Python 层lstm_35的输入0与层不兼容：预期ndim=3，发现ndim=4。收到完整形状：[无，1966，7059，256]

我正在为文本摘要创建一个单词级嵌入的seq2seq模型，我面临数据形状问题，请帮助。谢谢

        encoder_input=Input(shape=(max_encoder_seq_length,))
        embed_layer=Embedding(num_encoder_tokens,256,mask_zero=True)(encoder_input)
        encoder=LSTM(256,return_state=True,return_sequences=False)
        encoder_ouput,state_h,state_c=encoder(embed_layer)
        encoder_state=[state_h,state_c] 
        decoder_input=Input(shape=(max_decoder_seq_length,))
        de_embed=Embedding(num_decoder_tokens,256)(decoder_input)
        decoder=LSTM(256,return_state=True,return_sequences=True)
        decoder_output,_,_=decoder(de_embed,initial_state=encoder_state)
        decoder_dense=Dense(num_decoder_tokens,activation='softmax')
        decoder_output=decoder_dense(decoder_output)
        model=Model([encoder_input,decoder_input],decoder_output)
        model.compile(optimizer='adam',loss="categorical_crossentropy",metrics=['accuracy'])

由于输入的形状，训练时会出现错误。请帮助将我的数据重新塑造为当前形状

编码器数据形状：（501966，7059） 解码器数据形状：（50、69、1183） 解码器目标形状：（50、69、1183）

纪元1/35
警告：tensorflow:为输入张量（“input_37:0”，shape=（None，1966），dtype=float32）使用shape（None，1966）构造模型，但在具有不兼容形状（None，1966，7059）的输入上调用了该模型。
警告：tensorflow:为输入张量（“input_38:0”，shape=（None，69），dtype=float32）构造了形状（None，69）的模型，但在形状不兼容的输入（None，69，1183）上调用了该模型。
---------------------------------------------------------------------------
ValueError回溯（最近一次调用上次）
在（）
1模型拟合（[编码器输入数据，解码器输入数据]，解码器目标数据，
2批次尺寸=16，
---->3个时代=35）
ValueError:层lstm_35的输入0与层不兼容：预期ndim=3，发现ndim=4。收到完整形状：[无，1966，7059，256]

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我已尝试复制您的问题，并且能够成功地拟合模型，您可以遵循以下代码，该代码与您的架构相同，嵌入层的形状存在一些小问题，我已包含使用手套嵌入的嵌入层的权重，下面还提到了嵌入矩阵的细节

embedding_layer = Embedding(num_words, EMBEDDING_SIZE, weights=[embedding_matrix], input_length=max_input_len)
encoder_inputs_placeholder = Input(shape=(max_encoder_seq_length,))
x = embedding_layer(encoder_inputs_placeholder)
encoder = LSTM(LSTM_NODES, return_state=True)

encoder_outputs, h, c = encoder(x)
encoder_states = [h, c]
decoder_inputs_placeholder = Input(shape=(max_decoder_seq_length,))

decoder_embedding = Embedding(num_decoder_tokens, LSTM_NODES)
decoder_inputs_x = decoder_embedding(decoder_inputs_placeholder)

decoder_lstm = LSTM(LSTM_NODES, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(decoder_inputs_x, initial_state=encoder_states)
decoder_dense = Dense(num_decoder_tokens, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)
model = Model([encoder_inputs_placeholder,
  decoder_inputs_placeholder], decoder_outputs)
model.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

用于嵌入矩阵：

MAX_NUM_WORDS = 10000
EMBEDDING_SIZE = 100 # you can choose 200, 300 dimensions also, depending on the embedding file you use.
embeddings_dictionary = dict()

glove_file = open(r'/content/drive/My Drive/datasets/glove.6B.100d.txt', encoding="utf8")

for line in glove_file:
    records = line.split()
    word = records[0]
    vector_dimensions = asarray(records[1:], dtype='float32')
    embeddings_dictionary[word] = vector_dimensions
glove_file.close()

num_words = min(MAX_NUM_WORDS, len(word2idx_inputs) + 1)
embedding_matrix = zeros((num_words, EMBEDDING_SIZE))
for word, index in word2idx_inputs.items():
    embedding_vector = embeddings_dictionary.get(word)
    if embedding_vector is not None:
        embedding_matrix[index] = embedding_vector

Model: "model_2"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_5 (InputLayer)            (None, 16)           0                                            
__________________________________________________________________________________________________
input_6 (InputLayer)            (None, 59)           0                                            
__________________________________________________________________________________________________
embedding_5 (Embedding)         (None, 16, 100)      1000000     input_5[0][0]                    
__________________________________________________________________________________________________
embedding_6 (Embedding)         (None, 59, 64)       5824        input_6[0][0]                    
__________________________________________________________________________________________________
lstm_4 (LSTM)                   [(None, 64), (None,  42240       embedding_5[0][0]                
__________________________________________________________________________________________________
lstm_5 (LSTM)                   [(None, 59, 64), (No 33024       embedding_6[0][0]                
                                                                 lstm_4[0][1]                     
                                                                 lstm_4[0][2]                     
__________________________________________________________________________________________________
dense_2 (Dense)                 (None, 59, 91)       5915        lstm_5[0][0]                     
==================================================================================================
Total params: 1,087,003
Trainable params: 1,087,003
Non-trainable params: 0

型号摘要：

MAX_NUM_WORDS = 10000
EMBEDDING_SIZE = 100 # you can choose 200, 300 dimensions also, depending on the embedding file you use.
embeddings_dictionary = dict()

glove_file = open(r'/content/drive/My Drive/datasets/glove.6B.100d.txt', encoding="utf8")

for line in glove_file:
    records = line.split()
    word = records[0]
    vector_dimensions = asarray(records[1:], dtype='float32')
    embeddings_dictionary[word] = vector_dimensions
glove_file.close()

num_words = min(MAX_NUM_WORDS, len(word2idx_inputs) + 1)
embedding_matrix = zeros((num_words, EMBEDDING_SIZE))
for word, index in word2idx_inputs.items():
    embedding_vector = embeddings_dictionary.get(word)
    if embedding_vector is not None:
        embedding_matrix[index] = embedding_vector

Model: "model_2"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_5 (InputLayer)            (None, 16)           0                                            
__________________________________________________________________________________________________
input_6 (InputLayer)            (None, 59)           0                                            
__________________________________________________________________________________________________
embedding_5 (Embedding)         (None, 16, 100)      1000000     input_5[0][0]                    
__________________________________________________________________________________________________
embedding_6 (Embedding)         (None, 59, 64)       5824        input_6[0][0]                    
__________________________________________________________________________________________________
lstm_4 (LSTM)                   [(None, 64), (None,  42240       embedding_5[0][0]                
__________________________________________________________________________________________________
lstm_5 (LSTM)                   [(None, 59, 64), (No 33024       embedding_6[0][0]                
                                                                 lstm_4[0][1]                     
                                                                 lstm_4[0][2]                     
__________________________________________________________________________________________________
dense_2 (Dense)                 (None, 59, 91)       5915        lstm_5[0][0]                     
==================================================================================================
Total params: 1,087,003
Trainable params: 1,087,003
Non-trainable params: 0

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希望这能解决您的问题，学习愉快

嗨@TensorflowWarrior你能帮我查一下这个问题吗@Tensorflow Warrior你能帮我查一下这个问题吗