Tensorflow学习总结（二）

本篇博客利用tensorflow对MNIST_data数据实现手写数字的简单识别，作为深度学习入门的第一次尝试，感觉到了神经网络的神奇之处。

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1.代码部分

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

#载入数据集
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)

#定义每个批次的大小
batch_size = 100
#计算一共有多少个批次
n_batch = mnist.train.num_examples // batch_size

#定义两个placeholder
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])

#创建一个简单的神经网络
w = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
prediction = tf.nn.softmax(tf.matmul(x, w) + b)

#二次代价函数
loss = tf.reduce_mean(tf.square(y - prediction))
#使用梯度下降法
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

#初始化变量
init = tf.global_variables_initializer()


#求准确率
corret_prediction = tf.equal(tf.arg_max(y, 1), tf.argmax(prediction, 1))

accuracy = tf.reduce_mean(tf.cast(corret_prediction, tf.float32))


with tf.Session() as sess:
    sess.run(init)
    for epoch in range(21):
        for batch in range(n_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            sess.run(train_step, feed_dict={x:batch_xs, y:batch_ys})

        acc = sess.run(accuracy, feed_dict={x:mnist.test.images, y:mnist.test.labels})
        print("Iter" + str(epoch) + ", Testing Accuracy " + str(acc))

2.运行结果