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---
datasets:
- cifar10
metrics:
- accuracy
library_name: pytorch
pipeline_tag: image-classification
---
# mlp-cifar2-v2
Multi-layer perceptron (MLP) trained on CIFAR-2 (a subset of CIFAR-10 for classifying 'airplane' vs. 'bird').
`nn.BCEWithLogitsLoss` was used to train the model.
This model pertains to Exercise 2 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_2.ipynb
Experiment tracking: https://wandb.ai/sadhaklal/mlp-cifar2-v2
## Usage
```
!pip install -q datasets
from datasets import load_dataset
cifar10 = load_dataset("cifar10")
label_map = {0: 0.0, 2: 1.0}
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.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):
def __init__(self):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(3072, 64), # Hidden layer.
nn.Tanh(),
nn.Linear(64, 1) # Output layer.
)
def forward(self, x):
return self.mlp(x)
model = MLPForCIFAR2.from_pretrained("sadhaklal/mlp-cifar2-v2")
model.eval()
import torch.nn.functional as F
with torch.no_grad():
logits = model(batch)
proba = F.sigmoid(logits.squeeze())
pred = int(proba.item() > 0.5)
print(f"Predicted class: {class_names[pred]}")
print(f"Predicted class probabilities ('airplane' vs. 'bird'): {[proba.item(), 1 - proba.item()]}")
```
## Metric
Accuracy on `cifar2_val`: 0.829
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