双向 LSTM 和 LSTM 的区别是什么？

LSTM in its core, preserves information from inputs that has already passed through it using the hidden state.

Unidirectional LSTM only preserves information of the past because the only inputs it has seen are from the past.

Using bidirectional will run your inputs in two ways, one from past to future and one from future to past and what differs this approach from unidirectional is that in the LSTM that runs backwards you preserve information from the future and using the two hidden states combined you are able in any point in time to preserve information from both past and future.

What they are suited for is a very complicated question but BiLSTMs show very good results as they can understand context better, I will try to explain through an example.

Lets say we try to predict the next word in a sentence, on a high level what a unidirectional LSTM will see is

The boys went to ....

And will try to predict the next word only by this context, with bidirectional LSTM you will be able to see information further down the road for example

Forward LSTM:

The boys went to ...

Backward LSTM:

... and then they got out of the pool

You can see that using the information from the future it could be easier for the network to understand what the next word is.

Another use case of bidirectional LSTM might be for word classification in the text. They can see the past and future context of the word and are much better suited to classify the word.

Adding to Bluesummer's answer, here is how you would implement Bidirectional LSTM from scratch without calling BiLSTM module. This might better contrast the difference between a uni-directional and bi-directional LSTMs. As you see, we merge two LSTMs to create a bidirectional LSTM.

You can merge outputs of the forward and backward LSTMs by using either {'sum', 'mul', 'concat', 'ave'}.

left = Sequential()
left.add(LSTM(output_dim=hidden_units, init='uniform', inner_init='uniform',
forget_bias_init='one', return_sequences=True, activation='tanh',
inner_activation='sigmoid', input_shape=(99, 13)))
right = Sequential()
right.add(LSTM(output_dim=hidden_units, init='uniform', inner_init='uniform',
forget_bias_init='one', return_sequences=True, activation='tanh',
inner_activation='sigmoid', input_shape=(99, 13), go_backwards=True))


model = Sequential()
model.add(Merge([left, right], mode='sum'))


model.add(TimeDistributedDense(nb_classes))
model.add(Activation('softmax'))


sgd = SGD(lr=0.1, decay=1e-5, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd)
print("Train...")
model.fit([X_train, X_train], Y_train, batch_size=1, nb_epoch=nb_epoches, validation_data=([X_test, X_test], Y_test), verbose=1, show_accuracy=True)

In comparison to LSTM, BLSTM or BiLSTM has two networks, one access pastinformation in forward direction and another access future in the reverse direction. wiki

A new class Bidirectional is added as per official doc here: https://www.tensorflow.org/api_docs/python/tf/keras/layers/Bidirectional

model = Sequential()
model.add(Bidirectional(LSTM(10, return_sequences=True), input_shape=(5,
10)))

and activation function can be added like this:

model = Sequential()
model.add(Bidirectional(LSTM(num_channels,
implementation = 2, recurrent_activation = 'sigmoid'),
input_shape=(input_length, input_dim)))

Complete example using IMDB data will be like this.The result after 4 epoch.

Downloading data from https://s3.amazonaws.com/text-datasets/imdb.npz
17465344/17464789 [==============================] - 4s 0us/step
Train...
Train on 25000 samples, validate on 25000 samples
Epoch 1/4
25000/25000 [==============================] - 78s 3ms/step - loss: 0.4219 - acc: 0.8033 - val_loss: 0.2992 - val_acc: 0.8732
Epoch 2/4
25000/25000 [==============================] - 82s 3ms/step - loss: 0.2315 - acc: 0.9106 - val_loss: 0.3183 - val_acc: 0.8664
Epoch 3/4
25000/25000 [==============================] - 91s 4ms/step - loss: 0.1802 - acc: 0.9338 - val_loss: 0.3645 - val_acc: 0.8568
Epoch 4/4
25000/25000 [==============================] - 92s 4ms/step - loss: 0.1398 - acc: 0.9509 - val_loss: 0.3562 - val_acc: 0.8606

BiLSTM or BLSTM

import numpy as np
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Dropout, Embedding, LSTM, Bidirectional
from keras.datasets import imdb




n_unique_words = 10000 # cut texts after this number of words
maxlen = 200
batch_size = 128


(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=n_unique_words)
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
y_train = np.array(y_train)
y_test = np.array(y_test)


model = Sequential()
model.add(Embedding(n_unique_words, 128, input_length=maxlen))
model.add(Bidirectional(LSTM(64)))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))


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


print('Train...')
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=4,
validation_data=[x_test, y_test])

It can also be helpful in Time Series Forecasting problems, like predicting the electric consumption of a household. However, we can also use LSTM in this but Bidirectional LSTM will also do a better job in it.