两种序列模型之间的差异keras(带有和不带有RepeatVector) [英] Difference between two Sequence to Sequence Models keras (with and without RepeatVector)

问题描述

我试图了解在此处，以下内容:

I try to understand what the difference between this model describde here, the following one:

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

inputs = Input(shape=(timesteps, input_dim))
encoded = LSTM(latent_dim)(inputs)

decoded = RepeatVector(timesteps)(encoded)
decoded = LSTM(input_dim, return_sequences=True)(decoded)

sequence_autoencoder = Model(inputs, decoded)
encoder = Model(inputs, encoded)

，这里描述的序列到序列模型是 第二项描述

and the sequence to sequence model described here is second describion

有什么区别?第一个没有RepeatVector，而第二个没有RepeatVector?第一个模型是否不将解码器的隐藏状态作为预测的初始状态?

What is the difference ? The first one has the RepeatVector while the second does not have that? Is the first model not taking the decoders hidden state as inital state for the prediction ?

有没有一篇描述第一篇和第二篇的论文?

Are there a paper describing the first and second one ?

推荐答案

在使用RepeatVector的模型中，他们没有使用任何形式的花式预测，也没有处理状态.他们让模型在内部完成所有操作，并使用RepeatVector将(batch, latent_dim)向量(不是序列)转换为(batch, timesteps, latent_dim)(现在是适当的序列).

In the model using RepeatVector, they're not using any kind of fancy prediction, nor dealing with states. They're letting the model do everything internally and the RepeatVector is used to transform a (batch, latent_dim) vector (which is not a sequence) into a (batch, timesteps, latent_dim) (which is now a proper sequence).

现在，在另一个没有RepeatVector的模型中，秘密在于此附加功能:

Now, in the other model, without RepeatVector, the secret lies in this additional function:

def decode_sequence(input_seq):
    # Encode the input as state vectors.
    states_value = encoder_model.predict(input_seq)

    # Generate empty target sequence of length 1.
    target_seq = np.zeros((1, 1, num_decoder_tokens))
    # Populate the first character of target sequence with the start character.
    target_seq[0, 0, target_token_index['\t']] = 1.

    # Sampling loop for a batch of sequences
    # (to simplify, here we assume a batch of size 1).
    stop_condition = False
    decoded_sentence = ''
    while not stop_condition:
        output_tokens, h, c = decoder_model.predict([target_seq] + states_value)

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

        # Exit condition: either hit max length
        # or find stop character.
        if (sampled_char == '\n' or len(decoded_sentence) > max_decoder_seq_length):
            stop_condition = True

        # Update the target sequence (of length 1).
        target_seq = np.zeros((1, 1, num_decoder_tokens))
        target_seq[0, 0, sampled_token_index] = 1.

        # Update states
        states_value = [h, c]

    return decoded_sentence

这会基于stop_condition运行一个循环"，以一个一个地创建时间步长. (这样做的好处是使句子没有固定的长度).

This runs a "loop" based on a stop_condition for creating the time steps one by one. (The advantage of this is making sentences without a fixed length).

它还显式地获取每个步骤中生成的状态(以保持每个单独步骤之间的正确连接).

It also explicitly takes the states generated in each step (in order to keep the proper connection between each individual step).

• 模型1:通过重复潜在矢量来创建长度
• 模型2:通过循环执行新的步骤直到达到停止条件来创建长度

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