Keras：嵌入多维时间步长的图层

I am trying to feed two sentences in character level into the LSTM layer for classification. My samples are similar to the following and my labels are one hot encoded classes.

我试图将两个字符级别的句子输入LSTM层进行分类。我的样本类似于以下内容，我的标签是一个热门编码类。

label:

标签：

label array([1., 0., 0., 0., 0.])

sample:

样品：

array([['0', ' '],
   [' ', 'l'],
   ['1', 'e'],
   ['1', 't'],
   ['2', 'n'],
   ['8', 'i'],
   [' ', ' '],
   ['"', ';'],
   ['h', 'h'],
   ['t', 's'],
   ['t', 'o'],
   ['p', 't'],
   ['s', 'n'],
   [':', 'i'],
   ['/', 'c'],
   ['/', 'a'],
   ['w', 'm'],
   ['w', '('],
   ['w', ' '],
   ['.', '0'],
   ['e', '.'],
   ['x', '5'],
   ['a', '/'],
   ['m', 'a'],
   ['p', 'l'],
   ['l', 'l'],
   ['e', 'i'],
   ['.', 'z'],
   ['c', 'o'],
   ['o', 'm'],
   ['m', '"'],
   ['/', ' '],
   ['c', '"'],
   ['m', '/'],
   ['s', 'd'],
   ['/', 'a'],
   ['t', 'o'],
   ['i', 'l'],
   ['n', 'n'],
   ['a', 'w'],
   ['-', 'o'],
   ['a', 'd'],
   ['c', '-'],
   ['c', 'r'],
   ['e', 'o'],
   ['s', 'f'],
   ['s', '-'],
   ['-', 'r'],
   ['e', 'o'],
   ['d', 't'],
   ['i', 'i']], dtype='<U1')

I am trying to use the Embedding layer of Keras to map the characters into vectors. The embedding layer, however, only takes in single dimensional sequences. How can I adjust the network to take in multi dimensional sequence? Currently I have the following code that works for single dimensional samples. 51 is my lstm window size and 74 is the size of my vocabulary.

我正在尝试使用Keras的嵌入层将字符映射到矢量中。然而，嵌入层仅采用单维序列。如何调整网络以采用多维序列？目前，我有以下代码适用于单维样本。 51是我的lstm窗口大小，74是我的词汇量。

model = keras.models.Sequential()
model.add(keras.layers.Embedding(input_dim=74,
                                 output_dim=74,
                                 input_length=51))

model.add(keras.layers.Dropout(0.2))
model.add(keras.layers.LSTM(64,
                            dropout=0.5,
                            recurrent_dropout=0.5,
                            return_sequences=True,
                            input_shape=(51, 74)))
model.add(keras.layers.LSTM(64,
                            dropout=0.5,
                            recurrent_dropout=0.5))
model.add(keras.layers.Dense(num_classes, activation='sigmoid'))
model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

1 个解决方案

#1

Ok, I solved this problem by adding a Reshaped layer before Embedding, and then another reshape layer after embedding. Here is the code:

好吧，我通过在嵌入之前添加重塑图层，然后在嵌入之后添加另一个重塑图层来解决此问题。这是代码：

model = keras.models.Sequential()

model.add(keras.layers.Reshape((2 * lstm_window_size, 1), input_shape=(
    lstm_window_size, 2)))

model.add(keras.layers.Embedding(input_dim=vocab_size + 1,
                                 output_dim=100,
                                 input_length=lstm_window_size * 2))

model.add(keras.layers.Reshape((lstm_window_size, 200)))
model.add(keras.layers.Dropout(0.2))
model.add(keras.layers.LSTM(64,
                            dropout=0.5,
                            recurrent_dropout=0.5,
                            return_sequences=True,
                            input_shape=(lstm_window_size, 2)))
model.add(keras.layers.LSTM(64,
                            dropout=0.5,
                            recurrent_dropout=0.5))
model.add(keras.layers.Dense(num_classes, activation='sigmoid'))
model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])