tensorflow 多层感知机 分类mnist

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/root/data/", one_hot=True)

import tensorflow as tf

learning_rate = 0.001
training_epochs = 25
batch_size = 100
display_step = 1

n_hidden_1 = 256
n_hidden_2 = 256
n_input = 784
n_classes = 10

x = tf.placeholder("float",[None, n_input])
y = tf.placeholder("float",[None, n_classes])

def multilayer_perception(x, weights, biases):
   layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
   layer_1 = tf.nn.relu(layer_1)

   layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
   layer_2 = tf.nn.relu(layer_2)

   out_layer = tf.matmul(layer_2, weights['out']) + biases['out']

   return out_layer

weights = {
    '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]))
}

pred = multilayer_perception(x, weights, biases)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))

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

init = tf.initialize_all_variables()

with tf.Session() as sess:
    sess.run(init)

    for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples/batch_size)

        for i in range(total_batch):
            batch_x, batch_y = mnist.train.next_batch(batch_size)

            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,
                                                          y: batch_y})

            avg_cost += c / total_batch

        if epoch % display_step == 0:
            print "Epoch:", '%04d' % (epoch+1), "cost=", \
                "{:.9f}".format(avg_cost)
    print "Optimization Finished!"

    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))

    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})