使用tensorflow构建cnn对minist数据集进行分类。
代码来自网络。

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#coding:utf-8
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

mnist = input_data.read_data_sets('mnist/', one_hot=True)

sess = tf.InteractiveSession()
x = tf.placeholder("float", shape=[None,784])
y_ = tf.placeholder("float", shape=[None,10])

#初始化权重
def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev = 0.1)
    return tf.Variable(initial)
#初始化偏置项
def bias_variable(shape):
    initial = tf.constant(0.1, shape = shape )
    return tf.Variable(initial)

#卷积过程
def conv2d(x,w):
    return tf.nn.conv2d(x,w,
                        strides=[1,1,1,1],padding='SAME')
#池化过程
def max_pool_2x2(x):
    return tf.nn.max_pool(x,ksize=[1,2,2,1],
                        strides=[1,2,2,1],padding='SAME')

w_conv1 = weight_variable([5,5,1,32])
b_conv1=bias_variable([32])

#-1 表示图片数量不定
x_image=tf.reshape(x,[-1,28,28,1])

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)

keep_prob=tf.placeholder("float")
h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob)

w_fc2=weight_variable([1024,10])
b_fc2=bias_variable([10])

y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop,w_fc2)+b_fc2)

#计算交叉熵的代价函数
cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
#使用优化算法使得代价函数最小化
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
#找出预测正确的标签
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
#得出通过正确个数除以总数得出准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
sess.run(tf.initialize_all_variables())

for i in range(20000):
  batch = mnist.train.next_batch(50)
  if i%100 == 0:
    train_accuracy = accuracy.eval(feed_dict={
        x:batch[0], y_: batch[1], keep_prob: 1.0})
    print "step %d, training accuracy %g"%(i, train_accuracy)
  train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

print "test accuracy %g"%accuracy.eval(feed_dict={
    x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})