tf.contrib.learn

tf.contrib.learn is a high-level TensorFlow library that simplifies the mechanics of machine learning, including the following:

• running training loops
• running evaluation loops
• managing data sets
• managing feeding

Let us try to see the implementation of Linear regression on same data we used above using tf.contrib.learn. 

Output:

W: 0.252928     b: 0.802972
Final training loss: 0.153998
Final testing loss: 0.0777036

Let us try to understand the above code.

• The shape and type of feature matrix is declared using a list. Each element of the list defines the structure of a column. In above example, we have only 1 feature which stores real values and has been given a name X.

features = [tf.contrib.layers.real_valued_column("X")]

• Then, we need an estimator. An estimator is nothing but a pre-defined model with many useful methods and parameters. In above example, we use a Linear Regression model estimator.

estimator = tf.contrib.learn.LinearRegressor(feature_columns=features)

• For training purpose, we need to use an input function which is responsible for feeding data to estimator while training. It takes the feature column values as dictionary. Many other parameters like batch size, number of epochs, etc can be specified.

input_fn = tf.contrib.learn.io.numpy_input_fn({"X":train_X}, 
              train_y, batch_size=4, num_epochs=2000)

• To fit training data to estimator, we simply use fit method of estimator in which input function is passed as an argument.

estimator.fit(input_fn=input_fn)

• Once training is complete, we can get the value of different variables using get_variable_value method of estimator. You can get a list of all variables using get_variable_names method.

W = estimator.get_variable_value('linear/X/weight')[0][0]
b = estimator.get_variable_value('linear/bias_weight')[0]

• The mean squared error/loss can be computed as:

train_loss = estimator.evaluate(input_fn=input_fn)['loss']
test_loss = estimator.evaluate(input_fn=test_input_fn)['loss']

TensorFlow is an open-source machine learning library developed by Google. TensorFlow is used to build and train deep learning models as it facilitates the creation of computational graphs and efficient execution on various hardware platforms. The article provides an comprehensive overview of tensorflow.