keras_training.py 文件源码

def __init__(self, **kwargs):
        self.final_fc_nm = kwargs.get("final_fc_nm", 2048)
        if "final_fc_nm" in kwargs:
            kwargs.pop("final_fc_nm")
        super(KerasLenetExt2Model, self).__init__(**kwargs)
        norm_shape = self.norm_shape
        self.model = Sequential()
        self.model.add(Convolution2D(64, (3, 3), activation='relu',
                                input_shape=(norm_shape[0], norm_shape[1], 1)))

        self.model.add(Convolution2D(64, (3, 3), activation='relu'))
        self.model.add(MaxPooling2D(pool_size=(2,2)))
        self.model.add(Dropout(0.25))

        self.model.add(Convolution2D(128, (3, 3), activation='relu'))
        self.model.add(MaxPooling2D(pool_size=(2,2)))
        self.model.add(Dropout(0.25))

        self.model.add(Convolution2D(256, (3, 3), activation='relu'))
        self.model.add(MaxPooling2D(pool_size=(2,2)))
        self.model.add(Dropout(0.25))

        self.model.add(Flatten())
        self.model.add(Dense(self.final_fc_nm, activation='relu'))
        self.model.add(Dropout(0.5))
        self.model.add(Dense(self.max_n_label, activation='softmax'))

        # 8. Compile model
        self.model.compile(loss='categorical_crossentropy',
                      optimizer='adam',
                      metrics=['accuracy'])