transformer_mse = criterion(transformer_predictions, y_test).item() plt.bar(["RNN", "Transformer"], [rnn_mse, transformer_mse], color=["red", "blue"]) plt.ylabel("Mean Squared Error") plt.title("MSE Comparison") plt.tight_layout() plt.show() # 输出模型效果总结 print(f"RNN Trainin...
plt.title("Prediction Comparison (First 50 Samples)") plt.legend() # 训练时间比较 plt.subplot(2, 2, 3) times = [end_time_rnn - start_time_rnn, end_time_transformer - start_time_transformer] plt.bar(["RNN", "Transformer"], times, color=["red", "blue"]) plt.ylabel("Training Tim...
各类Transformer的计算复杂度如Table 1所示 Table 1: Complexity comparison with different Transformers: Reformer, Linear Transformer, Performer, AFT, MEGA. Here T denotes the sequence length, d the feature dimension, and c is MEGA’s chunk size of quadratic attention. RNN 以LSTM为例,RNN可以表示为如...
[4] Graves A. Supervised sequence labelling with recurrent neuralnetworks. 2012[J]. ISBN 9783642212703. URL http://books. google. com/books. [5] Prabhavalkar R, Rao K, Sainath T N, et al. A comparison ofsequence-to-sequence models for speech recognition[C]//Proc. Interspeech. 2017:93...
RWKV is an RNN with Transformer-level LLM performance, which can also be directly trained like a GPT transformer (parallelizable). And it's 100% attention-free. You only need the hidden state at position t to compute the state at position t+1. You can use the "GPT" mode to quickly ...
A comparison of LSTM and GRU networks for learning symbolic sequences evtor:深入理解lstm及其变种gru Understanding LSTM Networks 一文搞懂RNN(循环神经网络)基础篇 - 知乎 (zhihu.com) LSTM - 长短期记忆递归神经网络 - 知乎 (zhihu.com) 为什么目前的强化学习里深度网络很少用 transformer ,更多的是 lstm ...
时序预测:Transformer也逐渐应用于时间序列预测等任务,尤其是在长序列场景中。 整体对比下来: RNN依赖递归结构和隐藏状态,适合短期依赖任务,但难以捕捉长距离依赖,且训练速度较慢。 Transformer通过自注意力机制高效处理长序列任务,适合大规模并行计算,在自然语言处理和其他领域的长序列建模上表现出色。
RWKV: RNN with Transformer-level LLM Performance RWKV is a RNN with Transformer-level LLM performance, which can also be directly trained like a GPT transformer (parallelizable). And it's 100% attention-free. You only need the hidden state at position t to compute the state at position t...
交叉注意力融合时域、频域特征的FFT + CNN -Transformer-CrossAttention轴承故障识别模型 - 知乎 (zhihu....
RWKV is an RNN with Transformer-level LLM performance, which can also be directly trained like a GPT transformer (parallelizable). And it's 100% attention-free. You only need the hidden state at position t to compute the state at position t+1. You can use the "GPT" mode to quickly ...