4. Linear regression

import tensorflow as tf

import numpy as np

import matplotlib.pyplot as plt

x_data = np.random.rand(100)

noise = np.random.normal(0,0.01,x_data.shape)

y_data = x_data*0.1 + 0.2 + noise



plt.scatter(x_data, y_data)

plt.show()

# build a linear Model

d = tf.Variable(np.random.rand(1))

k = tf.Variable(np.random.rand(1))

y = k*x_data + d



# Quadratic cost function

loss = tf.losses.mean_squared_error(y_data, y)

# Define a gradient descent method optimizer

optimizer = tf.train.GradientDescentOptimizer(0.3)

# Minimize the cost function

train = optimizer.minimize(loss)



# Initialize variables

init= tf.global_variables_initializer()



with tf.Session() as sess:

 sess.run(init)

 for i in range(201):

 sess.run(train)

 if i%20==0:

 print(i,sess.run([k,d]) )

 y_pred = sess.run(y)

 plt.scatter(x_data,y_data)

 plt.plot(x_data,y_pred,'r-',lw=3)

 plt.show()

0 [array([0.42558686]), array([0.07772181])]

20 [array([0.24686251]), array([0.1212207])]

40 [array([0.17103131]), array([0.16282419])]

60 [array([0.13410329]), array([0.18308412])]

80 [array([0.1161202]), array([0.19295024])]

100 [array([0.10736286]), array([0.1977548])]

120 [array([0.10309823]), array([0.20009452])]

140 [array([0.10102146]), array([0.2012339])]

160 [array([0.10001012]), array([0.20178875])]

180 [array([0.09951763]), array([0.20205895])]

200 [array([0.09927779]), array([0.20219054])]

import tensorflow as tf

import numpy as np

import matplotlib.pyplot as plt

x_data = np.random. rand(100)

noise = np.random.normal(0,0.01,x_data.shape)

y_data = x_data*0.1 + 0.2 + noise



plt.scatter(x_data, y_data)

plt.show()

# Build a linear model

d = tf.Variable(np.random.rand(1))

k = tf.Variable(np.random.rand(1))

y = k*x_data + d



# Quadratic cost function

loss = tf.losses.mean_squared_error(y_data, y)

# Define a gradient descent method optimizer

optimizer = tf.train.GradientDescentOptimizer(0.3)

# Minimize the cost function

train = optimizer.minimize(loss)



# Initialize variables

init= tf.global_variables_initializer()



with tf.Session() as sess:

 sess.run(init)

 for i in range(201):

 sess.run(train)

 if i%20==0:

 print(i,sess.run([k,d]) )

 y_pred = sess.run(y)

 plt.scatter(x_data,y_data)

 plt.plot(x_data,y_pred,'r-',lw=3)

 plt.show()