LSTM KERAS功能API层输入形状误差

Question

我正在尝试使用多个输入输出模型使用预训练的单词嵌入嵌入层来构建keras functional api lstm层。

以下是我的代码：

#sequential padding text data
max_review_length = 300
text_seq_train = sequence.pad_sequences(text_train, maxlen=max_review_length)
text_seq_test = sequence.pad_sequences(text_test, maxlen=max_review_length)
print(text_seq_train.shape)
print(text_seq_test.shape)
# Loading pre-trained glove embedding file
embeddings_index = dict()
f = open('gdrive/My Drive/glove.6B.100d.txt')
for line in f:
    values = line.split()
    word = values[0]
    coefs = np.asarray(values[1:], dtype='float32')
    embeddings_index[word] = coefs
f.close()
print('Loaded %s word vectors.' % len(embeddings_index))
embedding_matrix = zeros((len(text_tokenizer.index_word), 100))
for word, i in text_tokenizer.index_word.items():
    embedding_vector = embeddings_index.get(word)
    if embedding_vector is not None:
        embedding_matrix[i] = embedding_vector
print(embedding_matrix.shape)   #weights for embedding layer

输出：

(76473, 300)
(32775, 300)
Loaded 400000 word vectors.
(55297, 100)

lstm part

input_layer = Input(shape=(300,))
embed = Embedding(input_dim = len(text_tokenizer.index_word) , output_dim = 100, input_length = len(text_seq_train[0]) ,weights=[embedding_matrix], trainable=False) (input_layer)
lstm = LSTM(100)(embed)
flat = Flatten() (lstm)

收到错误语句：

---

ValueError                                Traceback (most recent call last)
<ipython-input-131-9118c8229a4a> in <module>()
      2 embed = Embedding(input_dim = len(text_tokenizer.index_word) , output_dim = 100, input_length = len(text_seq_train[0]) ,weights=[embedding_matrix], trainable=False) (input_layer)
      3 lstm = LSTM(100)(embed)
----> 4 flat = Flatten() (lstm)
1 frames
/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py in assert_input_compatibility(self, inputs)
    325                                      self.name + ': expected min_ndim=' +
    326                                      str(spec.min_ndim) + ', found ndim=' +
--> 327                                      str(K.ndim(x)))
    328             # Check dtype.
    329             if spec.dtype is not None:
ValueError: Input 0 is incompatible with layer flatten_26: expected min_ndim=3, found ndim=2

我不知道我错过了什么，我出了什么问题。任何帮助将不胜感激。

Answer 1

您不必弄平LSTM输出。LSTM输出张量的形状(batch_size，单位(。

请参阅：https：//keras.io/layers/recurrent/

jawar