keras_LeNet.py 文件源码

def build(input_shape, classes):
        model = Sequential()
        # CONV => RELU => POOL
        model.add(Conv2D(20, kernel_size=5, padding="same",
            input_shape=input_shape))
        model.add(Activation("relu"))
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
        # CONV => RELU => POOL
        model.add(Conv2D(50, kernel_size=5, padding="same"))
        model.add(Activation("relu"))
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
        # Flatten => RELU layers
        model.add(Flatten())
        model.add(Dense(500))
        model.add(Activation("relu"))

        # a softmax classifier
        model.add(Dense(classes))
        model.add(Activation("softmax"))

        return model

# network and training