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如果这个问题看起来很愚蠢，那是因为我是 TensorFlow 的新手。我正在使用 TensorFlow 2 的 TFA seq2seq 实现来实现一个玩具 endocer-decoder 问题。在我想用 BeamSearchDecoder 更改我的 BasicDecoder 之前，API 是很容易理解的。我的问题是关于 BeamSearchDecoder 的 start_tokens 和 end_token 参数的初始化。

这是实现的副本，不胜感激。


tf.keras.backend.clear_session()
tf.random.set_seed(42)

enc_vocab_size = len(train_vocab) + 1
dec_vocab_size = len(target_vocab) + 1
embed_size = 10


import tensorflow_addons as tfa

encoder_inputs = keras.layers.Input(shape=[None], dtype=np.int32)
decoder_inputs = keras.layers.Input(shape=[None], dtype=np.int32)
sequence_lengths = keras.layers.Input(shape=[], dtype=np.int32)


encoder_embeddings = keras.layers.Embedding(enc_vocab_size, embed_size)(encoder_inputs)
encoder = keras.layers.LSTM(512, return_state = True)
encoder_outputs, state_h, state_c = encoder(encoder_embeddings)
encoder_state = [state_h, state_c]


sampler = tfa.seq2seq.sampler.TrainingSampler()


decoder_embeddings = keras.layers.Embedding(dec_vocab_size, embed_size)(decoder_inputs)
decoder_cell = keras.layers.LSTMCell(512)
output_layer = keras.layers.Dense(dec_vocab_size)



beam_width = 10
start_tokens = tf.zeros([32], tf.dtypes.int32)
end_token = tf.constant(1, tf.dtypes.int32)
decoder = tfa.seq2seq.beam_search_decoder.BeamSearchDecoder(cell = decoder_cell, beam_width = beam_width, output_layer = output_layer)
decoder_initial_state = tfa.seq2seq.beam_search_decoder.tile_batch(encoder_state, multiplier = beam_width)
outputs, _, _ = decoder(decoder_embeddings, start_tokens = start_tokens, end_token = end_token, initial_state = decoder_initial_state)
Y_proba = tf.nn.softmax(outputs.rnn_output)


model = keras.models.Model(inputs = [encoder_inputs, decoder_inputs], outputs = [Y_proba])
model.compile(loss="sparse_categorical_crossentropy", optimizer = 'adam', metrics = ['accuracy'])

错误恍惚：


---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-101-6cf083735ed0> in <module>()
     34 decoder = tfa.seq2seq.beam_search_decoder.BeamSearchDecoder(cell = decoder_cell, beam_width = beam_width, output_layer = output_layer)
     35 decoder_initial_state = tfa.seq2seq.beam_search_decoder.tile_batch(encoder_state, multiplier = beam_width)
---> 36 outputs, _, _ = decoder(decoder_embeddings, start_tokens = start_tokens, end_token = end_token, initial_state = decoder_initial_state)
     37 Y_proba = tf.nn.softmax(outputs.rnn_output)
     38 

1 frames
/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
     65     except Exception as e:  # pylint: disable=broad-except
     66       filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67       raise e.with_traceback(filtered_tb) from None
     68     finally:
     69       del filtered_tb

/usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py in wrapper(*args, **kwargs)
    690       except Exception as e:  # pylint:disable=broad-except
    691         if hasattr(e, 'ag_error_metadata'):
--> 692           raise e.ag_error_metadata.to_exception(e)
    693         else:
    694           raise

ValueError: Exception encountered when calling layer "beam_search_decoder" (type BeamSearchDecoder).

in user code:

    File "/usr/local/lib/python3.7/dist-packages/tensorflow_addons/seq2seq/beam_search_decoder.py", line 941, in call  *
        self,
    File "/usr/local/lib/python3.7/dist-packages/typeguard/__init__.py", line 262, in wrapper  *
        retval = func(*args, **kwargs)
    File "/usr/local/lib/python3.7/dist-packages/tensorflow_addons/seq2seq/decoder.py", line 430, in body  *
        (next_outputs, decoder_state, next_inputs, decoder_finished) = decoder.step(
    File "/usr/local/lib/python3.7/dist-packages/tensorflow_addons/seq2seq/beam_search_decoder.py", line 705, in step  *
        cell_outputs, next_cell_state = self._cell(
    File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 67, in error_handler  **
        raise e.with_traceback(filtered_tb) from None

    ValueError: Exception encountered when calling layer "lstm_cell_1" (type LSTMCell).
    
    Dimensions must be equal, but are 80 and 320 for '{{node beam_search_decoder/decoder/while/BeamSearchDecoderStep/lstm_cell_1/mul}} = Mul[T=DT_FLOAT](beam_search_decoder/decoder/while/BeamSearchDecoderStep/lstm_cell_1/Sigmoid_1, beam_search_decoder/decoder/while/BeamSearchDecoderStep/Reshape_2)' with input shapes: [320,80,2048], [320,512].
    
    Call arguments received:
      • inputs=tf.Tensor(shape=(320, None, 10), dtype=float32)
      • states=ListWrapper(['tf.Tensor(shape=(320, 512), dtype=float32)', 'tf.Tensor(shape=(320, 512), dtype=float32)'])
      • training=None


Call arguments received:
  • embedding=tf.Tensor(shape=(None, None, 10), dtype=float32)
  • start_tokens=tf.Tensor(shape=(32,), dtype=int32)
  • end_token=tf.Tensor(shape=(), dtype=int32)
  • initial_state=['tf.Tensor(shape=(None, 512), dtype=float32)', 'tf.Tensor(shape=(None, 512), dtype=float32)']
  • training=None
  • kwargs=<class 'inspect._empty'>
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