TensorFlow 实现Multilayer Perceptron

# Import MINST datafrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("MNIST_Data/data/", one_hot=True)import tensorflow as tf# Parameterslearning_rate = 0.001training_epochs = 15batch_size = 100display_step = 1# Network Parametersn_hidden_1 = 256 # 1st layer number of featuresn_hidden_2 = 256 # 2nd layer number of featuresn_input = 784 # MNIST data input (img shape: 28*28)n_classes = 10 # MNIST total classes (0-9 digits)# tf Graph inputx = tf.placeholder("float", [None, n_input])y = tf.placeholder("float", [None, n_classes])# Store layers weight & biasweights = {    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))}biases = {    'b1': tf.Variable(tf.random_normal([n_hidden_1])),    'b2': tf.Variable(tf.random_normal([n_hidden_2])),    'out': tf.Variable(tf.random_normal([n_classes]))}# Create modeldef multilayer_perceptron(x, weights, biases):    # Hidden layer with RELU activation    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])    layer_1 = tf.nn.relu(layer_1)    # Hidden layer with RELU activation    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])    layer_2 = tf.nn.relu(layer_2)    # Output layer with linear activation    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']    return out_layer# Construct modelpred = multilayer_perceptron(x, weights, biases)# Define loss and optimizercost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)# Initializing the variablesinit = tf.initialize_all_variables()# Launch the graphwith tf.Session() as sess:    sess.run(init)    # Training cycle    for epoch in range(training_epochs):        avg_cost = 0.        total_batch = int(mnist.train.num_examples/batch_size)        # Loop over all batches        for i in range(total_batch):            batch_x, batch_y = 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_x,                                                          y: batch_y})            # Compute average loss            avg_cost += c / total_batch        # Display logs per epoch step        if epoch % display_step == 0:            print "Epoch:", '%04d' % (epoch+1), "cost=", \                "{:.9f}".format(avg_cost)    print "Optimization Finished!"    # Test model    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))    # Calculate accuracy    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))    print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})