1. 输入数据

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
#数据中包含55000张训练图片，每张图片的分辨率是28×28，所以我们的训练网络输入应该是28×28=784个像素数据。

数据中包含55000张训练图片，每张图片的分辨率是28×28，所以我们的训练网络输入应该是28×28=784个像素数据。

 

2. 搭建网络

def add_layer(inputs, in_size, out_size, activation_function=None):
    # add one more layer and return the output of this layer
    with tf.name_scope('layer'):
        with tf.name_scope('weights'):
            Weights = tf.Variable(
            tf.random_normal([in_size, out_size],name='W'), 
            name='W')
        with tf.name_scope('biases'):
            biases = tf.Variable(tf.zeros([1, out_size]) + 0.1,name='b')
        with tf.name_scope('Wx_plus_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

# 搭建网络 
import tensorflow as tf
xs = tf.placeholder(tf.float32, [None, 784]) # 28x28
ys = tf.placeholder(tf.float32, [None, 10])
prediction = add_layer(xs, 784, 10, activation_function=tf.nn.softmax)
# 其中输入数据是784个特征，输出数据是10个特征，激励采用softmax函数

　　

损失函数：交叉熵损失

cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),reduction_indices=[1])) # loss

　

优化器：SGD

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
sess = tf.Session()
# tf.initialize_all_variables() 这种写法马上就要被废弃
# 替换成下面的写法:
sess.run(tf.global_variables_initializer())

　　

计算accuracy

def compute_accuracy(v_xs,v_ys):
    global prediction
    y_pre = sess.run(prediction,feed_dict={xs:v_xs})
    correct_prediction = tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    result = sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys})
    return result

　

3. 训练

# 训练
# 现在开始train，每次只取100张图片，免得数据太多训练太慢。
for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys})

    if i % 50 == 0:
        print(compute_accuracy(mnist.test.images, mnist.test.labels))

 

　　

　

　

 

 

 

 

 

 

 参考文献：

【1】莫烦Python