python MNIST分类 tensorflow

【核心代码】

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

##number 1 to 10
mnist = input_data.read_data_sets("MNIST_data",one_hot = True)

def add_layer(input, in_size, out_size, activation_function = None):
    Wights = tf.Variable(tf.random_normal([in_size,out_size]))
    biases = tf.Variable(tf.zeros([1,out_size]) 0.1)
    Wx_plus_b = tf.matmul(input,Wights)   biases
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    return outputs
def compute_accuracy(v_xs,v_ys):##估计准确度
    global prediction
    y_pre = sess.run(prediction,feed_dict={xs:v_xs})##预测值
    correct_prediction = tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))
    accurary = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    result = sess.run(accurary,feed_dict={xs:v_xs,ys:v_ys})
    return result

##define placeholder for inputs to network
xs = tf.placeholder(tf.float32,[None,784])###28*28 784个像素点
ys = tf.placeholder(tf.float32,[None,10])

##add output layer
prediction = add_layer(xs,784,10,activation_function=tf.nn.softmax)

##the error between prediction and real data
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),
                                              reduction_indices=[1]))##loss
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

sess = tf.Session()
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
    init = tf.initialize_all_variables()
else:
    init = tf.global_variables_initializer()
sess.run(init)

for i in range(1000):
    batch_xs,batch_ys = mnist.train.next_batch(100)##每次提取100个数据
    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))

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

##number 1 to 10
mnist = input_data.read_data_sets（“MNIST_data“one_hot = True）

def add_layer（input in_size out_size activation_function = None）:
    Wights = tf.Variable（tf.random_normal（[in_sizeout_size]））
    biases = tf.Variable（tf.zeros（[1out_size]）+0.1）
    Wx_plus_b = tf.matmul（inputWights） + biases
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function（Wx_plus_b）
    return outputs
def compute_accuracy（v_xsv_ys）:##估计准确度
    global prediction
    y_pre = sess.run（predictionfeed_dict={xs:v_xs}）##预测值
    correct_prediction = tf.equal（tf.argmax（y_pre1）tf.argmax（v_ys1））
    accurary = tf.reduce_mean（tf.cast（correct_pr