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   "metadata": {},
   "source": [
    "*Python Machine Learning 3rd Edition* by [Sebastian Raschka](https://sebastianraschka.com) & [Vahid Mirjalili](http://vahidmirjalili.com), Packt Publishing Ltd. 2019\n",
    "\n",
    "Code Repository: https://github.com/rasbt/python-machine-learning-book-3rd-edition\n",
    "\n",
    "Code License: [MIT License](https://github.com/rasbt/python-machine-learning-book-3rd-edition/blob/master/LICENSE.txt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 14: Going Deeper -- the Mechanics of TensorFlow (Part 3/3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sebastian Raschka & Vahid Mirjalili \n",
      "last updated: 2019-10-29 \n",
      "\n",
      "numpy 1.17.2\n",
      "scipy 1.3.1\n",
      "tensorflow 2.0.0\n",
      "tensorflow_datasets 1.3.0\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a \"Sebastian Raschka & Vahid Mirjalili\" -u -d -p numpy,scipy,tensorflow,tensorflow_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow_datasets as tfds\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Estimators for MNIST hand-written digit classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "BUFFER_SIZE = 10000\n",
    "BATCH_SIZE = 64\n",
    "NUM_EPOCHS = 20\n",
    "steps_per_epoch = np.ceil(60000 / BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(item):\n",
    "    image = item['image']\n",
    "    label = item['label']\n",
    "    image = tf.image.convert_image_dtype(\n",
    "        image, tf.float32)\n",
    "    image = tf.reshape(image, (-1,))\n",
    "\n",
    "    return {'image-pixels':image}, label[..., tf.newaxis]\n",
    "\n",
    "#Step 1: Defining the input functions (one for training and one for evaluation)\n",
    "## Step 1: Define the input function for training\n",
    "def train_input_fn():\n",
    "    datasets = tfds.load(name='mnist')\n",
    "    mnist_train = datasets['train']\n",
    "\n",
    "    dataset = mnist_train.map(preprocess)\n",
    "    dataset = dataset.shuffle(BUFFER_SIZE)\n",
    "    dataset = dataset.batch(BATCH_SIZE)\n",
    "    return dataset.repeat()\n",
    "\n",
    "## define input-function for evaluation:\n",
    "def eval_input_fn():\n",
    "    datasets = tfds.load(name='mnist')\n",
    "    mnist_test = datasets['test']\n",
    "    dataset = mnist_test.map(preprocess).batch(BATCH_SIZE)\n",
    "    return dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Step 2: feature column\n",
    "image_feature_column = tf.feature_column.numeric_column(\n",
    "    key='image-pixels', shape=(28*28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using config: {'_model_dir': 'models/mnist-dnn/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
      "graph_options {\n",
      "  rewrite_options {\n",
      "    meta_optimizer_iterations: ONE\n",
      "  }\n",
      "}\n",
      ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fe849bfb860>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n",
      "WARNING:tensorflow:From /Users/sebastian/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "WARNING:tensorflow:From /Users/sebastian/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n",
      "\u001b[1mDownloading and preparing dataset mnist (11.06 MiB) to /Users/sebastian/tensorflow_datasets/mnist/1.0.0...\u001b[0m\n"
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      "/Users/sebastian/miniconda3/lib/python3.7/site-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n",
      "/Users/sebastian/miniconda3/lib/python3.7/site-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n",
      "/Users/sebastian/miniconda3/lib/python3.7/site-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n",
      "/Users/sebastian/miniconda3/lib/python3.7/site-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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      "WARNING:tensorflow:From /Users/sebastian/miniconda3/lib/python3.7/site-packages/tensorflow_datasets/core/file_format_adapter.py:209: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use eager execution and: \n",
      "`tf.data.TFRecordDataset(path)`\n"
     ]
    },
    {
     "name": "stderr",
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      "Instructions for updating:\n",
      "Use eager execution and: \n",
      "`tf.data.TFRecordDataset(path)`\n"
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      "\u001b[1mDataset mnist downloaded and prepared to /Users/sebastian/tensorflow_datasets/mnist/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n",
      "INFO:tensorflow:Calling model_fn.\n"
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      "WARNING:tensorflow:From /Users/sebastian/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/optimizer_v2/adagrad.py:108: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
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      "Instructions for updating:\n",
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     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 0.42343718, step = 18700 (0.101 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving checkpoints for 18760 into models/mnist-dnn/model.ckpt.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving checkpoints for 18760 into models/mnist-dnn/model.ckpt.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Loss for final step: 0.22432359.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Loss for final step: 0.22432359.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow_estimator.python.estimator.canned.dnn.DNNClassifierV2 at 0x7fe849be4e10>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Step 3: instantiate the estimator\n",
    "dnn_classifier = tf.estimator.DNNClassifier(\n",
    "    feature_columns=[image_feature_column],\n",
    "    hidden_units=[32, 16],\n",
    "    n_classes=10,\n",
    "    model_dir='models/mnist-dnn/')\n",
    "\n",
    "\n",
    "## Step 4: train\n",
    "dnn_classifier.train(\n",
    "    input_fn=train_input_fn,\n",
    "    steps=NUM_EPOCHS * steps_per_epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Done calling model_fn.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Done calling model_fn.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Starting evaluation at 2019-10-29T21:22:53Z\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Starting evaluation at 2019-10-29T21:22:53Z\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Graph was finalized.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Graph was finalized.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from models/mnist-dnn/model.ckpt-18760\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from models/mnist-dnn/model.ckpt-18760\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Running local_init_op.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Running local_init_op.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Finished evaluation at 2019-10-29-21:22:55\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Finished evaluation at 2019-10-29-21:22:55\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving dict for global step 18760: accuracy = 0.8977, average_loss = 0.39452052, global_step = 18760, loss = 0.39615202\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving dict for global step 18760: accuracy = 0.8977, average_loss = 0.39452052, global_step = 18760, loss = 0.39615202\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 18760: models/mnist-dnn/model.ckpt-18760\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 18760: models/mnist-dnn/model.ckpt-18760\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'accuracy': 0.8977, 'average_loss': 0.39452052, 'loss': 0.39615202, 'global_step': 18760}\n"
     ]
    }
   ],
   "source": [
    "eval_result = dnn_classifier.evaluate(\n",
    "    input_fn=eval_input_fn)\n",
    "\n",
    "print(eval_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a custom Estimator from an existing Keras model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Set random seeds for reproducibility\n",
    "tf.random.set_seed(1)\n",
    "np.random.seed(1)\n",
    "\n",
    "## Create the data\n",
    "x = np.random.uniform(low=-1, high=1, size=(200, 2))\n",
    "y = np.ones(len(x))\n",
    "y[x[:, 0] * x[:, 1]<0] = 0\n",
    "\n",
    "x_train = x[:100, :]\n",
    "y_train = y[:100]\n",
    "x_valid = x[100:, :]\n",
    "y_valid = y[100:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Step 1: Define the input functions\n",
    "def train_input_fn(x_train, y_train, batch_size=8):\n",
    "    dataset = tf.data.Dataset.from_tensor_slices(\n",
    "        ({'input-features':x_train}, y_train.reshape(-1, 1)))\n",
    "\n",
    "    # Shuffle, repeat, and batch the examples.\n",
    "    return dataset.shuffle(100).repeat().batch(batch_size)\n",
    "\n",
    "def eval_input_fn(x_test, y_test=None, batch_size=8):\n",
    "    if y_test is None:\n",
    "        dataset = tf.data.Dataset.from_tensor_slices(\n",
    "            {'input-features':x_test})\n",
    "    else:\n",
    "        dataset = tf.data.Dataset.from_tensor_slices(\n",
    "            ({'input-features':x_test}, y_test.reshape(-1, 1)))\n",
    "\n",
    "\n",
    "    # Shuffle, repeat, and batch the examples.\n",
    "    return dataset.batch(batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[NumericColumn(key='input-features:', shape=(2,), default_value=None, dtype=tf.float32, normalizer_fn=None)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Step 2: Define the feature columns\n",
    "features = [\n",
    "    tf.feature_column.numeric_column(\n",
    "        key='input-features:', shape=(2,))\n",
    "]\n",
    "    \n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 4)                 12        \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 4)                 20        \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 4)                 20        \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 5         \n",
      "=================================================================\n",
      "Total params: 57\n",
      "Trainable params: 57\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "INFO:tensorflow:Using default config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using default config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using the Keras model provided.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using the Keras model provided.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using config: {'_model_dir': 'models/estimator-for-XOR/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
      "graph_options {\n",
      "  rewrite_options {\n",
      "    meta_optimizer_iterations: ONE\n",
      "  }\n",
      "}\n",
      ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fe839329780>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using config: {'_model_dir': 'models/estimator-for-XOR/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
      "graph_options {\n",
      "  rewrite_options {\n",
      "    meta_optimizer_iterations: ONE\n",
      "  }\n",
      "}\n",
      ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fe839329780>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
     ]
    }
   ],
   "source": [
    "## Step 3: Create the estimator: convert from a Keras model\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Input(shape=(2,), name='input-features'),\n",
    "    tf.keras.layers.Dense(units=4, activation='relu'),\n",
    "    tf.keras.layers.Dense(units=4, activation='relu'),\n",
    "    tf.keras.layers.Dense(units=4, activation='relu'),\n",
    "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
    "])\n",
    "\n",
    "model.summary()\n",
    "\n",
    "model.compile(optimizer=tf.keras.optimizers.SGD(),\n",
    "              loss=tf.keras.losses.BinaryCrossentropy(),\n",
    "              metrics=[tf.keras.metrics.BinaryAccuracy()])\n",
    "\n",
    "my_estimator = tf.keras.estimator.model_to_estimator(\n",
    "    keras_model=model,\n",
    "    model_dir='models/estimator-for-XOR/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Done calling model_fn.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Done calling model_fn.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Warm-starting with WarmStartSettings: WarmStartSettings(ckpt_to_initialize_from='models/estimator-for-XOR/keras/keras_model.ckpt', vars_to_warm_start='.*', var_name_to_vocab_info={}, var_name_to_prev_var_name={})\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Warm-starting with WarmStartSettings: WarmStartSettings(ckpt_to_initialize_from='models/estimator-for-XOR/keras/keras_model.ckpt', vars_to_warm_start='.*', var_name_to_vocab_info={}, var_name_to_prev_var_name={})\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Warm-starting from: models/estimator-for-XOR/keras/keras_model.ckpt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Warm-starting from: models/estimator-for-XOR/keras/keras_model.ckpt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Warm-starting variables only in TRAINABLE_VARIABLES.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Warm-starting variables only in TRAINABLE_VARIABLES.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Warm-started 8 variables.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Warm-started 8 variables.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Create CheckpointSaverHook.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Create CheckpointSaverHook.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Graph was finalized.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Graph was finalized.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Running local_init_op.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Running local_init_op.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving checkpoints for 0 into models/estimator-for-XOR/model.ckpt.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving checkpoints for 0 into models/estimator-for-XOR/model.ckpt.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 0.67599964, step = 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 0.67599964, step = 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 1031.16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 1031.16\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 0.66389585, step = 100 (0.098 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 0.66389585, step = 100 (0.098 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 2229.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 2229.1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 0.69760054, step = 200 (0.045 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 0.69760054, step = 200 (0.045 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 2045.16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 2045.16\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 0.64448076, step = 300 (0.049 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 0.64448076, step = 300 (0.049 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 2124.04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 2124.04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 0.7329457, step = 400 (0.047 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 0.7329457, step = 400 (0.047 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 2099.51\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 2099.51\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 0.735602, step = 500 (0.047 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 0.735602, step = 500 (0.047 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 2092.14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 2092.14\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 0.6452929, step = 600 (0.048 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 0.6452929, step = 600 (0.048 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 2012.06\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 2012.06\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 0.6274626, step = 700 (0.050 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 0.6274626, step = 700 (0.050 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 2081.3\n"
     ]
    },
    {
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 0.0021532348, step = 9900 (0.049 sec)\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 0.0021532348, step = 9900 (0.049 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving checkpoints for 10000 into models/estimator-for-XOR/model.ckpt.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving checkpoints for 10000 into models/estimator-for-XOR/model.ckpt.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Loss for final step: 0.00016663199.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Loss for final step: 0.00016663199.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow_estimator.python.estimator.estimator.EstimatorV2 at 0x7fe829ea0f28>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Step 4: use the estimator: train/evaluate/predict\n",
    "\n",
    "num_epochs = 200\n",
    "batch_size = 2\n",
    "steps_per_epoch = np.ceil(len(x_train) / batch_size)\n",
    "\n",
    "my_estimator.train(\n",
    "    input_fn=lambda: train_input_fn(x_train, y_train, batch_size),\n",
    "    steps=num_epochs * steps_per_epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Done calling model_fn.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Done calling model_fn.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Starting evaluation at 2019-10-29T21:23:01Z\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Starting evaluation at 2019-10-29T21:23:01Z\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Graph was finalized.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Graph was finalized.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from models/estimator-for-XOR/model.ckpt-10000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from models/estimator-for-XOR/model.ckpt-10000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Running local_init_op.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Running local_init_op.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Finished evaluation at 2019-10-29-21:23:01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Finished evaluation at 2019-10-29-21:23:01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving dict for global step 10000: binary_accuracy = 0.96, global_step = 10000, loss = 0.081909806\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving dict for global step 10000: binary_accuracy = 0.96, global_step = 10000, loss = 0.081909806\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: models/estimator-for-XOR/model.ckpt-10000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: models/estimator-for-XOR/model.ckpt-10000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'binary_accuracy': 0.96, 'loss': 0.081909806, 'global_step': 10000}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_estimator.evaluate(\n",
    "    input_fn=lambda: eval_input_fn(x_valid, y_valid, batch_size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Readers may ignore the next cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook ch14_part3.ipynb to script\n",
      "[NbConvertApp] Writing 5044 bytes to ch14_part3.py\n"
     ]
    }
   ],
   "source": [
    "! python ../.convert_notebook_to_script.py --input ch14_part3.ipynb --output ch14_part3.py"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
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 "nbformat": 4,
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}