GRU.py

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
'''
@Project :Awesome-DL-Models 
@File    :GRU.py
@Author  :JackHCC
@Date    :2022/3/14 18:52 
@Desc    :

'''
import torch
from torch import nn

from _utils import load_data_time_machine, train
from RNN import RNNModel, RNNModelScratch


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))

    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_hiddens, 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_hiddens, device):
    return (torch.zeros((batch_size, num_hiddens), 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)
        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,)


if __name__ == "__main__":
    batch_size, num_steps = 32, 35
    train_iter, vocab = load_data_time_machine(batch_size, num_steps)

    vocab_size, num_hiddens, device = len(vocab), 256, torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    num_epochs, lr = 500, 1

    num_inputs = vocab_size

    # GRU输入的参数与RNN一致
    gru_layer = nn.GRU(num_inputs, num_hiddens)

    # Use API
    # model = RNNModel(gru_layer, len(vocab))
    # model = model.to(device)

    # Do not use API
    model = RNNModelScratch(len(vocab), num_hiddens, device, get_params, init_gru_state, gru)

    train(model, train_iter, vocab, lr, num_epochs, device)