VGG模型灰度图像处理代码：构建与编译详解

```python import keras from keras.models import Sequential from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense # 创建一个简单的VGG模型 model = Sequential() model.add(Conv2D(64, (3, 3), activation='relu', input_shape=(None, None, 1))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D((2, 2))) model.add(Conv2D(128, (3, 3), activation='relu')) model.add(Conv2D(128, (3, 3), activation='relu')) model.add(MaxPooling2D((2, 2))) model.add(Conv2D(256, (3, 3), activation='relu')) model.add(Conv2D(256, (3, 3), activation='relu')) model.add(Conv2D(256, (3, 3), activation='relu')) model.add(MaxPooling2D((2, 2))) model.add(Conv2D(512, (3, 3), activation='relu')) model.add(Conv2D(512, (3, 3), activation='relu')) model.add(Conv2D(512, (3, 3), activation='relu')) model.add(MaxPooling2D((2, 2))) model.add(Conv2D(512, (3, 3), activation='relu')) model.add(Conv2D(512, (3, 3), activation='relu')) model.add(Conv2D(512, (3, 3), activation='relu')) model.add(MaxPooling2D((2, 2))) model.add(Flatten()) model.add(Dense(4096, activation='relu')) model.add(Dense(4096, activation='relu')) model.add(Dense(1000, activation='softmax')) model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) model.summary() ``` ######[AI写代码神器 | 853点数解答 | 2024-10-17 18:33:25]