courses/machine_learning/deepdive/10_recommend/content_based_using_neural_networks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Content-Based Filtering Using Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook relies on files created in the [content_based_preproc.ipynb](./content_based_preproc.ipynb) notebook. Be sure to run the code in there before completing this notebook.  \n",
    "Also, we'll be using the **python3** kernel from here on out so don't forget to change the kernel if it's still Python2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This lab illustrates:\n",
    "1. how to build feature columns for a model using tf.feature_column\n",
    "2. how to create custom evaluation metrics and add them to Tensorboard\n",
    "3. how to train a model and make predictions with the saved model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tensorflow Hub should already be installed. You can check that it is by using \"pip freeze\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "pip freeze | grep tensor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make sure we install the necessary version of tensorflow-hub. After doing the pip install below, click **\"Restart the kernel\"** on the notebook so that the Python environment picks up the new packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip3 install tensorflow-hub==0.7.0\n",
    "!pip3 install --upgrade tensorflow==1.15.3\n",
    "!pip3 install google-cloud-bigquery==1.10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import tensorflow_hub as hub\n",
    "import shutil\n",
    "\n",
    "PROJECT = 'cloud-training-demos' # REPLACE WITH YOUR PROJECT ID\n",
    "BUCKET = 'cloud-training-demos-ml' # REPLACE WITH YOUR BUCKET NAME\n",
    "REGION = 'us-central1' # REPLACE WITH YOUR BUCKET REGION e.g. us-central1\n",
    "\n",
    "# do not change these\n",
    "os.environ['PROJECT'] = PROJECT\n",
    "os.environ['BUCKET'] = BUCKET\n",
    "os.environ['REGION'] = REGION\n",
    "os.environ['TFVERSION'] = '1.15.3'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Updated property [core/project].\n",
      "Updated property [compute/region].\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "gcloud config set project $PROJECT\n",
    "gcloud config set compute/region $REGION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build the feature columns for the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To start, we'll load the list of categories, authors and article ids we created in the previous **Create Datasets** notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "categories_list = open(\"categories.txt\").read().splitlines()\n",
    "authors_list = open(\"authors.txt\").read().splitlines()\n",
    "content_ids_list = open(\"content_ids.txt\").read().splitlines()\n",
    "mean_months_since_epoch = 523"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below we'll define the feature columns to use in our model. If necessary, remind yourself the [various feature columns](https://www.tensorflow.org/api_docs/python/tf/feature_column) to use.  \n",
    "For the embedded_title_column feature column, use a Tensorflow Hub Module to create an embedding of the article title. Since the articles and titles are in German, you'll want to use a German language embedding module.  \n",
    "Explore the text embedding Tensorflow Hub modules [available here](https://alpha.tfhub.dev/). Filter by setting the language to 'German'. The 50 dimensional embedding should be sufficient for our purposes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedded_title_column = hub.text_embedding_column(\n",
    "    key=\"title\", \n",
    "    module_spec=\"https://tfhub.dev/google/nnlm-de-dim50/1\",\n",
    "    trainable=False)\n",
    "\n",
    "content_id_column = tf.feature_column.categorical_column_with_hash_bucket(\n",
    "    key=\"content_id\",\n",
    "    hash_bucket_size= len(content_ids_list) + 1)\n",
    "embedded_content_column = tf.feature_column.embedding_column(\n",
    "    categorical_column=content_id_column,\n",
    "    dimension=10)\n",
    "\n",
    "author_column = tf.feature_column.categorical_column_with_hash_bucket(key=\"author\",\n",
    "    hash_bucket_size=len(authors_list) + 1)\n",
    "embedded_author_column = tf.feature_column.embedding_column(\n",
    "    categorical_column=author_column,\n",
    "    dimension=3)\n",
    "\n",
    "category_column_categorical = tf.feature_column.categorical_column_with_vocabulary_list(\n",
    "    key=\"category\",\n",
    "    vocabulary_list=categories_list,\n",
    "    num_oov_buckets=1)\n",
    "category_column = tf.feature_column.indicator_column(category_column_categorical)\n",
    "\n",
    "months_since_epoch_boundaries = list(range(400,700,20))\n",
    "months_since_epoch_column = tf.feature_column.numeric_column(\n",
    "    key=\"months_since_epoch\")\n",
    "months_since_epoch_bucketized = tf.feature_column.bucketized_column(\n",
    "    source_column = months_since_epoch_column,\n",
    "    boundaries = months_since_epoch_boundaries)\n",
    "\n",
    "crossed_months_since_category_column = tf.feature_column.indicator_column(tf.feature_column.crossed_column(\n",
    "  keys = [category_column_categorical, months_since_epoch_bucketized], \n",
    "  hash_bucket_size = len(months_since_epoch_boundaries) * (len(categories_list) + 1)))\n",
    "\n",
    "feature_columns = [embedded_content_column,\n",
    "                   embedded_author_column,\n",
    "                   category_column,\n",
    "                   embedded_title_column,\n",
    "                   crossed_months_since_category_column] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the input function.\n",
    "\n",
    "Next we'll create the input function for our model. This input function reads the data from the csv files we created in the previous labs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "record_defaults = [[\"Unknown\"], [\"Unknown\"],[\"Unknown\"],[\"Unknown\"],[\"Unknown\"],[mean_months_since_epoch],[\"Unknown\"]]\n",
    "column_keys = [\"visitor_id\", \"content_id\", \"category\", \"title\", \"author\", \"months_since_epoch\", \"next_content_id\"]\n",
    "label_key = \"next_content_id\"\n",
    "def read_dataset(filename, mode, batch_size = 512):\n",
    "  def _input_fn():\n",
    "      def decode_csv(value_column):\n",
    "          columns = tf.decode_csv(value_column,record_defaults=record_defaults)\n",
    "          features = dict(zip(column_keys, columns))          \n",
    "          label = features.pop(label_key)         \n",
    "          return features, label\n",
    "\n",
    "      # Create list of files that match pattern\n",
    "      file_list = tf.io.gfile.glob(filename)\n",
    "\n",
    "      # Create dataset from file list\n",
    "      dataset = tf.data.TextLineDataset(file_list).map(decode_csv)\n",
    "\n",
    "      if mode == tf.estimator.ModeKeys.TRAIN:\n",
    "          num_epochs = None # indefinitely\n",
    "          dataset = dataset.shuffle(buffer_size = 10 * batch_size)\n",
    "      else:\n",
    "          num_epochs = 1 # end-of-input after this\n",
    "\n",
    "      dataset = dataset.repeat(num_epochs).batch(batch_size)\n",
    "      return dataset.make_one_shot_iterator().get_next()\n",
    "  return _input_fn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the model and train/evaluate\n",
    "\n",
    "\n",
    "Next, we'll build our model which recommends an article for a visitor to the Kurier.at website. Look through the code below. We use the input_layer feature column to create the dense input layer to our network. This is just a sigle layer network where we can adjust the number of hidden units as a parameter.\n",
    "\n",
    "Currently, we compute the accuracy between our predicted 'next article' and the actual 'next article' read next by the visitor. We'll also add an additional performance metric of top 10 accuracy to assess our model. To accomplish this, we compute the top 10 accuracy metric, add it to the metrics dictionary below and add it to the tf.summary so that this value is reported to Tensorboard as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_fn(features, labels, mode, params):\n",
    "  net = tf.feature_column.input_layer(features, params['feature_columns'])\n",
    "  for units in params['hidden_units']:\n",
    "        net = tf.layers.dense(net, units=units, activation=tf.nn.relu)\n",
    "   # Compute logits (1 per class).\n",
    "  logits = tf.layers.dense(net, params['n_classes'], activation=None) \n",
    "\n",
    "  predicted_classes = tf.argmax(logits, 1)\n",
    "  from tensorflow.python.lib.io import file_io\n",
    "    \n",
    "  with file_io.FileIO('content_ids.txt', mode='r') as ifp:\n",
    "    content = tf.constant([x.rstrip() for x in ifp])\n",
    "  predicted_class_names = tf.gather(content, predicted_classes)\n",
    "  if mode == tf.estimator.ModeKeys.PREDICT:\n",
    "    predictions = {\n",
    "        'class_ids': predicted_classes[:, tf.newaxis],\n",
    "        'class_names' : predicted_class_names[:, tf.newaxis],\n",
    "        'probabilities': tf.nn.softmax(logits),\n",
    "        'logits': logits,\n",
    "    }\n",
    "    return tf.estimator.EstimatorSpec(mode, predictions=predictions)\n",
    "  table = tf.contrib.lookup.index_table_from_file(vocabulary_file=\"content_ids.txt\")\n",
    "  labels = table.lookup(labels)\n",
    "  # Compute loss.\n",
    "  loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)\n",
    "\n",
    "  # Compute evaluation metrics.\n",
    "  accuracy = tf.metrics.accuracy(labels=labels,\n",
    "                                 predictions=predicted_classes,\n",
    "                                 name='acc_op')\n",
    "  top_10_accuracy = tf.metrics.mean(tf.nn.in_top_k(predictions=logits, \n",
    "                                                   targets=labels, \n",
    "                                                   k=10))\n",
    "  \n",
    "  metrics = {\n",
    "    'accuracy': accuracy,\n",
    "    'top_10_accuracy' : top_10_accuracy}\n",
    "  \n",
    "  tf.summary.scalar('accuracy', accuracy[1])\n",
    "  tf.summary.scalar('top_10_accuracy', top_10_accuracy[1])\n",
    "\n",
    "  if mode == tf.estimator.ModeKeys.EVAL:\n",
    "      return tf.estimator.EstimatorSpec(\n",
    "          mode, loss=loss, eval_metric_ops=metrics)\n",
    "\n",
    "  # Create training op.\n",
    "  assert mode == tf.estimator.ModeKeys.TRAIN\n",
    "\n",
    "  optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)\n",
    "  train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())\n",
    "  return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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 config: {'_global_id_in_cluster': 0, '_log_step_count_steps': 100, '_eval_distribute': None, '_protocol': None, '_task_type': 'worker', '_is_chief': True, '_task_id': 0, '_master': '', '_num_worker_replicas': 1, '_device_fn': None, '_experimental_distribute': None, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': 'content_based_model_trained', '_train_distribute': None, '_save_checkpoints_secs': 600, '_service': None, '_num_ps_replicas': 0, '_experimental_max_worker_delay_secs': None, '_session_config': allow_soft_placement: true\n",
      "graph_options {\n",
      "  rewrite_options {\n",
      "    meta_optimizer_iterations: ONE\n",
      "  }\n",
      "}\n",
      ", '_keep_checkpoint_max': 5, '_session_creation_timeout_secs': 7200, '_evaluation_master': '', '_tf_random_seed': None, '_save_checkpoints_steps': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fb78c627d68>, '_save_summary_steps': 100}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using config: {'_global_id_in_cluster': 0, '_log_step_count_steps': 100, '_eval_distribute': None, '_protocol': None, '_task_type': 'worker', '_is_chief': True, '_task_id': 0, '_master': '', '_num_worker_replicas': 1, '_device_fn': None, '_experimental_distribute': None, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': 'content_based_model_trained', '_train_distribute': None, '_save_checkpoints_secs': 600, '_service': None, '_num_ps_replicas': 0, '_experimental_max_worker_delay_secs': None, '_session_config': allow_soft_placement: true\n",
      "graph_options {\n",
      "  rewrite_options {\n",
      "    meta_optimizer_iterations: ONE\n",
      "  }\n",
      "}\n",
      ", '_keep_checkpoint_max': 5, '_session_creation_timeout_secs': 7200, '_evaluation_master': '', '_tf_random_seed': None, '_save_checkpoints_steps': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fb78c627d68>, '_save_summary_steps': 100}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Not using Distribute Coordinator.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Not using Distribute Coordinator.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Running training and evaluation locally (non-distributed).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Running training and evaluation locally (non-distributed).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps None or save_checkpoints_secs 600.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps None or save_checkpoints_secs 600.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.decode_csv is deprecated. Please use tf.io.decode_csv instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.decode_csv is deprecated. Please use tf.io.decode_csv instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-5-0d15d822f470>:25: DatasetV1.make_one_shot_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_one_shot_iterator(dataset)`.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-5-0d15d822f470>:25: DatasetV1.make_one_shot_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_one_shot_iterator(dataset)`.\n"
     ]
    },
    {
     "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": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column.py:206: EmbeddingColumn._get_dense_tensor (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column.py:206: EmbeddingColumn._get_dense_tensor (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:3182: HashedCategoricalColumn._get_sparse_tensors (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:3182: HashedCategoricalColumn._get_sparse_tensors (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column.py:2158: HashedCategoricalColumn._transform_feature (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column.py:2158: HashedCategoricalColumn._transform_feature (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:3122: HashedCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:3122: HashedCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/ops/embedding_ops.py:802: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/ops/embedding_ops.py:802: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column.py:207: EmbeddingColumn._variable_shape (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column.py:207: EmbeddingColumn._variable_shape (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column.py:206: IndicatorColumn._get_dense_tensor (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column.py:206: IndicatorColumn._get_dense_tensor (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column.py:2158: IndicatorColumn._transform_feature (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column.py:2158: IndicatorColumn._transform_feature (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4300: CrossedColumn._get_sparse_tensors (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4300: CrossedColumn._get_sparse_tensors (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column.py:2158: CrossedColumn._transform_feature (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column.py:2158: CrossedColumn._transform_feature (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4118: VocabularyListCategoricalColumn._get_sparse_tensors (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4118: VocabularyListCategoricalColumn._get_sparse_tensors (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column.py:2158: VocabularyListCategoricalColumn._transform_feature (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column.py:2158: VocabularyListCategoricalColumn._transform_feature (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4118: BucketizedColumn._get_sparse_tensors (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4118: BucketizedColumn._get_sparse_tensors (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column.py:2158: BucketizedColumn._transform_feature (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column.py:2158: BucketizedColumn._transform_feature (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column.py:2158: NumericColumn._transform_feature (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column.py:2158: NumericColumn._transform_feature (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4271: IndicatorColumn._variable_shape (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4271: IndicatorColumn._variable_shape (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4326: CrossedColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4326: CrossedColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4326: VocabularyListCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4326: VocabularyListCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-6-6b888c179a6d>:4: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.Dense instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-6-6b888c179a6d>:4: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.Dense instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/layers/core.py:187: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.__call__` method instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/layers/core.py:187: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.__call__` method instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:\n",
      "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "  * https://github.com/tensorflow/io (for I/O related ops)\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:\n",
      "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "  * https://github.com/tensorflow/io (for I/O related ops)\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/training/adagrad.py:76: 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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/jupyter/.local/lib/python3.5/site-packages/tensorflow_core/python/training/adagrad.py:76: 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"
     ]
    },
    {
     "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: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 content_based_model_trained/model.ckpt.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving checkpoints for 0 into content_based_model_trained/model.ckpt.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 9.657101, step = 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 9.657101, step = 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 7.47224\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 7.47224\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 5.4171796, step = 101 (13.385 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 5.4171796, step = 101 (13.385 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 7.64064\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 7.64064\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 5.166601, step = 201 (13.088 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 5.166601, step = 201 (13.088 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 7.75395\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 7.75395\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 5.139056, step = 301 (12.896 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 5.139056, step = 301 (12.896 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 7.6323\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 7.6323\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 5.171594, step = 401 (13.103 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 5.171594, step = 401 (13.103 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 7.76031\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 7.76031\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 4.9596405, step = 501 (12.886 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 4.9596405, step = 501 (12.886 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 7.66465\n"
     ]
    },
    {
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     "output_type": "stream",
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      "INFO:tensorflow:global_step/sec: 7.66465\n"
     ]
    },
    {
     "name": "stdout",
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     "text": [
      "INFO:tensorflow:loss = 4.946377, step = 601 (13.047 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 4.946377, step = 601 (13.047 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "INFO:tensorflow:global_step/sec: 7.66215\n"
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    },
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    },
    {
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     "text": [
      "INFO:tensorflow:loss = 5.1287856, step = 701 (13.051 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 5.1287856, step = 701 (13.051 sec)\n"
     ]
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    {
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     "output_type": "stream",
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      "INFO:tensorflow:global_step/sec: 7.59581\n"
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      "INFO:tensorflow:loss = 5.0491443, step = 801 (13.165 sec)\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 5.0491443, step = 801 (13.165 sec)\n"
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    {
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      "INFO:tensorflow:global_step/sec: 7.58225\n"
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    {
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      "INFO:tensorflow:loss = 5.1082306, step = 901 (13.189 sec)\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 5.1082306, step = 901 (13.189 sec)\n"
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    {
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      "INFO:tensorflow:global_step/sec: 7.57405\n"
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    },
    {
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     "text": [
      "INFO:tensorflow:loss = 4.889172, step = 1001 (13.203 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 4.889172, step = 1001 (13.203 sec)\n"
     ]
    },
    {
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      "INFO:tensorflow:global_step/sec: 7.41508\n"
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      "INFO:tensorflow:global_step/sec: 7.41508\n"
     ]
    },
    {
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     "text": [
      "INFO:tensorflow:loss = 5.073477, step = 1101 (13.487 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 5.073477, step = 1101 (13.487 sec)\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 4.95774\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 4.95774\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 4.863147, step = 1201 (20.170 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 4.863147, step = 1201 (20.170 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 7.5227\n"
     ]
    },
    {
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      "INFO:tensorflow:global_step/sec: 7.5227\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 5.0129967, step = 1301 (13.293 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 5.0129967, step = 1301 (13.293 sec)\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 7.57467\n"
     ]
    },
    {
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      "INFO:tensorflow:global_step/sec: 7.57467\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 4.9426913, step = 1401 (13.202 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 4.9426913, step = 1401 (13.202 sec)\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 7.69566\n"
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    },
    {
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     "output_type": "stream",
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      "INFO:tensorflow:global_step/sec: 7.69566\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 4.945592, step = 1501 (12.994 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 4.945592, step = 1501 (12.994 sec)\n"
     ]
    },
    {
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      "INFO:tensorflow:global_step/sec: 7.78895\n"
     ]
    },
    {
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      "INFO:tensorflow:global_step/sec: 7.78895\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 4.924628, step = 1601 (12.839 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 4.924628, step = 1601 (12.839 sec)\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 7.72259\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 7.72259\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 4.900159, step = 1701 (12.948 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 4.900159, step = 1701 (12.948 sec)\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 7.7355\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 7.7355\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 4.8644915, step = 1801 (12.928 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 4.8644915, step = 1801 (12.928 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 7.65909\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 7.65909\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 4.845366, step = 1901 (13.056 sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 4.845366, step = 1901 (13.056 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving checkpoints for 2000 into content_based_model_trained/model.ckpt.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving checkpoints for 2000 into content_based_model_trained/model.ckpt.\n"
     ]
    },
    {
     "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:Saver not created because there are no variables in the graph to restore\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\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 2020-06-12T19:25:37Z\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Starting evaluation at 2020-06-12T19:25:37Z\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 content_based_model_trained/model.ckpt-2000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from content_based_model_trained/model.ckpt-2000\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 2020-06-12-19:25:43\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Finished evaluation at 2020-06-12-19:25:43\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.056213133, global_step = 2000, loss = 4.9000645, top_10_accuracy = 0.31876245\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.056213133, global_step = 2000, loss = 4.9000645, top_10_accuracy = 0.31876245\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2000: content_based_model_trained/model.ckpt-2000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2000: content_based_model_trained/model.ckpt-2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Loss for final step: 4.885457.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Loss for final step: 4.885457.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "({'accuracy': 0.056213133,\n",
       "  'global_step': 2000,\n",
       "  'loss': 4.9000645,\n",
       "  'top_10_accuracy': 0.31876245},\n",
       " [])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outdir = 'content_based_model_trained'\n",
    "shutil.rmtree(outdir, ignore_errors = True) # start fresh each time\n",
    "#tf.summary.FileWriterCache.clear() # ensure filewriter cache is clear for TensorBoard events file\n",
    "estimator = tf.estimator.Estimator(\n",
    "    model_fn=model_fn,\n",
    "    model_dir = outdir,\n",
    "    params={\n",
    "     'feature_columns': feature_columns,\n",
    "      'hidden_units': [200, 100, 50],\n",
    "      'n_classes': len(content_ids_list)\n",
    "    })\n",
    "\n",
    "train_spec = tf.estimator.TrainSpec(\n",
    "    input_fn = read_dataset(\"training_set.csv\", tf.estimator.ModeKeys.TRAIN),\n",
    "    max_steps = 2000)\n",
    "\n",
    "eval_spec = tf.estimator.EvalSpec(\n",
    "    input_fn = read_dataset(\"test_set.csv\", tf.estimator.ModeKeys.EVAL),\n",
    "    steps = None,\n",
    "    start_delay_secs = 30,\n",
    "    throttle_secs = 60)\n",
    "\n",
    "tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This takes a while to complete but in the end, I get about **30% top 10 accuracy**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make predictions with the trained model. \n",
    "\n",
    "With the model now trained, we can make predictions by calling the predict method on the estimator. Let's look at how our model predicts on the first five examples of the training set.  \n",
    "To start, we'll create a new file 'first_5.csv' which contains the first five elements of our training set. We'll also save the target values to a file 'first_5_content_ids' so we can compare our results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000593816586876859,230814320,Stars & Kultur,Kritik an Meghan Markle immer lauter,Elisabeth Spitzer,562,299837992\n",
      "1001769331926555188,299836255,News,Blümel Kneissl &Co.: Das sind die Fixstarter,,574,299826767\n",
      "1001769331926555188,299826767,Lifestyle,Titanic-Regisseur: Darum musste Jack sterben,Elisabeth Mittendorfer,574,299921761\n",
      "1001769331926555188,299912085,News,Erster ÖBB-Containerzug nach China unterwegs,Stefan Hofer,574,299836841\n",
      "1001769331926555188,299836841,News,\"ÖVP will Studiengebühren FPÖ in Verhandlungen \"\"flexibel\"\"\",Raffaela Lindorfer,574,299915880\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "head -5 training_set.csv > first_5.csv\n",
    "head first_5.csv\n",
    "awk -F \"\\\"*,\\\"*\" '{print $2}' first_5.csv > first_5_content_ids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall, to make predictions on the trained model we pass a list of examples through the input function. Complete the code below to make predicitons on the examples contained in the \"first_5.csv\" file we created above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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:Saver not created because there are no variables in the graph to restore\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\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: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 content_based_model_trained/model.ckpt-2000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from content_based_model_trained/model.ckpt-2000\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"
     ]
    }
   ],
   "source": [
    "output = list(estimator.predict(input_fn=read_dataset(\"first_5.csv\", tf.estimator.ModeKeys.PREDICT)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:2: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "recommended_content_ids = [np.asscalar(d[\"class_names\"]).decode('UTF-8') for d in output]\n",
    "content_ids = open(\"first_5_content_ids\").read().splitlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we map the content id back to the article title. Let's compare our model's recommendation for the first example. This can be done in BigQuery. Look through the query below and make sure it is clear what is being returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current title: b'Kritik an Meghan Markle immer lauter' \n",
      "Recommended title: b'Matthias Reim spricht \\xc3\\xbcber Absturz & Comeback'\n"
     ]
    }
   ],
   "source": [
    "from google.cloud import bigquery\n",
    "recommended_title_sql=\"\"\"\n",
    "#standardSQL\n",
    "SELECT\n",
    "(SELECT MAX(IF(index=6, value, NULL)) FROM UNNEST(hits.customDimensions)) AS title\n",
    "FROM `cloud-training-demos.GA360_test.ga_sessions_sample`,   \n",
    "  UNNEST(hits) AS hits\n",
    "WHERE \n",
    "  # only include hits on pages\n",
    "  hits.type = \"PAGE\"\n",
    "  AND (SELECT MAX(IF(index=10, value, NULL)) FROM UNNEST(hits.customDimensions)) = \\\"{}\\\"\n",
    "LIMIT 1\"\"\".format(recommended_content_ids[0])\n",
    "\n",
    "current_title_sql=\"\"\"\n",
    "#standardSQL\n",
    "SELECT\n",
    "(SELECT MAX(IF(index=6, value, NULL)) FROM UNNEST(hits.customDimensions)) AS title\n",
    "FROM `cloud-training-demos.GA360_test.ga_sessions_sample`,   \n",
    "  UNNEST(hits) AS hits\n",
    "WHERE \n",
    "  # only include hits on pages\n",
    "  hits.type = \"PAGE\"\n",
    "  AND (SELECT MAX(IF(index=10, value, NULL)) FROM UNNEST(hits.customDimensions)) = \\\"{}\\\"\n",
    "LIMIT 1\"\"\".format(content_ids[0])\n",
    "recommended_title = bigquery.Client().query(recommended_title_sql).to_dataframe()['title'].tolist()[0].encode('utf-8').strip()\n",
    "current_title = bigquery.Client().query(current_title_sql).to_dataframe()['title'].tolist()[0].encode('utf-8').strip()\n",
    "print(\"Current title: {} \".format(current_title))\n",
    "print(\"Recommended title: {}\".format(recommended_title))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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