Library of pytoan
Introduction
Installing
pip install pytoan
Usage
- Example model with MNIST
from pytoan.pytorch import Learning
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
from pathlib import Path
# Hyper parameters
num_classes = 10
batch_size = 100
learning_rate = 0.001
# MNIST dataset
train_dataset = torchvision.datasets.MNIST(root='../../data/',
train=True,
transform=transforms.ToTensor(),
download=True)
test_dataset = torchvision.datasets.MNIST(root='../../data/',
train=False,
transform=transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False,
pin_memory=True)
class ConvNet(nn.Module):
def __init__(self, num_classes=10):
super(ConvNet, self).__init__()
self.layer1 = nn.Sequential(
nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2))
self.layer2 = nn.Sequential(
nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2))
self.fc = nn.Linear(7*7*32, num_classes)
def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = out.reshape(out.size(0), -1)
out = self.fc(out)
return out
def accuracy_score(output, target):
with torch.no_grad():
pred = torch.argmax(output, dim=1)
assert pred.shape[0] == len(target)
correct = 0
correct += torch.sum(pred == target).item()
return correct / len(target)
model = ConvNet(num_classes)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)
metric_ftns = [accuracy_score]
device = [0]
num_epoch = 100
gradient_clipping = 0.1
gradient_accumulation_steps = 1
early_stopping = 10
validation_frequency = 1
tensorboard = True
checkpoint_dir = Path('./', type(model).__name__)
checkpoint_dir.mkdir(exist_ok=True, parents=True)
resume_path = None
learning = Learning(model=model,
criterion=criterion,
optimizer=optimizer,
scheduler = scheduler,
metric_ftns=metric_ftns,
device=device,
num_epoch=num_epoch,
grad_clipping = gradient_clipping,
grad_accumulation_steps = gradient_accumulation_steps,
early_stopping = early_stopping,
validation_frequency = validation_frequency,
tensorboard = tensorboard,
checkpoint_dir = checkpoint_dir,
resume_path=resume_path)
- For Training and Validation
learning.train(train_loader, test_loader)
Log:
- For Testing
learning.test(test_loader) # but not complete
1 maintainer
Release files for pytoan 0.7.2.2.9
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
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|---|---|---|---|---|
| pytoan-0.7.2.2.9-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 24.0 kB
Release files / pytoan-0.7.2.2.9.tar.gz
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