基于tensorflow的CNN卷积神经网络对手写字MNIST数据集的分类器

这篇用一种更简洁的方式写CNN神经网络

上代码：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import  input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
#define conv layer
def conv_layer(inputs, channels_in, channels_out):
    w = tf.Variable(tf.truncated_normal([5,5,channels_in, channels_out], stddev=0.1))
    b = tf.Variable(tf.constant(0.1, shape=[channels_out]))
    conv = tf.nn.conv2d(inputs, w, strides=[1, 1, 1, 1], padding='SAME')  #调用conv2d
    act = tf.nn.relu(conv + b)    # conv + bias
    return act
#define fully connection layer
def fc_layer(inputs, channels_in, channels_out, activate_function=None):
    w = tf.Variable(tf.truncated_normal([channels_in, channels_out]))
    b = tf.Variable(tf.zeros([channels_out])+0.1)
    fc = tf.matmul(inputs, w) + b
    if activate_function is None:
        res = fc
    else:
        res = activate_function(fc)
    return res
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])
x_image = tf.reshape(x, [-1, 28, 28, 1])    
keep_prob = tf.placeholder(tf.float32)
conv1 = conv_layer(x_image, 1, 32)
pool1 = tf.nn.max_pool(conv1, ksize=[1, 1, 1, 1], strides=[1, 2, 2, 1], padding='SAME')    # 14*14*32

conv2 = conv_layer(pool1, 32, 64)
pool2 = tf.nn.max_pool(conv2, ksize=[1, 1, 1, 1], strides=[1, 2, 2, 1], padding='SAME')    
flattended = tf.reshape(pool2, [-1, 7*7*64])
fc1 = fc_layer(flattended, 7 * 7 * 64, 1024, tf.nn.relu)
fc1 = tf.nn.dropout(fc1, keep_prob)
logits =  fc_layer(fc1, 1024, 10)
cross_entropy = tf.reduce_mean(
                        tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y)
)
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
for i in range(10000):
    #batch = mnist.train.next_batch(100)
    images, labels = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x:images, y:labels,keep_prob:1})
    if i%50==0:
        print(sess.run(accuracy, feed_dict={x:mnist.test.images[:1000], y:mnist.test.labels[:1000], keep_prob:1}))
        #print(sess.run(accuracy, feed_dict={x:batch[0], y:batch[1]}))
sess.close()

1.这段代码封装了CNN的每一层

2.这段代码还用到了droup out 防止过拟合

3.不知道为什么，这样的写法似乎比不封装要慢，最终准确率也无法到达98 99