我对神经网络比较陌生，所以请原谅我的无知。我正在尝试改编keras BLSTM示例here。该示例读取文本并将其分类为0或1。我想要一个BLSTM，它做的非常类似于POS标记，尽管并不需要诸如lemmatizing或其他高级功能之类的额外功能，我只想要一个基本模型。我的数据是一个句子列表，每个单词的类别为1-8。我想训练一个BLSTM，可以使用此数据来预测一个看不见的句子中每个单词的类别。
例如输入= ['The'，'dog'，'is'，'red']给出输出= [2、4、3、7]
如果keras示例不是最佳途径，那么我欢迎其他建议。
我目前有这个:
'''Train a Bidirectional LSTM.'''
from __future__ import print_function
import numpy as np
from keras.preprocessing import sequence
from keras.models import Model
from keras.layers import Dense, Dropout, Embedding, LSTM, Input, merge
from prep_nn import prep_scan
np.random.seed(1337) # for reproducibility
max_features = 20000
batch_size = 16
maxlen = 18
print('Loading data...')
(X_train, y_train), (X_test, y_test) = prep_scan(nb_words=max_features,
test_split=0.2)
print(len(X_train), 'train sequences')
print(len(X_test), 'test sequences')
print("Pad sequences (samples x time)")
# type issues here? float/int?
X_train = sequence.pad_sequences(X_train, value=0.)
X_test = sequence.pad_sequences(X_test, value=0.) # pad with zeros
print('X_train shape:', X_train.shape)
print('X_test shape:', X_test.shape)
# need to pad y too, because more than 1 ouput value, not classification?
y_train = sequence.pad_sequences(np.array(y_train), value=0.)
y_test = sequence.pad_sequences(np.array(y_test), value=0.)
print('y_train shape:', X_train.shape)
print('y_test shape:', X_test.shape)
# this is the placeholder tensor for the input sequences
sequence = Input(shape=(maxlen,), dtype='int32')
# this embedding layer will transform the sequences of integers
# into vectors of size 128
embedded = Embedding(max_features, 128, input_length=maxlen)(sequence)
# apply forwards LSTM
forwards = LSTM(64)(embedded)
# apply backwards LSTM
backwards = LSTM(64, go_backwards=True)(embedded)
# concatenate the outputs of the 2 LSTMs
merged = merge([forwards, backwards], mode='concat', concat_axis=-1)
after_dp = Dropout(0.5)(merged)
# number after dense has to corresponse to output matrix?
output = Dense(17, activation='sigmoid')(after_dp)
model = Model(input=sequence, output=output)
# try using different optimizers and different optimizer configs
model.compile('adam', 'categorical_crossentropy', metrics=['accuracy'])
print('Train...')
model.fit(X_train, y_train,
batch_size=batch_size,
nb_epoch=4,
validation_data=[X_test, y_test])
X_test_new = np.array([[0,0,0,0,0,0,0,0,0,12,3,55,4,34,5,45,3,9],[0,0,0,0,0,0,0,1,7,65,34,67,34,23,24,67,54,43,]])
classes = model.predict(X_test_new, batch_size=16)
print(classes)
我的输出是正确的尺寸，但给我浮点数0-1。我认为这是因为它仍在寻找二进制分类。有人知道怎么修这个东西吗？
解决了
只需确保标签是每个二进制数组即可:
(X_train, y_train), (X_test, y_test), maxlen, word_ids, tags_ids = prep_model(
nb_words=nb_words, test_len=75)
W = (y_train > 0).astype('float')
print(len(X_train), 'train sequences')
print(int(len(X_train)*val_split), 'validation sequences')
print(len(X_test), 'heldout sequences')
# this is the placeholder tensor for the input sequences
sequence = Input(shape=(maxlen,), dtype='int32')
# this embedding layer will transform the sequences of integers
# into vectors of size 256
embedded = Embedding(nb_words, output_dim=hidden,
input_length=maxlen, mask_zero=True)(sequence)
# apply forwards LSTM
forwards = LSTM(output_dim=hidden, return_sequences=True)(embedded)
# apply backwards LSTM
backwards = LSTM(output_dim=hidden, return_sequences=True,
go_backwards=True)(embedded)
# concatenate the outputs of the 2 LSTMs
merged = merge([forwards, backwards], mode='concat', concat_axis=-1)
after_dp = Dropout(0.15)(merged)
# TimeDistributed for sequence
# change activation to sigmoid?
output = TimeDistributed(
Dense(output_dim=nb_classes,
activation='softmax'))(after_dp)
model = Model(input=sequence, output=output)
# try using different optimizers and different optimizer configs
# loss=binary_crossentropy, optimizer=rmsprop
model.compile(loss='categorical_crossentropy',
metrics=['accuracy'], optimizer='adam',
sample_weight_mode='temporal')
print('Train...')
model.fit(X_train, y_train,
batch_size=batch_size,
nb_epoch=epochs,
shuffle=True,
validation_split=val_split,
sample_weight=W)
最佳答案
解决了。主要问题是将分类类别的数据重塑为二进制数组。还使用了TimeDistributed，并将return_sequences设置为True。
关于python - keras BLSTM用于序列标记，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/37307421/