**TensorFlow Deep Learning**

Create a new Jupyter notebook with python 2.7 kernel. Name it as **TensorFlow Deep Learning**. Let’s import all the required modules.

import tensorflow as tf import tempfile import pandas as pd import urllib

Define Base Feature Columns that will be the building blocks used by both the wide part and the deep part of the model.

tf.logging.set_verbosity(tf.logging.ERROR) # Categorical base columns. gender = tf.contrib.layers.sparse_column_with_keys(column_name="gender", keys=["Female", "Male"]) race = tf.contrib.layers.sparse_column_with_keys(column_name="race", keys=[ "Amer-Indian-Eskimo", "Asian-Pac-Islander", "Black", "Other", "White"]) education = tf.contrib.layers.sparse_column_with_hash_bucket("education", hash_bucket_size=1000) relationship = tf.contrib.layers.sparse_column_with_hash_bucket("relationship", hash_bucket_size=100) workclass = tf.contrib.layers.sparse_column_with_hash_bucket("workclass", hash_bucket_size=100) occupation = tf.contrib.layers.sparse_column_with_hash_bucket("occupation", hash_bucket_size=1000) native_country = tf.contrib.layers.sparse_column_with_hash_bucket("native_country", hash_bucket_size=1000) # Continuous base columns. age = tf.contrib.layers.real_valued_column("age") age_buckets = tf.contrib.layers.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65]) education_num = tf.contrib.layers.real_valued_column("education_num") capital_gain = tf.contrib.layers.real_valued_column("capital_gain") capital_loss = tf.contrib.layers.real_valued_column("capital_loss") hours_per_week = tf.contrib.layers.real_valued_column("hours_per_week")

The wide model is a linear model with a wide set of sparse and crossed feature columns:

wide_columns = [ gender, native_country, education, occupation, workclass, relationship, age_buckets, tf.contrib.layers.crossed_column([education, occupation], hash_bucket_size=int(1e4)), tf.contrib.layers.crossed_column([native_country, occupation], hash_bucket_size=int(1e4)), tf.contrib.layers.crossed_column([age_buckets, education, occupation], hash_bucket_size=int(1e6)) ]

The Deep Model: Neural Network with Embeddings

deep_columns = [ tf.contrib.layers.embedding_column(workclass, dimension=8), tf.contrib.layers.embedding_column(education, dimension=8), tf.contrib.layers.embedding_column(gender, dimension=8), tf.contrib.layers.embedding_column(relationship, dimension=8), tf.contrib.layers.embedding_column(native_country, dimension=8), tf.contrib.layers.embedding_column(occupation, dimension=8), age, education_num, capital_gain, capital_loss, hours_per_week ]

Combining Wide and Deep Models into one

model_dir = tempfile.mkdtemp() m = tf.contrib.learn.DNNLinearCombinedClassifier( fix_global_step_increment_bug=True, model_dir=model_dir, linear_feature_columns=wide_columns, dnn_feature_columns=deep_columns, dnn_hidden_units=[100, 50])

Process input data

# Define the column names for the data sets. COLUMNS = ["age", "workclass", "fnlwgt", "education", "education_num", "marital_status", "occupation", "relationship", "race", "gender", "capital_gain", "capital_loss", "hours_per_week", "native_country", "income_bracket"] LABEL_COLUMN = 'label' CATEGORICAL_COLUMNS = ["workclass", "education", "marital_status", "occupation", "relationship", "race", "gender", "native_country"] CONTINUOUS_COLUMNS = ["age", "education_num", "capital_gain", "capital_loss", "hours_per_week"] # Download the training and test data to temporary files. # Alternatively, you can download them yourself and change train_file and # test_file to your own paths. train_file = tempfile.NamedTemporaryFile() test_file = tempfile.NamedTemporaryFile() urllib.urlretrieve("http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/adult.data", train_file.name) urllib.urlretrieve("http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/adult.test", test_file.name) # Read the training and test data sets into Pandas dataframe. df_train = pd.read_csv(train_file, names=COLUMNS, skipinitialspace=True) df_test = pd.read_csv(test_file, names=COLUMNS, skipinitialspace=True, skiprows=1) df_train[LABEL_COLUMN] = (df_train['income_bracket'].apply(lambda x: '>50K' in x)).astype(int) df_test[LABEL_COLUMN] = (df_test['income_bracket'].apply(lambda x: '>50K' in x)).astype(int) def input_fn(df): # Creates a dictionary mapping from each continuous feature column name (k) to # the values of that column stored in a constant Tensor. continuous_cols = {k: tf.constant(df[k].values) for k in CONTINUOUS_COLUMNS} # Creates a dictionary mapping from each categorical feature column name (k) # to the values of that column stored in a tf.SparseTensor. categorical_cols = {k: tf.SparseTensor( indices=[[i, 0] for i in range(df[k].size)], values=df[k].values, dense_shape=[df[k].size, 1]) for k in CATEGORICAL_COLUMNS} # Merges the two dictionaries into one. feature_cols = dict(continuous_cols.items() + categorical_cols.items()) # Converts the label column into a constant Tensor. label = tf.constant(df[LABEL_COLUMN].values) # Returns the feature columns and the label. return feature_cols, label def train_input_fn(): return input_fn(df_train) def eval_input_fn(): return input_fn(df_test)

Training and evaluating The Model

m.fit(input_fn=train_input_fn, steps=200) results = m.evaluate(input_fn=eval_input_fn, steps=1) for key in sorted(results): print("%s: %s" % (key, results[key]))

Continue for part 4, http://intellij.my/2017/08/08/tensorflow-tutorial-for-data-scientist-part-4/

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