1.GRU的原理

1.1重置门和更新门

1.2候选隐藏状态

 

1.3隐状态 

2. GRU的代码实现

#导包
import torch
from torch import nn
import dltools


#加载数据
batch_size, num_steps = 32, 35
train_iter, vocab = dltools.load_data_time_machine(batch_size, num_steps)


#封装函数：实现初始化模型参数
def get_params(vocab_size, num_hiddens, device):
    num_inputs = num_outputs = vocab_size
    
    def normal(shape):
        return torch.randn(size=shape, device=device) * 0.01
    
    def three():
        return (normal((num_inputs, num_hiddens)),
               normal((num_hiddens, num_hiddens)),
               torch.zeros(num_hiddens, device=device))
    
    # 更新门参数
    W_xz, W_hz, b_z = three()
    # 重置门
    W_xr, W_hr, b_r = three()
    # 候选隐藏状态参数
    W_xh, W_hh, b_h = three()
    
    # 输出层参数
    W_hq = normal((num_hiddens, num_outputs))
    b_q = torch.zeros(num_outputs, device=device)
    params = [W_xz, W_hz, b_z, W_xr, W_hr, b_r, W_xh, W_hh, b_h, W_hq, b_q]
    
    for param in params:
        param.requires_grad_(True)
        
    return params


#定义函数：初始化隐藏状态
def init_gru_state(batch_size, num_hiddens, device):
    return (torch.zeros((batch_size, num_hiddens), device=device))


#定义函数：构建GRU网络结构
def gru(inputs, state, params):
    [W_xz, W_hz, b_z, W_xr, W_hr, b_r, W_xh, W_hh, b_h, W_hq, b_q] = params
    H, = state
    outputs = []
    
    for X in inputs:
        Z = torch.sigmoid((X @ W_xz) + (H @ W_hz) + b_z)
        R = torch.sigmoid((X @ W_xr) + (H @ W_hr) + b_r)
        H_tilda = torch.tanh((X @ W_xh) + ((R * H) @ W_hh) + b_h)
        H = Z * H + (1 - Z) * H_tilda
        Y = H @ W_hq + b_q
        outputs.append(Y)
        
    return torch.cat(outputs, dim=0), (H, )


#训练和预测
vocab_size, num_hiddens, device = len(vocab), 256, dltools.try_gpu()
num_epochs, lr = 500, 5
model = dltools.RNNModelScratch(len(vocab), num_hiddens, device, get_params, init_gru_state, gru)
dltools.train_ch8(model, train_iter, vocab, lr, num_epochs, device)

 

3.pytorch 简洁实现版_GRU调包实现 

num_inputs = vocab_size
#创建网络层
gru_layer = nn.GRU(num_inputs, num_hiddens)
#建模
model = dltools.RNNModel(gru_layer, len(vocab))
#将模型转到device上
model = model.to(device)
#模型训练
dltools.train_ch8(model, train_iter, vocab, lr, num_epochs, device)

 

4.知识点个人理解