Task5: multi type emotion analysis

In this study , We will have 6 Data sets of classes perform classification
You can use jupyter notebook function ！！！

import torch
from torchtext.legacy import data
from torchtext.legacy import datasets
import random

SEED = 1234
torch.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
TEXT = data.Field(tokenize = 'spacy',tokenizer_language = 'en_core_web_sm')
LABEL = data.LabelField()
train_data, test_data = datasets.TREC.splits(TEXT, LABEL, fine_grained=False)
train_data, valid_data = train_data.split(random_state = random.seed(SEED))
#  Building a vocabulary 
MAX_VOCAB_SIZE = 25_000
TEXT.build_vocab(train_data, 
                 max_size = MAX_VOCAB_SIZE, 
                 vectors = "glove.6B.100d", 
                 unk_init = torch.Tensor.normal_)
LABEL.build_vocab(train_data)
#  Build iterators 
BATCH_SIZE = 64
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_iterator, valid_iterator, test_iterator = data.BucketIterator.splits(
    (train_data, valid_data, test_data), 
    batch_size = BATCH_SIZE, 
    device = device)
#  Model building 
import torch.nn as nn
import torch.nn.functional as F
class CNN(nn.Module):
    def __init__(self, vocab_size, embedding_dim, n_filters, filter_sizes, output_dim, 
                 dropout, pad_idx):
        
        super().__init__()        
        self.embedding = nn.Embedding(vocab_size, embedding_dim)        
        self.convs = nn.ModuleList([
                                    nn.Conv2d(in_channels = 1, 
                                              out_channels = n_filters, 
                                              kernel_size = (fs, embedding_dim)) 
                                    for fs in filter_sizes
                                    ])       
        self.fc = nn.Linear(len(filter_sizes) * n_filters, output_dim)       
        self.dropout = nn.Dropout(dropout)        
    def forward(self, text):       
        #text = [sent len, batch size] 
        text = text.permute(1, 0)                
        #text = [batch size, sent len]
        embedded = self.embedding(text)   
        #embedded = [batch size, sent len, emb dim]
        embedded = embedded.unsqueeze(1)
        #embedded = [batch size, 1, sent len, emb dim]
        conved = [F.relu(conv(embedded)).squeeze(3) for conv in self.convs]
        #conv_n = [batch size, n_filters, sent len - filter_sizes[n]]
        pooled = [F.max_pool1d(conv, conv.shape[2]).squeeze(2) for conv in conved]
        #pooled_n = [batch size, n_filters] 
        cat = self.dropout(torch.cat(pooled, dim = 1))
        #cat = [batch size, n_filters * len(filter_sizes)] 
        return self.fc(cat)
#  Model parameter settings 
INPUT_DIM = len(TEXT.vocab)
EMBEDDING_DIM = 100
N_FILTERS = 100
FILTER_SIZES = [2,3,4]
OUTPUT_DIM = len(LABEL.vocab)
DROPOUT = 0.5
PAD_IDX = TEXT.vocab.stoi[TEXT.pad_token]
model = CNN(INPUT_DIM, EMBEDDING_DIM, N_FILTERS, FILTER_SIZES, OUTPUT_DIM, DROPOUT, PAD_IDX)
#  Load pre training model 
pretrained_embeddings = TEXT.vocab.vectors
model.embedding.weight.data.copy_(pretrained_embeddings)
#  use 0 Initialize unknown weights and padding Parameters 
UNK_IDX = TEXT.vocab.stoi[TEXT.unk_token]
model.embedding.weight.data[UNK_IDX] = torch.zeros(EMBEDDING_DIM)
model.embedding.weight.data[PAD_IDX] = torch.zeros(EMBEDDING_DIM)
#  Set up loss
import torch.optim as optim
optimizer = optim.Adam(model.parameters())
criterion = nn.CrossEntropyLoss()
model = model.to(device)
criterion = criterion.to(device)
#  Calculation accuracy 
def categorical_accuracy(preds, y):
    """ Returns accuracy per batch, i.e. if you get 8/10 right, this returns 0.8, NOT 8 """
    top_pred = preds.argmax(1, keepdim = True)
    correct = top_pred.eq(y.view_as(top_pred)).sum()
    acc = correct.float() / y.shape[0]
    return acc
#  Training 
def train(model, iterator, optimizer, criterion):
    
    epoch_loss = 0
    epoch_acc = 0    
    model.train()    
    for batch in iterator:        
        optimizer.zero_grad()        
        predictions = model(batch.text)        
        loss = criterion(predictions, batch.label)        
        acc = categorical_accuracy(predictions, batch.label)        
        loss.backward()        
        optimizer.step()        
        epoch_loss += loss.item()
        epoch_acc += acc.item()        
    return epoch_loss / len(iterator), epoch_acc / len(iterator)

#  evaluation 
def evaluate(model, iterator, criterion):    
    epoch_loss = 0
    epoch_acc = 0    
    model.eval()    
    with torch.no_grad():    
        for batch in iterator:
            predictions = model(batch.text)            
            loss = criterion(predictions, batch.label)            
            acc = categorical_accuracy(predictions, batch.label)
            epoch_loss += loss.item()
            epoch_acc += acc.item()        
    return epoch_loss / len(iterator), epoch_acc / len(iterator)

#  Time statistics 
import time

def epoch_time(start_time, end_time):
    elapsed_time = end_time - start_time
    elapsed_mins = int(elapsed_time / 60)
    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))
    return elapsed_mins, elapsed_secs

#  Training models 
N_EPOCHS = 5

best_valid_loss = float('inf')

for epoch in range(N_EPOCHS):

    start_time = time.time()
    
    train_loss, train_acc = train(model, train_iterator, optimizer, criterion)
    valid_loss, valid_acc = evaluate(model, valid_iterator, criterion)
    
    end_time = time.time()

    epoch_mins, epoch_secs = epoch_time(start_time, end_time)
    
    if valid_loss < best_valid_loss:
        best_valid_loss = valid_loss
        torch.save(model.state_dict(), 'tut5-model.pt')
    
    print(f'Epoch: {
      epoch+1:02} | Epoch Time: {
      epoch_mins}m {
      epoch_secs}s')
    print(f'\tTrain Loss: {
      train_loss:.3f} | Train Acc: {
      train_acc*100:.2f}%')
    print(f'\t Val. Loss: {
      valid_loss:.3f} | Val. Acc: {
      valid_acc*100:.2f}%')

#  test model 
model.load_state_dict(torch.load('tut5-model.pt'))
test_loss, test_acc = evaluate(model, test_iterator, criterion)
print(f'Test Loss: {
      test_loss:.3f} | Test Acc: {
      test_acc*100:.2f}%')