用于黑白图像识别的 AlexNet 架构

Question

我想尝试用黑白图像在 AlexNet CNN 上进行实验，我在 AlexNet 实验中所知道的是使用具有 3 个颜色通道的 RGB 图像，而我要做的实验只有 1 个颜色通道。在为黑白图像设计 AlexNet 架构时，我仍然感到困惑。 下面是来自 AlexNet 的架构示例。

问题是，我能否让架构看起来像示例图像，但使用黑白图像而不是 RGB 图像？

Answer 1

您只需更改输入尺寸。黑白图像只有一个通道而不是 3 个。这里是完全适应的模型

image_dim = (224,224,1) # black and white images
n_classes = 10

model = Sequential()

# 1st Convolutional Layer
model.add(Conv2D(filters=96, input_shape=image_dim, kernel_size=(11,11), strides=(4,4), padding="valid"))
model.add(Activation("relu"))
# Max Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding="valid"))

# 2nd Convolutional Layer
model.add(Conv2D(filters=256, kernel_size=(11,11), strides=(1,1), padding="valid"))
model.add(Activation("relu"))
# Max Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding="valid"))

# 3rd Convolutional Layer
model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding="valid"))
model.add(Activation("relu"))

# 4th Convolutional Layer
model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding="valid"))
model.add(Activation("relu"))

# 5th Convolutional Layer
model.add(Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding="valid"))
model.add(Activation("relu"))
# Max Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding="valid"))

# Passing it to a Fully Connected layer
model.add(Flatten())
# 1st Fully Connected Layer
model.add(Dense(4096))
model.add(Activation("relu"))
# Add Dropout to prevent overfitting
model.add(Dropout(0.4))

# 2nd Fully Connected Layer
model.add(Dense(4096))
model.add(Activation("relu"))
# Add Dropout
model.add(Dropout(0.4))

# 3rd Fully Connected Layer
model.add(Dense(1000))
model.add(Activation("relu"))
# Add Dropout
model.add(Dropout(0.4))

# Output Layer
model.add(Dense(n_classes))
model.add(Activation("softmax"))

# Compile the model
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics="accuracy")

model.summary()