TF：TF下CNN实现mnist数据集预测 96%采用placeholder用法+2层C及其max_pool法+隐藏层dropout法+输出层softmax法+目标函数cross_entropy法+AdamOptimizer算法

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 compute_accuracy(v_xs, v_ys):
    global prediction
    y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1})
    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, keep_prob: 1})
    return result

def weight_variable(shape):      
    initial = tf.truncated_normal(shape, stddev=0.1) 
    return tf.Variable(initial)
def bias_variable(shape):        
    initial = tf.constant(0.1, shape=shape)      return tf.Variable(initial)

def conv2d(x, W):                 
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') 

def max_pool_2x2(x):              
    return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')

xs = tf.placeholder(tf.float32, [None, 784]) # 28x28
ys = tf.placeholder(tf.float32, [None, 10])
keep_prob = tf.placeholder(tf.float32)
x_image = tf.reshape(xs, [-1, 28, 28, 1])  

## conv1 layer；
W_conv1 = weight_variable([5,5, 1,32]) 
b_conv1 = bias_variable([32])        
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) 
h_pool1 = max_pool_2x2(h_conv1)                        

W_conv2 = weight_variable([5,5, 32, 64]) 
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) 
h_pool2 = max_pool_2x2(h_conv2)                          

W_fc1 = weight_variable([7*7*64, 1024]) 
b_fc1 = bias_variable([1024])          
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])           
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)              

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) 

# 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.AdamOptimizer(1e-4).minimize(cross_entropy)    

sess = tf.Session()
# important step
sess.run(tf.global_variables_initializer())

for i in range(10):  
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: 0.5}) 
    if i % 50 == 0:
        print(compute_accuracy(
            mnist.test.images, mnist.test.labels))