Python TypeError:（'；关键字参数未理解：'；，'；子样本'；）

我正在训练一个Keras模型，但它抛出了一个错误

我用不起作用的Conv2D替换了卷积2d

---------------------------------------------------------------------------
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')

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它明确表示子样本是未知的。

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尝试将“子样本”替换为“跨步”，在keras的最新版本中，它被称为“跨步”。

它明确表示子样本是未知的。 尝试将“子样本”替换为“跨步”，在keras的最新版本中，它被称为“跨步”。

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 model
model = nvidia_model()
print(model.summary())

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 model
model = nvidia_model()
print(model.summary())

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