ظهور الخطأ ValueError: Input 0 of layer simple_rnn_20 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 16) أثناء تدريب شبكة RNNs

أحاول تدريب شبكة عصبية (هذه الشبكة عبارة عن تجميع طبقات RNN) لكن يظهر لي الخطأ في الكود التالي:

from keras.layers import Dense,Embedding,SimpleRNN
from keras.datasets import imdb
from keras.preprocessing import sequence
from keras.models import Sequential
max_features = 1000
maxlen = 20
batch_size = 64
print('Loading data...')
(input_train, y_train), (input_test, y_test) = imdb.load_data(
num_words=max_features)
print(len(input_train), 'train sequences')
print(len(input_test), 'test sequences')
print('Pad sequences (samples x time)')
input_train = sequence.pad_sequences(input_train, maxlen=maxlen)
input_test = sequence.pad_sequences(input_test, maxlen=maxlen)
print('input_train shape:', input_train.shape)
model = Sequential()
Loading data...
25000 train sequences
25000 test sequences
Pad sequences (samples x time)
input_train shape: (25000, 20)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-45-586f07f93ca3> in <module>
     18 model.add(Embedding(max_features, 16))
     19 model.add(SimpleRNN(16))
---> 20 model.add(SimpleRNN(16))
     21 model.add(Dense(1, activation='sigmoid'))
     22 model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])

~\anaconda3\lib\site-packages\tensorflow\python\training\tracking\base.py in _method_wrapper(self, *args, **kwargs)
    515     self._self_setattr_tracking = False  # pylint: disable=protected-access
    516     try:
--> 517       result = method(self, *args, **kwargs)
    518     finally:
    519       self._self_setattr_tracking = previous_value  # pylint: disable=protected-access

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\sequential.py in add(self, layer)
    221       # If the model is being built continuously on top of an input layer:
    222       # refresh its output.
--> 223       output_tensor = layer(self.outputs[0])
    224       if len(nest.flatten(output_tensor)) != 1:
    225         raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)

~\anaconda3\lib\site-packages\tensorflow\python\keras\layers\recurrent.py in __call__(self, inputs, initial_state, constants, **kwargs)
    658 
    659     if initial_state is None and constants is None:
--> 660       return super(RNN, self).__call__(inputs, **kwargs)
    661 
    662     # If any of `initial_state` or `constants` are specified and are Keras

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in __call__(self, *args, **kwargs)
    950     if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
    951       return self._functional_construction_call(inputs, args, kwargs,
--> 952                                                 input_list)
    953 
    954     # Maintains info about the `Layer.call` stack.

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in _functional_construction_call(self, inputs, args, kwargs, input_list)
   1089         # Check input assumptions set after layer building, e.g. input shape.
   1090         outputs = self._keras_tensor_symbolic_call(
-> 1091             inputs, input_masks, args, kwargs)
   1092 
   1093         if outputs is None:

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in _keras_tensor_symbolic_call(self, inputs, input_masks, args, kwargs)
    820       return nest.map_structure(keras_tensor.KerasTensor, output_signature)
    821     else:
--> 822       return self._infer_output_signature(inputs, args, kwargs, input_masks)
    823 
    824   def _infer_output_signature(self, inputs, args, kwargs, input_masks):

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in _infer_output_signature(self, inputs, args, kwargs, input_masks)
    860           # overridden).
    861           # TODO(kaftan): do we maybe_build here, or have we already done it?
--> 862           self._maybe_build(inputs)
    863           outputs = call_fn(inputs, *args, **kwargs)
    864 

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in _maybe_build(self, inputs)
   2683     if not self.built:
   2684       input_spec.assert_input_compatibility(
-> 2685           self.input_spec, inputs, self.name)
   2686       input_list = nest.flatten(inputs)
   2687       if input_list and self._dtype_policy.compute_dtype is None:

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\input_spec.py in assert_input_compatibility(input_spec, inputs, layer_name)
    221                          'expected ndim=' + str(spec.ndim) + ', found ndim=' +
    222                          str(ndim) + '. Full shape received: ' +
--> 223                          str(tuple(shape)))
    224     if spec.max_ndim is not None:
    225       ndim = x.shape.rank

ValueError: Input 0 of layer simple_rnn_20 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 16)


model.add(Embedding(max_features, 16))
model.add(SimpleRNN(16))
model.add(SimpleRNN(16))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
history = model.fit(input_train, y_train,
epochs=2,
batch_size=128,
validation_split=0.2)