 2.3755 - val_loss: 7.0230\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 1.8921 - val_loss: 4.4862\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 1.5530 - val_loss: 2.9327\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 1.3134 - val_loss: 1.9684\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 1.1418 - val_loss: 1.3865\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 1.0182 - val_loss: 1.0533\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.9288 - val_loss: 0.8780\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.8635 - val_loss: 0.7901\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.8151 - val_loss: 0.7545\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.7788 - val_loss: 0.7543\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.7517 - val_loss: 0.7656\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.7310 - val_loss: 0.7843\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.7150 - val_loss: 0.8019\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.7023 - val_loss: 0.8338\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.6922 - val_loss: 0.8568\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.6840 - val_loss: 0.8807\n",
      "Epoch 21/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.6772 - val_loss: 0.9006\n",
      "Epoch 22/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.6714 - val_loss: 0.9174\n",
      "Epoch 23/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.6664 - val_loss: 0.9244\n",
      "Epoch 24/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.6620 - val_loss: 0.9322\n",
      "3870/3870==============================] - 0s 18us/sample - loss: 0.6630\n",
      "7740/7740==============================] - 0s 18us/sample - loss: 0.6590\n",
      "[CV]  learning_rate=0.0003099230412972121, n_hidden=0, n_neurons=49, total=   9.3s\n",
      "[CV] learning_rate=0.0003099230412972121, n_hidden=0, n_neurons=49 ...\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 186us/sample - loss: 7.6323 - val_loss: 25.5284\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 5.6953 - val_loss: 23.8079\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 4.3243 - val_loss: 22.5988\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 3.3435 - val_loss: 21.7504\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 2.6349 - val_loss: 21.1517\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 2.1182 - val_loss: 20.7329\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 1.7387 - val_loss: 20.4467\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 1.4579 - val_loss: 20.2513\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 1.2488 - val_loss: 20.1218\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 1.0923 - val_loss: 20.0459\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.9745 - val_loss: 20.0054\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.8853 - val_loss: 19.9929\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.8176 - val_loss: 20.0016\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.7657 - val_loss: 20.0277\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.7258 - val_loss: 20.0580\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.6949 - val_loss: 20.1003\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.6708 - val_loss: 20.1535\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.6517 - val_loss: 20.2118\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.6366 - val_loss: 20.2723\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.6245 - val_loss: 20.3361\n",
      "Epoch 21/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.6147 - val_loss: 20.4004\n",
      "Epoch 22/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.6066 - val_loss: 20.4709\n",
      "3870/3870==============================] - 0s 19us/sample - loss: 1.0966\n",
      "7740/7740==============================] - 0s 19us/sample - loss: 0.6029\n",
      "[CV]  learning_rate=0.0003099230412972121, n_hidden=0, n_neurons=49, total=   8.5s\n",
      "[CV] learning_rate=0.0003099230412972121, n_hidden=0, n_neurons=49 ...\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 1s 190us/sample - loss: 6.1563 - val_loss: 7.5813\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 4.4870 - val_loss: 4.9091\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 3.3694 - val_loss: 3.3099\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 2.6024 - val_loss: 2.3615\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 2.0669 - val_loss: 1.7867\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 1.6871 - val_loss: 1.4386\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 1.4146 - val_loss: 1.2291\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 1.2174 - val_loss: 1.1002\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 1.0735 - val_loss: 1.0217\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.9677 - val_loss: 0.9778\n",
      "Epoch 11/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7740/7740==============================] - 0s 41us/sample - loss: 0.8893 - val_loss: 0.9589\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.8309 - val_loss: 0.9552\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.7872 - val_loss: 0.9557\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.7544 - val_loss: 0.9513\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.7293 - val_loss: 0.9388\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 40us/sample - loss: 0.7100 - val_loss: 0.9532\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.6950 - val_loss: 0.9560\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.6833 - val_loss: 0.9489\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.6738 - val_loss: 0.9457\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.6661 - val_loss: 0.9418\n",
      "Epoch 21/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.6596 - val_loss: 0.9483\n",
      "Epoch 22/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.6543 - val_loss: 0.9425\n",
      "Epoch 23/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.6496 - val_loss: 0.9462\n",
      "Epoch 24/100\n",
      "7740/7740==============================] - 0s 42us/sample - loss: 0.6455 - val_loss: 0.9454\n",
      "Epoch 25/100\n",
      "7740/7740==============================] - 0s 41us/sample - loss: 0.6418 - val_loss: 0.9493\n",
      "3870/3870==============================] - 0s 19us/sample - loss: 0.6453\n",
      "7740/7740==============================] - 0s 19us/sample - loss: 0.6392\n",
      "[CV]  learning_rate=0.0003099230412972121, n_hidden=0, n_neurons=49, total=   9.6s\n",
      "[CV] learning_rate=0.0033625641252688094, n_hidden=2, n_neurons=42 ...\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 2s 198us/sample - loss: 1.4103 - val_loss: 10.4662\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.7060 - val_loss: 2.2811\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.5362 - val_loss: 0.6905\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4711 - val_loss: 0.4921\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.4409 - val_loss: 0.4266\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4216 - val_loss: 0.4118\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.4088 - val_loss: 0.4009\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3988 - val_loss: 0.4154\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3909 - val_loss: 0.4006\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.3841 - val_loss: 0.4143\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.3791 - val_loss: 0.3797\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3741 - val_loss: 0.3795\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3728 - val_loss: 0.4014\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3679 - val_loss: 0.3928\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3644 - val_loss: 0.3847\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3607 - val_loss: 0.3928\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3585 - val_loss: 0.3690\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3555 - val_loss: 0.3735\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3541 - val_loss: 0.3832\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3532 - val_loss: 0.3555\n",
      "Epoch 21/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3494 - val_loss: 0.3622\n",
      "Epoch 22/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3468 - val_loss: 0.3702\n",
      "Epoch 23/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3473 - val_loss: 0.3483\n",
      "Epoch 24/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3428 - val_loss: 0.3414\n",
      "Epoch 25/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3427 - val_loss: 0.3615\n",
      "Epoch 26/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3404 - val_loss: 0.3867\n",
      "Epoch 27/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3391 - val_loss: 0.3508\n",
      "Epoch 28/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3371 - val_loss: 0.3394\n",
      "Epoch 29/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3357 - val_loss: 0.3513\n",
      "Epoch 30/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3342 - val_loss: 0.3378\n",
      "Epoch 31/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3334 - val_loss: 0.3405\n",
      "Epoch 32/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3315 - val_loss: 0.3481\n",
      "Epoch 33/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3315 - val_loss: 0.3382\n",
      "Epoch 34/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3302 - val_loss: 0.3343\n",
      "Epoch 35/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3292 - val_loss: 0.3571\n",
      "Epoch 36/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3283 - val_loss: 0.3505\n",
      "Epoch 37/100\n",
      "7740/7740==============================] - 0s 49us/sample - loss: 0.3276 - val_loss: 0.3297\n",
      "Epoch 38/100\n",
      "7740/7740==============================] - 0s 50us/sample - loss: 0.3266 - val_loss: 0.3383\n",
      "Epoch 39/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.3257 - val_loss: 0.3643\n",
      "Epoch 40/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.3251 - val_loss: 0.3222\n",
      "Epoch 41/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.3241 - val_loss: 0.3348\n",
      "Epoch 42/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3233 - val_loss: 0.3520\n",
      "Epoch 43/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3232 - val_loss: 0.3246\n",
      "Epoch 44/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3219 - val_loss: 0.3336\n",
      "Epoch 45/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3210 - val_loss: 0.3166\n",
      "Epoch 46/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3208 - val_loss: 0.3608\n",
      "Epoch 47/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3197 - val_loss: 0.3382\n",
      "Epoch 48/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3186 - val_loss: 0.3433\n",
      "Epoch 49/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3182 - val_loss: 0.3619\n",
      "Epoch 50/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3177 - val_loss: 0.3293\n",
      "Epoch 51/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3170 - val_loss: 0.3655\n",
      "Epoch 52/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3166 - val_loss: 0.3101\n",
      "Epoch 53/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3154 - val_loss: 0.3254\n",
      "Epoch 54/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.3147 - val_loss: 0.3178\n",
      "Epoch 55/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.3134 - val_loss: 0.3503\n",
      "Epoch 56/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3134 - val_loss: 0.3077\n",
      "Epoch 57/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3129 - val_loss: 0.3464\n",
      "Epoch 58/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.3117 - val_loss: 0.3092\n",
      "Epoch 59/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3110 - val_loss: 0.3122\n",
      "Epoch 60/100\n",
      "7740/7740==============================] - 0s 43us/sample - loss: 0.3100 - val_loss: 0.3087\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 61/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.3091 - val_loss: 0.3551\n",
      "Epoch 62/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.3089 - val_loss: 0.3220\n",
      "Epoch 63/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3086 - val_loss: 0.3056\n",
      "Epoch 64/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.3081 - val_loss: 0.3513\n",
      "Epoch 65/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.3085 - val_loss: 0.3083\n",
      "Epoch 66/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.3067 - val_loss: 0.3171\n",
      "Epoch 67/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3057 - val_loss: 0.3058\n",
      "Epoch 68/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3056 - val_loss: 0.3163\n",
      "Epoch 69/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3042 - val_loss: 0.3383\n",
      "Epoch 70/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.3037 - val_loss: 0.3036\n",
      "Epoch 71/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3032 - val_loss: 0.3290\n",
      "Epoch 72/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.3034 - val_loss: 0.3035\n",
      "Epoch 73/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3026 - val_loss: 0.3729\n",
      "Epoch 74/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.3028 - val_loss: 0.3131\n",
      "Epoch 75/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.3019 - val_loss: 0.2991\n",
      "Epoch 76/100\n",
      "7740/7740==============================] - 0s 49us/sample - loss: 0.3006 - val_loss: 0.3087\n",
      "Epoch 77/100\n",
      "7740/7740==============================] - 0s 51us/sample - loss: 0.2992 - val_loss: 0.3376\n",
      "Epoch 78/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.2999 - val_loss: 0.3140\n",
      "Epoch 79/100\n",
      "7740/7740==============================] - 0s 51us/sample - loss: 0.2998 - val_loss: 0.3791\n",
      "Epoch 80/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.2990 - val_loss: 0.2971\n",
      "Epoch 81/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.2982 - val_loss: 0.3305\n",
      "Epoch 82/100\n",
      "7740/7740==============================] - 0s 50us/sample - loss: 0.2979 - val_loss: 0.3194\n",
      "Epoch 83/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.2976 - val_loss: 0.2947\n",
      "Epoch 84/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.2972 - val_loss: 0.3701\n",
      "Epoch 85/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.2967 - val_loss: 0.2957\n",
      "Epoch 86/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.2975 - val_loss: 0.3093\n",
      "Epoch 87/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.2963 - val_loss: 0.2942\n",
      "Epoch 88/100\n",
      "7740/7740==============================] - 0s 51us/sample - loss: 0.2953 - val_loss: 0.3078\n",
      "Epoch 89/100\n",
      "7740/7740==============================] - 0s 59us/sample - loss: 0.2943 - val_loss: 0.3352\n",
      "Epoch 90/100\n",
      "7740/7740==============================] - 0s 49us/sample - loss: 0.2946 - val_loss: 0.3200\n",
      "Epoch 91/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.2943 - val_loss: 0.2949\n",
      "Epoch 92/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.2930 - val_loss: 0.3618\n",
      "Epoch 93/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.2935 - val_loss: 0.2930\n",
      "Epoch 94/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.2923 - val_loss: 0.3236\n",
      "Epoch 95/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.2931 - val_loss: 0.2975\n",
      "Epoch 96/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.2916 - val_loss: 0.3895\n",
      "Epoch 97/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.2920 - val_loss: 0.2945\n",
      "Epoch 98/100\n",
      "7740/7740==============================] - 0s 44us/sample - loss: 0.2909 - val_loss: 0.3804\n",
      "Epoch 99/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.2907 - val_loss: 0.2922\n",
      "Epoch 100/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.2896 - val_loss: 0.3793\n",
      "3870/3870==============================] - 0s 19us/sample - loss: 0.3224\n",
      "7740/7740==============================] - 0s 20us/sample - loss: 0.2909\n",
      "[CV]  learning_rate=0.0033625641252688094, n_hidden=2, n_neurons=42, total=  37.1s\n",
      "[CV] learning_rate=0.0033625641252688094, n_hidden=2, n_neurons=42 ...\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 2s 209us/sample - loss: 1.1931 - val_loss: 0.9041\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 49us/sample - loss: 0.6067 - val_loss: 0.6543\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.5368 - val_loss: 1.2742\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4912 - val_loss: 1.1536\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4628 - val_loss: 1.0382\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 53us/sample - loss: 0.4411 - val_loss: 0.6116\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 53us/sample - loss: 0.4259 - val_loss: 0.4852\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.4138 - val_loss: 0.3940\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 49us/sample - loss: 0.4038 - val_loss: 0.3999\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.3976 - val_loss: 0.4216\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 63us/sample - loss: 0.3890 - val_loss: 0.5039\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 52us/sample - loss: 0.3857 - val_loss: 0.5955\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 52us/sample - loss: 0.3801 - val_loss: 0.6782\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 51us/sample - loss: 0.3761 - val_loss: 0.7260\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 50us/sample - loss: 0.3714 - val_loss: 0.7824\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 50us/sample - loss: 0.3690 - val_loss: 0.7678\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 52us/sample - loss: 0.3653 - val_loss: 0.8185\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 51us/sample - loss: 0.3625 - val_loss: 0.8134\n",
      "3870/3870==============================] - 0s 23us/sample - loss: 0.3845\n",
      "7740/7740==============================] - 0s 22us/sample - loss: 0.3592\n",
      "[CV]  learning_rate=0.0033625641252688094, n_hidden=2, n_neurons=42, total=   8.8s\n",
      "[CV] learning_rate=0.0033625641252688094, n_hidden=2, n_neurons=42 ...\n",
      "Train on 7740 samples, validate on 3870 samples\n",
      "Epoch 1/100\n",
      "7740/7740==============================] - 2s 215us/sample - loss: 1.0993 - val_loss: 2.9006\n",
      "Epoch 2/100\n",
      "7740/7740==============================] - 0s 50us/sample - loss: 0.6998 - val_loss: 3.9137\n",
      "Epoch 3/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.6035 - val_loss: 1.0577\n",
      "Epoch 4/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.5328 - val_loss: 1.0056\n",
      "Epoch 5/100\n",
      "7740/7740==============================] - 0s 49us/sample - loss: 0.4815 - val_loss: 0.4702\n",
      "Epoch 6/100\n",
      "7740/7740==============================] - 0s 49us/sample - loss: 0.4553 - val_loss: 0.5195\n",
      "Epoch 7/100\n",
      "7740/7740==============================] - 0s 49us/sample - loss: 0.4350 - val_loss: 0.4672\n",
      "Epoch 8/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4223 - val_loss: 0.4911\n",
      "Epoch 9/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.4119 - val_loss: 0.4449\n",
      "Epoch 10/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.4055 - val_loss: 0.4392\n",
      "Epoch 11/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.3983 - val_loss: 0.3689\n",
      "Epoch 12/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3890 - val_loss: 0.6488\n",
      "Epoch 13/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.3864 - val_loss: 0.3798\n",
      "Epoch 14/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3791 - val_loss: 0.7182\n",
      "Epoch 15/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.3808 - val_loss: 0.7215\n",
      "Epoch 16/100\n",
      "7740/7740==============================] - 0s 48us/sample - loss: 0.3781 - val_loss: 1.3052\n",
      "Epoch 17/100\n",
      "7740/7740==============================] - 0s 45us/sample - loss: 0.3822 - val_loss: 1.6169\n",
      "Epoch 18/100\n",
      "7740/7740==============================] - 0s 46us/sample - loss: 0.3878 - val_loss: 1.8493\n",
      "Epoch 19/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.3851 - val_loss: 0.4229\n",
      "Epoch 20/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.3640 - val_loss: 0.7876\n",
      "Epoch 21/100\n",
      "7740/7740==============================] - 0s 47us/sample - loss: 0.3656 - val_loss: 0.3743\n",
      "3870/3870==============================] - 0s 21us/sample - loss: 0.3581\n",
      "7740/7740==============================] - 0s 21us/sample - loss: 0.3617\n",
      "[CV]  learning_rate=0.0033625641252688094, n_hidden=2, n_neurons=42, total=   9.6s\n",
      "Train on 11610 samples, validate on 3870 samples\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:  6.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "11610/11610==============================] - 2s 155us/sample - loss: 1.0606 - val_loss: 6.2728\n",
      "Epoch 2/100\n",
      "11610/11610==============================] - 1s 44us/sample - loss: 0.6068 - val_loss: 3.4547\n",
      "Epoch 3/100\n",
      "11610/11610==============================] - 1s 44us/sample - loss: 0.5038 - val_loss: 0.5592\n",
      "Epoch 4/100\n",
      "11610/11610==============================] - 1s 44us/sample - loss: 0.4442 - val_loss: 0.4589\n",
      "Epoch 5/100\n",
      "11610/11610==============================] - 1s 45us/sample - loss: 0.4186 - val_loss: 0.4900\n",
      "Epoch 6/100\n",
      "11610/11610==============================] - 1s 45us/sample - loss: 0.4032 - val_loss: 0.4061\n",
      "Epoch 7/100\n",
      "11610/11610==============================] - 1s 45us/sample - loss: 0.3923 - val_loss: 0.4028\n",
      "Epoch 8/100\n",
      "11610/11610==============================] - 0s 43us/sample - loss: 0.3847 - val_loss: 0.4166\n",
      "Epoch 9/100\n",
      "11610/11610==============================] - 0s 41us/sample - loss: 0.3797 - val_loss: 0.3926\n",
      "Epoch 10/100\n",
      "11610/11610==============================] - 1s 43us/sample - loss: 0.3731 - val_loss: 0.4093\n",
      "Epoch 11/100\n",
      "11610/11610==============================] - 0s 42us/sample - loss: 0.3694 - val_loss: 0.4151\n",
      "Epoch 12/100\n",
      "11610/11610==============================] - 0s 42us/sample - loss: 0.3655 - val_loss: 0.4099\n",
      "Epoch 13/100\n",
      "11610/11610==============================] - 0s 41us/sample - loss: 0.3650 - val_loss: 0.3563\n",
      "Epoch 14/100\n",
      "11610/11610==============================] - 0s 41us/sample - loss: 0.3591 - val_loss: 0.3790\n",
      "Epoch 15/100\n",
      "11610/11610==============================] - 0s 41us/sample - loss: 0.3587 - val_loss: 0.3461\n",
      "Epoch 16/100\n",
      "11610/11610==============================] - 0s 41us/sample - loss: 0.3540 - val_loss: 0.3415\n",
      "Epoch 17/100\n",
      "11610/11610==============================] - 0s 41us/sample - loss: 0.3534 - val_loss: 0.3837\n",
      "Epoch 18/100\n",
      "11610/11610==============================] - 0s 41us/sample - loss: 0.3513 - val_loss: 0.3385\n",
      "Epoch 19/100\n",
      "11610/11610==============================] - 0s 41us/sample - loss: 0.3493 - val_loss: 0.3932\n",
      "Epoch 20/100\n",
      "11610/11610==============================] - 0s 42us/sample - loss: 0.3469 - val_loss: 0.3591\n",
      "Epoch 21/100\n",
      "11610/11610==============================] - 0s 42us/sample - loss: 0.3447 - val_loss: 0.3575\n",
      "Epoch 22/100\n",
      "11610/11610==============================] - 0s 43us/sample - loss: 0.3431 - val_loss: 0.3749\n",
      "Epoch 23/100\n",
      "11610/11610==============================] - 0s 41us/sample - loss: 0.3420 - val_loss: 0.3371\n",
      "Epoch 24/100\n",
      "11610/11610==============================] - 0s 42us/sample - loss: 0.3413 - val_loss: 0.3487\n",
      "Epoch 25/100\n",
      "11610/11610==============================] - 0s 42us/sample - loss: 0.3387 - val_loss: 0.3370\n",
      "Epoch 26/100\n",
      "11610/11610==============================] - 0s 42us/sample - loss: 0.3372 - val_loss: 0.3546\n",
      "Epoch 27/100\n",
      "11610/11610==============================] - 1s 43us/sample - loss: 0.3361 - val_loss: 0.3292\n",
      "Epoch 28/100\n",
      "11610/11610==============================] - 0s 41us/sample - loss: 0.3338 - val_loss: 0.3308\n",
      "Epoch 29/100\n",
      "11610/11610==============================] - 0s 42us/sample - loss: 0.3338 - val_loss: 0.3421\n",
      "Epoch 30/100\n",
      "11610/11610==============================] - 0s 42us/sample - loss: 0.3322 - val_loss: 0.3192\n",
      "Epoch 31/100\n",
      "11610/11610==============================] - 0s 41us/sample - loss: 0.3314 - val_loss: 0.3422\n",
      "Epoch 32/100\n",
      "11610/11610==============================] - 1s 43us/sample - loss: 0.3290 - val_loss: 0.3896\n",
      "Epoch 33/100\n",
      "11610/11610==============================] - 1s 43us/sample - loss: 0.3299 - val_loss: 0.3142\n",
      "Epoch 34/100\n",
      "11610/11610==============================] - 0s 42us/sample - loss: 0.3292 - val_loss: 0.3256\n",
      "Epoch 35/100\n",
      "11610/11610==============================] - 0s 42us/sample - loss: 0.3258 - val_loss: 0.3807\n",
      "Epoch 36/100\n",
      "11610/11610==============================] - 0s 42us/sample - loss: 0.3250 - val_loss: 0.3248\n",
      "Epoch 37/100\n",
      "11610/11610==============================] - 0s 42us/sample - loss: 0.3246 - val_loss: 0.3701\n",
      "Epoch 38/100\n",
      "11610/11610==============================] - 0s 41us/sample - loss: 0.3232 - val_loss: 0.3243\n",
      "Epoch 39/100\n",
      "11610/11610==============================] - 0s 42us/sample - loss: 0.3218 - val_loss: 0.3620\n",
      "Epoch 40/100\n",
      "11610/11610==============================] - 0s 42us/sample - loss: 0.3209 - val_loss: 0.3705\n",
      "Epoch 41/100\n",
      "11610/11610==============================] - 0s 42us/sample - loss: 0.3208 - val_loss: 0.3338\n",
      "Epoch 42/100\n",
      "11610/11610==============================] - 0s 42us/sample - loss: 0.3190 - val_loss: 0.4084\n",
      "Epoch 43/100\n",
      "11610/11610==============================] - 0s 43us/sample - loss: 0.3187 - val_loss: 0.3582\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score='raise-deprecating',\n",
       "          estimator=<tensorflow.python.keras.wrappers.scikit_learn.KerasRegressor object at 0x152a86be0>,\n",
       "          fit_params=None, iid='warn', n_iter=10, n_jobs=None,\n",
       "          param_distributions={'n_hidden': [0, 1, 2, 3], 'n_neurons': array([ 1,  2, ..., 98, 99]), 'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x13bd002b0>},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring=None, 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),\n",
    "    \"learning_rate\": reciprocal(3e-4, 3e-2),\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,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.0033625641252688094, 'n_hidden': 2, 'n_neurons': 42}"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.35500098818545706"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.wrappers.scikit_learn.KerasRegressor at 0x13bab2ef0>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5160/5160==============================] - 0s 19us/sample - loss: 0.3252\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-0.3251738954422086"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.engine.sequential.Sequential at 0x138a9af60>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = rnd_search_cv.best_estimator_.model\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5160/5160==============================] - 0s 22us/sample - loss: 0.3252\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.3251738954422086"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. to 9."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "See appendix A."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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