from __future__ import print_functionimport tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline# Parameters
learning_rate = 0.01
training_epochs = 1000
display_step = 50# Generate the training data
train_X = np.linspace(-1, 1, 200)
train_Y = 2*train_X + np.random.randn(*train_X.shape)*0.2n_samples = train_X.shape[0]# tf Graph Input
X = tf.placeholder("float")
Y = tf.placeholder("float")# Initialize the variable w and b
W = tf.Variable(np.random.randn(), name="weight")
b = tf.Variable(np.random.randn(), name="bias")# Define the linear model
pred = tf.add(tf.mul(X, W), b)# Mean squared error
cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)# Build the optimizer
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)# Initializing the variables
init = tf.initialize_all_variables()# Launch the graph
with tf.Session() as sess:sess.run(init)# Fit all training datafor epoch in range(training_epochs):for (x, y) in zip(train_X, train_Y):sess.run(optimizer, feed_dict={X: x, Y: y})# Display logs per epoch stepif (epoch+1) % display_step == 0:c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \"W=", sess.run(W), "b=", sess.run(b))print("Optimization Finished!")training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')# Graphic displayplt.plot(train_X, train_Y, 'ro', label='Original data')plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')plt.legend()plt.show()# Generate the test datatest_X = np.linspace(-1, 1, 100)test_Y = 2*test_X + np.random.randn(*test_X.shape)*0.2print("Testing... (Mean square loss Comparison)")testing_cost = sess.run(tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * test_X.shape[0]),feed_dict={X: test_X, Y: test_Y})  # same function as cost aboveprint("Testing cost=", testing_cost)print("Absolute mean square loss difference:", abs(training_cost - testing_cost))plt.plot(test_X, test_Y, 'bo', label='Testing data')plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')plt.legend()plt.show()