tensoflow练习3：卷积神经网络用于分类

再次利用卷积神经网络对手写体进行识别；卷积神经网络是一种非常强大的神经网络模型（可应用于图像识别，语音处理等领域）。下面将给出源码：

#coding = utf-8import tensorflow as tfimport numpy as npfrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets('MNIST_data/',one_hot=True)n_output_layer = 10#定义待训练的神经网络def convoluional_neural_network(data):    weights = {'w_conv1':tf.Variable(tf.random_normal([5,5,1,32])),               'w_conv2':tf.Variable(tf.random_normal([5,5,32,64])),               'w_fc':tf.Variable(tf.random_normal([7*7*64,1024])),               'out':tf.Variable(tf.random_normal([1024,n_output_layer])),               }    biases = { 'b_conv1':tf.Variable(tf.random_normal([32])),               'b_conv2':tf.Variable(tf.random_normal([64])),               'b_fc':tf.Variable(tf.random_normal([1024])),               'out':tf.Variable(tf.random_normal([n_output_layer]))    }    data = tf.reshape(data,[-1,28,28,1])    conv1 = tf.nn.relu(tf.add(tf.nn.conv2d(data,weights['w_conv1'],strides=[1,1,1,1],padding='SAME'),biases['b_conv1']))    #池化    conv1 = tf.nn.max_pool(conv1,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')    #第二层    conv2 = tf.nn.relu(tf.add(tf.nn.conv2d(conv1,weights['w_conv2'],strides=[1,1,1,1],padding='SAME'),biases['b_conv2']))    conv2 = tf.nn.max_pool(conv2,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')    #全连接层    fc = tf.reshape(conv2,[-1,7*7*64])    fc = tf.nn.relu(tf.add(tf.matmul(fc,weights['w_fc']),biases['b_fc']))#输出节点为1024    #dropout 可选    output = tf.add(tf.matmul(fc,weights['out']),biases['out'])    return outputbatch_size = 100X= tf.placeholder('float',[None,28*28])Y= tf.placeholder('float')def train_neural_network(X, Y):    predict = convoluional_neural_network(X)    cost_func = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(predict,Y))    optimizer = tf.train.AdamOptimizer().minimize(cost_func)    epochs = 13    with tf.Session() as sess:        sess.run(tf.global_variables_initializer())        epoch_loss = 0        print('training begins:')        for epoch in range(epochs):            for i in range(int(mnist.train.num_examples/batch_size)):                x, y  = mnist.train.next_batch(batch_size)                _, c = sess.run([optimizer,cost_func],feed_dict={X:x,Y:y})                epoch_loss += c            print(epoch, ' : ', epoch_loss)        correct = tf.equal(tf.argmax(predict,1),tf.argmax(Y,1))        accuracy = tf.reduce_mean(tf.cast(correct,'float'))        print('精确率:', accuracy.eval({X:mnist.test.images,Y:mnist.test.labels}))train_neural_network(X,Y)

相同部分就不加描述了，这里主要讲一下卷积过程；
（1）第一次卷积与池化：（特征个数从1变为32）

conv1 = tf.nn.relu(tf.add(tf.nn.conv2d(data,weights['w_conv1'],strides=[1,1,1,1],padding='SAME'),biases['b_conv1']))    #池化    conv1 = tf.nn.max_pool(conv1,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')

padding=’SAME’时，表示0边距；strides=[1,1,1,1]：表示1步长；卷积后尺寸不变；然后池化操作变为14*14；
（2）第二次卷积与池化将尺寸变为7*7；（32变为64）

#第二层    conv2 = tf.nn.relu(tf.add(tf.nn.conv2d(conv1,weights['w_conv2'],strides=[1,1,1,1],padding='SAME'),biases['b_conv2']))    conv2 = tf.nn.max_pool(conv2,ksize=[1,2,2,1],strides=[1,2,2,1],padding

（3）全连接

fc = tf.reshape(conv2,[-1,7*7*64])fc=tf.nn.relu(tf.add(tf.matmul(fc,weights['w_fc']),biases['b_fc']))#输出节点为1024

结果：