我在训练一个Keras模型时遇到了错误。
我将Convolution2D替换成了Conv2D，但这不起作用。
---------------------------------------------------------------------------TypeError Traceback (most recent call last)<ipython-input-99-e85c5751f266> in <module>() 26 model.compile(loss='mse', optimizer=optimizer) 27 return model---> 28 model = nvidia_model() 29 print(model.summary())5 frames/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/generic_utils.py in validate_kwargs(kwargs, allowed_kwargs, error_message) 776 for kwarg in kwargs: 777 if kwarg not in allowed_kwargs:--> 778 raise TypeError(error_message, kwarg) 779 780 TypeError: ('Keyword argument not understood:', 'subsample')修改后的代码
我目前使用的是Keras 2.2.4
定义NVIDIA模型
def nvidia_model(): model = Sequential() model.add(Conv2D(24, 5, 5, strides=(2, 2), input_shape=(66, 200, 3), activation='elu')) model.add(Conv2D(36, 5, 5, strides=(2, 2), activation='elu')) model.add(Conv2D(48, 5, 5, strides=(2, 2), activation='elu')) model.add(Conv2D(64, 3, 3, activation='elu')) model.add(Conv2D(64, 3, 3, activation='elu'))# model.add(Dropout(0.5)) model.add(Flatten()) model.add(Dense(100, activation = 'elu'))# model.add(Dropout(0.5)) model.add(Dense(50, activation = 'elu'))# model.add(Dropout(0.5)) model.add(Dense(10, activation = 'elu'))# model.add(Dropout(0.5)) model.add(Dense(1)) optimizer = Adam(lr=1e-3) model.compile(loss='mse', optimizer=optimizer) return modelmodel = nvidia_model()print(model.summary())回答：
尝试以下方法:
def nvidia_model(): model = Sequential() model.add(Conv2D(24,(5,5), strides=(2, 2), input_shape=(66, 200, 3), activation='elu')) model.add(Conv2D(36, (5,5), strides=(2, 2), activation='elu')) model.add(Conv2D(48, (5,5), strides=(2, 2), activation='elu')) model.add(Conv2D(64, (3,3), activation='elu')) model.add(Conv2D(64, (3,3), activation='elu'))# model.add(Dropout(0.5)) model.add(Flatten()) model.add(Dense(100, activation = 'elu'))# model.add(Dropout(0.5)) model.add(Dense(50, activation = 'elu'))# model.add(Dropout(0.5)) model.add(Dense(10, activation = 'elu'))# model.add(Dropout(0.5)) model.add(Dense(1)) optimizer = Adam(lr=1e-3) model.compile(loss='mse', optimizer=optimizer) return modelmodel = nvidia_model()print(model.summary())