# -*- coding: utf-8 -*- ''' Created on 2018年4月20日@author: user ''' import tensorflow as tf # Import MINST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)# Parameters learning_rate = 0.01 training_epochs = 25 batch_size = 100 display_step = 1# tf Graph Input x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784 y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes# Set model weights W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10]))# Construct model pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax# Minimize error using cross entropy cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1)) # Gradient Descent optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)# Initializing the variables init = tf.initialize_all_variables()# Launch the graph with tf.Session() as sess:sess.run(init)# Training cyclefor epoch in range(training_epochs):avg_cost = 0.total_batch = int(mnist.train.num_examples/batch_size)# Loop over all batchesfor i in range(total_batch):batch_xs, batch_ys = mnist.train.next_batch(batch_size)# Run optimization op (backprop) and cost op (to get loss value)_, c = sess.run([optimizer, cost], feed_dict={x: batch_xs,y: batch_ys})# Compute average lossavg_cost += c / total_batch# Display logs per epoch stepif (epoch+1) % display_step == 0:print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)print "Optimization Finished!"# Test modelcorrect_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))# Calculate accuracyaccuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})

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