Question

我正在尝试将两个字符级别的句子输入LSTM图层进行分类。我的示例与以下内容类似，我的标签是一个热门编码类。

标签：

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

示例：

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')

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是我的窗口大小，74是我词汇量的大小。

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'])

Answer 1

好的，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'])

Keras：嵌入多维时间步长的图层

1 个答案: