# tensorflow linear_regression 实例解析

2017/01/22 00:40

import tensorflow as tfimport numpy
import matplotlib.pyplot as plt
rng = numpy.random
# Parameters
learning_rate = 0.01
training_epochs = 2000
display_step = 50
# Training Data
train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3])
n_samples = train_X.shape[0]
# tf Graph Input
#placeholder类似JDBC里的PrepareStatement
X = tf.placeholder("float")
Y = tf.placeholder("float")
# Create Model
# Set model weights
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")# Construct a linear model
activation = tf.add(tf.mul(X, W), b)#拟合 X * W + b
# Minimize the squared errors
# reduce_sum就是求和
# cost是真实值y与拟合值h<hypothesis>之间的距离
cost = tf.reduce_sum(tf.pow(activation-Y, 2))/(2*n_samples)
#L2 loss
# Initializing the variables
init = tf.initialize_all_variables()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
# Fit all training data
# training_epochs是迭代次数
for 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 step

if epoch % display_step == 0:
print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(sess.run(cost, feed_dict={X: train_X, Y:train_Y})), \
"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'
# Testing example, as requested (Issue #2)
test_X = numpy.asarray([6.83,4.668,8.9,7.91,5.7,8.7,3.1,2.1])
test_Y = numpy.asarray([1.84,2.273,3.2,2.831,2.92,3.24,1.35,1.03])
print "Testing... (L2 loss Comparison)"
testing_cost = sess.run(tf.reduce_sum(tf.pow(activation-Y, 2))/(2*test_X.shape[0]),
feed_dict={X: test_X, Y: test_Y}) #same function as cost above
print "Testing cost=", testing_cost
print "Absolute l2 loss difference:", abs(training_cost - testing_cost)
#Graphic display
plt.plot(train_X, train_Y, 'ro', label='Original data')
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()

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