vgg16代码实现图像分类 python

以下是一个使用Python实现VGG16进行图像分类的简单代码示例：

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from tensorflow.keras import Model

# 定义VGG16模型
class VGG16(Model):
    def __init__(self, num_classes):
        super(VGG16, self).__init__()
        self.conv1 = Conv2D(64, 3, activation='relu', padding='same')
        self.conv2 = Conv2D(64, 3, activation='relu', padding='same')
        self.pool1 = MaxPooling2D(2, 2)
        self.conv3 = Conv2D(128, 3, activation='relu', padding='same')
        self.conv4 = Conv2D(128, 3, activation='relu', padding='same')
        self.pool2 = MaxPooling2D(2, 2)
        self.conv5 = Conv2D(256, 3, activation='relu', padding='same')
        self.conv6 = Conv2D(256, 3, activation='relu', padding='same')
        self.conv7 = Conv2D(256, 3, activation='relu', padding='same')
        self.pool3 = MaxPooling2D(2, 2)
        self.conv8 = Conv2D(512, 3, activation='relu', padding='same')
        self.conv9 = Conv2D(512, 3, activation='relu', padding='same')
        self.conv10 = Conv2D(512, 3, activation='relu', padding='same')
        self.pool4 = MaxPooling2D(2, 2)
        self.conv11 = Conv2D(512, 3, activation='relu', padding='same')
        self.conv12 = Conv2D(512, 3, activation='relu', padding='same')
        self.conv13 = Conv2D(512, 3, activation='relu', padding='same')
        self.pool5 = MaxPooling2D(2, 2)
        self.flatten = Flatten()
        self.fc1 = Dense(4096, activation='relu')
        self.fc2 = Dense(4096, activation='relu')
        self.fc3 = Dense(num_classes, activation='softmax')

    def call(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.pool1(x)
        x = self.conv3(x)
        x = self.conv4(x)
        x = self.pool2(x)
        x = self.conv5(x)
        x = self.conv6(x)
        x = self.conv7(x)
        x = self.pool3(x)
        x = self.conv8(x)
        x = self.conv9(x)
        x = self.conv10(x)
        x = self.pool4(x)
        x = self.conv11(x)
        x = self.conv12(x)
        x = self.conv13(x)
        x = self.pool5(x)
        x = self.flatten(x)
        x = self.fc1(x)
        x = self.fc2(x)
        output = self.fc3(x)
        return output

# 设置参数
num_classes = 10
learning_rate = 0.001

# 加载数据
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
x_train = x_train.reshape(x_train.shape<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
-  *1* [Python实现Vgg16经典网络架构代码](https://blog.csdn.net/weixin_40911806/article/details/129708964)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"]
-  *2* *3* [卷积神经网络——vgg16网络及其python实现](https://blog.csdn.net/abc1234abcdefg/article/details/125495965)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"]
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