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How to train your model with PyTorch

Stan Mtete answered on June 19, 2023 Popularity 9/10 Helpfulness 4/10

answer How to train your model with PyTorch

How to train your model with PyTorch

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import torch
import torch.nn as nn
import torch.optim as optim
from torchtext.data import Field, TabularDataset, BucketIterator

# Step 1: Prepare the data
text_field = Field(tokenize='spacy', lower=True)
label_field = Field(sequential=False, use_vocab=False)

train_data, test_data = TabularDataset.splits(
    path='path_to_dataset_folder',
    train='train.csv',
    test='test.csv',
    format='csv',
    fields=[('text', text_field), ('label', label_field)]
)

text_field.build_vocab(train_data)

# Step 2: Define the model
class TextClassifier(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim):
        super(TextClassifier, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.fc = nn.Linear(embedding_dim, hidden_dim)
        self.output = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        embedded = self.embedding(x)
        hidden = torch.mean(embedded, dim=1)
        out = torch.relu(self.fc(hidden))
        return self.output(out)

vocab_size = len(text_field.vocab)
embedding_dim = 100
hidden_dim = 256
output_dim = 2

model = TextClassifier(vocab_size, embedding_dim, hidden_dim, output_dim)

# Step 3: Define loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters())

# Step 4: Train the model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
train_iterator, test_iterator = BucketIterator.splits(
    (train_data, test_data),
    batch_size=32,
    device=device
)

num_epochs = 10
for epoch in range(num_epochs):
    model.train()
    for batch in train_iterator:
        optimizer.zero_grad()
        text = batch.text
        labels = batch.label
        predictions = model(text).squeeze(1)
        loss = criterion(predictions, labels)
        loss.backward()
        optimizer.step()

# Step 5: Evaluate the model
model.eval()
correct = 0
total = 0
with torch.no_grad():
    for batch in test_iterator:
        text = batch.text
        labels = batch.label
        predictions = model(text).squeeze(1)
        predicted_labels = torch.argmax(predictions, dim=1)
        correct += (predicted_labels == labels).sum().item()
        total += labels.size(0)

accuracy = correct / total
print(f"Accuracy: {accuracy * 100}%")

Popularity 9/10 Helpfulness 4/10 Language python

Source: chat.openai.com

Tags: model python pytorch

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Contributed on Jun 19 2023

Stan Mtete

0 Answers Avg Quality 2/10

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