具有可变句子长度的Keras LSTM-传递给模型的Numpy数组列表而不是预期的模型大小

Question

我已经成功实现了LSTM教程来生成音乐。但是，我正在努力为语言创建一个（主要兴趣）。我有一个单词索引，这是我的数据中的两个示例句子。

样本预测变量：

[[1],
 [1, 6],
 [1, 6, 241],
 [1, 6, 241, 252],
 [1, 6, 241, 252, 11],
 [1, 6, 241, 252, 11, 59],
 [1, 6, 241, 252, 11, 59, 2],
 [1, 6, 241, 252, 11, 59, 2, 62],
 [1, 6, 241, 252, 11, 59, 2, 62, 663],
 [1, 6, 241, 252, 11, 59, 2, 62, 663, 41],
 [1],
 [1, 3],
 [1, 3, 216],
 [1, 3, 216, 227],
 [1, 3, 216, 227, 26],
 [1, 3, 216, 227, 26, 30],
 [1, 3, 216, 227, 26, 30, 5]]

样本标签：

[[6],
[241],
[252],
[11],
[59],
[2],
[62],
[663],
[41],
[1],
[3],
[216],
[227],
[26],
[30],
[5],
[1]]

损失应按以下方式计算： 我想最小化。

我的LSTM代码是

from keras.models import Model
from keras import layers
from keras import Input

vocabulary_size = len(word_index)
dimensions = 200


text_input = Input(shape=(None,))
embedded = layers.Embedding(vocabulary_size, dimensions)(text_input)
encoded = layers.LSTM(vocabulary_size)(embedded)
output = layers.Dense(vocabulary_size, activation='softmax')(encoded)
model = Model(text_input, output)
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['acc'])
model.summary()
model.fit(x, y, epochs=10, batch_size=1)

为了适应可变的句子长度，我设置了

• batch_size = 1，因为句子长度可变
从shape到Input的
• (None, )

但是，出现以下错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-64-95228d843a72> in <module>()
     25               metrics=['acc'])
     26 model.summary()
---> 27 model.fit(x, y, epochs=1, batch_size=1)

C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
    950             sample_weight=sample_weight,
    951             class_weight=class_weight,
--> 952             batch_size=batch_size)
    953         # Prepare validation data.
    954         do_validation = False

C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
    749             feed_input_shapes,
    750             check_batch_axis=False,  # Don't enforce the batch size.
--> 751             exception_prefix='input')
    752 
    753         if y is not None:

C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    100                 'Expected to see ' + str(len(names)) + ' array(s), '
    101                 'but instead got the following list of ' +
--> 102                 str(len(data)) + ' arrays: ' + str(data)[:200] + '...')
    103         elif len(names) > 1:
    104             raise ValueError(

ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 1 array(s), but instead got the following list of 17 arrays: [array([[1]]), array([[1],
       [6]]), array([[  1],
       [  6],
       [241]]), array([[  1],
       [  6],
       [241],
       [252]]), array([[  1],
       [  6],
       [241],
       [252],
 ...

我没有使用列表列表，而是尝试将它们转换为numpy数组列表，但这并没有改变错误。在这里建议： keras list of Numpy arrays not the size model expected

x = [np.array(i) for i in x]
y = [np.array(i) for i in y]

即使我故意构造模型以处理可变长度的数组，为什么仍会收到此错误？

由于我的预测变量（x）的格式，发生了错误。至少，我认为这是错误所指示的。

Answer 1

我可能记错了，但是我相信你应该做类似

y = y.reshape(-1)

以获得Keras在这里想要的标签列表。同样，batch_size为1的训练也会影响您的表现。建议您使用固定数量的0预先填充数据，在LSTM中设置mask=True并使用标准的批量大小