卷积神经网络(CNN)介绍与实践（二）

# CNN 代码
def convolutional(x,keep_prob):
    def conv2d(x,W):
        return tf.nn.conv2d(x,W,[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')
    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)
    x_image=tf.reshape(x,[-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_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)
    # full_connetion
    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)
    # dropout 随机扔掉一些值，防止过拟合
    h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob)
    W_fc2=weight_variable([1024,10])
    b_fc2=bias_variable([10])
    y=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)
    return y,[W_conv1,b_conv1,W_conv2,b_conv2,W_fc1,b_fc1,W_fc2,b_fc2]