层“lstm_1”的输入 0 与层不兼容：预期 ndim=3，发现 ndim=2。收到完整形状：（无，256）

Question

我正在尝试使用 tensorflow2 构建一个生成式 LSTM 模型。我不熟悉在 tensorflow 中使用 LSTM 层。代码如下：：

vec = layers.TextVectorization(output_sequence_length=maxlen,
                               max_tokens=379)
vec.adapt(use_for_vocab(train))

voc = vec.get_vocabulary()
voc_size = len(voc)

embed = layers.Embedding(input_dim=voc_size,
                         output_dim=256,
                         mask_zero=True)

inp_word = layers.Input(shape=(maxlen+2,), # maxlen is the maximum length of the sentence in the text
                   name="word_input")      # 2 is added to accommodate start_token and end_token
x_word = embed(inp_word)
x_word = layers.Dropout(0.5)(x_word)
x_word = layers.LSTM(256, return_sequences=True)(x_word)
ops_word = layers.GlobalAveragePooling1D(name="word_gap")(x_word)

我能够毫无错误地编译模型。但是，在拟合模型时，它给出了以下错误：

 File "/usr/local/lib/python3.9/dist-packages/keras/engine/training.py", line 1284, in train_function  *
        return step_function(self, iterator)
    File "/usr/local/lib/python3.9/dist-packages/keras/engine/training.py", line 1268, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/usr/local/lib/python3.9/dist-packages/keras/engine/training.py", line 1249, in run_step  **
        outputs = model.train_step(data)
    File "/usr/local/lib/python3.9/dist-packages/keras/engine/training.py", line 1050, in train_step
        y_pred = self(x, training=True)
    File "/usr/local/lib/python3.9/dist-packages/keras/utils/traceback_utils.py", line 70, in error_handler
        raise e.with_traceback(filtered_tb) from None
    File "/usr/local/lib/python3.9/dist-packages/keras/engine/input_spec.py", line 235, in assert_input_compatibility
        raise ValueError(

    ValueError: Exception encountered when calling layer 'caption_model' (type Functional).
    
    Input 0 of layer "lstm_5" is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 128)

无法理解为什么尽管使 return_sequences 为真，但输入序列仍然是二维的。任何帮助将不胜感激。

Answer 1

当你为 nlp 嵌入时，你可以这样做：

嵌入（vocab_size，embedding_dim）

我不知道你用的是什么嵌入

所以而不是 x_word = embed(inp_word) 你可以使用 x_word = layers.Embedding(vocab_size, embedding_dim)(inp_word)