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シーケンスラベリング用のkerasBLSTM

私はニューラルネットに比較的慣れていないので、無知を許してください。私はkerasBLSTMの例を適応させようとしています ここ 。この例では、テキストを読み取り、0または1に分類します。品詞タグ付けやその他の高度な機能などの追加機能は必要ありませんが、基本モデルだけが必要です。POSタグ付けと非常によく似たBLSTMが必要です。私のデータは文のリストであり、各単語にはカテゴリ1〜8が割り当てられています。このデータを使用して、見えない文の各単語のカテゴリを予測できるBLSTMをトレーニングしたいと思います。

例えばinput = ['The'、 'dog'、 'is'、 'red']はoutput = [2、4、3、7]を与えます

ケラスの例が最善のルートではない場合、私は他の提案を受け入れます。

私は現在これを持っています：

'''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)

私の出力は正しい次元ですが、フロート0-1を与えています。これは、まだバイナリ分類を探しているためだと思います。誰かがこれを修正する方法を知っていますか？

解決済み

ラベルがそれぞれバイナリ配列であることを確認してください。

(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)
pythonneural-networkkeraslstm
12
ChrisDH

解決しました。主な問題は、分類カテゴリのデータをバイナリ配列として再形成することでした。また、TimeDistributedを使用し、return_sequencesをTrueに設定しました。

4
ChrisDH

私はこのスレッドが非常に古いことを知っていますが、私が助けてくれることを願っています。

バイナリモデルのモデルを変更しました。

sequence = Input(shape=(X_train.shape[1],), dtype='int32')

embedded = Embedding(max_fatures,embed_dim,input_length=X_train.shape[1], 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 = concatenate([forwards, backwards])
after_dp = Dropout(0.15)(merged)
# add now layer LSTM without return_sequence
lstm_normal = LSTM(hidden)(merged)

# TimeDistributed for sequence
# change activation to sigmoid?
#output = TimeDistributed(Dense(output_dim=2,activation='sigmoid'))(after_dp)
#I changed output layer TimeDistributed for a Dense, for the problem of dimensionality and output_dim = 1 (output binary) 
output = Dense(output_dim=1,activation='sigmoid')(lstm_normal)

model = Model(input=sequence, output=output)

# try using different optimizers and different optimizer configs
# loss=binary_crossentropy, optimizer=rmsprop
# I changed modelo compile by to binary and remove sample_weight_mode parameter
model.compile(loss='binary_crossentropy',
              metrics=['accuracy'], optimizer='adam',
              )

print(model.summary())


###################################
#this is the line of training

model.fit(X_train, Y_train,
          batch_size=128,
          epochs=10,
          shuffle=True,
          validation_split=0.2,
          #sample_weight=W
         )

#In this moment work fine.....
Train on 536000 samples, validate on 134000 samples
Epoch 1/10
536000/536000 [==============================] - 1814s 3ms/step - loss: 0.4794 - acc: 0.7679 - val_loss: 0.4624 - val_acc: 0.7784
Epoch 2/10
536000/536000 [==============================] - 1829s 3ms/step - loss: 0.4502 - acc: 0.7857 - val_loss: 0.4551 - val_acc: 0.7837
Epoch 3/10
 99584/536000 [====>.........................] - ETA: 23:10 - loss: 0.4291 - acc: 0.7980
0
Chevelle