In [1]:

    

import tensorflow as tf
import numpy as np


    

In [2]:

    

%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt


    

In [3]:

    

# Set up the data with a noisy linear relationship between X and Y.
num_examples = 50
X = np.array([np.linspace(-2, 4, num_examples), np.linspace(-6, 6, num_examples)])


    

In [5]:

    

X = np.array([np.linspace(-2, 4, num_examples), np.linspace(-6, 6, num_examples)])
plt.figure(figsize=(4,4))
plt.scatter(X[0], X[1])
plt.show()


    

In [6]:

    

X += np.random.randn(2, num_examples)
plt.figure(figsize=(4,4))
plt.scatter(X[0], X[1])
plt.show()


    

In [7]:

    

weights = np.polyfit(X[0], X[1], 1)
plt.figure(figsize=(4,4))
plt.scatter(X[0], X[1])
line_x_range = (-3, 5)
plt.plot(line_x_range, [weights[1] + a * weights[0] for a in line_x_range], "g", alpha=0.8)
plt.show()


    

In [8]:

    

x, y = X
x_with_bias = np.array([(1., a) for a in x]).astype(np.float32)

losses = []
training_steps = 50
learning_rate = 0.002

with tf.Session() as sess:
  # Set up all the tensors, variables, and operations.
  input = tf.constant(x_with_bias)
  target = tf.constant(np.transpose([y]).astype(np.float32))
  weights = tf.Variable(tf.random_normal([2, 1], 0, 0.1))
  
  tf.initialize_all_variables().run()
                      
  yhat = tf.matmul(input, weights)
  yerror = tf.sub(yhat, target)
  loss = tf.nn.l2_loss(yerror)
  
  update_weights = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
  
  for _ in range(training_steps):
    # Repeatedly run the operations, updating the TensorFlow variable.
    update_weights.run()
    losses.append(loss.eval())
    #print _  #It takes on value from 0 to 49
    #print losses  #this shows losses array keep increasing in size: [18] , [18, 13],...

  # Training is done, get the final values for the graphs
  betas = weights.eval()
  yhat = yhat.eval()

# Show the fit and the loss over time.
fig, (ax1, ax2) = plt.subplots(1, 2)  #show subplot in 1 row, 2 columns ?
plt.subplots_adjust(wspace=.3)
fig.set_size_inches(10, 4)

ax1.scatter(x, y, alpha=.7)
ax1.scatter(x, np.transpose(yhat)[0], c="g", alpha=.6)
line_x_range = (-4, 6)
ax1.plot(line_x_range, [betas[0] + a * betas[1] for a in line_x_range], "g", alpha=0.6)

ax2.plot(range(0, training_steps), losses)
ax2.set_ylabel("Loss")
ax2.set_xlabel("Training steps")
plt.show()