Skip to main content

A minimal library for plotting training progress in Jupyter/Colab

Project description

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

Installation

pip install tinygraphs

Options

train_losses: (Required) List of training loss values to plot

val_losses: (Required) List of validation loss values to plot

epoch: (Required) Current epoch number for updating the plot

legend_loc: Location of the legend on the plot (default: "upper right")

updating_title: Boolean flag to indicate if the title should update with epoch progress (default: True)

legend: Boolean flag to show/hide the legend (default: True)

x_label: Label for the x-axis

y_label: Label for the y-axis

title: Main title of the plot

dark_mode: Boolean flag to switch between light and dark themes (default: True)

Usage

import tinygraphs as tg

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
    tg.plot(train_losses, val_losses, epoch, legend_loc = "upper right", updating_title = False, legend = True, x_label = "x title", y_label = "y title", title = "title", dark_mode = False)

Other Usage Options

    tg.plot(train_losses, val_losses, epoch, legend_loc = "upper right", updating_title = False, legend = True, x_label = "Epoch", y_label = "Loss", title = "Loss Graph", dark_mode = True)

Dark Mode is Awesome!

    tg.plot(train_losses, val_losses, epoch, theme = 'jet')

Beautiful Loss Graph With Barely Any Code!

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

tinygraphs-2.0.0.tar.gz (4.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

tinygraphs-2.0.0-py3-none-any.whl (4.4 kB view details)

Uploaded Python 3

File details

Details for the file tinygraphs-2.0.0.tar.gz.

File metadata

  • Download URL: tinygraphs-2.0.0.tar.gz
  • Upload date:
  • Size: 4.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.3

File hashes

Hashes for tinygraphs-2.0.0.tar.gz
Algorithm Hash digest
SHA256 8264f679363cf696722f7f0b7b533366da55e4db0c66153f37a096bda9f1af2f
MD5 6f72ce9db4adf2a8e060208b584bd2a7
BLAKE2b-256 ce599be29a637a9c34363d415746b4564b49eaa7f9ab7030c45fb55e505c2a85

See more details on using hashes here.

File details

Details for the file tinygraphs-2.0.0-py3-none-any.whl.

File metadata

  • Download URL: tinygraphs-2.0.0-py3-none-any.whl
  • Upload date:
  • Size: 4.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.3

File hashes

Hashes for tinygraphs-2.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 da8814999b0d35630489de03cb2865052480a55c6005f86c111f0a6bd9dd7529
MD5 4d2b244a69d56d9a766661bee436c830
BLAKE2b-256 7e8704eb73013bbf096ffc7403b3c6b85deaa7057783ff510740062b942095ef

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page