Metadata-Version: 2.2
Name: tinygraphs
Version: 0.0.2
Summary: A minimal library for plotting training progress in Jupyter/Colab
Author-email: rkal <rohkal505@gmail.com>
License: MIT License
        
        Copyright (c) 2024 [YOUR NAME]
        
        Permission is hereby granted, free of charge, to any person obtaining a copy 
        of this software and associated documentation files (the "Software"), to deal 
        in the Software without restriction, including without limitation the rights 
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 
        copies of the Software, and to permit persons to whom the Software is 
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in 
        all copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN 
        THE SOFTWARE.
Keywords: training,plotting,machine-learning,notebook
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: matplotlib
Requires-Dist: IPython

# tinyGraphs

A minimal library for plotting training progress in Jupyter/COLAB notebooks.

## Installation

pip install tinyGraphs

## Usage

```python

train_losses, val_losses = [], []
epochs = 3

for epoch in range(epochs):
    # Training
    model.train()
    running_train_loss = 0
    for x, y in train_loader:
        x, y = x.to(device), y.to(device)
        optimizer.zero_grad()
        loss = criterion(model(x), y)
        loss.backward()
        optimizer.step()
        running_train_loss += loss.item()
    train_losses.append(running_train_loss / len(train_loader))

    # Validation
    model.eval()
    running_val_loss = 0
    with torch.no_grad():
        for x, y in val_loader:
            x, y = x.to(device), y.to(device)
            loss = criterion(model(x), y)
            running_val_loss += loss.item()
    val_losses.append(running_val_loss / len(val_loader))

    # Plot using tinyGraphs
    plot_training_progress(train_losses, val_losses, epoch, y_max = 0.5)
