2层DNN，Mnist的使用，交叉熵（cross_entropy)log + 1e-6， test集算accuracy用tf代码归为网络一部分

2层DNN，Mnist的使用，交叉熵（cross_entropy)log + 1e-6， test集算accuracy用tf代码归为网络一部分

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

def add_layer(input, in_size, out_size, name='layer', activation=None):
    with tf.variable_scope(name):
        Weight = tf.get_variable(name='Weight', shape=(in_size, out_size), dtype=tf.float32, initializer=tf.random_normal_initializer())
        bias = tf.get_variable(name='bias', shape=(out_size), dtype=tf.float32, initializer=tf.constant_initializer(0.1))
        Wx_b = tf.matmul(input, Weight) + bias
        if activation != None:
            output = activation(Wx_b)
        else:
            output = Wx_b
    return output

#placehoder
x = tf.placeholder(name='x', shape=(None, 784), dtype=tf.float32)
y_label = tf.placeholder(name='y_label', shape=(None, 10), dtype=tf.float32)

#network structure
hidden_size = 128
y_hidden = add_layer(x, 784, 128, name='hidden', activation=tf.nn.relu)
y = add_layer(y_hidden, 128, 10, name='output', activation=tf.nn.softmax)

#loss and train
# cross_entropy = tf.reduce_mean(tf.reduce_sum(-y_label * tf.log(y), reduction_indices=-1))
# train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_label * tf.log(y + 0.000001),
                                              reduction_indices=[1]))       # loss
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
#test structure
correct = tf.equal(tf.arg_max(y, 1), tf.arg_max(y_label, 1))
acuracy = tf.reduce_mean(tf.cast(correct, 'float'))
    # acuracy = 1



#run sess
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for steps in range(1000):
        batch_x, batch_y = mnist.train.next_batch(100)
        _, loss = sess.run([train_step, cross_entropy], feed_dict={x:batch_x, y_label:batch_y})
        if steps % 50 == 0:
            print(loss)
            accuracy = sess.run(acuracy, feed_dict={x:mnist.test.images, y_label:mnist.test.labels})
            print(steps, accuracy)