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python人工智能tensorflow構(gòu)建卷積神經(jīng)網(wǎng)絡(luò)CNN

更新時(shí)間：2022年05月05日 10:04:26 作者：Bubbliiiing

學(xué)習(xí)神經(jīng)網(wǎng)絡(luò)已經(jīng)有一段時(shí)間，從普通的BP神經(jīng)網(wǎng)絡(luò)到LSTM長(zhǎng)短期記憶網(wǎng)絡(luò)都有一定的了解，但是從未系統(tǒng)的把整個(gè)神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)記錄下來(lái)，我相信這些小記錄可以幫助我更加深刻的理解神經(jīng)網(wǎng)絡(luò)

學(xué)習(xí)神經(jīng)網(wǎng)絡(luò)已經(jīng)有一段時(shí)間，從普通的BP神經(jīng)網(wǎng)絡(luò)到LSTM長(zhǎng)短期記憶網(wǎng)絡(luò)都有一定的了解，但是從未系統(tǒng)的把整個(gè)神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)記錄下來(lái)，我相信這些小記錄可以幫助我更加深刻的理解神經(jīng)網(wǎng)絡(luò)。

簡(jiǎn)介

卷積神經(jīng)網(wǎng)絡(luò)（Convolutional Neural Networks, CNN）是一類包含卷積計(jì)算且具有深度結(jié)構(gòu)的前饋神經(jīng)網(wǎng)絡(luò)（Feedforward Neural Networks），是深度學(xué)習(xí)（deep learning）的代表算法之一。

其主要結(jié)構(gòu)分為輸入層、隱含層、輸出層。

在tensorboard中，其結(jié)構(gòu)如圖所示：

對(duì)于卷積神經(jīng)網(wǎng)絡(luò)而言，其輸入層、輸出層與平常的卷積神經(jīng)網(wǎng)絡(luò)無(wú)異。

但其隱含層可以分為三個(gè)部分，分別是卷積層（對(duì)輸入數(shù)據(jù)進(jìn)行特征提?。?、池化層（特征選擇和信息過(guò)濾）、全連接層（等價(jià)于傳統(tǒng)前饋神經(jīng)網(wǎng)絡(luò)中的隱含層）。

隱含層介紹

1、卷積層

卷積將輸入圖像放進(jìn)一組卷積濾波器，每個(gè)濾波器激活圖像中的某些特征。

假設(shè)一副黑白圖像為5*5的大小，像這樣：

利用如下卷積器進(jìn)行卷積：

卷積結(jié)果為：

卷積過(guò)程可以提取特征，卷積神經(jīng)網(wǎng)絡(luò)是根據(jù)特征來(lái)完成分類的。

在tensorflow中，卷積層的重要函數(shù)是：

tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, name=None)

其中：

1、input是輸入量，shape是[batch, height, width, channels]。；

2、filter是使用的卷積核；

3、strides是步長(zhǎng)，其格式[1,step,step,1]，step指的是在圖像卷積的每一維的步長(zhǎng)；

4、padding：string類型的量，只能是"SAME","VALID"其中之一，SAME表示卷積前后圖像面積不變。

2、池化層

池化層用于在卷積層進(jìn)行特征提取后，輸出的特征圖會(huì)被傳遞至池化層進(jìn)行特征選擇和信息過(guò)濾。

常見的池化是最大池化，最大池化指的是取出這些被卷積后的數(shù)據(jù)的最大值，就是取出其最大特征。

假設(shè)其池化窗口為2X2，步長(zhǎng)為2。

原圖像為：

池化后為：

在tensorflow中，池化層的重要函數(shù)是：

tf.nn.max_pool(value, ksize, strides, padding, data_format, name)

1、value：池化層的輸入，一般池化層接在卷積層后面，shape是[batch, height, width, channels]。

2、ksize：池化窗口的大小，取一個(gè)四維向量，一般是[1, in_height, in_width, 1]。

3、strides：和卷積類似，窗口在每一個(gè)維度上滑動(dòng)的步長(zhǎng)，也是[1, stride,stride, 1]。

4、padding：和卷積類似，可以取’VALID’ 或者’SAME’。

這是tensorboard中卷積層和池化層的連接結(jié)構(gòu)：

3、全連接層

全連接層與普通神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)相同，如圖所示：

具體實(shí)現(xiàn)代碼

卷積層、池化層與全連接層實(shí)現(xiàn)代碼

def conv2d(x,W,step,pad): #用于進(jìn)行卷積，x為輸入值，w為卷積核
    return tf.nn.conv2d(x,W,strides = [1,step,step,1],padding = pad)
def max_pool_2X2(x,step,pad):	#用于池化，x為輸入值，step為步數(shù)
    return tf.nn.max_pool(x,ksize = [1,2,2,1],strides= [1,step,step,1],padding = pad)
def weight_variable(shape):		#用于獲得W
    initial = tf.truncated_normal(shape,stddev = 0.1) #從截?cái)嗟恼龖B(tài)分布中輸出隨機(jī)值
    return tf.Variable(initial)
def bias_variable(shape):		#獲得bias
    initial = tf.constant(0.1,shape=shape)  #生成普通值
    return tf.Variable(initial)
def add_layer(inputs,in_size,out_size,n_layer,activation_function = None,keep_prob = 1):	
#用于添加全連接層
    layer_name = 'layer_%s'%n_layer
    with tf.name_scope(layer_name):
        with tf.name_scope("Weights"):
            Weights = tf.Variable(tf.truncated_normal([in_size,out_size],stddev = 0.1),name = "Weights")
            tf.summary.histogram(layer_name+"/weights",Weights)
        with tf.name_scope("biases"):
            biases = tf.Variable(tf.zeros([1,out_size]) + 0.1,name = "biases")
            tf.summary.histogram(layer_name+"/biases",biases)
        with tf.name_scope("Wx_plus_b"):
            Wx_plus_b = tf.matmul(inputs,Weights) + biases
            tf.summary.histogram(layer_name+"/Wx_plus_b",Wx_plus_b)
        if activation_function == None :
            outputs = Wx_plus_b 
        else:
            outputs = activation_function(Wx_plus_b)
        print(activation_function)
        outputs = tf.nn.dropout(outputs,keep_prob)
        tf.summary.histogram(layer_name+"/outputs",outputs)
        return outputs
def add_cnn_layer(inputs, in_z_dim, out_z_dim, n_layer, conv_step = 1, pool_step = 2, padding = "SAME"):
#用于生成卷積層和池化層
    layer_name = 'layer_%s'%n_layer
    with tf.name_scope(layer_name):
        with tf.name_scope("Weights"):
            W_conv = weight_variable([5,5,in_z_dim,out_z_dim])
        with tf.name_scope("biases"):
            b_conv = bias_variable([out_z_dim])
        with tf.name_scope("conv"):
        #卷積層
            h_conv = tf.nn.relu(conv2d(inputs, W_conv, conv_step, padding)+b_conv) 
        with tf.name_scope("pooling"):
        #池化層
            h_pool = max_pool_2X2(h_conv, pool_step, padding)
    return h_pool

全部代碼

import tensorflow as tf 
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data",one_hot = "true")
def conv2d(x,W,step,pad):
    return tf.nn.conv2d(x,W,strides = [1,step,step,1],padding = pad)
def max_pool_2X2(x,step,pad):
    return tf.nn.max_pool(x,ksize = [1,2,2,1],strides= [1,step,step,1],padding = pad)
def weight_variable(shape):
    initial = tf.truncated_normal(shape,stddev = 0.1) #從截?cái)嗟恼龖B(tài)分布中輸出隨機(jī)值
    return tf.Variable(initial)
def bias_variable(shape):
    initial = tf.constant(0.1,shape=shape)  #生成普通值
    return tf.Variable(initial)
def add_layer(inputs,in_size,out_size,n_layer,activation_function = None,keep_prob = 1):
    layer_name = 'layer_%s'%n_layer
    with tf.name_scope(layer_name):
        with tf.name_scope("Weights"):
            Weights = tf.Variable(tf.truncated_normal([in_size,out_size],stddev = 0.1),name = "Weights")
            tf.summary.histogram(layer_name+"/weights",Weights)
        with tf.name_scope("biases"):
            biases = tf.Variable(tf.zeros([1,out_size]) + 0.1,name = "biases")
            tf.summary.histogram(layer_name+"/biases",biases)
        with tf.name_scope("Wx_plus_b"):
            Wx_plus_b = tf.matmul(inputs,Weights) + biases
            tf.summary.histogram(layer_name+"/Wx_plus_b",Wx_plus_b)
        if activation_function == None :
            outputs = Wx_plus_b 
        else:
            outputs = activation_function(Wx_plus_b)
        print(activation_function)
        outputs = tf.nn.dropout(outputs,keep_prob)
        tf.summary.histogram(layer_name+"/outputs",outputs)
        return outputs
def add_cnn_layer(inputs, in_z_dim, out_z_dim, n_layer, conv_step = 1, pool_step = 2, padding = "SAME"):
    layer_name = 'layer_%s'%n_layer
    with tf.name_scope(layer_name):
        with tf.name_scope("Weights"):
            W_conv = weight_variable([5,5,in_z_dim,out_z_dim])
        with tf.name_scope("biases"):
            b_conv = bias_variable([out_z_dim])
        with tf.name_scope("conv"):
            h_conv = tf.nn.relu(conv2d(inputs, W_conv, conv_step, padding)+b_conv) 
        with tf.name_scope("pooling"):
            h_pool = max_pool_2X2(h_conv, pool_step, padding)
    return h_pool
def compute_accuracy(x_data,y_data):
    global prediction
    y_pre = sess.run(prediction,feed_dict={xs:x_data,keep_prob:1})
    correct_prediction = tf.equal(tf.arg_max(y_data,1),tf.arg_max(y_pre,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    result = sess.run(accuracy,feed_dict = {xs:batch_xs,ys:batch_ys,keep_prob:1})
    return result
keep_prob = tf.placeholder(tf.float32)
xs = tf.placeholder(tf.float32,[None,784])
ys = tf.placeholder(tf.float32,[None,10])
x_image = tf.reshape(xs,[-1,28,28,1])
h_pool1 = add_cnn_layer(x_image, in_z_dim = 1, out_z_dim = 32, n_layer = "cnn1",)
h_pool2 = add_cnn_layer(h_pool1, in_z_dim = 32, out_z_dim = 64, n_layer = "cnn2",)
h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64])
h_fc1_drop = add_layer(h_pool2_flat, 7*7*64, 1024, "layer1", activation_function = tf.nn.relu, keep_prob = keep_prob)
prediction = add_layer(h_fc1_drop, 1024, 10, "layer2", activation_function = tf.nn.softmax, keep_prob = 1)
with tf.name_scope("loss"):
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=ys,logits = prediction),name = 'loss')
    tf.summary.scalar("loss",loss)
train = tf.train.AdamOptimizer(1e-4).minimize(loss)
init = tf.initialize_all_variables()
merged = tf.summary.merge_all()
with tf.Session() as sess:
    sess.run(init)
    write = tf.summary.FileWriter("logs/",sess.graph)
    for i in range(5000):
        batch_xs,batch_ys = mnist.train.next_batch(100)
        sess.run(train,feed_dict = {xs:batch_xs,ys:batch_ys,keep_prob:0.5})
        if i % 100 == 0:
            print(compute_accuracy(mnist.test.images,mnist.test.labels))

以上就是python人工智能tensorflow構(gòu)建卷積神經(jīng)網(wǎng)絡(luò)CNN的詳細(xì)內(nèi)容，更多關(guān)于tensorflow構(gòu)建卷積神經(jīng)網(wǎng)絡(luò)CNN的資料請(qǐng)關(guān)注腳本之家其它相關(guān)文章！

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