TensorFlow MNIST CNN 代码

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_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):
    #stride:[1,x_movement,y_movement,1]
    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")

#image with and height
#result length
wh = 28
rl = 10

xs = tf.placeholder(tf.float32,[None,wh*wh])
ys = tf.placeholder(tf.float32,[None,rl])
keep_prob = tf.placeholder(tf.float32)

x_image=tf.reshape(xs,[-1,wh,wh,1])

#patch 5*5 in size 1 ,out size 32
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])

#[7,7,64]=>[7*7*64]
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,rl])
B_fc2 = bias_variable([rl])
prediction = tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+B_fc2)

cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1]))
#1e-4=0.0001
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

sess = tf.Session()

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, keep_prob: 0.5})
    if i % 50 == 0:
        print(compute_accuracy(
            mnist.test.images[:1000], mnist.test.labels[:1000]))

sess.close()

这次运行代码计算时间非常长，而且跑到后面，电脑开始明显的发热。排风扇也开始响了。

最后准确率到达了0.964