Notebooks/C12_N2_Custom Models and Training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 12 - Custom Models and Training with TensorFlow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part II: Custom Models and Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR:root:Line magic function `%tensorflow_version` not found.\n",
      "/Users/dongdongdongdong/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "# Python ≥3.5 is required\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)\n",
    "\n",
    "# Scikit-Learn ≥0.20 is required\n",
    "import sklearn\n",
    "assert sklearn.__version__ >= \"0.20\"\n",
    "\n",
    "try:\n",
    "    # %tensorflow_version only exists in Colab.\n",
    "    %tensorflow_version 2.x\n",
    "except Exception:\n",
    "    pass\n",
    "\n",
    "# TensorFlow ≥2.0 is required\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\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",
    "tf.random.set_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 = \"deep\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customizing Models and Training Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by loading and preparing the California housing dataset. We first load it, then split it into a training set, a validation set and a test set, and finally we scale it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing = fetch_california_housing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train_full, X_test, y_train_full, y_test = train_test_split(housing.data, \n",
    "                                               housing.target.reshape(-1, 1),\n",
    "                                               random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(X_train_full, y_train_full,\n",
    "                                                     random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_valid_scaled = scaler.transform(X_valid)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom Loss Function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Huber loss:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def huber_fn(y_true, y_pred):\n",
    "    error = y_true - y_pred\n",
    "    is_small_error = tf.abs(error) < 1\n",
    "    squared_loss = tf.square(error) / 2\n",
    "    linear_loss = tf.abs(error) - 0.5\n",
    "    return tf.where(is_small_error, squared_loss, linear_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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c4vRzt3evtc2aSS6IjrZ25kxHd3ca4E/rYT7NVbWztTbdWrsYqAh0z9VbAgdddZUUYtWo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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9425259d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 3.5))\n",
    "z = np.linspace(-4, 4, 200)\n",
    "plt.plot(z, huber_fn(0, z), 'b-', linewidth=2, label='huber($z$)')\n",
    "plt.plot(z, z**2 / 2, 'b:', linewidth=1, label=r'$\\frac{1}{2}z^2$')\n",
    "plt.plot([-1, -1], [0, huber_fn(0., -1.)], 'r--')\n",
    "plt.plot([1, 1], [0, huber_fn(0., 1.)], 'r--')\n",
    "plt.gca().axhline(y=0, color='k')\n",
    "plt.gca().axvline(x=0, color='k')\n",
    "plt.axis([-4, 4, 0, 4])\n",
    "plt.grid(True)\n",
    "plt.xlabel('$z$')\n",
    "plt.legend(fontsize=14)\n",
    "plt.title('Huber Loss', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_shape = X_train.shape[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Dense(30, activation='selu',\n",
    "                       kernel_initializer='lecun_normal',\n",
    "                       input_shape=input_shape),\n",
    "    keras.layers.Dense(1)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss=huber_fn,\n",
    "              optimizer='nadam',\n",
    "              metrics=['mae'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11610 samples, validate on 3870 samples\n",
      "Epoch 1/2\n",
      "11610/11610 [==============================] - 1s 122us/sample - loss: 0.6229 - mae: 0.9934 - val_loss: 0.2816 - val_mae: 0.5838\n",
      "Epoch 2/2\n",
      "11610/11610 [==============================] - 1s 54us/sample - loss: 0.2190 - mae: 0.5173 - val_loss: 0.2365 - val_mae: 0.5216\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f94212b3780>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_scaled, y_train,\n",
    "          epochs=2,\n",
    "          validation_data=(X_valid_scaled, y_valid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving/Loading Models with Custom Objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save('my_model_with_a_custom_loss.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = keras.models.load_model('my_model_with_a_custom_loss.h5',\n",
    "                                custom_objects={'huber_fn': huber_fn})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11610 samples, validate on 3870 samples\n",
      "Epoch 1/2\n",
      "11610/11610 [==============================] - 1s 75us/sample - loss: 0.2048 - mae: 0.4976 - val_loss: 0.2126 - val_mae: 0.4968\n",
      "Epoch 2/2\n",
      "11610/11610 [==============================] - 0s 41us/sample - loss: 0.2002 - mae: 0.4911 - val_loss: 0.1978 - val_mae: 0.4826\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f94261f14a8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_scaled, y_train,\n",
    "          epochs=2,\n",
    "          validation_data=(X_valid_scaled, y_valid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a function that creates a configured loss function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_huber(threshold=1.0):\n",
    "    def huber_fn(y_true, y_pred):\n",
    "        error = y_true - y_pred\n",
    "        is_small_error = tf.abs(error) < threshold\n",
    "        squared_loss = tf.square(error) / 2\n",
    "        linear_loss = threshold * tf.abs(error) - threshold**2 / 2\n",
    "        return tf.where(is_small_error, squared_loss, linear_loss)\n",
    "    return huber_fn        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss=create_huber(2.0),\n",
    "              optimizer='nadam',\n",
    "              metrics=['mae'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11610 samples, validate on 3870 samples\n",
      "Epoch 1/2\n",
      "11610/11610 [==============================] - 1s 73us/sample - loss: 0.2224 - mae: 0.4894 - val_loss: 0.2410 - val_mae: 0.4816\n",
      "Epoch 2/2\n",
      "11610/11610 [==============================] - 1s 62us/sample - loss: 0.2180 - mae: 0.4845 - val_loss: 0.2252 - val_mae: 0.4751\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f94259b7b70>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_scaled, y_train,\n",
    "          epochs=2,\n",
    "          validation_data=(X_valid_scaled, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save('my_model_with_a_custom_loss_threshold_2.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = keras.models.load_model('my_model_with_a_custom_loss_threshold_2.h5',\n",
    "                                custom_objects={'huber_fn': create_huber(2.0)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11610 samples, validate on 3870 samples\n",
      "Epoch 1/2\n",
      "11610/11610 [==============================] - 1s 86us/sample - loss: 0.2145 - mae: 0.4794 - val_loss: 0.2060 - val_mae: 0.4654\n",
      "Epoch 2/2\n",
      "11610/11610 [==============================] - 1s 59us/sample - loss: 0.2118 - mae: 0.4767 - val_loss: 0.2022 - val_mae: 0.4564\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f94096a0978>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_scaled, y_train,\n",
    "          epochs=2,\n",
    "          validation_data=(X_valid_scaled, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class HuberLoss(keras.losses.Loss):\n",
    "    \n",
    "    def __init__(self, threshold=1.0, **kwargs):\n",
    "        self.threshold = threshold\n",
    "        super().__init__(**kwargs)\n",
    "        \n",
    "    def call(self, y_true, y_pred):\n",
    "        error = y_true - y_pred\n",
    "        is_small_error = tf.abs(error) < self.threshold\n",
    "        squared_loss = tf.square(error) / 2\n",
    "        linear_loss = self.threshold * tf.abs(error) - self.threshold**2 / 2\n",
    "        return tf.where(is_small_error, squared_loss, linear_loss)\n",
    "    \n",
    "    def get_config(self):\n",
    "        base_config = super().get_config()\n",
    "        return {**base_config, 'threshold': self.threshold}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Dense(30, activation='selu',\n",
    "                       kernel_initializer='lecun_normal',\n",
    "                       input_shape=input_shape),\n",
    "    keras.layers.Dense(1)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss=HuberLoss(2.0),\n",
    "              optimizer='nadam',\n",
    "              metrics=['mae'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11610 samples, validate on 3870 samples\n",
      "Epoch 1/2\n",
      "11610/11610 [==============================] - 1s 81us/sample - loss: 0.6982 - mae: 0.8827 - val_loss: 0.3284 - val_mae: 0.5434\n",
      "Epoch 2/2\n",
      "11610/11610 [==============================] - 1s 49us/sample - loss: 0.2399 - mae: 0.5068 - val_loss: 0.2621 - val_mae: 0.5015\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f940a743240>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_scaled, y_train,\n",
    "          epochs=2,\n",
    "          validation_data=(X_valid_scaled, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save('my_model_with_a_custom_loss_class.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "model = keras.models.load_model('my_model_with_a_custom_loss_class.h5',\n",
    "                                custom_objects={'HuberLoss': HuberLoss})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.loss.threshold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other Custom Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom Activation Function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def my_softplus(z):\n",
    "    return tf.math.log(tf.exp(z) + 1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom Initializer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def my_glorot_initializer(shape, dtype=tf.float32):\n",
    "    stddev = tf.sqrt(2. / (shape[0] + shape[1]))\n",
    "    return tf.random.normal(shape, stddev=stddev, dtype=dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom Regularizer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def my_l1_regularizer(weights):\n",
    "    return tf.reduce_sum(tf.abs(0.01 * weights))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom Constraint:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def my_positive_weights(weights):\n",
    "    return tf.where(weights < 0., tf.zeros_like(weights), weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "layer = keras.layers.Dense(1, activation=my_softplus,\n",
    "                           kernel_initializer=my_glorot_initializer,\n",
    "                           kernel_regularizer=my_l1_regularizer,\n",
    "                           kernel_constraint=my_positive_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Dense(30, activation='selu',\n",
    "                       kernel_initializer='lecun_normal',\n",
    "                       input_shape=input_shape),\n",
    "    keras.layers.Dense(1, activation=my_softplus,\n",
    "                       kernel_initializer=my_glorot_initializer,\n",
    "                       kernel_regularizer=my_l1_regularizer,\n",
    "                       kernel_constraint=my_positive_weights)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss='mse',\n",
    "              optimizer='nadam',\n",
    "              metrics=['mae'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11610 samples, validate on 3870 samples\n",
      "Epoch 1/2\n",
      "11610/11610 [==============================] - 1s 80us/sample - loss: 1.4943 - mae: 0.8850 - val_loss: 1.5203 - val_mae: 0.5619\n",
      "Epoch 2/2\n",
      "11610/11610 [==============================] - 1s 52us/sample - loss: 0.5918 - mae: 0.5268 - val_loss: 1.5022 - val_mae: 0.5096\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f940bae0fd0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_scaled, y_train,\n",
    "          epochs=2,\n",
    "          validation_data=(X_valid_scaled, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save('my_model_with_many_custom_parts.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "my_glorot_initializer() missing 1 required positional argument: 'shape'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-41-f771eb734e87>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m                 \u001b[0;34m'my_glorot_initializer'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmy_glorot_initializer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                 \u001b[0;34m'my_l1_regularizer'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmy_l1_regularizer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m                 \u001b[0;34m'my_positive_weights'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmy_positive_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m             })\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/saving/hdf5_format.py\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects, compile)\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0mmodel_config\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_config\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m     model = model_config_lib.model_from_config(model_config,\n\u001b[0;32m--> 215\u001b[0;31m                                                custom_objects=custom_objects)\n\u001b[0m\u001b[1;32m    216\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m     \u001b[0;31m# set weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/saving/model_config.py\u001b[0m in \u001b[0;36mmodel_from_config\u001b[0;34m(config, custom_objects)\u001b[0m\n\u001b[1;32m     53\u001b[0m                     '`Sequential.from_config(config)`?')\n\u001b[1;32m     54\u001b[0m   \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdeserialize\u001b[0m  \u001b[0;31m# pylint: disable=g-import-not-at-top\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mdeserialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustom_objects\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/layers/serialization.py\u001b[0m in \u001b[0;36mdeserialize\u001b[0;34m(config, custom_objects)\u001b[0m\n\u001b[1;32m     93\u001b[0m       \u001b[0mmodule_objects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mglobs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m       \u001b[0mcustom_objects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustom_objects\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m       printable_module_name='layer')\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/utils/generic_utils.py\u001b[0m in \u001b[0;36mdeserialize_keras_object\u001b[0;34m(identifier, module_objects, custom_objects, printable_module_name)\u001b[0m\n\u001b[1;32m    190\u001b[0m             custom_objects=dict(\n\u001b[1;32m    191\u001b[0m                 \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_GLOBAL_CUSTOM_OBJECTS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 192\u001b[0;31m                 list(custom_objects.items())))\n\u001b[0m\u001b[1;32m    193\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mCustomObjectScope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcustom_objects\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    194\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\u001b[0m in \u001b[0;36mfrom_config\u001b[0;34m(cls, config, custom_objects)\u001b[0m\n\u001b[1;32m    349\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mlayer_config\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlayer_configs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    350\u001b[0m       layer = layer_module.deserialize(layer_config,\n\u001b[0;32m--> 351\u001b[0;31m                                        custom_objects=custom_objects)\n\u001b[0m\u001b[1;32m    352\u001b[0m       \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    353\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mbuild_input_shape\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/layers/serialization.py\u001b[0m in \u001b[0;36mdeserialize\u001b[0;34m(config, custom_objects)\u001b[0m\n\u001b[1;32m     93\u001b[0m       \u001b[0mmodule_objects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mglobs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m       \u001b[0mcustom_objects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustom_objects\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m       printable_module_name='layer')\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/utils/generic_utils.py\u001b[0m in \u001b[0;36mdeserialize_keras_object\u001b[0;34m(identifier, module_objects, custom_objects, printable_module_name)\u001b[0m\n\u001b[1;32m    192\u001b[0m                 list(custom_objects.items())))\n\u001b[1;32m    193\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mCustomObjectScope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcustom_objects\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 194\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    195\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m       \u001b[0;31m# Then `cls` may be a function returning a class.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36mfrom_config\u001b[0;34m(cls, config)\u001b[0m\n\u001b[1;32m    437\u001b[0m         \u001b[0mA\u001b[0m \u001b[0mlayer\u001b[0m \u001b[0minstance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m     \"\"\"\n\u001b[0;32m--> 439\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mcompute_output_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/layers/core.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, units, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, **kwargs)\u001b[0m\n\u001b[1;32m    947\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mactivations\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactivation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    948\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_bias\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0muse_bias\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 949\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel_initializer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minitializers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkernel_initializer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    950\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias_initializer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minitializers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbias_initializer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    951\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel_regularizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mregularizers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkernel_regularizer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(identifier)\u001b[0m\n\u001b[1;32m    189\u001b[0m   \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midentifier\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m     \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'class_name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midentifier\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'config'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 191\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mdeserialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    192\u001b[0m   \u001b[0;32melif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midentifier\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0midentifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py\u001b[0m in \u001b[0;36mdeserialize\u001b[0;34m(config, custom_objects)\u001b[0m\n\u001b[1;32m    178\u001b[0m       \u001b[0mmodule_objects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodule_objects\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m       \u001b[0mcustom_objects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustom_objects\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m       printable_module_name='initializer')\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/utils/generic_utils.py\u001b[0m in \u001b[0;36mdeserialize_keras_object\u001b[0;34m(identifier, module_objects, custom_objects, printable_module_name)\u001b[0m\n\u001b[1;32m    199\u001b[0m       \u001b[0mcustom_objects\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcustom_objects\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mCustomObjectScope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcustom_objects\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mcls_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    202\u001b[0m   \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midentifier\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m     \u001b[0mfunction_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0midentifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: my_glorot_initializer() missing 1 required positional argument: 'shape'"
     ]
    }
   ],
   "source": [
    "model = keras.models.load_model('my_model_with_many_custom_parts.h5',\n",
    "            custom_objects={\n",
    "                'my_softplus': my_softplus,\n",
    "                'my_glorot_initializer': my_glorot_initializer,\n",
    "                'my_l1_regularizer': my_l1_regularizer,\n",
    "                'my_positive_weights': my_positive_weights\n",
    "            })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class MyL1Regularizer(keras.regularizers.Regularizer):\n",
    "    \n",
    "    def __init__(self, factor):\n",
    "        self.factor = factor\n",
    "    \n",
    "    def __call__(self, weights):\n",
    "        return tf.reduce_sum(tf.abs(self.factor * weights))\n",
    "    \n",
    "    def get_config(self):\n",
    "        return {'factor': self.factor}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Dense(30, activation='selu',\n",
    "                       kernel_initializer='lecun_normal',\n",
    "                       input_shape=input_shape),\n",
    "    keras.layers.Dense(1, activation=my_softplus,\n",
    "                       kernel_initializer=my_glorot_initializer,\n",
    "                       kernel_regularizer=MyL1Regularizer(0.01),\n",
    "                       kernel_constraint=my_positive_weights)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss='mse',\n",
    "              optimizer='nadam',\n",
    "              metrics=['mae'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.fit(X_train_scaled, y_train,\n",
    "          epochs=2,\n",
    "          validation_data=(X_valid_scaled, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save('my_model_with_many_custom_parts.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "model = keras.models.load_model(\n",
    "    \"my_model_with_many_custom_parts.h5\",\n",
    "    custom_objects={\n",
    "       \"MyL1Regularizer\": MyL1Regularizer,\n",
    "       \"my_positive_weights\": my_positive_weights,\n",
    "       \"my_glorot_initializer\": my_glorot_initializer,\n",
    "       \"my_softplus\": my_softplus,\n",
    "    })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Dense(30, activation='selu',\n",
    "                       kernel_initializer='lecun_normal',\n",
    "                       input_shape=input_shape),\n",
    "    keras.layers.Dense(1)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss='mse',\n",
    "              optimizer='nadam',\n",
    "              metrics=[create_huber(2.0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "11610/11610 [==============================] - 1s 57us/sample - loss: 0.5220 - huber_fn: 0.2336\n",
      "Epoch 2/2\n",
      "11610/11610 [==============================] - 0s 39us/sample - loss: 0.4993 - huber_fn: 0.2252\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f9407dbf588>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_scaled, y_train,\n",
    "          epochs=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning:** if you use the same function as the loss and a metric, you may be surprised to see different results. This is generally just due to floating point precision errors: even though the mathematical equations are equivalent, the operations are not run in the same order, which can lead to small differences. Moreover, when using sample weights, there's more than just precision errors:\n",
    "\n",
    "- the loss since the start of the epoch is the mean of all batch losses seen so far. Each batch loss is the sum of the weighted instance losses divided by the batch size (not the sum of weights, so the batch loss is not the weighted mean of the losses).\n",
    "- the metric since the start of the epoch is equal to the sum of weighted instance losses divided by sum of all weights seen so far. In other words, it is the weighted mean of all the instance losses. Not the same thing.\n",
    "\n",
    "If you do the math, you will find that loss = metric * mean of sample weights (plus some floating point precision error)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss=create_huber(2.0),\n",
    "              optimizer='nadam',\n",
    "              metrics=[create_huber(2.0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "11610/11610 [==============================] - 1s 58us/sample - loss: 0.1427 - huber_fn: 0.2222\n",
      "Epoch 2/2\n",
      "11610/11610 [==============================] - 0s 37us/sample - loss: 0.1377 - huber_fn: 0.2200\n"
     ]
    }
   ],
   "source": [
    "sample_weight = np.random.rand(len(y_train))\n",
    "\n",
    "history = model.fit(X_train_scaled, y_train,\n",
    "                    epochs=2,\n",
    "                    sample_weight=sample_weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.14271765617231785, 0.1106913447000173)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history['loss'][0], history.history['huber_fn'][0] * sample_weight.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Streaming Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "precision = keras.metrics.Precision()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=68088, shape=(), dtype=float32, numpy=0.8>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision([0, 1, 1, 1, 0, 1, 0, 1], [1, 1, 0, 1, 0, 1, 0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=68138, shape=(), dtype=float32, numpy=0.5>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision([0, 1, 0, 0, 1, 0, 1, 1], [1, 0, 1, 1, 0, 0, 0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=68148, shape=(), dtype=float32, numpy=0.5>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Variable 'true_positives:0' shape=(1,) dtype=float32, numpy=array([4.], dtype=float32)>,\n",
       " <tf.Variable 'false_positives:0' shape=(1,) dtype=float32, numpy=array([4.], dtype=float32)>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision.variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "precision.reset_states()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a streaming metric:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class HuberMetric(keras.metrics.Metric):\n",
    "    \n",
    "    def __init__(self, threshold=1.0, **kwargs):\n",
    "        super().__init__(**kwargs) # handles base args (e.g., dtype)\n",
    "        self.threshold = threshold\n",
    "        self.total = self.add_weight('total', initializer='zeros')\n",
    "        self.count = self.add_weight('count', initializer='zeros')\n",
    "        \n",
    "    def huber_fn(self, y_true, y_pred): # workaround\n",
    "        error = y_true - y_pred\n",
    "        is_small_error = tf.abs(error) < self.threshold\n",
    "        squared_loss = tf.square(error) / 2\n",
    "        linear_loss = self.threshold * tf.abs(error) - self.threshold** 2 / 2\n",
    "        return tf.where(is_small_error, squared_loss, linear_loss)\n",
    "    \n",
    "    def update_state(self, y_true, y_pred, sample_weight=None):\n",
    "        metric = self.huber_fn(y_true, y_pred)\n",
    "        self.total.assign_add(tf.reduce_sum(metric))\n",
    "        self.count.assign_add(tf.cast(tf.size(y_true), tf.float32))\n",
    "        \n",
    "    def result(self):\n",
    "        return self.total / self.count\n",
    "    \n",
    "    def get_config(self):\n",
    "        base_config = super().get_config()\n",
    "        return {**base_config, 'threshold': self.threshold}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m = HuberMetric(2.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=68202, shape=(), dtype=float32, numpy=14.0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m(tf.constant([[2.]]), tf.constant([[10.]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=68206, shape=(), dtype=float32, numpy=14.0>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Variable 'total:0' shape=() dtype=float32, numpy=14.0>,\n",
       " <tf.Variable 'count:0' shape=() dtype=float32, numpy=1.0>]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Variable 'total:0' shape=() dtype=float32, numpy=0.0>,\n",
       " <tf.Variable 'count:0' shape=() dtype=float32, numpy=0.0>]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.reset_states()\n",
    "m.variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check that the `HuberMetric` class works well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Dense(30, activation='selu',\n",
    "                       kernel_initializer='lecun_normal',\n",
    "                       input_shape=input_shape),\n",
    "    keras.layers.Dense(1)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss=create_huber(2.0),\n",
    "              optimizer='nadam',\n",
    "              metrics=[HuberMetric(2.0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "11610/11610 [==============================] - 1s 58us/sample - loss: 0.8717 - huber_metric: 0.8717\n",
      "Epoch 2/2\n",
      "11610/11610 [==============================] - 0s 38us/sample - loss: 0.2567 - huber_metric: 0.2567\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f940c959eb8>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_scaled.astype(np.float32), y_train.astype(np.float32), \n",
    "          epochs=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save(\"my_model_with_a_custom_metric.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like it works fine! More simply, we could have created the class like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class HuberMetric(keras.metrics.Mean):\n",
    "    \n",
    "    def __init__(self, threshold=1.0, name='HuberMetric', dtype=None):\n",
    "        self.threshold = threshold\n",
    "        self.huber_fn = create_huber(threshold)\n",
    "        super().__init__(name=name, dtype=dtype)\n",
    "        \n",
    "    def update_state(self, y_true, y_pred, sample_weight=None):\n",
    "        metric = self.huber_fn(y_true, y_pred)\n",
    "        super(HuberMetric, self).update_state(metric, sample_weight)\n",
    "        \n",
    "    def get_config(self):\n",
    "        base_config = super().get_config()\n",
    "        return {**base_config, \"threshold\": self.threshold}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This class handles shapes better, and it also supports sample weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Dense(30, activation=\"selu\", \n",
    "                       kernel_initializer=\"lecun_normal\",\n",
    "                       input_shape=input_shape),\n",
    "    keras.layers.Dense(1),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss=keras.losses.Huber(2.0), \n",
    "              optimizer=\"nadam\",\n",
    "              weighted_metrics=[HuberMetric(2.0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "11610/11610 [==============================] - 1s 68us/sample - loss: 0.4336 - HuberMetric: 0.8739\n",
      "Epoch 2/2\n",
      "11610/11610 [==============================] - 0s 36us/sample - loss: 0.1298 - HuberMetric: 0.2615\n"
     ]
    }
   ],
   "source": [
    "sample_weight = np.random.rand(len(y_train))\n",
    "\n",
    "history = model.fit(X_train_scaled.astype(np.float32), y_train.astype(np.float32),\n",
    "                    epochs=2, \n",
    "                    sample_weight=sample_weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.43364625671472556, 0.43364639481949674)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history[\"loss\"][0], history.history[\"HuberMetric\"][0] * sample_weight.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save(\"my_model_with_a_custom_metric_v2.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "11610/11610 [==============================] - 0s 38us/sample - loss: 0.2378 - HuberMetric: 0.2378\n",
      "Epoch 2/2\n",
      "11610/11610 [==============================] - 0s 37us/sample - loss: 0.2279 - HuberMetric: 0.2279\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f940daa7c88>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_scaled.astype(np.float32), y_train.astype(np.float32), epochs=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning:** In TF 2.2, tf.keras adds an extra first metric in `model.metrics` at position 0 (see TF issue #38150). This forces us to use `model.metrics[-1]` rather than `model.metrics[0]` to access the HuberMetric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metrics[-1].threshold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "exponential_layer = keras.layers.Lambda(lambda x: tf.exp(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=88116, shape=(3,), dtype=float32, numpy=array([0.36787945, 1.        , 2.7182817 ], dtype=float32)>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exponential_layer([-1., 0., 1.])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding an exponential layer at the output of a regression model can be useful if the values to predict are positive and with very different scales (e.g., 0.001, 10., 10000):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Dense(30, activation='relu',\n",
    "                       input_shape=input_shape),\n",
    "    keras.layers.Dense(1),\n",
    "    exponential_layer\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss='mse',\n",
    "              optimizer='nadam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11610 samples, validate on 3870 samples\n",
      "Epoch 1/5\n",
      "11610/11610 [==============================] - 1s 64us/sample - loss: 13681365654862.8613 - val_loss: inf\n",
      "Epoch 2/5\n",
      "11610/11610 [==============================] - 1s 51us/sample - loss: 493578.6405 - val_loss: inf\n",
      "Epoch 3/5\n",
      "11610/11610 [==============================] - 1s 47us/sample - loss: 274951.7519 - val_loss: inf\n",
      "Epoch 4/5\n",
      "11610/11610 [==============================] - 1s 48us/sample - loss: 129439.4402 - val_loss: inf\n",
      "Epoch 5/5\n",
      "11610/11610 [==============================] - 1s 46us/sample - loss: 72170.3607 - val_loss: inf\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f940e70c6a0>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_scaled, y_train,\n",
    "          epochs=5,\n",
    "          validation_data=(X_valid_scaled, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5160/5160 [==============================] - 0s 18us/sample - loss: 1728.5436\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1728.5435571418252"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a subclass of the `keras.layers.Layer` class to implement a simplified version of the `Dense` layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class MyDense(keras.layers.Layer):\n",
    "    \n",
    "    def __init__(self, units, activation=None, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.units = units\n",
    "        self.activation = keras.activations.get(activation)\n",
    "        \n",
    "    def build(self, batch_input_shape):\n",
    "        self.kernel = self.add_weight(\n",
    "            name='kernel',\n",
    "            shape=[batch_input_shape[-1], self.units],\n",
    "            initializer='glorot_normal')\n",
    "        self.bias = self.add_weight(\n",
    "            name='bias',\n",
    "            shape=[self.units],\n",
    "            initializer='zeros')\n",
    "        super().build(batch_input_shape) # must be here\n",
    "        \n",
    "    def call(self, X):\n",
    "        return self.activation(X @ self.kernel + self.bias)\n",
    "    \n",
    "    def compute_output_shape(self, batch_input_shape):\n",
    "        return tf.TensorShape(batch_input_shape.as_list()[:-1] + [self.units])\n",
    "    \n",
    "    def get_config(self):\n",
    "        base_config = super().get_config()\n",
    "        return {**base_config, \n",
    "                'units': self.units,\n",
    "                'activation': keras.activations.serialize(self.activation)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    MyDense(30, activation=\"relu\", \n",
    "            input_shape=input_shape),\n",
    "    MyDense(1)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss=\"mse\", \n",
    "              optimizer=\"nadam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11610 samples, validate on 3870 samples\n",
      "Epoch 1/2\n",
      "11610/11610 [==============================] - 1s 73us/sample - loss: 2.2287 - val_loss: 0.8403\n",
      "Epoch 2/2\n",
      "11610/11610 [==============================] - 1s 49us/sample - loss: 0.6441 - val_loss: 0.6282\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f940f39ab38>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_scaled, y_train, \n",
    "          epochs=2,\n",
    "          validation_data=(X_valid_scaled, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5160/5160 [==============================] - 0s 21us/sample - loss: 0.5475\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5475413522054983"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save(\"my_model_with_a_custom_layer.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = keras.models.load_model(\"my_model_with_a_custom_layer.h5\",\n",
    "                                custom_objects={\"MyDense\": MyDense})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class MyMultiLayer(keras.layers.Layer):\n",
    "    \n",
    "    def call(self, X):\n",
    "        X1, X2 = X\n",
    "        return X1 + X2, X1 * X2\n",
    "    \n",
    "    def compute_output_shape(self, batch_input_shape):\n",
    "        batch_input_shape1, batch_input_shape2 = batch_input_shape\n",
    "        return [batch_input_shape1, batch_input_shape2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inputs1 = keras.layers.Input(shape=[2])\n",
    "inputs2 = keras.layers.Input(shape=[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "outputs1, outputs2 = MyMultiLayer()((inputs1, inputs2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a layer with a different behavior during training and testing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class AddGaussianNoise(keras.layers.Layer):\n",
    "    \n",
    "    def __init__(self, stddev, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.stddev = stddev\n",
    "        \n",
    "    def call(self, X, training=True):\n",
    "        if training:\n",
    "            noise = tf.random.normal(tf.shape(X), stddev=self.stddev)\n",
    "            return X + noise\n",
    "        else:\n",
    "            return X\n",
    "    \n",
    "    def compute_output_shape(self, batch_input_shape):\n",
    "        return batch_input_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss='mse',\n",
    "              optimizer='nadam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11610 samples, validate on 3870 samples\n",
      "Epoch 1/2\n",
      "11610/11610 [==============================] - 1s 74us/sample - loss: 0.4878 - val_loss: 0.5836\n",
      "Epoch 2/2\n",
      "11610/11610 [==============================] - 1s 52us/sample - loss: 0.4226 - val_loss: 0.4909\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f94107c7ef0>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_scaled, y_train, \n",
    "          epochs=2,\n",
    "          validation_data=(X_valid_scaled, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5160/5160 [==============================] - 0s 18us/sample - loss: 0.4025\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.4024852337763291"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's implement a custom `ResidualBlock`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_new_scaled = X_test_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class ResidualBlock(keras.layers.Layer):\n",
    "    \n",
    "    def __init__(self, n_layers, n_neurons, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.hidden = [keras.layers.Dense(n_neurons,\n",
    "                                          activation='elu',\n",
    "                                          kernel_initializer='he_normal') for _ in range(n_layers)]\n",
    "        \n",
    "    def call(self, inputs):\n",
    "        Z = inputs\n",
    "        for layer in self.hidden:\n",
    "            Z = layer(Z)\n",
    "        return inputs + Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class ResidualRegressor(keras.models.Model):\n",
    "    \n",
    "    def __init__(self, output_dim, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.hidden1 = keras.layers.Dense(30, activation='elu',\n",
    "                                          kernel_initializer='he_normal')\n",
    "        self.block1 = ResidualBlock(2, 30)\n",
    "        self.block2 = ResidualBlock(2, 30)\n",
    "        self.out = keras.layers.Dense(output_dim)\n",
    "        \n",
    "    def call(self, inputs):\n",
    "        Z = self.hidden1(inputs)\n",
    "        for _ in range(1 + 3):\n",
    "            Z = self.block1(Z)\n",
    "        Z = self.block2(Z)\n",
    "        return self.out(Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = ResidualRegressor(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss='mse',\n",
    "              optimizer='nadam',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "11610/11610 [==============================] - 1s 127us/sample - loss: 7.5186 - accuracy: 0.0019\n",
      "Epoch 2/5\n",
      "11610/11610 [==============================] - 1s 69us/sample - loss: 1.2075 - accuracy: 0.0023\n",
      "Epoch 3/5\n",
      "11610/11610 [==============================] - 1s 73us/sample - loss: 0.9630 - accuracy: 0.0026\n",
      "Epoch 4/5\n",
      "11610/11610 [==============================] - 1s 72us/sample - loss: 1.1591 - accuracy: 0.0027\n",
      "Epoch 5/5\n",
      "11610/11610 [==============================] - 1s 67us/sample - loss: 0.6449 - accuracy: 0.0028\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train_scaled, y_train,\n",
    "                    epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5160/5160 [==============================] - 0s 31us/sample - loss: 0.5202 - accuracy: 0.0017\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_new_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save_weights('my_custom_model.ckpt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loss and Metrics based on Model Internals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's build a custom regression MLP model composed of a stack of five hiddent layers plus an output layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class ReconstructingRegressor(keras.models.Model):\n",
    "    \n",
    "    def __init__(self, output_dim, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.hidden = [keras.layers.Dense(30, activation=\"selu\",\n",
    "                                          kernel_initializer=\"lecun_normal\")\n",
    "                       for _ in range(5)]\n",
    "        self.out = keras.layers.Dense(output_dim)\n",
    "        \n",
    "    def build(self, batch_input_shape):\n",
    "        n_inputs = batch_input_shape[-1]\n",
    "        self.reconstruct = keras.layers.Dense(n_inputs)\n",
    "        super().build(batch_input_shape)\n",
    "        \n",
    "    def call(self, inputs, training=True):\n",
    "        Z = inputs\n",
    "        for layer in self.hidden:\n",
    "            Z = layer(Z)\n",
    "        reconstruction = self.reconstruct(Z)\n",
    "        recon_loss = tf.reduce_mean(tf.square(reconstruction - loss))\n",
    "        self.add_loss(0.05 * recon_loss)\n",
    "        return self.out(Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = ReconstructingRegressor(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss='mse',\n",
    "              optimizer='nadam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "history = model.fit(X_train_scaled, y_train, \n",
    "                    epochs=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing Gradients with Autodiff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first consider a simple toy function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def f(w1, w2):\n",
    "    return 3 * w1 ** 2 + 2 * w1 * w2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ f(w_1, w_2) = 3w_1^2 + 2w_1w_2 $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By approximation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "w1, w2 = 5, 3\n",
    "eps = 1e-6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.000003007075065"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(f(w1 + eps, w2) - f(w1, w2)) / eps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.000000003174137"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(f(w1, w2 + eps) - f(w1, w2)) / eps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By autodiff:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "w1, w2 = tf.Variable(5.), tf.Variable(3.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.GradientTape() as tape:\n",
    "    z = f(w1, w2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gradients = tape.gradient(z, [w1, w2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: id=138861, shape=(), dtype=float32, numpy=36.0>,\n",
       " <tf.Tensor: id=138832, shape=(), dtype=float32, numpy=10.0>]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gradients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tape is automatically erased immediately after you call its `gradient()` method, so you will get an exception if you try to call `gradient()` twice:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "__enter__",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-120-a2b45155bcec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGradientTape\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtape\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: __enter__"
     ]
    }
   ],
   "source": [
    "with tf.GradientTape as tape:\n",
    "    z = f(w1, w2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "GradientTape.gradient can only be called once on non-persistent tapes.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-121-583f2ebb9935>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdz_dw1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgradient\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# => tensor 36.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdz_dw2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgradient\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# RuntimeError!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/backprop.py\u001b[0m in \u001b[0;36mgradient\u001b[0;34m(self, target, sources, output_gradients, unconnected_gradients)\u001b[0m\n\u001b[1;32m    918\u001b[0m     \"\"\"\n\u001b[1;32m    919\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tape\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 920\u001b[0;31m       raise RuntimeError(\"GradientTape.gradient can only be called once on \"\n\u001b[0m\u001b[1;32m    921\u001b[0m                          \"non-persistent tapes.\")\n\u001b[1;32m    922\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_recording\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: GradientTape.gradient can only be called once on non-persistent tapes."
     ]
    }
   ],
   "source": [
    "dz_dw1 = tape.gradient(z, w1) # => tensor 36.0\n",
    "dz_dw2 = tape.gradient(z, w2) # RuntimeError!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.GradientTape(persistent=True) as tape:\n",
    "    z = f(w1, w2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dz_dw1 = tape.gradient(z, w1)\n",
    "dz_dw2 = tape.gradient(z, w2) # works now\n",
    "del tape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: id=138906, shape=(), dtype=float32, numpy=36.0>,\n",
       " <tf.Tensor: id=138909, shape=(), dtype=float32, numpy=10.0>)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dz_dw1, dz_dw2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the tape will only track operations involving variables, so if you try to compute the gradient of `z` with regard to anything other than a variable, the result will be `None`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c1, c2 = tf.constant(5.), tf.constant(3.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.GradientTape() as tape:\n",
    "    z = f(c1, c2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gradients = tape.gradient(z, [c1, c2])\n",
    "gradients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, you can force the tape to watch any tensors you like, to record every operation that involves them. You can then compute gradients with regard to these tensors, as if they were variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.GradientTape() as tape:\n",
    "    tape.watch(c1)\n",
    "    tape.watch(c2)\n",
    "    z = f(c1, c2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gradients = tape.gradient(z, [c1, c2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: id=138989, shape=(), dtype=float32, numpy=36.0>,\n",
       " <tf.Tensor: id=138960, shape=(), dtype=float32, numpy=10.0>]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gradients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In some cases you may want to stop gradients from backpropagating through some part of your neural network. To do this, you must use the `tf.stop_gradient()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def f(w1, w2):\n",
    "    return 3 * w1 ** 2 + tf.stop_gradient(2 * w1 * w2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.GradientTape() as tape:\n",
    "    z = f(w1, w2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: id=139020, shape=(), dtype=float32, numpy=30.0>, None]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gradients = tape.gradient(z, [w1, w2])\n",
    "gradients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, you may occasionally run into some numerical issues when computing gradients.\n",
    "\n",
    "For example, if you compute the gradients of the `my_softplus()` function for large inputs, the result will be `NaN`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = tf.Variable([100.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.GradientTape() as tape:\n",
    "    z = my_softplus(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: id=139060, shape=(1,), dtype=float32, numpy=array([nan], dtype=float32)>]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tape.gradient(z, [x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.GradientTape() as tape:\n",
    "    z1 = f(w1, w2 + 2.)\n",
    "    z2 = f(w1, w2 + 5.)\n",
    "    z3 = f(w1, w2 + 7.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: id=139185, shape=(), dtype=float32, numpy=90.0>, None]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tape.gradient([z1, z2, z3], [w1, w2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.GradientTape(persistent=True) as tape:\n",
    "    z1 = f(w1, w2 + 2.)\n",
    "    z2 = f(w1, w2 + 5.)\n",
    "    z3 = f(w1, w2 + 7.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Attempt to convert a value (None) with an unsupported type (<class 'NoneType'>) to a Tensor.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-140-b5b4c77088c1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m tf.reduce_sum(tf.stack([tape.gradient(z, [w1, w2]) for z in (z1, z2, z3)]),\n\u001b[0m\u001b[1;32m      2\u001b[0m               axis=0)\n\u001b[1;32m      3\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mtape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/util/dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    178\u001b[0m     \u001b[0;34m\"\"\"Call target, and fall back on dispatchers if there is a TypeError.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m       \u001b[0;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py\u001b[0m in \u001b[0;36mstack\u001b[0;34m(values, axis, name)\u001b[0m\n\u001b[1;32m   1011\u001b[0m       \u001b[0;32mpass\u001b[0m  \u001b[0;31m# Input list contains non-constant tensors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1013\u001b[0;31m   \u001b[0mvalue_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shape_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1014\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mvalue_shape\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1015\u001b[0m     \u001b[0mexpanded_num_dims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue_shape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mconvert_to_tensor\u001b[0;34m(value, dtype, name, preferred_dtype, dtype_hint)\u001b[0m\n\u001b[1;32m   1048\u001b[0m   preferred_dtype = deprecation.deprecated_argument_lookup(\n\u001b[1;32m   1049\u001b[0m       \"dtype_hint\", dtype_hint, \"preferred_dtype\", preferred_dtype)\n\u001b[0;32m-> 1050\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mconvert_to_tensor_v2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreferred_dtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1051\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1052\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mconvert_to_tensor_v2\u001b[0;34m(value, dtype, dtype_hint, name)\u001b[0m\n\u001b[1;32m   1106\u001b[0m       \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1107\u001b[0m       \u001b[0mpreferred_dtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype_hint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1108\u001b[0;31m       as_ref=False)\n\u001b[0m\u001b[1;32m   1109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36minternal_convert_to_tensor\u001b[0;34m(value, dtype, name, as_ref, preferred_dtype, ctx, accept_symbolic_tensors)\u001b[0m\n\u001b[1;32m   1184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1185\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1186\u001b[0;31m       \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconversion_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mas_ref\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1188\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py\u001b[0m in \u001b[0;36m_autopacking_conversion_function\u001b[0;34m(v, dtype, name, as_ref)\u001b[0m\n\u001b[1;32m   1115\u001b[0m   \u001b[0;32melif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0minferred_dtype\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1116\u001b[0m     \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_cast_nested_seqs_to_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1117\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0m_autopacking_helper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m\"packed\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py\u001b[0m in \u001b[0;36m_autopacking_helper\u001b[0;34m(list_or_tuple, dtype, name)\u001b[0m\n\u001b[1;32m   1066\u001b[0m           \u001b[0;31m# convertible-to-tensor types, such as numpy arrays.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1067\u001b[0m           elems_as_tensors.append(\n\u001b[0;32m-> 1068\u001b[0;31m               constant_op.constant(elem, dtype=dtype, name=str(i)))\n\u001b[0m\u001b[1;32m   1069\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mgen_array_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melems_as_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscope\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1070\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconstant\u001b[0;34m(value, dtype, shape, name)\u001b[0m\n\u001b[1;32m    243\u001b[0m   \"\"\"\n\u001b[1;32m    244\u001b[0m   return _constant_impl(value, dtype, shape, name, verify_shape=False,\n\u001b[0;32m--> 245\u001b[0;31m                         allow_broadcast=True)\n\u001b[0m\u001b[1;32m    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36m_constant_impl\u001b[0;34m(value, dtype, shape, name, verify_shape, allow_broadcast)\u001b[0m\n\u001b[1;32m    251\u001b[0m   \u001b[0mctx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m     \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_to_eager_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    254\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconvert_to_eager_tensor\u001b[0;34m(value, ctx, dtype)\u001b[0m\n\u001b[1;32m    112\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m   \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEagerTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Attempt to convert a value (None) with an unsupported type (<class 'NoneType'>) to a Tensor."
     ]
    }
   ],
   "source": [
    "tf.reduce_sum(tf.stack([tape.gradient(z, [w1, w2]) for z in (z1, z2, z3)]),\n",
    "              axis=0)\n",
    "del tape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.GradientTape(persistent=True) as hessian_tape:\n",
    "    with tf.GradientTape() as jacobian_tape:\n",
    "        z = f(w1, w2)\n",
    "    jacobians = jacobian_tape.gradient(z, [w1, w2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute '_id'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-142-80f0714df78d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m hessians = [hessian_tape.gradient(jacobian, [w1, w2])\n\u001b[0;32m----> 2\u001b[0;31m             for jacobian in jacobians]\n\u001b[0m",
      "\u001b[0;32m<ipython-input-142-80f0714df78d>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      1\u001b[0m hessians = [hessian_tape.gradient(jacobian, [w1, w2])\n\u001b[0;32m----> 2\u001b[0;31m             for jacobian in jacobians]\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/backprop.py\u001b[0m in \u001b[0;36mgradient\u001b[0;34m(self, target, sources, output_gradients, unconnected_gradients)\u001b[0m\n\u001b[1;32m    954\u001b[0m         \u001b[0mflat_sources\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    955\u001b[0m         \u001b[0moutput_gradients\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_gradients\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 956\u001b[0;31m         unconnected_gradients=unconnected_gradients)\n\u001b[0m\u001b[1;32m    957\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    958\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_persistent\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/imperative_grad.py\u001b[0m in \u001b[0;36mimperative_grad\u001b[0;34m(tape, target, sources, output_gradients, unconnected_gradients)\u001b[0m\n\u001b[1;32m     70\u001b[0m       \u001b[0msources\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m       \u001b[0moutput_gradients\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m       compat.as_str(unconnected_gradients.value))\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute '_id'"
     ]
    }
   ],
   "source": [
    "hessians = [hessian_tape.gradient(jacobian, [w1, w2])\n",
    "            for jacobian in jacobians]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del hessian_tape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "@tf.custom_gradient\n",
    "\n",
    "def my_better_softplus(z):\n",
    "    exp = tf.exp(z)\n",
    "    def my_softplus_gradients(grad):\n",
    "        return grad / (1 + 1 / exp)\n",
    "    return tf.math.log(exp + 1), my_softplus_gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def my_better_softplus(z):\n",
    "    return tf.where(z > 30., z, tf.math.log(tf.exp(z) + 1.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: id=139405, shape=(1,), dtype=float32, numpy=array([1000.], dtype=float32)>,\n",
       " [<tf.Tensor: id=139429, shape=(1,), dtype=float32, numpy=array([nan], dtype=float32)>])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = tf.Variable([1000.])\n",
    "with tf.GradientTape() as tape:\n",
    "    z = my_better_softplus(x)\n",
    "\n",
    "z, tape.gradient(z, [x])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom Training Loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let’s build a simple model. No need to compile it, since we will handle the training loop manually:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l2_reg = keras.regularizers.l2(0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Dense(30, activation='elu',\n",
    "                       kernel_initializer='he_normal',\n",
    "                       kernel_regularizer=l2_reg),\n",
    "    keras.layers.Dense(1,\n",
    "                       kernel_regularizer=l2_reg)\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let’s create a tiny function that will randomly sample a batch of instances from the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def random_batch(X, y, batch_size=32):\n",
    "    idx = np.random.randint(len(X), size=batch_size)\n",
    "    return X[idx], y[idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s also define a function that will display the training status, including the number of steps, the total number of steps, the mean loss since the start of the epoch (i.e., we will use the `Mean` metric to compute it), and other metrics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_status_bar(iteration, total, loss, metrics=None):\n",
    "    metrics = ' - '.join(['{}: {:.4f}'.format(m.name, m.result()) \n",
    "                          for m in [loss] + (metrics or [])])\n",
    "    end = '' if iteration < total else '\\n'\n",
    "    print('\\r{}/{} - '.format(iteration, total) + metrics, end=end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50/50 - loss: 0.0900 - mean_square: 858.5000\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "mean_loss = keras.metrics.Mean(name='loss')\n",
    "mean_square = keras.metrics.Mean(name='mean_square')\n",
    "\n",
    "for i in range(1, 50+1):\n",
    "    loss = 1 / i\n",
    "    mean_loss(loss)\n",
    "    mean_square(i ** 2)\n",
    "    print_status_bar(i, 50, mean_loss, [mean_square])\n",
    "    time.sleep(0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A fancier version with a progress bar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def progress_bar(iteration, total, size=30):\n",
    "    running = iteration < total\n",
    "    c = \">\" if running else \"=\"\n",
    "    p = (size - 1) * iteration // total\n",
    "    fmt = \"{{:-{}d}}/{{}} [{{}}]\".format(len(str(total)))\n",
    "    params = [iteration, total, \"=\" * p + c + \".\" * (size - p - 1)]\n",
    "    return fmt.format(*params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' 3500/10000 [=>....]'"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "progress_bar(3500, 10000, size=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_status_bar(iteration, total, loss, metrics=None, size=30):\n",
    "    metrics = \" - \".join([\"{}: {:.4f}\".format(m.name, m.result())\n",
    "                         for m in [loss] + (metrics or [])])\n",
    "    end = \"\" if iteration < total else \"\\n\"\n",
    "    print(\"\\r{} - {}\".format(progress_bar(iteration, total), metrics), end=end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50/50 [==============================] - loss: 0.0900 - mean_square: 858.5000\n"
     ]
    }
   ],
   "source": [
    "mean_loss = keras.metrics.Mean(name=\"loss\")\n",
    "mean_square = keras.metrics.Mean(name=\"mean_square\")\n",
    "\n",
    "for i in range(1, 50 + 1):\n",
    "    loss = 1 / i\n",
    "    mean_loss(loss)\n",
    "    mean_square(i ** 2)\n",
    "    print_status_bar(i, 50, mean_loss, [mean_square])\n",
    "    time.sleep(0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With that, let’s get down to business! First, we need to define some hyperparameters and choose the optimizer, the loss function, and the metrics (just the MAE in this example):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_epochs = 5\n",
    "batch_size = 32\n",
    "n_steps = len(X_train) // batch_size\n",
    "optimizer = keras.optimizers.Nadam(lr=0.01)\n",
    "loss_fn = keras.losses.mean_squared_error\n",
    "mean_loss = keras.metrics.Mean()\n",
    "metrics = [keras.metrics.MeanAbsoluteError()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "11610/11610 [==============================] - mean: 1.4384 - mean_absolute_error: 0.58792\n",
      "Epoch 2/5\n",
      "11610/11610 [==============================] - mean: 0.6721 - mean_absolute_error: 0.5281\n",
      "Epoch 3/5\n",
      "11610/11610 [==============================] - mean: 0.6342 - mean_absolute_error: 0.5185\n",
      "Epoch 4/5\n",
      "11610/11610 [==============================] - mean: 0.6394 - mean_absolute_error: 0.5187\n",
      "Epoch 5/5\n",
      "11610/11610 [==============================] - mean: 0.6434 - mean_absolute_error: 0.5216\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(1, n_epochs + 1):\n",
    "    print('Epoch {}/{}'.format(epoch, n_epochs))\n",
    "    \n",
    "    for step in range(1, n_steps + 1):\n",
    "        X_batch, y_batch = random_batch(X_train_scaled, y_train)\n",
    "        with tf.GradientTape() as tape:\n",
    "            y_pred = model(X_batch)\n",
    "            main_loss = tf.reduce_mean(loss_fn(y_batch, y_pred))\n",
    "            loss = tf.add_n([main_loss] + model.losses)\n",
    "        gradients = tape.gradient(loss, model.trainable_variables)\n",
    "        optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
    "        \n",
    "        for variable in model.variables:\n",
    "            if variable.constraint is not None:\n",
    "                variable.assign(variable.constraint(variable))\n",
    "        mean_loss(loss)\n",
    "        \n",
    "        for metric in metrics:\n",
    "            metric(y_batch, y_pred)\n",
    "        print_status_bar(step * batch_size, len(y_train), mean_loss, metrics)\n",
    "    print_status_bar(len(y_train), len(y_train), mean_loss, metrics)\n",
    "    \n",
    "    for metric in [mean_loss] + metrics:\n",
    "        metric.reset_states()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "To run this cell, please install tqdm, ipywidgets and restart Jupyter\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    from tqdm.notebook import trange\n",
    "    from collections import OrderedDict\n",
    "    with trange(1, n_epochs + 1, desc=\"All epochs\") as epochs:\n",
    "        for epoch in epochs:\n",
    "            with trange(1, n_steps + 1, desc=\"Epoch {}/{}\".format(epoch, n_epochs)) as steps:\n",
    "                for step in steps:\n",
    "                    X_batch, y_batch = random_batch(X_train_scaled, y_train)\n",
    "                    with tf.GradientTape() as tape:\n",
    "                        y_pred = model(X_batch)\n",
    "                        main_loss = tf.reduce_mean(loss_fn(y_batch, y_pred))\n",
    "                        loss = tf.add_n([main_loss] + model.losses)\n",
    "                    gradients = tape.gradient(loss, model.trainable_variables)\n",
    "                    optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
    "                    for variable in model.variables:\n",
    "                        if variable.constraint is not None:\n",
    "                            variable.assign(variable.constraint(variable))                    \n",
    "                    status = OrderedDict()\n",
    "                    mean_loss(loss)\n",
    "                    status[\"loss\"] = mean_loss.result().numpy()\n",
    "                    for metric in metrics:\n",
    "                        metric(y_batch, y_pred)\n",
    "                        status[metric.name] = metric.result().numpy()\n",
    "                    steps.set_postfix(status)\n",
    "            for metric in [mean_loss] + metrics:\n",
    "                metric.reset_states()\n",
    "except ImportError as ex:\n",
    "    print(\"To run this cell, please install tqdm, ipywidgets and restart Jupyter\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TensorFlow Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cube(x):\n",
    "    return x ** 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cube(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=804710, shape=(), dtype=float32, numpy=8.0>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cube(tf.constant(2.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TensorFlow Function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.eager.def_function.Function at 0x7f941432d7f0>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_cube = tf.function(cube)\n",
    "tf_cube"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=804717, shape=(), dtype=int32, numpy=8>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_cube(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=804727, shape=(), dtype=float32, numpy=8.0>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_cube(tf.constant(2.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TF Functions and Concrete Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.framework.func_graph.FuncGraph at 0x7f94142f9f28>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concrete_function = tf_cube.get_concrete_function(tf.constant(2.0))\n",
    "concrete_function.graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=804732, shape=(), dtype=float32, numpy=8.0>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concrete_function(tf.constant(2.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concrete_function is tf_cube.get_concrete_function(tf.constant(2.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Function Definitions and Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.framework.func_graph.FuncGraph at 0x7f94142f9f28>"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concrete_function.graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Operation 'x' type=Placeholder>,\n",
       " <tf.Operation 'pow/y' type=Const>,\n",
       " <tf.Operation 'pow' type=Pow>,\n",
       " <tf.Operation 'Identity' type=Identity>]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ops = concrete_function.graph.get_operations()\n",
    "ops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor 'x:0' shape=() dtype=float32>,\n",
       " <tf.Tensor 'pow/y:0' shape=() dtype=float32>]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pow_op = ops[2]\n",
    "list(pow_op.inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor 'pow:0' shape=() dtype=float32>]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pow_op.outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Operation 'x' type=Placeholder>"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concrete_function.graph.get_operation_by_name('x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Identity:0' shape=() dtype=float32>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concrete_function.graph.get_tensor_by_name('Identity:0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name: \"__inference_cube_804726\"\n",
       "input_arg {\n",
       "  name: \"x\"\n",
       "  type: DT_FLOAT\n",
       "}\n",
       "output_arg {\n",
       "  name: \"identity\"\n",
       "  type: DT_FLOAT\n",
       "}"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concrete_function.function_def.signature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How TF Functions Trace Python Functions to Extract Their Computation Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def tf_cube(x):\n",
    "    print(\"print:\", x)\n",
    "    return x ** 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "print: Tensor(\"x:0\", shape=(), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "result = tf_cube(tf.constant(2.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=804745, shape=(), dtype=float32, numpy=8.0>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "print: 2\n",
      "print: 3\n",
      "print: Tensor(\"x:0\", shape=(1, 2), dtype=float32)\n",
      "print: Tensor(\"x:0\", shape=(None, 2), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "result = tf_cube(2)\n",
    "result = tf_cube(3)\n",
    "result = tf_cube(tf.constant([[1., 2.]])) # New shape: trace!\n",
    "result = tf_cube(tf.constant([[3., 4.], [5., 6.]])) # New shape: trace!\n",
    "result = tf_cube(tf.constant([[7., 8.], [9., 10.], [11., 12.]])) # no trace"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to specify a particular input signature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "@tf.function(input_signature=[tf.TensorSpec([None, 28, 28], tf.float32)])\n",
    "def shrink(images):\n",
    "    print(\"Tracing\", images)\n",
    "    return images[:, ::2, ::2] # drop half the rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tracing Tensor(\"images:0\", shape=(None, 28, 28), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "img_batch_1 = tf.random.uniform(shape=[100, 28, 28])\n",
    "img_batch_2 = tf.random.uniform(shape=[50, 28, 28])\n",
    "preprocessed_images = shrink(img_batch_1) # Traces the function.\n",
    "preprocessed_images = shrink(img_batch_2) # Reuses the same concrete function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python inputs incompatible with input_signature: inputs ((<tf.Tensor: id=804812, shape=(2, 2, 2), dtype=float32, numpy=\n",
      "array([[[0.7413678 , 0.62854624],\n",
      "        [0.01738465, 0.3431449 ]],\n",
      "\n",
      "       [[0.51063764, 0.3777541 ],\n",
      "        [0.07321596, 0.02137029]]], dtype=float32)>,)), input_signature ((TensorSpec(shape=(None, 28, 28), dtype=tf.float32, name=None),))\n"
     ]
    }
   ],
   "source": [
    "img_batch_3 = tf.random.uniform(shape=[2, 2, 2])\n",
    "\n",
    "try:\n",
    "    preprocessed_images = shrink(img_batch_3)  # rejects unexpected types or shapes\n",
    "except ValueError as ex:\n",
    "    print(ex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Autograph To Capture Control Flow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A \"static\" `for` loop using `range()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def add_10(x):\n",
    "    for i in range(10):\n",
    "        x += 1\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=804841, shape=(), dtype=int32, numpy=15>"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_10(tf.constant(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Operation 'x' type=Placeholder>,\n",
       " <tf.Operation 'add/y' type=Const>,\n",
       " <tf.Operation 'add' type=Add>,\n",
       " <tf.Operation 'add_1/y' type=Const>,\n",
       " <tf.Operation 'add_1' type=Add>,\n",
       " <tf.Operation 'add_2/y' type=Const>,\n",
       " <tf.Operation 'add_2' type=Add>,\n",
       " <tf.Operation 'add_3/y' type=Const>,\n",
       " <tf.Operation 'add_3' type=Add>,\n",
       " <tf.Operation 'add_4/y' type=Const>,\n",
       " <tf.Operation 'add_4' type=Add>,\n",
       " <tf.Operation 'add_5/y' type=Const>,\n",
       " <tf.Operation 'add_5' type=Add>,\n",
       " <tf.Operation 'add_6/y' type=Const>,\n",
       " <tf.Operation 'add_6' type=Add>,\n",
       " <tf.Operation 'add_7/y' type=Const>,\n",
       " <tf.Operation 'add_7' type=Add>,\n",
       " <tf.Operation 'add_8/y' type=Const>,\n",
       " <tf.Operation 'add_8' type=Add>,\n",
       " <tf.Operation 'add_9/y' type=Const>,\n",
       " <tf.Operation 'add_9' type=Add>,\n",
       " <tf.Operation 'Identity' type=Identity>]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_10.get_concrete_function(tf.constant(5)).graph.get_operations()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A \"dynamic\" loop using `tf.while_loop()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def add_10(x):\n",
    "    condition = lambda i, x: tf.less(i, 10)\n",
    "    body = lambda i, x: (tf.add(i, 1), tf.add(x, 1))\n",
    "    final_i, final_x = tf.while_loop(condition, body, [tf.constant(0), x])\n",
    "    return final_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=804887, shape=(), dtype=int32, numpy=15>"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_10(tf.constant(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Operation 'x' type=Placeholder>,\n",
       " <tf.Operation 'Const' type=Const>,\n",
       " <tf.Operation 'while/maximum_iterations' type=Const>,\n",
       " <tf.Operation 'while/loop_counter' type=Const>,\n",
       " <tf.Operation 'while' type=While>,\n",
       " <tf.Operation 'while/Identity' type=Identity>,\n",
       " <tf.Operation 'while/Identity_1' type=Identity>,\n",
       " <tf.Operation 'while/Identity_2' type=Identity>,\n",
       " <tf.Operation 'while/Identity_3' type=Identity>,\n",
       " <tf.Operation 'Identity' type=Identity>]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_10.get_concrete_function(tf.constant(5)).graph.get_operations()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A \"dynamic\" `for` loop using `tf.range()` (captured by autograph):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def add_10(x):\n",
    "    for i in tf.range(10):\n",
    "        x = x + 1\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Operation 'x' type=Placeholder>,\n",
       " <tf.Operation 'range/start' type=Const>,\n",
       " <tf.Operation 'range/limit' type=Const>,\n",
       " <tf.Operation 'range/delta' type=Const>,\n",
       " <tf.Operation 'range' type=Range>,\n",
       " <tf.Operation 'Const' type=Const>,\n",
       " <tf.Operation 'while/maximum_iterations' type=Const>,\n",
       " <tf.Operation 'while/loop_counter' type=Const>,\n",
       " <tf.Operation 'while' type=While>,\n",
       " <tf.Operation 'while/Identity' type=Identity>,\n",
       " <tf.Operation 'while/Identity_1' type=Identity>,\n",
       " <tf.Operation 'while/Identity_2' type=Identity>,\n",
       " <tf.Operation 'while/Identity_3' type=Identity>,\n",
       " <tf.Operation 'while/Identity_4' type=Identity>,\n",
       " <tf.Operation 'Identity' type=Identity>]"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_10.get_concrete_function(tf.constant(0)).graph.get_operations()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Variables and Other Resources in TF Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "counter = tf.Variable(0)\n",
    "\n",
    "@tf.function\n",
    "def increment(counter, c=1):\n",
    "    return counter.assign_add(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=804969, shape=(), dtype=int32, numpy=2>"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "increment(counter)\n",
    "increment(counter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name: \"counter\"\n",
       "type: DT_RESOURCE"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function_def = increment.get_concrete_function(counter).function_def\n",
    "function_def.signature.input_arg[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "counter = tf.Variable(0)\n",
    "\n",
    "@tf.function\n",
    "def increment(c=1):\n",
    "    return counter.assign_add(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=804987, shape=(), dtype=int32, numpy=2>"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "increment()\n",
    "increment()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name: \"assignaddvariableop_resource\"\n",
       "type: DT_RESOURCE"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function_def = increment.get_concrete_function().function_def\n",
    "function_def.signature.input_arg[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Counter:\n",
    "    def __init__(self):\n",
    "        self.counter = tf.Variable(0)\n",
    "\n",
    "    @tf.function\n",
    "    def increment(self, c=1):\n",
    "        return self.counter.assign_add(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=805005, shape=(), dtype=int32, numpy=2>"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = Counter()\n",
    "c.increment()\n",
    "c.increment()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"from __future__ import print_function\\n\\ndef tf__add_10(x):\\n  try:\\n    with ag__.function_scope('add_10'):\\n      do_return = False\\n      retval_ = None\\n\\n      def loop_body(loop_vars, x_1):\\n        with ag__.function_scope('loop_body'):\\n          i = loop_vars\\n          x_1 += 1\\n          return x_1,\\n      x, = ag__.for_stmt(ag__.converted_call('range', tf, ag__.ConversionOptions(recursive=True, verbose=0, strip_decorators=(ag__.convert, ag__.do_not_convert, ag__.converted_call), force_conversion=False, optional_features=ag__.Feature.ALL, internal_convert_user_code=True), (10,), {}), None, loop_body, (x,))\\n      do_return = True\\n      retval_ = x\\n      return retval_\\n  except:\\n    ag__.rewrite_graph_construction_error(ag_source_map__)\\n\\n\\n\\ntf__add_10.autograph_info__ = {}\\n\""
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@tf.function\n",
    "def add_10(x):\n",
    "    for i in tf.range(10):\n",
    "        x += 1\n",
    "    return x\n",
    "\n",
    "tf.autograph.to_code(add_10.python_function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def display_tf_code(func):\n",
    "    from IPython.display import display, Markdown\n",
    "    if hasattr(func, \"python_function\"):\n",
    "        func = func.python_function\n",
    "    code = tf.autograph.to_code(func)\n",
    "    display(Markdown('```python\\n{}\\n```'.format(code)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```python\n",
       "from __future__ import print_function\n",
       "\n",
       "def tf__add_10(x):\n",
       "  try:\n",
       "    with ag__.function_scope('add_10'):\n",
       "      do_return = False\n",
       "      retval_ = None\n",
       "\n",
       "      def loop_body(loop_vars, x_1):\n",
       "        with ag__.function_scope('loop_body'):\n",
       "          i = loop_vars\n",
       "          x_1 += 1\n",
       "          return x_1,\n",
       "      x, = ag__.for_stmt(ag__.converted_call('range', tf, ag__.ConversionOptions(recursive=True, verbose=0, strip_decorators=(ag__.convert, ag__.do_not_convert, ag__.converted_call), force_conversion=False, optional_features=ag__.Feature.ALL, internal_convert_user_code=True), (10,), {}), None, loop_body, (x,))\n",
       "      do_return = True\n",
       "      retval_ = x\n",
       "      return retval_\n",
       "  except:\n",
       "    ag__.rewrite_graph_construction_error(ag_source_map__)\n",
       "\n",
       "\n",
       "\n",
       "tf__add_10.autograph_info__ = {}\n",
       "\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_tf_code(add_10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using TF Functions with tf.keras (or Not)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, tf.keras will automatically convert your custom code into TF Functions, no need to use `tf.function()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Custom loss function\n",
    "def my_mse(y_true, y_pred):\n",
    "    print(\"Tracing loss my_mse()\")\n",
    "    return tf.reduce_mean(tf.square(y_pred - y_true))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Custom metric function\n",
    "def my_mae(y_true, y_pred):\n",
    "    print(\"Tracing metric my_mae()\")\n",
    "    return tf.reduce_mean(tf.abs(y_pred - y_true))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Custom layer\n",
    "class MyDense(keras.layers.Layer):\n",
    "    \n",
    "    def __init__(self, units, activation=None, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.units = units\n",
    "        self.activation = keras.activations.get(activation)\n",
    "\n",
    "    def build(self, input_shape):\n",
    "        self.kernel = self.add_weight(name='kernel', \n",
    "                                      shape=(input_shape[1], self.units),\n",
    "                                      initializer='uniform',\n",
    "                                      trainable=True)\n",
    "        self.biases = self.add_weight(name='bias', \n",
    "                                      shape=(self.units,),\n",
    "                                      initializer='zeros',\n",
    "                                      trainable=True)\n",
    "        super().build(input_shape)\n",
    "\n",
    "    def call(self, X):\n",
    "        print(\"Tracing MyDense.call()\")\n",
    "        return self.activation(X @ self.kernel + self.biases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Custom model\n",
    "class MyModel(keras.models.Model):\n",
    "    \n",
    "    def __init__(self, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.hidden1 = MyDense(30, activation=\"relu\")\n",
    "        self.hidden2 = MyDense(30, activation=\"relu\")\n",
    "        self.output_ = MyDense(1)\n",
    "\n",
    "    def call(self, input):\n",
    "        print(\"Tracing MyModel.call()\")\n",
    "        hidden1 = self.hidden1(input)\n",
    "        hidden2 = self.hidden2(hidden1)\n",
    "        concat = keras.layers.concatenate([input, hidden2])\n",
    "        output = self.output_(concat)\n",
    "        return output\n",
    "\n",
    "model = MyModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss=my_mse, \n",
    "              optimizer=\"nadam\", \n",
    "              metrics=[my_mae])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing metric my_mae()\n",
      "Tracing metric my_mae()\n",
      "Tracing loss my_mse()\n",
      "Train on 11610 samples, validate on 3870 samples\n",
      "Epoch 1/2\n",
      "11610/11610 [==============================] - 1s 77us/sample - loss: 1.3164 - my_mae: 0.7935 - val_loss: 0.5629 - val_my_mae: 0.4915\n",
      "Epoch 2/2\n",
      "11610/11610 [==============================] - 1s 54us/sample - loss: 0.4434 - my_mae: 0.4779 - val_loss: 0.4852 - val_my_mae: 0.4528\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f941490e860>"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_scaled, y_train, \n",
    "          epochs=2,\n",
    "          validation_data=(X_valid_scaled, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5160/5160 [==============================] - 0s 15us/sample - loss: 0.4235 - my_mae: 0.4608\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.42349057040473287, 0.46075788]"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can turn this off by creating the model with `dynamic=True` (or calling `super().__init__(dynamic=True, **kwargs)` in the model's constructor):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = MyModel(dynamic=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss=my_mse, \n",
    "              optimizer=\"nadam\", \n",
    "              metrics=[my_mae])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "   32/11610 [..............................] - ETA: 20s - loss: 7.0258 - my_mae: 2.3381Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "  160/11610 [..............................] - ETA: 8s - loss: 5.9069 - my_mae: 2.1625 Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "  256/11610 [..............................] - ETA: 7s - loss: 5.8042 - my_mae: 2.1386Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "  352/11610 [..............................] - ETA: 7s - loss: 5.9022 - my_mae: 2.1438Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "  480/11610 [>.............................] - ETA: 6s - loss: 5.8398 - my_mae: 2.1271Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "  608/11610 [>.............................] - ETA: 6s - loss: 5.6199 - my_mae: 2.0787Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "  704/11610 [>.............................] - ETA: 6s - loss: 5.6674 - my_mae: 2.0915Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "  800/11610 [=>............................] - ETA: 6s - loss: 5.5208 - my_mae: 2.0641Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
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      "  896/11610 [=>............................] - ETA: 6s - loss: 5.4744 - my_mae: 2.0544Tracing MyModel.call()\n",
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      " 1056/11610 [=>............................] - ETA: 6s - loss: 5.4103 - my_mae: 2.0429Tracing MyModel.call()\n",
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      " 1120/11610 [=>............................] - ETA: 6s - loss: 5.4388 - my_mae: 2.0478Tracing MyModel.call()\n",
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      " 1184/11610 [==>...........................] - ETA: 6s - loss: 5.4293 - my_mae: 2.0469Tracing MyModel.call()\n",
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      " 1280/11610 [==>...........................] - ETA: 6s - loss: 5.3484 - my_mae: 2.0305Tracing MyModel.call()\n",
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      " 1344/11610 [==>...........................] - ETA: 7s - loss: 5.2965 - my_mae: 2.0182Tracing MyModel.call()\n",
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      " 1408/11610 [==>...........................] - ETA: 7s - loss: 5.2511 - my_mae: 2.0090Tracing MyModel.call()\n",
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      " 1504/11610 [==>...........................] - ETA: 7s - loss: 5.1618 - my_mae: 1.9874Tracing MyModel.call()\n",
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      "Tracing loss my_mse()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " 1568/11610 [===>..........................] - ETA: 7s - loss: 5.1026 - my_mae: 1.9734Tracing MyModel.call()\n",
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      " 1632/11610 [===>..........................] - ETA: 7s - loss: 5.0269 - my_mae: 1.9550Tracing MyModel.call()\n",
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      " 1696/11610 [===>..........................] - ETA: 7s - loss: 4.9305 - my_mae: 1.9304Tracing MyModel.call()\n",
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      " 2656/11610 [=====>........................] - ETA: 6s - loss: 3.7649 - my_mae: 1.5912Tracing MyModel.call()\n",
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      " 2752/11610 [======>.......................] - ETA: 6s - loss: 3.6672 - my_mae: 1.5598Tracing MyModel.call()\n",
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      " 2848/11610 [======>.......................] - ETA: 6s - loss: 3.5704 - my_mae: 1.5289Tracing MyModel.call()\n",
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      " 2944/11610 [======>.......................] - ETA: 6s - loss: 3.4856 - my_mae: 1.5035Tracing MyModel.call()\n",
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      " 3040/11610 [======>.......................] - ETA: 6s - loss: 3.3932 - my_mae: 1.4737Tracing MyModel.call()\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tracing loss my_mse()\n",
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      " 3136/11610 [=======>......................] - ETA: 6s - loss: 3.3092 - my_mae: 1.4466Tracing MyModel.call()\n",
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      " 3232/11610 [=======>......................] - ETA: 6s - loss: 3.2261 - my_mae: 1.4198Tracing MyModel.call()\n",
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      " 3296/11610 [=======>......................] - ETA: 6s - loss: 3.1830 - my_mae: 1.4061Tracing MyModel.call()\n",
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      " 3392/11610 [=======>......................] - ETA: 5s - loss: 3.1180 - my_mae: 1.3855Tracing MyModel.call()\n",
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      " 3648/11610 [========>.....................] - ETA: 5s - loss: 2.9563 - my_mae: 1.3369Tracing MyModel.call()\n",
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      " 3744/11610 [========>.....................] - ETA: 5s - loss: 2.8931 - my_mae: 1.3159Tracing MyModel.call()\n",
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      " 3840/11610 [========>.....................] - ETA: 5s - loss: 2.8340 - my_mae: 1.2966Tracing MyModel.call()\n",
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      " 3936/11610 [=========>....................] - ETA: 5s - loss: 2.7790 - my_mae: 1.2791Tracing MyModel.call()\n",
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      " 4032/11610 [=========>....................] - ETA: 5s - loss: 2.7281 - my_mae: 1.2635Tracing MyModel.call()\n",
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      " 4128/11610 [=========>....................] - ETA: 5s - loss: 2.6907 - my_mae: 1.2513Tracing MyModel.call()\n",
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      " 4224/11610 [=========>....................] - ETA: 5s - loss: 2.6421 - my_mae: 1.2352Tracing MyModel.call()\n",
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      " 4320/11610 [==========>...................] - ETA: 5s - loss: 2.5987 - my_mae: 1.2216Tracing MyModel.call()\n",
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      " 4384/11610 [==========>...................] - ETA: 5s - loss: 2.5684 - my_mae: 1.2115Tracing MyModel.call()\n",
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      " 4480/11610 [==========>...................] - ETA: 5s - loss: 2.5250 - my_mae: 1.1972Tracing MyModel.call()\n",
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      " 4576/11610 [==========>...................] - ETA: 5s - loss: 2.4846 - my_mae: 1.1845Tracing MyModel.call()\n",
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     ]
    },
    {
     "name": "stdout",
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     "text": [
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      " 5408/11610 [============>.................] - ETA: 4s - loss: 2.2010 - my_mae: 1.0905Tracing MyModel.call()\n",
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      " 5856/11610 [==============>...............] - ETA: 4s - loss: 2.0764 - my_mae: 1.0500Tracing MyModel.call()\n",
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     ]
    },
    {
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    },
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    },
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      "11610/11610 [==============================] - 10s 823us/sample - loss: 1.3164 - my_mae: 0.7935 - val_loss: 0.5624 - val_my_mae: 0.4913\n",
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      "   32/11610 [..............................] - ETA: 7s - loss: 0.4505 - my_mae: 0.5092Tracing MyModel.call()\n",
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      "  128/11610 [..............................] - ETA: 7s - loss: 0.5761 - my_mae: 0.5342Tracing MyModel.call()\n",
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      "  416/11610 [>.............................] - ETA: 8s - loss: 0.4482 - my_mae: 0.4972Tracing MyModel.call()\n",
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      "  512/11610 [>.............................] - ETA: 8s - loss: 0.4283 - my_mae: 0.4848Tracing MyModel.call()\n",
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      "  608/11610 [>.............................] - ETA: 8s - loss: 0.4372 - my_mae: 0.4879Tracing MyModel.call()\n",
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      "  672/11610 [>.............................] - ETA: 8s - loss: 0.4336 - my_mae: 0.4871Tracing MyModel.call()\n",
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      "  768/11610 [>.............................] - ETA: 8s - loss: 0.4328 - my_mae: 0.4915Tracing MyModel.call()\n",
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      "  832/11610 [=>............................] - ETA: 8s - loss: 0.4308 - my_mae: 0.4889Tracing MyModel.call()\n",
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      "  896/11610 [=>............................] - ETA: 8s - loss: 0.4385 - my_mae: 0.4908Tracing MyModel.call()\n",
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      "  992/11610 [=>............................] - ETA: 8s - loss: 0.4372 - my_mae: 0.4882Tracing MyModel.call()\n",
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      " 1056/11610 [=>............................] - ETA: 8s - loss: 0.4433 - my_mae: 0.4897Tracing MyModel.call()\n",
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      " 1152/11610 [=>............................] - ETA: 7s - loss: 0.4377 - my_mae: 0.4870Tracing MyModel.call()\n",
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      " 1248/11610 [==>...........................] - ETA: 7s - loss: 0.4397 - my_mae: 0.4889Tracing MyModel.call()\n",
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      " 1344/11610 [==>...........................] - ETA: 7s - loss: 0.4544 - my_mae: 0.4946Tracing MyModel.call()\n",
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      " 1440/11610 [==>...........................] - ETA: 7s - loss: 0.4534 - my_mae: 0.4958Tracing MyModel.call()\n",
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      " 1536/11610 [==>...........................] - ETA: 7s - loss: 0.4500 - my_mae: 0.4943Tracing MyModel.call()\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " 1632/11610 [===>..........................] - ETA: 6s - loss: 0.4482 - my_mae: 0.4938Tracing MyModel.call()\n",
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      " 1792/11610 [===>..........................] - ETA: 6s - loss: 0.4462 - my_mae: 0.4950Tracing MyModel.call()\n",
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      " 1856/11610 [===>..........................] - ETA: 6s - loss: 0.4516 - my_mae: 0.4974Tracing MyModel.call()\n",
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      " 1952/11610 [====>.........................] - ETA: 6s - loss: 0.4546 - my_mae: 0.4995Tracing MyModel.call()\n",
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      " 2048/11610 [====>.........................] - ETA: 6s - loss: 0.4490 - my_mae: 0.4974Tracing MyModel.call()\n",
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      " 2176/11610 [====>.........................] - ETA: 6s - loss: 0.4474 - my_mae: 0.4967Tracing MyModel.call()\n",
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      " 2272/11610 [====>.........................] - ETA: 6s - loss: 0.4623 - my_mae: 0.5026Tracing MyModel.call()\n",
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      " 2368/11610 [=====>........................] - ETA: 6s - loss: 0.4692 - my_mae: 0.5056Tracing MyModel.call()\n",
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      " 2464/11610 [=====>........................] - ETA: 6s - loss: 0.4720 - my_mae: 0.5067Tracing MyModel.call()\n",
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      " 2560/11610 [=====>........................] - ETA: 6s - loss: 0.4677 - my_mae: 0.5048Tracing MyModel.call()\n",
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      " 2656/11610 [=====>........................] - ETA: 6s - loss: 0.4678 - my_mae: 0.5055Tracing MyModel.call()\n",
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      " 2752/11610 [======>.......................] - ETA: 6s - loss: 0.4732 - my_mae: 0.5068Tracing MyModel.call()\n",
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      " 2816/11610 [======>.......................] - ETA: 6s - loss: 0.4699 - my_mae: 0.5057Tracing MyModel.call()\n",
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      " 2912/11610 [======>.......................] - ETA: 6s - loss: 0.4672 - my_mae: 0.5036Tracing MyModel.call()\n",
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      " 3008/11610 [======>.......................] - ETA: 6s - loss: 0.4650 - my_mae: 0.5023Tracing MyModel.call()\n",
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      " 3104/11610 [=======>......................] - ETA: 6s - loss: 0.4609 - my_mae: 0.5000Tracing MyModel.call()\n",
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     ]
    },
    {
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     "text": [
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      " 3200/11610 [=======>......................] - ETA: 6s - loss: 0.4676 - my_mae: 0.5019Tracing MyModel.call()\n",
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      " 4576/11610 [==========>...................] - ETA: 4s - loss: 0.4655 - my_mae: 0.4958Tracing MyModel.call()\n",
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      " 4672/11610 [===========>..................] - ETA: 4s - loss: 0.4669 - my_mae: 0.4961Tracing MyModel.call()\n",
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     ]
    },
    {
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     "text": [
      "Tracing metric my_mae()\n",
      " 4768/11610 [===========>..................] - ETA: 4s - loss: 0.4634 - my_mae: 0.4944Tracing MyModel.call()\n",
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      " 4864/11610 [===========>..................] - ETA: 4s - loss: 0.4646 - my_mae: 0.4954Tracing MyModel.call()\n",
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      " 4960/11610 [===========>..................] - ETA: 4s - loss: 0.4633 - my_mae: 0.4945Tracing MyModel.call()\n",
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      " 6112/11610 [==============>...............] - ETA: 3s - loss: 0.4542 - my_mae: 0.4889Tracing MyModel.call()\n",
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    },
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     ]
    },
    {
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     "text": [
      "Tracing metric my_mae()\n",
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    },
    {
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      "11610/11610 [==============================] - 10s 833us/sample - loss: 0.4432 - my_mae: 0.4787 - val_loss: 0.5055 - val_my_mae: 0.4452\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f94147f9748>"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_scaled, y_train, \n",
    "          epochs=2,\n",
    "          validation_data=(X_valid_scaled, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
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      "5160/5160 [==============================] - 1s 268us/sample - loss: 0.4201 - my_mae: 0.4539\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.41954182115969835, 0.45391732]"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you can compile a model with `run_eagerly=True`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = MyModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss=my_mse, \n",
    "              optimizer=\"nadam\", \n",
    "              metrics=[my_mae], \n",
    "              run_eagerly=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f94150399e8>"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_scaled[:64], y_train[:64], \n",
    "          epochs=1,\n",
    "          validation_data=(X_valid_scaled[:64], y_valid[:64]), verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n",
      "Tracing MyModel.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing MyDense.call()\n",
      "Tracing loss my_mse()\n",
      "Tracing metric my_mae()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[5.508231163024902, 2.056763]"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_scaled[:64], y_test[:64], verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom Optimizers\n",
    "\n",
    "Defining custom optimizers is not very common, but in case you are one of the happy few who gets to write one, here is an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class MyMomentumOptimizer(keras.optimizers.Optimizer):\n",
    "    \n",
    "    def __init__(self, learning_rate=0.001, momentum=0.9, name=\"MyMomentumOptimizer\", **kwargs):\n",
    "        \"\"\"Call super().__init__() and use _set_hyper() to store hyperparameters\"\"\"\n",
    "        super().__init__(name, **kwargs)\n",
    "        self._set_hyper(\"learning_rate\", kwargs.get(\"lr\", learning_rate)) # handle lr=learning_rate\n",
    "        self._set_hyper(\"decay\", self._initial_decay) # \n",
    "        self._set_hyper(\"momentum\", momentum)\n",
    "    \n",
    "    def _create_slots(self, var_list):\n",
    "        \"\"\"For each model variable, create the optimizer variable associated with it.\n",
    "        TensorFlow calls these optimizer variables \"slots\".\n",
    "        For momentum optimization, we need one momentum slot per model variable.\n",
    "        \"\"\"\n",
    "        for var in var_list:\n",
    "            self.add_slot(var, \"momentum\")\n",
    "\n",
    "    @tf.function\n",
    "    def _resource_apply_dense(self, grad, var):\n",
    "        \"\"\"Update the slots and perform one optimization step for one model variable\n",
    "        \"\"\"\n",
    "        var_dtype = var.dtype.base_dtype\n",
    "        lr_t = self._decayed_lr(var_dtype) # handle learning rate decay\n",
    "        momentum_var = self.get_slot(var, \"momentum\")\n",
    "        momentum_hyper = self._get_hyper(\"momentum\", var_dtype)\n",
    "        momentum_var.assign(momentum_var * momentum_hyper - (1. - momentum_hyper)* grad)\n",
    "        var.assign_add(momentum_var * lr_t)\n",
    "\n",
    "    def _resource_apply_sparse(self, grad, var):\n",
    "        raise NotImplementedError\n",
    "\n",
    "    def get_config(self):\n",
    "        base_config = super().get_config()\n",
    "        return {\n",
    "            **base_config,\n",
    "            \"learning_rate\": self._serialize_hyperparameter(\"learning_rate\"),\n",
    "            \"decay\": self._serialize_hyperparameter(\"decay\"),\n",
    "            \"momentum\": self._serialize_hyperparameter(\"momentum\"),\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([keras.layers.Dense(1, input_shape=[8])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss=\"mse\", \n",
    "              optimizer=MyMomentumOptimizer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "11610/11610 [==============================] - 0s 41us/sample - loss: 3.8083\n",
      "Epoch 2/5\n",
      "11610/11610 [==============================] - 0s 33us/sample - loss: 1.4868\n",
      "Epoch 3/5\n",
      "11610/11610 [==============================] - 0s 27us/sample - loss: 0.9177\n",
      "Epoch 4/5\n",
      "11610/11610 [==============================] - 0s 30us/sample - loss: 0.7591\n",
      "Epoch 5/5\n",
      "11610/11610 [==============================] - 0s 28us/sample - loss: 0.70490s - loss: 0.725 - ETA: 0s - loss: 0.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f941507a6d8>"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_scaled, y_train, epochs=5)"
   ]
  }
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