《动手学深度学习》(PyTorch版)代码注释 - 32 【RNN_with_zero】_王国平动手学pytorch代码注释

说明

本博客代码来自开源项目：《动手学深度学习》(PyTorch版)
并且在博主学习的理解上对代码进行了大量注释，方便理解各个函数的原理和用途

配置环境

使用环境：python3.8
平台：Windows10
IDE：PyCharm

此节说明

此节对应书本上5.7节
此节功能为：使用重复元素的网络（VGG）
由于次节相对容易理解，代码注释量较少

并且，值得注意的是，次节中博主对其中数据操作有些困惑，自己重新实现了一版，在文末附上

代码

# 本书链接https://tangshusen.me/Dive-into-DL-PyTorch/#/chapter03_DL-basics/3.8_mlp
# 6.4 循环神经网络的从零开始实现
# 注释：黄文俊
# E-mail：hurri_cane@qq.com

import time
import math
import numpy as np
import torch
from torch import nn, optim
import torch.nn.functional as F

import sys
sys.path.append("..")
import d2lzh_pytorch as d2l
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

(corpus_indices, char_to_idx, idx_to_char, vocab_size) = d2l.load_data_jay_lyrics()

def one_hot(x, n_class, dtype=torch.float32):
    # X shape: (batch), output shape: (batch, n_class)
    x = x.long()
    res = torch.zeros(x.shape[0], n_class, dtype=dtype, device=x.device)
    res.scatter_(1, x.view(-1, 1), 1)
    # res.scatter_(dim, index, src) 的参数有 3 个
    # dim：沿着哪个维度进行索引
    # index：用来 scatter 的元素索引
    # src：用来 scatter 的源元素，可以是一个标量或一个张量

    return res

x = torch.tensor([0, 2])
print(one_hot(x, vocab_size))



# 本函数已保存在d2lzh_pytorch包中方便以后使用
def to_onehot(X, n_class):
    # X shape: (batch, seq_len), output: seq_len elements of (batch, n_class)
    return [one_hot(X[:, i], n_class) for i in range(X.shape[1])]

X = torch.arange(10).view(2, 5)
# print(X[:, 0])
inputs = to_onehot(X, vocab_size)
print(len(inputs), inputs[0].shape)



# 初始化模型参数

num_inputs, num_hiddens, num_outputs = vocab_size, 256, vocab_size
print('will use', device)

def get_params():
    def _one(shape):
        ts = torch.tensor(np.random.normal(0, 0.01, size=shape), device=device, dtype=torch.float32)
        return torch.nn.Parameter(ts, requires_grad=True)

    # 隐藏层参数
    W_xh = _one((num_inputs, num_hiddens))
    W_hh = _one((num_hiddens, num_hiddens))
    b_h = torch.nn.Parameter(torch.zeros(num_hiddens, device=device, requires_grad=True))
    # 输出层参数
    W_hq = _one((num_hiddens, num_outputs))
    b_q = torch.nn.Parameter(torch.zeros(num_outputs, device=device, requires_grad=True))
    return nn.ParameterList([W_xh, W_hh, b_h, W_hq, b_q])

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


def rnn(inputs, state, params):
    # inputs和outputs皆为num_steps个形状为(batch_size, vocab_size)的矩阵
    W_xh, W_hh, b_h, W_hq, b_q = params
    H, = state
    outputs = []
    for X in inputs:
        H = torch.tanh(torch.matmul(X, W_xh) + torch.matmul(H, W_hh) + b_h)
        Y = torch.matmul(H, W_hq) + b_q
        outputs.append(Y)
    return outputs, (H,)

state = init_rnn_state(X.shape[0], num_hiddens, device)
inputs = to_onehot(X.to(device), vocab_size)
params = get_params()
outputs, state_new = rnn(inputs, state, params)
print(len(outputs), outputs[0].shape, state_new[0].shape)

print("*"*50)

# 本函数已保存在d2lzh_pytorch包中方便以后使用
def predict_rnn(prefix, num_chars, rnn, params, init_rnn_state,
                num_hiddens, vocab_size, device, idx_to_char, char_to_idx):
    state = init_rnn_state(1, num_hiddens, device)
    output = [char_to_idx[prefix[0]]]
    for t in range(num_chars + len(prefix) - 1):
        # 将上一时间步的输出作为当前时间步的输入
        X = to_onehot(torch.tensor([[output[-1]]], device=device), vocab_size)
        # 计算输出和更新隐藏状态
        (Y, state) = rnn(X, state, params)
        # 下一个时间步的输入是prefix里的字符或者当前的最佳预测字符
        if t < len(prefix) - 1:
            output.append(char_to_idx[prefix[t + 1]])
        else:
            output.append(int(Y[0].argmax(dim=1).item()))
    return ''.join([idx_to_char[i] for i in output])


predict_res = predict_rnn('分开', 10, rnn, params, init_rnn_state, num_hiddens, vocab_size,
            device, idx_to_char, char_to_idx)
print(predict_res)

print("*"*50)


# 裁剪梯度
# 本函数已保存在d2lzh_pytorch包中方便以后使用
def grad_clipping(params, theta, device):
    norm = torch.tensor([0.0], device=device)
    for param in params:
        norm += (param.grad.data ** 2).sum()
    norm = norm.sqrt().item()
    if norm > theta:
        for param in params:
            param.grad.data *= (theta / norm)



#  定义模型训练函数
# 本函数已保存在d2lzh_pytorch包中方便以后使用
def train_and_predict_rnn(rnn, get_params, init_rnn_state, num_hiddens,
                          vocab_size, device, corpus_indices, idx_to_char,
                          char_to_idx, is_random_iter, num_epochs, num_steps,
                          lr, clipping_theta, batch_size, pred_period,
                          pred_len, prefixes):
    if is_random_iter:
        data_iter_fn = d2l.data_iter_random
    else:
        data_iter_fn = d2l.data_iter_consecutive
    params = get_params()
    loss = nn.CrossEntropyLoss()

    for epoch in range(num_epochs):
        if not is_random_iter:  # 如使用相邻采样，在epoch开始时初始化隐藏状态
            state = init_rnn_state(batch_size, num_hiddens, device)
        l_sum, n, start = 0.0, 0, time.time()
        data_iter = data_iter_fn(corpus_indices, batch_size, num_steps, device)
        for X, Y in data_iter:
            # Y为X的下一步的真实值，可理解为X的真实标签
            if is_random_iter:  # 如使用随机采样，在每个小批量更新前初始化隐藏状态
                state = init_rnn_state(batch_size, num_hiddens, device)
            else:
            # 否则需要使用detach函数从计算图分离隐藏状态, 这是为了
            # 使模型参数的梯度计算只依赖一次迭代读取的小批量序列(防止梯度计算开销太大)
                for s in state:
                    s.detach_()

            inputs = to_onehot(X, vocab_size)
            '''
            在这里有必要做一下说明：
            在本实例中：inputs = to_onehot(X, vocab_size)
            X为小批量样本，为32*35的张量，其中32为，32表示这个小批量包含32个样本，35表示每个样本长度为35个字符
            在做to_onehot（）变换后得到的inputs的长度为35*32*1027
            inputs为包含35个32*1027矩阵的张量
            1027为字典长度
            
            上面的操作可以理解为：
            将32个样本的第一个字拿出来，做成一个onehot矩阵，依次往后，将第35个字拿完，组成35个矩阵
            其实很奇怪的是，为什么要这样做，这样做的目的很不明了
            因为直接做成32个矩阵，每个矩阵对应着35个连续的字也可以达到做成onehot形式来输入进网络的目的
            
            并且，因为上面这种变换操作，导致了后面求损失函数时，标签Y需要进行 
            行列转换 torch.transpose(Y, 0, 1) ；这样才能和这里的变换对上
            '''



            '''
            下面是对上面要转换input的解释：
            通过针对上面问题进行更改代码，更改后代码为：ResNet_Modify.py
            在修改过程正就发现如果按照上面说的32个样本做成32个矩阵
            不仅会导致在get_params()函数中W_xh的设置要确定字典长度外
            学习的效果也很差，后面的数字一直处于重复状态（这个目前还没搞清楚是什么原因导致的）
            
            
            '''
            # outputs有num_steps个形状为(batch_size, vocab_size)的矩阵
            (outputs, state) = rnn(inputs, state, params)
            # 拼接之后形状为(num_steps * batch_size, vocab_size)
            outputs = torch.cat(outputs, dim=0)
            # Y的形状是(batch_size, num_steps)，转置后再变成长度为
            # batch * num_steps 的向量，这样跟输出的行一一对应

            # a  =torch.transpose(Y, 0, 1)
            # b = a.contiguous()
            # c = b.view(-1)
            y = torch.transpose(Y, 0, 1).contiguous().view(-1)
            # 使用交叉熵损失计算平均分类误差
            l = loss(outputs, y.long())

            # 梯度清0
            if params[0].grad is not None:
                for param in params:
                    param.grad.data.zero_()
            l.backward()
            grad_clipping(params, clipping_theta, device)  # 裁剪梯度
            d2l.sgd(params, lr, 1)  # 因为误差已经取过均值，梯度不用再做平均
            l_sum += l.item() * y.shape[0]
            n += y.shape[0]

        if (epoch + 1) % pred_period == 0:
            print('epoch %d, perplexity %f, time %.2f sec' % (
                epoch + 1, math.exp(l_sum / n), time.time() - start))
            for prefix in prefixes:
                print(' -', predict_rnn(prefix, pred_len, rnn, params, init_rnn_state,
                    num_hiddens, vocab_size, device, idx_to_char, char_to_idx))



num_epochs, num_steps, batch_size, lr, clipping_theta = 250, 35, 32, 1e2, 1e-2
pred_period, pred_len, prefixes = 50, 50, ['分开', '不分开']

# 随机采样训练模型
train_and_predict_rnn(rnn, get_params, init_rnn_state, num_hiddens,
                      vocab_size, device, corpus_indices, idx_to_char,
                      char_to_idx, True, num_epochs, num_steps, lr,
                      clipping_theta, batch_size, pred_period, pred_len,
                      prefixes)


print("*"*50)
# 相邻采样训练模型
train_and_predict_rnn(rnn, get_params, init_rnn_state, num_hiddens,
                      vocab_size, device, corpus_indices, idx_to_char,
                      char_to_idx, False, num_epochs, num_steps, lr,
                      clipping_theta, batch_size, pred_period, pred_len,
                      prefixes)




print("*"*50)
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修改之后的代码

# 本书链接https://tangshusen.me/Dive-into-DL-PyTorch/#/chapter03_DL-basics/3.8_mlp
# 6.4 循环神经网络的修改
# 注释：黄文俊
# E-mail：hurri_cane@qq.com

import time
import math
import numpy as np
import torch
from torch import nn, optim
import torch.nn.functional as F

import sys

sys.path.append("..")
import d2lzh_pytorch as d2l

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

(corpus_indices, char_to_idx, idx_to_char, vocab_size) = d2l.load_data_jay_lyrics()


def one_hot(x, n_class, dtype=torch.float32):
    # X shape: (batch), output shape: (batch, n_class)
    x = x.long()
    res = torch.zeros(x.shape[0], n_class, dtype=dtype, device=x.device)
    res.scatter_(1, x.view(-1, 1), 1)
    # res.scatter_(dim, index, src) 的参数有 3 个
    # dim：沿着哪个维度进行索引
    # index：用来 scatter 的元素索引
    # src：用来 scatter 的源元素，可以是一个标量或一个张量

    return res


x = torch.tensor([0, 2])
print(one_hot(x, vocab_size))


# 本函数已保存在d2lzh_pytorch包中方便以后使用
def to_onehot(X, n_class):
    # X shape: (batch, seq_len), output: seq_len elements of (batch, n_class)
    return [one_hot(X[i, :], n_class) for i in range(X.shape[0])]


X = torch.arange(10).view(2, 5)
# print(X[:, 0])
inputs = to_onehot(X, vocab_size)
print(len(inputs), inputs[0].shape)

# 初始化模型参数

num_inputs, num_hiddens, num_outputs = vocab_size, 256, vocab_size
print('will use', device)


def get_params():
    def _one(shape):
        ts = torch.tensor(np.random.normal(0, 0.01, size=shape), device=device, dtype=torch.float32)
        return torch.nn.Parameter(ts, requires_grad=True)

    # 隐藏层参数
    W_xh = _one((1027, num_hiddens))
    W_hh = _one((num_hiddens, num_hiddens))
    b_h = torch.nn.Parameter(torch.zeros(num_hiddens, device=device, requires_grad=True))
    # 输出层参数
    W_hq = _one((num_hiddens, num_outputs))
    b_q = torch.nn.Parameter(torch.zeros(num_outputs, device=device, requires_grad=True))
    return nn.ParameterList([W_xh, W_hh, b_h, W_hq, b_q])


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


def rnn(inputs, state, params):
    # inputs和outputs皆为num_steps个形状为(batch_size, vocab_size)的矩阵
    W_xh, W_hh, b_h, W_hq, b_q = params
    H, = state
    outputs = []
    for X in inputs:
        # a = torch.matmul(X, W_xh)
        # b = torch.matmul(H, W_hh)
        # c = b_h
        # ans = a + b + c
        H = torch.tanh(torch.matmul(X, W_xh) + torch.matmul(H, W_hh) + b_h)
        Y = torch.matmul(H, W_hq) + b_q
        outputs.append(Y)
    return outputs, (H,)


state = init_rnn_state(X.shape[1], num_hiddens, device)
inputs = to_onehot(X.to(device), vocab_size)
params = get_params()
outputs, state_new = rnn(inputs, state, params)
print(len(outputs), outputs[0].shape, state_new[0].shape)

print("*" * 50)


# 本函数已保存在d2lzh_pytorch包中方便以后使用
def predict_rnn(prefix, num_chars, rnn, params, init_rnn_state,
                num_hiddens, vocab_size, device, idx_to_char, char_to_idx):
    state = init_rnn_state(1, num_hiddens, device)
    output = [char_to_idx[prefix[0]]]
    for t in range(num_chars + len(prefix) - 1):
        # 将上一时间步的输出作为当前时间步的输入
        X = to_onehot(torch.tensor([[output[-1]]], device=device), vocab_size)
        # 计算输出和更新隐藏状态
        (Y, state) = rnn(X, state, params)
        # 下一个时间步的输入是prefix里的字符或者当前的最佳预测字符
        if t < len(prefix) - 1:
            output.append(char_to_idx[prefix[t + 1]])
        else:
            output.append(int(Y[0].argmax(dim=1).item()))
    return ''.join([idx_to_char[i] for i in output])


predict_res = predict_rnn('分开', 10, rnn, params, init_rnn_state, num_hiddens, vocab_size,
                          device, idx_to_char, char_to_idx)
print(predict_res)

print("*" * 50)


# 裁剪梯度
# 本函数已保存在d2lzh_pytorch包中方便以后使用
def grad_clipping(params, theta, device):
    norm = torch.tensor([0.0], device=device)
    for param in params:
        norm += (param.grad.data ** 2).sum()
    norm = norm.sqrt().item()
    if norm > theta:
        for param in params:
            param.grad.data *= (theta / norm)


#  定义模型训练函数
# 本函数已保存在d2lzh_pytorch包中方便以后使用
def train_and_predict_rnn(rnn, get_params, init_rnn_state, num_hiddens,
                          vocab_size, device, corpus_indices, idx_to_char,
                          char_to_idx, is_random_iter, num_epochs, num_steps,
                          lr, clipping_theta, batch_size, pred_period,
                          pred_len, prefixes):
    if is_random_iter:
        data_iter_fn = d2l.data_iter_random
    else:
        data_iter_fn = d2l.data_iter_consecutive
    params = get_params()
    loss = nn.CrossEntropyLoss()

    for epoch in range(num_epochs):
        if not is_random_iter:  # 如使用相邻采样，在epoch开始时初始化隐藏状态
            state = init_rnn_state(num_steps, num_hiddens, device)
        l_sum, n, start = 0.0, 0, time.time()
        data_iter = data_iter_fn(corpus_indices, batch_size, num_steps, device)
        for X, Y in data_iter:
            # Y为X的下一步的真实值，可理解为X的真实标签
            if is_random_iter:  # 如使用随机采样，在每个小批量更新前初始化隐藏状态
                state = init_rnn_state(num_steps, num_hiddens, device)
            else:
                # 否则需要使用detach函数从计算图分离隐藏状态, 这是为了
                # 使模型参数的梯度计算只依赖一次迭代读取的小批量序列(防止梯度计算开销太大)
                for s in state:
                    s.detach_()

            inputs = to_onehot(X, vocab_size)

            # outputs有num_steps个形状为(batch_size, vocab_size)的矩阵
            (outputs, state) = rnn(inputs, state, params)
            # 拼接之后形状为(num_steps * batch_size, vocab_size)
            outputs = torch.cat(outputs, dim=0)
            # Y的形状是(batch_size, num_steps)，转置后再变成长度为
            # batch * num_steps 的向量，这样跟输出的行一一对应

            # a  =torch.transpose(Y, 0, 1)
            # b = a.contiguous()
            # c = b.view(-1)
            y = Y.contiguous().view(-1)
            # 使用交叉熵损失计算平均分类误差
            l = loss(outputs, y.long())

            # 梯度清0
            if params[0].grad is not None:
                for param in params:
                    param.grad.data.zero_()
            l.backward()
            grad_clipping(params, clipping_theta, device)  # 裁剪梯度
            d2l.sgd(params, lr, 1)  # 因为误差已经取过均值，梯度不用再做平均
            l_sum += l.item() * y.shape[0]
            n += y.shape[0]

        if (epoch + 1) % pred_period == 0:
            print('epoch %d, perplexity %f, time %.2f sec' % (
                epoch + 1, math.exp(l_sum / n), time.time() - start))
            for prefix in prefixes:
                print(' -', predict_rnn(prefix, pred_len, rnn, params, init_rnn_state,
                                        num_hiddens, vocab_size, device, idx_to_char, char_to_idx))


num_epochs, num_steps, batch_size, lr, clipping_theta = 250, 35, 32, 1e2, 1e-2
pred_period, pred_len, prefixes = 50, 50, ['分开', '不分开']

# 随机采样训练模型
train_and_predict_rnn(rnn, get_params, init_rnn_state, num_hiddens,
                      vocab_size, device, corpus_indices, idx_to_char,
                      char_to_idx, True, num_epochs, num_steps, lr,
                      clipping_theta, batch_size, pred_period, pred_len,
                      prefixes)


print("*"*50)
# 相邻采样训练模型
train_and_predict_rnn(rnn, get_params, init_rnn_state, num_hiddens,
                      vocab_size, device, corpus_indices, idx_to_char,
                      char_to_idx, False, num_epochs, num_steps, lr,
                      clipping_theta, batch_size, pred_period, pred_len,
                      prefixes)




print("*" * 50)
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