MLlab10-2.py

#Xavier for MNIST
import tensorflow as tf
import random
from tensorflow.examples.tutorials.mnist import input_data
tf.set_random_seed(777)

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
X = tf.placeholder(tf.float32, [None, 784])
Y = tf.placeholder(tf.float32, [None, 10])

W1 = tf.get_variable('W1', shape=[784,256],initializer=tf.contrib.layers.xavier_initializer())
b1 = tf.Variable(tf.random_normal([256]))
L1 = tf.nn.relu(tf.matmul(X,W1)+b1)

W2 = tf.get_variable('W2', shape=[256,256], initializer=tf.contrib.layers.xavier_initializer())
b2 = tf.Variable(tf.random_normal([256]))
L2 = tf.nn.relu(tf.matmul(L1, W2)+b2)

W3 = tf.get_variable('W3', shape=[256,10], initializer=tf.contrib.layers.xavier_initializer())
b3 = tf.Variable(tf.random_normal([10]))
hypothesis = tf.matmul(L2,W3) + b3

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=hypothesis, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=0.01).minimize(cost)

traning_epoches = 15
batch_size = 100

sess = tf.Session()
sess.run(tf.global_variables_initializer())

for epoch in range(traning_epoches):
    avg_cost = 0
    total_batch = int(mnist.train.num_examples / batch_size)
    for i in range(total_batch):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        feed_dict = {X:batch_xs, Y:batch_ys}
        c, _ = sess.run([cost, optimizer], feed_dict=feed_dict)
        avg_cost += c / total_batch
    print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))
print('Learning Finished!')

correct_prediction = tf.equal(tf.arg_max(hypothesis, 1), tf.arg_max(Y,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, dtype=tf.float32))
print('Accuracy: ', sess.run(accuracy, feed_dict={X:mnist.test.images, Y: mnist.test.labels}))