【深度学习笔记（十一）】之VGG13模型tensorflow2代码复现（附详细注释）

爱被打了一巴掌 2023-10-07 14:44 37阅读 0赞

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**老规矩–妹妹镇楼：**

![20200721223424816.JPG][]

### 一． VGG模型 ###

前面已经介绍过了VGG网络模型，一共13层，这里说的13层指的是10层卷积层和3层全连接层，并没有包括池化层。下面代码详细地将VGG的13层网络模型复现，并用CIFAR100数据集进行训练，测试。

代码中附有详细的注释，从数据的预处理，到训练，再到测试。

### 二． 代码实现 ###

import tensorflow as tf
    from tensorflow.keras import layers, optimizers, datasets, Sequential
    import os
    
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    tf.random.set_seed(2345)
    
    #VGG13总共13层，指的是10层卷积层和3层全连接层
    
    """
    一共5个单元的卷积层和池化层，每个单元2个卷积层和一个池化层
    """
    
    #卷积层和池化层
    conv_layers = [
    
        #第一个单元 2个卷积和一个池化
    
        layers.Conv2D(64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
        layers.Conv2D(64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
        layers.MaxPool2D(pool_size=[2, 2], strides=2, padding="same"),
    
        #第二个单元
        layers.Conv2D(128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
        layers.Conv2D(128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
        layers.MaxPool2D(pool_size=[2, 2], strides=2, padding="same"),
    
        #第三个单元
        layers.Conv2D(256, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
        layers.Conv2D(256, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
        layers.MaxPool2D(pool_size=[2, 2], strides=2, padding="same"),
    
        #第四个单元
        layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
        layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
        layers.MaxPool2D(pool_size=[2, 2], strides=2, padding="same"),
    
        #第五个单元
        layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
        layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
        layers.MaxPool2D(pool_size=[2, 2], strides=2, padding="same"),
    
    ]
    
    #数据预处理
    def preprocess(x, y):
        #数据到0-1之间
        x = tf.cast(x, dtype=tf.float32) / 255.
        y = tf.cast(y, dtype=tf.int32)
        return x,y
    
    #数据自动下载加载
    (x, y), (x_test, y_test) = datasets.cifar100.load_data()
    print(x.shape, y.shape, x_test.shape, y_test.shape)
    #标签 (64, 1)，需要squeeze掉 1
    y = tf.squeeze(y, axis=1)
    y_test = tf.squeeze(y_test, axis=1)
    
    #数据的处理
    #训练数据的处理
    train_db = tf.data.Dataset.from_tensor_slices((x, y))
    train_db = train_db.shuffle(1000).map(preprocess).batch(64)
    #测试数据的处理
    test_db = tf.data.Dataset.from_tensor_slices((x_test, y_test))
    test_db = test_db.map(preprocess).batch(64)
    
    #取出一个sample查看 每个样本数据的尺寸，标签的尺寸，最大值最小值
    sample = next(iter(train_db))
    print("sample:", sample[0].shape, sample[1].shape,
          tf.reduce_min(sample[0]), tf.reduce_max(sample[0]))
    
    
    
    
    def main():
    
        #卷积层
        conv_net = Sequential(conv_layers)
    
        #test卷积层和池化层
        #conv_net.build(input_shape=[None, 32, 32, 3])
        # x = tf.random.normal([4, 32, 32, 3])
        # out = conv_net(x)
        # print(out.shape)
    
        #全连接层
        fc_net = Sequential([
            layers.Dense(256, activation=tf.nn.relu),
            layers.Dense(128, activation=tf.nn.relu),
            layers.Dense(100, activation=None),
        ])
    
        conv_net.build(input_shape=[None, 32, 32, 3])
        fc_net.build(input_shape=[None, 512])
    
        #设置优化器，优化学习率
        optimizer = optimizers.Adam(lr=1e-4)
    
        #每个层键的参数变量结合起来
        variables = conv_net.trainable_variables + fc_net.trainable_variables
    
        #处理完数据后，开始训练
        for epoch in range(50):
            for step, (x, y) in enumerate(train_db):
                #自动求解梯度
                with tf.GradientTape() as tape:
                    #卷积层和池化层
                    #[b, 32, 32, 3] -> [b, 1, 1, 512]
                    out = conv_net(x)
                    #flatten -> [b, 512]
                    out = tf.reshape(out, [-1, 512])
                    #全连接层
                    #[b, 512] -> [b, 100]
                    logits = fc_net(out)
    
                    #走完卷积层，池化层，全连接层得到输出值
                    #求解Loss值,需要将y的尺寸补齐到logits的尺寸
                    #[b] -> [b, 100]
                    y_onehot = tf.one_hot(y, depth=100)
                    loss = tf.losses.categorical_crossentropy(y_onehot, logits, from_logits=True)
                    #计算Loss的均值，每个样本有一个loss均值
                    loss = tf.reduce_mean(loss)
    
                #反向传播，对所有的层间参数进行求导
                grads = tape.gradient(loss, variables)
                #更新梯度
                optimizer.apply_gradients(zip(grads, variables))
    
                #每100个样本打印一次结果
                if step%100 == 0:
                    print(epoch, step, "loss:", float(loss))
    
            #测试模型
            total_num = 0
            total_correct = 0
            for x, y in test_db:
                out = conv_net(x)
                out = tf.reshape(out, [-1, 512])
                logits = fc_net(out)
                #softmax转换成概率
                prob = tf.nn.softmax(logits, axis=1)
                #选择概率最大的作为预测值
                pred = tf.argmax(prob, axis=1)
                pred = tf.cast(pred, dtype=tf.int32)
    
                #计算正确预测的数量
                correct = tf.cast(tf.equal(pred, y), dtype=tf.int32)
                correct = tf.reduce_sum(correct)
    
                total_num += x.shape[0]
                total_correct += int(correct)
    
            #计算正确率
            acc = total_correct / total_num
            print(epoch, "acc:", acc)
    
    
    
    if __name__ == '__main__':
        main()

[20200721223424816.JPG]: https://img-blog.csdnimg.cn/20200721223424816.JPG