RNN.py

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
'''
@Project :Awesome-DL-Models 
@File    :RNN.py
@Author  :JackHCC
@Date    :2022/3/14 16:31 
@Desc    :

'''
import torch
from torch import nn
import torch.nn.functional as F

from _utils import load_data_time_machine, train


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

    # 隐藏层参数
    W_xh = normal((num_inputs, num_hiddens))
    W_hh = normal((num_hiddens, num_hiddens))
    b_h = torch.zeros(num_hiddens, device=device)
    # 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_xh, W_hh, b_h, W_hq, b_q]
    for param in params:
        param.requires_grad_(True)
    return params


def init_rnn_state(batch_size, num_hiddens, device):
    return (torch.zeros((batch_size, num_hiddens), device=device), )


def rnn(inputs, state, params):
    # inputs的形状：(时间步数量，批量大小，词表大小)
    W_xh, W_hh, b_h, W_hq, b_q = params
    H, = state
    outputs = []
    # X的形状：(批量大小，词表大小)
    for X in inputs:
        H = torch.tanh(torch.mm(X, W_xh) + torch.mm(H, W_hh) + b_h)
        Y = torch.mm(H, W_hq) + b_q
        outputs.append(Y)
    return torch.cat(outputs, dim=0), (H,)


class RNNModelScratch:
    def __init__(self, vocab_size, num_hiddens, device,
                 get_params, init_state, forward_fn):
        self.vocab_size, self.num_hiddens = vocab_size, num_hiddens
        self.params = get_params(vocab_size, num_hiddens, device)
        self.init_state, self.forward_fn = init_state, forward_fn

    def __call__(self, X, state):
        X = F.one_hot(X.T, self.vocab_size).type(torch.float32)
        return self.forward_fn(X, state, self.params)

    def begin_state(self, batch_size, device):
        return self.init_state(batch_size, self.num_hiddens, device)


class RNNModel(nn.Module):
    """循环神经网络模型"""

    def __init__(self, rnn_layer, vocab_size, **kwargs):
        super(RNNModel, self).__init__(**kwargs)
        self.rnn = rnn_layer
        self.vocab_size = vocab_size
        self.num_hiddens = self.rnn.hidden_size
        # 如果RNN是双向的（之后将介绍），num_directions应该是2，否则应该是1
        if not self.rnn.bidirectional:
            self.num_directions = 1
            self.linear = nn.Linear(self.num_hiddens, self.vocab_size)
        else:
            self.num_directions = 2
            self.linear = nn.Linear(self.num_hiddens * 2, self.vocab_size)

    def forward(self, inputs, state):
        X = F.one_hot(inputs.T.long(), self.vocab_size)
        X = X.to(torch.float32)
        Y, state = self.rnn(X, state)
        # 全连接层首先将Y的形状改为(时间步数*批量大小,隐藏单元数)
        # 它的输出形状是(时间步数*批量大小,词表大小)。
        output = self.linear(Y.reshape((-1, Y.shape[-1])))
        return output, state

    def begin_state(self, device, batch_size=1):
        if not isinstance(self.rnn, nn.LSTM):
            # nn.GRU以张量作为隐状态
            return torch.zeros((self.num_directions * self.rnn.num_layers,
                                batch_size, self.num_hiddens),
                               device=device)
        else:
            # nn.LSTM以元组作为隐状态
            return (torch.zeros((
                self.num_directions * self.rnn.num_layers,
                batch_size, self.num_hiddens), device=device),
                    torch.zeros((
                        self.num_directions * self.rnn.num_layers,
                        batch_size, self.num_hiddens), device=device))


if __name__ == "__main__":
    # Step 1: Preprocess Data include build Vocab and data Iteration, Setting Parameters
    batch_size, num_steps = 32, 35
    train_iter, vocab = load_data_time_machine(batch_size, num_steps)
    num_hiddens = 256

    # Step 2: Call RNN
    # 注意这里第一个参数是RNN的输入X的维度，这里因为是One-Hot编码，所有维度大小即词库大小， 第二个参数是隐藏层H的维度，第三个参数为隐藏层数
    rnn_layer = nn.RNN(len(vocab), num_hiddens, 1)
    # 设置参数H: 尺寸为(隐藏层数, Batch_Size, 隐藏单元维度)
    H = torch.zeros((1, batch_size, num_hiddens))
    # 输入变量X: 尺寸(序列长度, Batch_Size, 输出向量维度)
    X = torch.rand(size=(num_steps, batch_size, len(vocab)))
    Y, H_new = rnn_layer(X, H)
    print(Y.shape, H.shape)

    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    # Use API
    # net = RNNModel(rnn_layer, vocab_size=len(vocab))
    # net = net.to(device)

    # Do not use API
    net = RNNModelScratch(len(vocab), num_hiddens, device, get_params, init_rnn_state, rnn)
    print(net)

    num_epochs, lr = 500, 1
    # Step 3: Train Model
    train(net, train_iter, vocab, lr, num_epochs, device)