Supprimer 'main.py'

main.py

@@ -1,83 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from torchtext import data
-from gensim.corpora import WikiCorpus
-from transformers import GPT2Tokenizer, GPT2Model
-from functions import *
-
-# Define the hyperparameters
-num_layers = 2
-batch_size = 32
-hidden_dim = 256
-
-# Load the GPT2 model
-tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
-model = GPT2Model.from_pretrained('gpt2')
-
-# Load the data
-wiki_corpus = WikiCorpus('enwiki-latest-pages-articles.xml.bz2')
-stackoverflow_corpus = data.TabularDataset('stackoverflow.csv', format='csv', fields=['text'])
-
-# Preprocess the data
-wiki_data = [text for text in wiki_corpus]
-stackoverflow_data = [text for text in stackoverflow_corpus]
-
-# Convert the data to a format compatible with PyTorch
-wiki_data = torch.tensor(wiki_data)
-stackoverflow_data = torch.tensor(stackoverflow_data)
-
-# Define the Adam optimizer
-optimizer = optim.Adam(model.parameters(), lr=0.001)
-
-# Define the loss function
-criterion = nn.CrossEntropyLoss()
-
-# Train the model
-num_epochs=10
-labels = torch.tensor([0, 1, 1, 0, 0, 1, 0, 1, 0, 1])
-
-def adjust_learning_rate(optimizer, epoch):
-    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
-    lr = 0.001 * (0.1 ** (epoch // 30))
-    for param_group in optimizer.param_groups:
-        param_group['lr'] = lr
-
-for epoch in range(num_epochs):
-    # Forward pass
-    outputs = model(wiki_data, stackoverflow_data)
-    # Calculate the loss
-    loss = criterion(outputs, labels)
-    # Backward pass
-    loss.backward()
-    # Update the parameters
-    optimizer.step()
-    # Reset the gradients
-    optimizer.zero_grad()
-    # Evaluate the model
-    accuracy = evaluate(model, wiki_data)
-    # Save the model weights and states
-    torch.save(model.state_dict(), 'model.pth')
-    # Adjust the learning rate
-    adjust_learning_rate(optimizer, epoch)
-
-
-# Define the model
-class GPT(nn.Module):
-    def __init__(self, vocab_size, embedding_dim, hidden_dim, num_layers):
-        super().__init__()
-        self.embedding = nn.Embedding(vocab_size, embedding_dim)
-        self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers, batch_first=True)
-        self.fc = nn.Linear(hidden_dim, vocab_size)
-        self.gpt2 = model
-    
-    def forward(self, x):
-        # Embed the input
-        x = self.embedding(x)
-        # Pass through the GPT2 model
-        x = self.gpt2(x)
-        # Pass through the LSTM
-        x, _ = self.lstm(x)
-        # Pass through the fully connected layer
-        x = self.fc(x)
-        return x