循环神经网络(GRU)

import torch
from torch import nn
from d2l import torch as d2l

batch_size, num_steps = 32, 35
train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)


# 下一步是初始化模型参数。 我们从标准差为 的高斯分布中提取权重，
# 并将偏置项设为 超参数num_hidden定义隐藏单元的数量， 实例化与更新门、重置门、候选隐状态和输出层相关的所有权重和偏置。

def get_params(vocab_size, num_hidden, 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_hidden)),
                normal((num_hidden, num_hidden)),
                torch.zeros(num_hidden, device=device))

    w_xz, w_hz, b_z = three()  # 更新门参数
    w_xr, w_hr, b_r = three()  # 重置门参数
    w_xh, w_hh, b_h = three()  # 候选隐状态参数

    # 输出层参数

    w_hq = normal((num_hidden, num_outputs))
    b_q = torch.zeros(num_outputs, device=device)

    # 附加梯度

    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


def init_gru_state(batch_size, num_hidden, device):
    return (torch.zeros((batch_size, num_hidden), device=device),)


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)  # @ 是矩阵乘法
        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,)


vocab_size, num_hidden, device = len(vocab), 256, d2l.try_gpu()
num_epochs, lr = 500, 1
model = d2l.RNNModelScratch(len(vocab), num_hidden, device, get_params,
                            init_gru_state, gru)
d2l.train_ch8(model, train_iter, vocab, lr, num_epochs, device)

 

__EOF__

本文作者：Sakurajimamai
本文链接：https://www.cnblogs.com/o-Sakurajimamai-o/p/17689960.html
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