CodeSnips

python

import torch.nn as nn

class SmallCNN(nn.Module):
    def __init__(self, num_classes: int) -> None:
        super().__init__()
        self.features = nn.Sequential(

Convolutional neural networks for image classification in PyTorch

pytorch cnn computer-vision

by Dr. Elena Vasquez 1 tab

python

import pandas as pd
import torch
from PIL import Image
from torch.utils.data import Dataset, DataLoader

class ProductImageDataset(Dataset):

Custom Datasets and DataLoaders for robust training input pipelines

pytorch dataloader dataset

by Dr. Elena Vasquez 1 tab

python

import torch

best_val_loss = float('inf')

for epoch in range(1, num_epochs + 1):
    model.train()

A clean PyTorch training loop with validation and checkpoints

pytorch training-loop checkpoints

by Dr. Elena Vasquez 1 tab

python

import torch

device = 'cuda' if torch.cuda.is_available() else 'cpu'
features = torch.tensor([[1.0, 2.0], [3.0, 4.0]], requires_grad=True, device=device)
weights = torch.tensor([[0.2], [0.8]], requires_grad=True, device=device)

PyTorch tensor basics and automatic differentiation

pytorch tensors autograd

by Dr. Elena Vasquez 1 tab

python

import torch.nn as nn
from torchvision.models import resnet50, ResNet50_Weights

model = resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)

for parameter in model.parameters():

Transfer learning with pretrained torchvision backbones

pytorch transfer-learning torchvision

by Dr. Elena Vasquez 1 tab