How to implement sequence-to-sequence learning in Keras?

6 months ago

2 minutes

Implementing sequence-to-sequence learning in Keras is typically done by using keras.layers.LSTM or keras.layers.GRU to build the encoder and decoder. Here is an example of implementing a basic sequence-to-sequence model.

from keras.models import Model
from keras.layers import Input, LSTM, Dense

# 定义输入序列长度和输出序列长度
encoder_seq_length = 20
decoder_seq_length = 20
num_encoder_tokens = 100
num_decoder_tokens = 100

# 定义编码器
encoder_inputs = Input(shape=(encoder_seq_length, num_encoder_tokens))
encoder = LSTM(256, return_state=True)
encoder_outputs, state_h, state_c = encoder(encoder_inputs)
encoder_states = [state_h, state_c]

# 定义解码器
decoder_inputs = Input(shape=(decoder_seq_length, num_decoder_tokens))
decoder_lstm = LSTM(256, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state=encoder_states)
decoder_dense = Dense(num_decoder_tokens, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)

# 定义模型
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)

# 编译模型
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')

# 训练模型
model.fit([encoder_input_data, decoder_input_data], decoder_target_data,
          batch_size=64,
          epochs=100,
          validation_split=0.2)

In this example, we first specified the input sequence length for the encoder and decoder, as well as the output sequence length. We also determined the number of tokens for both input and output. Next, we defined the network architecture for the encoder and decoder, which includes LSTM and Dense layers. Finally, we established the entire sequence-to-sequence model, compiled it, and trained the model.

In practice, it is necessary to adjust the model’s hyperparameters and network structure based on specific data and tasks in order to achieve better performance.