TF：TF分类问题之MNIST手写50000数据集实现87.4%准确率识别：SGD法+softmax法+cross_entropy法—Jason niu

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data 
# number 1 to 10 data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True) 

def add_layer(inputs, in_size, out_size, activation_function=None,):
    # add one more layer and return the output of this layer
    Weights = tf.Variable(tf.random_normal([in_size, out_size]))
    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1,)
    Wx_plus_b = tf.matmul(inputs, Weights) + 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):      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

# define placeholder for inputs to network
xs = tf.placeholder(tf.float32, [None, 784]) 
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]))      
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) 
sess = tf.Session()
# important step
sess.run(tf.global_variables_initializer())

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))