# -*- coding: utf-8 -*- ''' Created on 2018年4月20日@author: user ''' import tensorflow as tf import numpyrng = 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.multiply (X, W), b)# Minimize the squared errors cost = tf.reduce_sum(tf.pow(activation-Y, 2))/(2*n_samples) #L2 loss 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 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 % 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)

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