mlp-cifar2

Multi-layer perceptron (MLP) trained on CIFAR-2 (a subset of CIFAR-10 for classifying 'airplane' vs. 'bird').

RandomCrop(24) was applied on the training set images, and Resize(24) was applied on the validation set images.

This model pertains to Exercise 1 of Chapter 7 of the book "Deep Learning with PyTorch" by Eli Stevens, Luca Antiga, and Thomas Viehmann.

Code: https://github.com/sambitmukherjee/dlwpt-exercises/blob/main/chapter_7/exercise_1.ipynb

Experiment tracking: https://wandb.ai/sadhaklal/mlp-cifar2

Usage

!pip install -q datasets

from datasets import load_dataset

cifar10 = load_dataset("cifar10")
label_map = {0: 0, 2: 1}
class_names = ['airplane', 'bird']
cifar2_train = [(example['img'], label_map[example['label']]) for example in cifar10['train'] if example['label'] in [0, 2]]
cifar2_val = [(example['img'], label_map[example['label']]) for example in cifar10['test'] if example['label'] in [0, 2]]

example = cifar2_val[0]
img, label = example

import torch
from torchvision.transforms import v2

val_tfms = v2.Compose([
    v2.Resize(24),
    v2.ToImage(),
    v2.ToDtype(torch.float32, scale=True),
    v2.Normalize(mean=[0.4915, 0.4823, 0.4468], std=[0.2470, 0.2435, 0.2616])
])
img = val_tfms(img)
batch = img.reshape(-1).unsqueeze(0) # Flatten.

import torch.nn as nn
from huggingface_hub import PyTorchModelHubMixin

class MLPForCIFAR2(nn.Module, PyTorchModelHubMixin):
    """Multi-layer perceptron (MLP) for classifying 'airplane' vs. 'bird' in the CIFAR-2 dataset (a subset of CIFAR-10)."""

    def __init__(self):
        super().__init__()
        self.mlp = nn.Sequential(
            nn.Linear(1728, 1024), # Hidden layer.
            nn.Tanh(),
            nn.Linear(1024, 512), # Hidden layer.
            nn.Tanh(),
            nn.Linear(512, 128), # Hidden layer.
            nn.Tanh(),
            nn.Linear(128, 2) # Output layer.
        )

    def forward(self, x):
        return self.mlp(x)

model = MLPForCIFAR2.from_pretrained("sadhaklal/mlp-cifar2")
model.eval()

import torch.nn.functional as F

with torch.no_grad():
    logits = model(batch)
    pred = logits[0].argmax().item()
    proba = F.softmax(logits, dim=1)

print(f"Predicted class: {class_names[pred]}")
print(f"Predicted class probabilities ('airplane' vs. 'bird'): {proba[0].tolist()}")

Metric

Accuracy on cifar2_val: 0.8090