动手创建一个简单的神经网络(MNIST)

#-*-coding:utf-8-*-
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot = True)

import tensorflow as tf
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

y = tf.nn.softmax(tf.matmul(x, W) + b)

#reduction_indices=[1],每一行相加，等于axis
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y),axis=1)) #交叉熵
# cross_entropy=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y,labels=y_))

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for i in range(1000):
        batch_xs, batch_ys = mnist.train.next_batch(100)
        sess.run(train_step, feed_dict={x:  batch_xs, y_: batch_ys})

        correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

        # 将x的数据格式转化成dtype.例如，原来x的数据格式是bool，
        # 那么将其转化成float以后，就能够将其转化成0和1的序列
    print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))