Question

我正在训练一个seq2seq模型进行拼写校正，改编自https://github.com/MajorTal/DeepSpell。

我的模型很好地学会了如何纠正拼写错误，但由于某种原因，它也打印出尾随无意义的字符。例如：

错字：用于装饰的一些新鲜的cZhives

正确：一些新鲜的韭菜装饰

预测：一些新鲜的韭菜为garnishhngghnnn））））））ssssssssssssssss

Keras模型的架构如下：

model = Sequential()

# Add masking layer
model.add(Masking(mask_value=0., input_shape=(None, len(chars))))

# "Encode" the input sequence using an RNN, producing an output of hidden_size
# note: in a situation where your input sequences have a variable length,
# use input_shape=(None, nb_feature).
for layer_number in range(CONFIG.input_layers):
    model.add(recurrent.LSTM(CONFIG.hidden_size, input_shape=(None, len(chars)), kernel_initializer=CONFIG.initialization,
                             return_sequences=layer_number + 1 < CONFIG.input_layers))
    model.add(Dropout(CONFIG.amount_of_dropout))
# For the decoder's input, we repeat the encoded input for each time step
model.add(RepeatVector(output_len))
# The decoder RNN could be multiple layers stacked or a single layer
for _ in range(CONFIG.output_layers):
    model.add(recurrent.LSTM(CONFIG.hidden_size, return_sequences=True, kernel_initializer=CONFIG.initialization))
    model.add(Dropout(CONFIG.amount_of_dropout))

# For each of step of the output sequence, decide which character should be chosen
model.add(TimeDistributed(Dense(len(chars), kernel_initializer=CONFIG.initialization)))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model

知道我做错了吗？

LSTM模型打印尾随垃圾字符

0 个答案: