基于CNN的MNIST手写体识别代码

import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

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


def compute_accuracy(v_xs, v_ys):
    global prediction
    y_pre = sess.run(prediction, feed_dict={xs: v_xs})
    correct_prediction = tf.equal(tf.arg_max(y_pre, 1), tf.arg_max(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



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

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(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(h_conv2)

h_pool_flat = tf.reshape(h_pool2, [-1, 7*7*64])
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
h_fc1= tf.matmul(h_pool_flat,W_fc1)+b_fc1

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
h_fc2 = tf.matmul(h_fc1, W_fc2)+b_fc2

prediction = tf.nn.softmax(h_fc2)
loss = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(loss)

init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
for step in range(1000):
    xs_batch, ys_batch = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={xs: xs_batch, ys: ys_batch})
    if step%50 ==0:
        result1 = compute_accuracy(mnist.test.images, mnist.test.labels)
        print(result1)

运行结果:

Extracting MNIST_data\train-images-idx3-ubyte.gz
Extracting MNIST_data\train-labels-idx1-ubyte.gz
Extracting MNIST_data\t10k-images-idx3-ubyte.gz
Extracting MNIST_data\t10k-labels-idx1-ubyte.gz
0.1437
0.8058
0.8864
0.9173
0.9276
0.946
0.9536
0.9496
0.9595
0.9616
0.9616
0.9626
0.9619
0.9666
0.9721
0.9706
0.9695
0.9708
0.9755
0.972