CustomModelLearn.py

import numpy as np
import tensorflow as tf
# Declare list of features, we only have one real-valued feature
def model(features, labels, mode):
 # Build a linear model and predict values
 W = tf.get_variable("W", [1], dtype=tf.float64)
 b = tf.get_variable("b", [1], dtype=tf.float64)
 y = W*features['x'] + b
 # Loss sub-graph
 loss = tf.reduce_sum(tf.square(y - labels))
 # Training sub-graph
 global_step = tf.train.get_global_step()
 optimizer = tf.train.GradientDescentOptimizer(0.01)
 train = tf.group(optimizer.minimize(loss),
 tf.assign_add(global_step, 1))
 # ModelFnOps connects subgraphs we built to the
 # appropriate functionality.
 return tf.contrib.learn.ModelFnOps(
 mode=mode, predictions=y,
 loss=loss,
 train_op=train)

estimator = tf.contrib.learn.Estimator(model_fn=model)

# define our data set
x = np.array([1., 2., 3., 4.])
y = np.array([0., -1., -2., -3.])
input_fn = tf.contrib.learn.io.numpy_input_fn({"x": x}, y, 4, num_epochs=1000)

# train
estimator.fit(input_fn=input_fn, steps=1000)
# evaluate our model
print(estimator.evaluate(input_fn=input_fn, steps=10))