博客存档TensorFlow入门一 1.4编程练习

 

 import tensorflow as tf
 import numpy
 import matplotlib.pyplot as plt
 #from sklearn.model_selection import train_test_split
 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
 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)

 # Minimize the squared errors
 cost = tf.reduce_sum(tf.pow(activation-Y, 2))/(2*n_samples)   #L2 loss

  #reduce_sum:把里面的平方求和
  # pow(x,y):这个是表示x的y次幂。

 optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

 #Gradient descent

 # Initializing the variables
 init = tf.initialize_all_variables()

 # Launch the graph
 with tf.Session() as sess:
     sess.run(init)

     # Fit all training data
     for epoch in range(training_epochs):
         for (x, y) in zip(train_X, train_Y):
             sess.run(optimizer, feed_dict={X: x, Y: y})
               #zip:对应的元素打包成一个个元组
         #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!")
     print("cost=", sess.run(cost, feed_dict={X: train_X, Y: train_Y}), \
           "W=", sess.run(W), "b=", sess.run(b))

     #Graphic display
     plt.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()