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29、PyTorch RNN的原理及其手写复现_哔哩哔哩_bilibili

笔记连接: https://pan.baidu.com/s/1_Sm7ptEiJtTTq3vQWgOTNg?pwd=2rei 提取码: 2rei

import torch
import torch.nn as nn

bs,T=2,3  # 批大小，输入序列长度
input_size,hidden_size = 2,3 # 输入特征大小，隐含层特征大小
input = torch.randn(bs,T,input_size)  # 随机初始化一个输入特征序列
h_prev = torch.zeros(bs,hidden_size) # 初始隐含状态

# step1 调用pytorch RNN API
rnn = nn.RNN(input_size,hidden_size,batch_first=True)
rnn_output,state_finall = rnn(input,h_prev.unsqueeze(0))print(rnn_output)
print(state_finall)

# step2 手写 rnn_forward函数，实现RNN的计算原理
def rnn_forward(input,weight_ih,weight_hh,bias_ih,bias_hh,h_prev):bs,T,input_size = input.shapeh_dim = weight_ih.shape[0]h_out = torch.zeros(bs,T,h_dim) # 初始化一个输出（状态）矩阵for t in range(T):x = input[:,t,:].unsqueeze(2)  # 获取当前时刻的输入特征，bs*input_size*1w_ih_batch = weight_ih.unsqueeze(0).tile(bs,1,1) # bs * h_dim * input_sizew_hh_batch = weight_hh.unsqueeze(0).tile(bs,1,1)# bs * h_dim * h_dimw_times_x = torch.bmm(w_ih_batch,x).squeeze(-1) # bs*h_dimw_times_h = torch.bmm(w_hh_batch,h_prev.unsqueeze(2)).squeeze(-1) # bs*h_himh_prev = torch.tanh(w_times_x + bias_ih + w_times_h + bias_hh)h_out[:,t,:] = h_prevreturn h_out,h_prev.unsqueeze(0)

# 验证结果
custom_rnn_output,custom_state_finall = rnn_forward(input,rnn.weight_ih_l0,rnn.weight_hh_l0,rnn.bias_ih_l0,rnn.bias_hh_l0,h_prev)
print(custom_rnn_output)
print(custom_state_finall)

print(torch.allclose(rnn_output,custom_rnn_output))
print(torch.allclose(state_finall,custom_state_finall))

# step3 手写一个 bidirectional_rnn_forward函数，实现双向RNN的计算原理
def bidirectional_rnn_forward(input,weight_ih,weight_hh,bias_ih,bias_hh,h_prev,weight_ih_reverse,weight_hh_reverse,bias_ih_reverse,bias_hh_reverse,h_prev_reverse):bs,T,input_size = input.shapeh_dim = weight_ih.shape[0]h_out = torch.zeros(bs,T,h_dim*2) # 初始化一个输出（状态）矩阵，注意双向是两倍的特征大小forward_output = rnn_forward(input,weight_ih,weight_hh,bias_ih,bias_hh,h_prev)[0]  # forward layerbackward_output = rnn_forward(torch.flip(input,[1]),weight_ih_reverse,weight_hh_reverse,bias_ih_reverse, bias_hh_reverse,h_prev_reverse)[0] # backward layer# 将input按照时间的顺序翻转h_out[:,:,:h_dim] = forward_outputh_out[:,:,h_dim:] = torch.flip(backward_output,[1]) #需要再翻转一下 才能和forward output拼接h_n = torch.zeros(bs,2,h_dim)  # 要最后的状态连接h_n[:,0,:] = forward_output[:,-1,:]h_n[:,1,:] = backward_output[:,-1,:]h_n = h_n.transpose(0,1)return h_out,h_n# return h_out,h_out[:,-1,:].reshape((bs,2,h_dim)).transpose(0,1)# 验证一下 bidirectional_rnn_forward的正确性
bi_rnn = nn.RNN(input_size,hidden_size,batch_first=True,bidirectional=True)
h_prev = torch.zeros((2,bs,hidden_size))
bi_rnn_output,bi_state_finall = bi_rnn(input,h_prev)for k,v in bi_rnn.named_parameters():print(k,v)

custom_bi_rnn_output,custom_bi_state_finall = bidirectional_rnn_forward(input,bi_rnn.weight_ih_l0,bi_rnn.weight_hh_l0,bi_rnn.bias_ih_l0,bi_rnn.bias_hh_l0,h_prev[0],bi_rnn.weight_ih_l0_reverse,bi_rnn.weight_hh_l0_reverse,bi_rnn.bias_ih_l0_reverse,bi_rnn.bias_hh_l0_reverse,h_prev[1])

print("Pytorch API output")
print(bi_rnn_output)
print(bi_state_finall)print("\n custom bidirectional_rnn_forward function output:")
print(custom_bi_rnn_output)
print(custom_bi_state_finall)
print(torch.allclose(bi_rnn_output,custom_bi_rnn_output))
print(torch.allclose(bi_state_finall,custom_bi_state_finall))