Tensorflow机器学习入门——MINIST数据集识别

参考网站：http://www.tensorfly.cn/tfdoc/tutorials/mnist_beginners.html

import tensorflow as tf

def add_layer(inputs, in_size, out_size, activation_function=None):
    Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W')
    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')
    Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases)
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    return outputs

#加载数据
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

#构建计算图
x = tf.placeholder("float", [None, 784])
y_ = tf.placeholder("float", [None,10])
y=add_layer(x,784,10,activation_function=tf.nn.softmax)

#损失与训练
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

#计算准确率
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

#训练1000步
init = tf.initialize_all_variables()
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})
        if i%100==0:
            print (sess.run(accuracy, feed_dict={x: batch_xs, y_: batch_ys}))
    #验证准确率
    print (sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))