我的时间序列预测变压器模型的训练损失和准确度都在下降

Posted 2023-03-25

【中文标题】我的时间序列预测变压器模型的训练损失和准确度都在下降

【英文标题】：Training Loss and Accuracy both decreasing for my transformer model for Time Series Prediction

【发布时间】：2020-12-31 07:16:46

【问题描述】：

我正在使用一个变压器模型来预测外汇市场。我转换了开盘价数据并计算了每 30 分钟间隔之间的差异。并将差额转换为代币。通过将 log1.5 应用于差异来获得令牌。我用了 6 年时间获得了 28 种代币。 14-27代表牛市，0-13代表熊市。 我在 PyTorch 中创建了一个变压器模型并应用了数据。

import torch 
import math 
import numpy as np
import copy
from torch import nn
from torch.utils.data import TensorDataset
from torch.utils.data import DataLoader
import ast 
from numpy import load
import torch.nn as nn
import random
import time
import matplotlib.pyplot as plt


class Embedder(nn.Module):
    def __init__(self, vocab_size, d_model):
        super().__init__()
        # print(vocab_size,d_model)
        self.embed = nn.Embedding(vocab_size+1, d_model,padding_idx=0)
    def forward(self, x):
        # print(x.shape)
        # print("Embed",self.embed(x).shape)
        return self.embed(x)

class PositionalEncoder(nn.Module):
    def __init__(self, d_model, max_seq_len = 500):
        super().__init__()
        self.d_model = d_model
        
        # create constant 'pe' matrix with values dependant on 
        # pos and i
        pe = torch.zeros(max_seq_len, d_model)
        for pos in range(max_seq_len):
            for i in range(0, d_model, 2):
                pe[pos, i] = \
                math.sin(pos / (10000 ** ((2 * i)/d_model)))
                pe[pos, i + 1] = \
                math.cos(pos / (10000 ** ((2 * (i + 1))/d_model)))
                
        pe = pe.unsqueeze(0)
        self.register_buffer('pe', pe)
 
    
    def forward(self, x):
        x = x * math.sqrt(self.d_model)
        seq_len = x.size(1)
        x = x + torch.autograd.Variable(self.pe[:,:seq_len],requires_grad=False)
        return x

def attention(q, k, v, d_k, mask=None, dropout=None):
    
    scores = torch.matmul(q, k.transpose(-2, -1)) /  math.sqrt(d_k)
    if mask is not None:
        mask = mask.unsqueeze(1)
        scores = scores.masked_fill(mask == 0, -1e9)
        scores = torch.nn.functional.softmax(scores, dim=-1)
    
    if dropout is not None:
        scores = dropout(scores)
        
    output = torch.matmul(scores, v)
    return output


class MultiHeadAttention(nn.Module):
    def __init__(self, heads, d_model, dropout = 0.1):
        super().__init__()
        
        self.d_model = d_model
        self.d_k = d_model // heads
        self.h = heads
        
        self.q_linear = nn.Linear(d_model, d_model)
        self.v_linear = nn.Linear(d_model, d_model)
        self.k_linear = nn.Linear(d_model, d_model)
        self.dropout = nn.Dropout(dropout)
        self.out = nn.Linear(d_model, d_model)
    
    def forward(self, q, k, v, mask=None):
        
        bs = q.size(0)
        
        # perform linear operation and split into h heads
        
        k = self.k_linear(k).view(bs, -1, self.h, self.d_k)
        q = self.q_linear(q).view(bs, -1, self.h, self.d_k)
        v = self.v_linear(v).view(bs, -1, self.h, self.d_k)
        
        # transpose to get dimensions bs * h * sl * d_model
       
        k = k.transpose(1,2)
        q = q.transpose(1,2)
        v = v.transpose(1,2)
        # calculate attention using function we will define next
        scores = attention(q, k, v, self.d_k, mask, self.dropout)
        
        # concatenate heads and put through final linear layer
        concat = scores.transpose(1,2).contiguous()\
        .view(bs, -1, self.d_model)
        
        output = self.out(concat)
    
        return output

class FeedForward(nn.Module):
    def __init__(self, d_model, d_ff=512, dropout = 0.1):
        super().__init__() 
        # We set d_ff as a default to 2048
        self.linear_1 = nn.Linear(d_model, d_ff)
        self.dropout = nn.Dropout(dropout)
        self.linear_2 = nn.Linear(d_ff, d_model)
    def forward(self, x):
        x = self.dropout(torch.nn.functional.relu(self.linear_1(x)))
        x = self.linear_2(x)
        return x

class Norm(nn.Module):
    def __init__(self, d_model, eps = 1e-6):
        super().__init__()
    
        self.size = d_model
        # create two learnable parameters to calibrate normalisation
        self.alpha = nn.Parameter(torch.ones(self.size))
        self.bias = nn.Parameter(torch.zeros(self.size))
        self.eps = eps
    def forward(self, x):
        norm = self.alpha * (x - x.mean(dim=-1, keepdim=True)) \
        / (x.std(dim=-1, keepdim=True) + self.eps) + self.bias
        return norm

class EncoderLayer(nn.Module):
    def __init__(self, d_model, heads, dropout = 0.1):
        super().__init__()
        self.norm_1 = Norm(d_model)
        self.norm_2 = Norm(d_model)
        self.attn = MultiHeadAttention(heads, d_model)
        self.ff = FeedForward(d_model)
        self.dropout_1 = nn.Dropout(dropout)
        self.dropout_2 = nn.Dropout(dropout)
        
    def forward(self, x, mask):
        x2 = self.norm_1(x)
        x = x + self.dropout_1(self.attn(x2,x2,x2,mask))
        x2 = self.norm_2(x)
        x = x + self.dropout_2(self.ff(x2))
        return x
    
class DecoderLayer(nn.Module):
    def __init__(self, d_model, heads, dropout=0.1):
        super().__init__()
        self.norm_1 = Norm(d_model)
        self.norm_2 = Norm(d_model)
        self.norm_3 = Norm(d_model)
        
        self.dropout_1 = nn.Dropout(dropout)
        self.dropout_2 = nn.Dropout(dropout)
        self.dropout_3 = nn.Dropout(dropout)
        
        self.attn_1 = MultiHeadAttention(heads, d_model)
        self.attn_2 = MultiHeadAttention(heads, d_model)
        self.ff = FeedForward(d_model).cuda()
        # self.ff = FeedForward(d_model)

    def forward(self, x, e_outputs, src_mask, trg_mask):
        x2 = self.norm_1(x)
        x = x + self.dropout_1(self.attn_1(x2, x2, x2, trg_mask))
        x2 = self.norm_2(x)
        x = x + self.dropout_2(self.attn_2(x2, e_outputs, e_outputs,
        src_mask))
        x2 = self.norm_3(x)
        x = x + self.dropout_3(self.ff(x2))
        return x
# We can then build a convenient cloning function that can generate multiple layers:
def get_clones(module, N):
    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])

class Encoder(nn.Module):
    def __init__(self, vocab_size, d_model, N, heads):
        super().__init__()
        self.N = N
        self.embed = Embedder(vocab_size, d_model)
        self.pe = PositionalEncoder(d_model)
        self.layers = get_clones(EncoderLayer(d_model, heads), N)
        self.norm = Norm(d_model)
        
    def forward(self, src, mask):
        x = self.embed(src)
        x = self.pe(x)
        for i in range(self.N):
            x = self.layers[i](x, mask)
        return self.norm(x)
    
class Decoder(nn.Module):
    def __init__(self, vocab_size, d_model, N, heads):
        super().__init__()
        self.N = N
        self.embed = Embedder(vocab_size, d_model)
        self.pe = PositionalEncoder(d_model)
        self.layers = get_clones(DecoderLayer(d_model, heads), N)
        self.norm = Norm(d_model)
    def forward(self, trg, e_outputs, src_mask, trg_mask):
        x = self.embed(trg)
        x = self.pe(x)
        for i in range(self.N):
            x = self.layers[i](x, e_outputs, src_mask, trg_mask)
        return self.norm(x)

class Transformer(nn.Module):
    def __init__(self, src_vocab, trg_vocab, d_model, N, heads):
        super().__init__()
        self.encoder = Encoder(src_vocab, d_model, N, heads)
        self.decoder = Decoder(trg_vocab, d_model, N, heads)
        self.out = nn.Linear(d_model, trg_vocab)

    def forward(self, src, trg, src_mask, trg_mask):
        e_outputs = self.encoder(src, src_mask)
        d_output = self.decoder(trg, e_outputs, src_mask, trg_mask)
        output = self.out(d_output)
        return output


def batchify(data, bsz):
    nbatch = data.size(0) // bsz
    data = data.narrow(0, 0, nbatch * bsz)
    data = data.view(bsz, -1).t().contiguous()
    return data

bptt = 128
class CustomDataLoader:
    def __init__(self,source):
        print("Source",source.shape)
        self.batches = list(range(0, source.size(0) - 2*bptt))
        # random.shuffle(self.batches)
        # print(self.batches)
        self.data = source
        self.sample = random.sample(self.batches,120)

    def batchcount(self):
        return len(self.batches)

    def shuffle_batches(self):
        random.shuffle(self.batches)

    def get_batch_from_batches(self,i):
        if i==0:
            random.shuffle(self.batches)
        ind = self.batches[i]
        seq_len = min(bptt,len(self.data)-1-ind)
        src = self.data[ind:ind+seq_len]
        tar = self.data[ind+seq_len-3:ind+seq_len-3+seq_len+1]
        return src,tar
        
    def get_batch(self,i):
        # print(i,len(self.batches))
        ind = self.sample[i]
        seq_len = min(bptt,len(self.data)-1-ind)
        src = self.data[ind:ind+seq_len]
        tar = self.data[ind+seq_len-3:ind+seq_len-3+seq_len+1]
        # tar = tar.view(-1)
        if(i==len(self.sample)-1):
            random.sample(self.batches,60)
            # print("Data shuffled",self.batches[:10])
        return src,tar

def get_batch(source, i):
    seq_len = min(bptt, len(source) - 1 - i)
    data = source[i:i+seq_len]
    target = source[i+seq_len-3:i+seq_len-3+seq_len]
    return data, target

def plot_multiple(data,legend):
    fig,ax = plt.subplots()
    for line in data:
        plt.plot(list(range(len(line))),line)
    plt.legend(legend)
    plt.show()


def plot_subplots(data,legends,name):
    names = ['Accuracy', 'Loss']
    plt.figure(figsize=(10, 5))
    for i in range(len(data)):
        plt.subplot(121+i)
        plt.plot(list(range(0,len(data[i])*3,3)),data[i])
        plt.title(legends[i])
        plt.xlabel("Epochs")
    plt.savefig(name)

def evaluate(eval_model, data_source):
    eval_model.eval() # Turn on the evaluation mode
    total_loss = 0.
    ntokens = 28
    count = 0
    with torch.no_grad():
        cum_loss = 0
        acc_count = 0
        accs = 0
        print(data_source.shape)
        for batch, i in enumerate(range(0, data_source.size(0) - bptt*2, bptt)):
            data, targets = get_batch(data_source, i)
            # data,targets = dataLoader.get_batch(i)
            data = data.transpose(0,1).contiguous()
            targets= targets.transpose(0,1).contiguous()
            trg_input = targets[:,:-1]
            trg_output = targets[:,1:].contiguous().view(-1)
            src_mask , trg_mask = create_masks(data,trg_input)
            output = model(data,trg_input,src_mask,trg_mask)
            output = output.view(-1,output.size(-1))
            loss = torch.nn.functional.cross_entropy(output,trg_output-1)
            accs += ((torch.argmax(output,dim=1)==trg_output).sum().item()/output.size(0))
            # accs += ((torch.argmax(output,dim=1)==targets).sum().item()/output.size(0))
            cum_loss += loss
            count+=1
        # print(epoch,"Loss: ",(cum_loss/count),"Accuracy ",accs/count)

    return cum_loss/ (count), accs/count

def nopeak_mask(size,cuda_enabled):
    np_mask = np.triu(np.ones((1, size, size)),
    k=1).astype('uint8')
    np_mask =  torch.autograd.Variable(torch.from_numpy(np_mask) == 0)

    if cuda_enabled:
      np_mask = np_mask.cuda()
    return np_mask

def create_masks(src, trg):
    src_mask = (src != 0).unsqueeze(-2)
    if trg is not None:
        trg_mask = (trg != 0).unsqueeze(-2)
        size = trg.size(1) # get seq_len for matrix
        # print("Sequence lenght in mask ",size)
        np_mask = nopeak_mask(size,True)
        # print(np_mask.shape,trg_mask.shape)
        if trg.is_cuda:
            np_mask.cuda()
        trg_mask = trg_mask & np_mask
    else:
        trg_mask = None
    return src_mask, trg_mask

def create_padding_mask(seq):
  seq = tf.cast(tf.math.equal(seq, 0), tf.float32)
  
  # add extra dimensions to add the padding
  # to the attention logits.
  return seq[:, tf.newaxis, tf.newaxis, :]  # (batch_size, 1, 1, seq_len)


if __name__ == '__main__':
    data = []
    dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    procsd_data = load("Eavg_open.npy")
    print(set(procsd_data[:,0]))
    train_data =torch.tensor(procsd_data)[:30000*2]
    print(train_data.shape)
    val_data = torch.tensor(procsd_data)[30000*2:35000*2]
    test_data = torch.tensor(procsd_data)[35000*2:]
    train_data = train_data.to(dev)
    val_data = val_data.to(dev)
    test_data = test_data.to(dev)

    # train_data = train_data.transpose(1,0).contiguous()
    # val_data = val_data.transpose(1,0).contiguous()

    batch_size = 32
    ntokens = 28
    train_data = batchify(train_data,batch_size)
    # print(train_data.shape)
    val_data = batchify(val_data,batch_size)
    test_data = batchify(train_data,batch_size)
    # model = Transformer(n_blocks=3,d_model=256,n_heads=8,d_ff=256,dropout=0.5)
    model = Transformer(28,28,64,3,4)
    # model = torch.load("modela")
    for p in model.parameters():
        if p.dim() > 1:
            nn.init.xavier_uniform_(p)

    model.to(dev)
    criterion = nn.CrossEntropyLoss()
    lr = 0.00001 # learning rate
    
    optim = torch.optim.Adam(model.parameters(), lr=0.0001, betas=(0.9, 0.98), eps=1e-9)
    #########training starts###########

    accuracies = []
    lossies = []
    val_loss = []
    val_accuracy = []
    dataLoader = CustomDataLoader(train_data)
    _onehot = torch.eye(29)
    for epoch in range(500):
        count = 0
        cum_loss = 0
        acc_count = 0
        accs = 0
        s = time.time()
        # for i in range(len(range(0, train_data.size(0) - bptt))):
        model.train()
        # dataLoader.shuffle_batches()
        for i in range(300):
            # data, targets = get_batch(train_data, i)
            # d = time.time()
            hh = time.time()
            data,targets = dataLoader.get_batch_from_batches(i)
            data = data.transpose(0,1).contiguous()
            targets= targets.transpose(0,1).contiguous()
            # print(data.shape,targets.shape)
            trg_input = targets[:,:-1]
            trg_output = targets[:,1:].contiguous().view(-1)
            # print(data.shape,trg_input.shape)
            src_mask , trg_mask = create_masks(data,trg_input)
            # print("Source Mask",src_mask)
            # print("Target Mask",trg_mask)
            output = model(data,trg_input,src_mask,trg_mask)
            # output = output.view(-1,28)
            output = output.view(-1,output.size(-1))

            loss = torch.nn.functional.cross_entropy(output,trg_output-1)
            accuracy = ((torch.argmax(output,dim=1)==trg_output).sum().item()/output.size(0))
            accs += accuracy
            cum_loss += loss.item();
            loss.backward()
            optim.step()
            model.zero_grad()
            optim.zero_grad()
            print(i," Batch Loss", loss.item()," Batch Accuracy ",accuracy," Time taken ",time.time()-hh)
            
            count+=1
            
        data,targets = None,None
        print(epoch,"Loss: ",(cum_loss/count),"Accuracy ",accs/count," Time Taken: ",time.time()-s)
        if(epoch%3==0):
            lossies.append(cum_loss/count)
            accuracies.append(accs/count)
            legend = ["accuracy","Loss"]
            plot_subplots([accuracies,lossies],legend,"A&L_v1")
            print("Valdata",val_data.shape)
            eval_loss,eval_acc = evaluate(model,val_data)
            val_accuracy.append(eval_acc)
            val_loss.append(eval_loss)
            plot_subplots([val_accuracy,val_loss],legend,"Val A&L_v1")
            print(epoch,"Loss: ",(cum_loss/count),"Accuracy ",accs/count," Valid_loss: ",eval_loss," Valid_accuracy: ",eval_acc)
            if len(val_loss)>0 and eval_loss < val_loss[-1]:
                val_loss.append(eval_loss)
                torch.save(model,"evalModel")
            else:
                val_loss.append(eval_loss)
                torch.save(model,"evalModel")
        if(epoch%5==0):
            torch.save(model,"modela")

我在训练时得到以下损失和准确率：

是什么导致了这种行为？ 我的标记化方法错了吗？ 是否需要在数据中添加任何时间嵌入？

【参考方案1】：

其实我在计算准确率时犯了一个小错误。

accuracy = ((torch.argmax(output,dim=1)==trg_output).sum().item()/output.size(0))

这里 trg_output 的标记从 1 到 n 编号，但用于输出的 argmax 函数返回的范围从 0 到 n-1。所以这导致了这个问题。

所以我将上面的行修改为

accuracy = ((torch.argmax(output,dim=1)==(trg_output-1) ).sum().item()/output.size(0))

评估函数也应如此。