PyTorch笔记 - GRU(Gated Recurrent Unit)网络结构

Posted 2023-03-03 SpikeKing

LSTM with peephole connections

• Paper：Learning Precise Timing with LSTM Recurrent Networks

即 w*x + w*h_(t-1) + w*c_(t-1)，t-1为下标的，都是初始状态。

torch.nn.GRU：

• r是reset，重置门
• z是update，更新门
• n是new，最新门

参数量：LSTM的0.75倍，计算量也是LSTM的0.75倍

• GRU的图中，缺少“+”，即h_(t-1) * r + x_t，同时z_t和1-z_t写反了。

Paper：Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

参数数目对比，LSTM vs. GRU：

import torch
import torch.nn as nn
lstm_layer = nn.LSTM(3, 5)
gru_layer = nn.GRU(3, 5)
print(sum(p.numel() for p in lstm_layer.parameters()))
print(sum(p.numel() for p in gru_layer.parameters()))

GRU只有h_0，没有c_0

# step5 逐行实现GRU网络
def gru_forward(input, initial_state, w_ih, w_hh, b_ih, b_hh):
    prev_h = initial_state
    bs, T, i_size = input.shape
    h_size = w_ih.shape[0] // 3  # GPU只有3组，LSTM有4组
    
    # 对权重扩维，复制batch_size倍
    batch_w_ih = w_ih.unsqueeze(0).tile(bs, 1, 1)  # 复制bs倍
    batch_w_hh = w_hh.unsqueeze(0).tile(bs, 1, 1)
    
    output = torch.zeros(bs, T, h_size)  # GRU网络的输出状态序列
    
    for t in range(T):
        x = input[:, t, :]  # t时刻GRU cell的输入特征向量, [bs, i_size]
        w_times_x = torch.bmm(batch_w_ih, x.unsqueeze(-1))  # [bs, 3*h_size, 1]
        w_times_x = w_times_x.squeeze(-1)  # [bs, 3*h_size]
        
        w_times_h_prev = torch.bmm(batch_w_hh, prev_h.unsqueeze(-1))  # [bs, 3*h_size, 1]
        w_times_h_prev = w_times_h_prev.squeeze(-1)  # [bs, 3*h_size]
        
        r_t = torch.sigmoid(w_times_x[:, :h_size] + w_times_h_prev[:, :h_size] + \\
                            b_ih[:h_size] + b_hh[:h_size])  # 重置门
        z_t = torch.sigmoid(w_times_x[:, h_size:h_size*2] + w_times_h_prev[:, h_size:h_size*2] + \\
                            b_ih[h_size:h_size*2] + b_hh[h_size:h_size*2])  # 更新门
        n_t = torch.tanh(w_times_x[:, h_size*2:h_size*3] + b_ih[h_size*2:h_size*3] + \\
                         r_t*(w_times_h_prev[:, h_size*2:h_size*3] + b_hh[h_size*2:h_size*3]))  # 最新门
        
        prev_h = (1-z_t)*n_t + z_t*prev_h  # 增量更新隐含状态
        output[:, t, :] = prev_h
        
    return output, prev_h

# 测试函数的正确性
bs, T, i_size, h_size = 2, 3, 4, 5
input = torch.randn(bs, T, i_size)  # 输入序列
h_0 = torch.randn(bs, h_size)  # proj是对h进行压缩

# 调用官方GRU API
gru_layer = nn.GRU(i_size, h_size, batch_first=True)
# input带batch, h_0也需要增加1维
output, h_final = gru_layer(input, h_0.unsqueeze(0))
print(f'[Info] output: \\noutput')

for k, v in gru_layer.named_parameters():
    print(k, v.shape)
    
output_custom, h_final_custom = gru_forward(input, h_0, gru_layer.weight_ih_l0, gru_layer.weight_hh_l0, \\
                                            gru_layer.bias_ih_l0, gru_layer.bias_hh_l0)
print(f'[Info] output_custom: \\noutput_custom')