seq2seq 完整代码

以下是一个简单的seq2seq模型的完整代码示例：

import numpy as np
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, LSTM, Dense

# 定义输入序列的最大长度和词汇表大小
max_encoder_seq_length = 50
max_decoder_seq_length = 50
num_encoder_tokens = 10000
num_decoder_tokens = 5000

# 定义编码器输入
encoder_inputs = Input(shape=(None, num_encoder_tokens))
encoder = LSTM(256, return_state=True)
encoder_outputs, state_h, state_c = encoder(encoder_inputs)
encoder_states = [state_h, state_c]

# 定义解码器输入
decoder_inputs = Input(shape=(None, 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)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')

# 训练模型
encoder_input_data = np.random.rand(1000, max_encoder_seq_length, num_encoder_tokens)
decoder_input_data = np.random.rand(1000, max_decoder_seq_length, num_decoder_tokens)
decoder_target_data = np.random.rand(1000, max_decoder_seq_length, num_decoder_tokens)

model.fit([encoder_input_data, decoder_input_data], decoder_target_data, batch_size=64, epochs=10)

# 使用训练好的模型进行预测
encoder_model = Model(encoder_inputs, encoder_states)

decoder_state_input_h = Input(shape=(256,))
decoder_state_input_c = Input(shape=(256,))
decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
decoder_outputs, state_h, state_c = decoder_lstm(decoder_inputs, initial_state=decoder_states_inputs)
decoder_states = [state_h, state_c]
decoder_outputs = decoder_dense(decoder_outputs)
decoder_model = Model([decoder_inputs] + decoder_states_inputs, [decoder_outputs] + decoder_states)

# 定义解码器的辅助函数
def decode_sequence(input_seq):
    states_value = encoder_model.predict(input_seq)

    target_seq = np.zeros((1, 1, num_decoder_tokens))
    target_seq[0, 0, target_token_index['<START>']] = 1.

    stop_condition = False
    decoded_sentence = ''
    while not stop_condition:
        output_tokens, h, c = decoder_model.predict([target_seq] + states_value)

        sampled_token_index = np.argmax(output_tokens[0, -1, :])
        sampled_char = reverse_target_char_index[sampled_token_index]
        decoded_sentence += sampled_char

        if (sampled_char == '<END>' or len(decoded_sentence) > max_decoder_seq_length):
            stop_condition = True

        target_seq = np.zeros((1, 1, num_decoder_tokens))
        target_seq[0, 0, sampled_token_index] = 1.

        states_value = [h, c]

    return decoded_sentence

这只是一个简单的示例，实际使用中可能需要根据具体任务进行调整和优化。注意，上述代码中的数据是随机生成的，并未实际训练模型。在实际使用中，你需要准备自己的训练数据，并根据任务需求进行适当的修改。