包含一个池化层和两个卷积层每个卷积层包含128个3x3的卷积核输出尺寸为112112128。同样进入第三块包含一个池化层和三个256个3x3的卷积核的卷积层输出尺寸为5656256。然后进入第四块包含一个池化层和三个卷积层每个卷积层包含512个3x3的卷积核输出为2828512的尺寸。改写
为代码形式如下：
model = Sequential()
第一块
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', padding='same', input_shape=(224,224,3))) model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2, 2)))
第二块
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu', padding='same')) model.add(Conv2D(128, kernel_size=(3, 3), activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2, 2)))
第三块
model.add(Conv2D(256, kernel_size=(3, 3), activation='relu', padding='same')) model.add(Conv2D(256, kernel_size=(3, 3), activation='relu', padding='same')) model.add(Conv2D(256, kernel_size=(3, 3), activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2, 2)))
第四块
model.add(Conv2D(512, kernel_size=(3, 3), activation='relu', padding='same')) model.add(Conv2D(512, kernel_size=(3, 3), activation='relu', padding='same')) model.add(Conv2D(512, kernel_size=(3, 3), activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2, 2))
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