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-2.0.3.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-2.0.3-py3-none-any.whl (4.4 kB view details)

Uploaded Python 3

File details

Details for the file tinygraphs-2.0.3.tar.gz.

File metadata

  • Download URL: tinygraphs-2.0.3.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-2.0.3.tar.gz
Algorithm Hash digest
SHA256 137257c06f262e608a53f5b90e6aa88e30bb13ebba37cb1858c8de8999ed6bc6
MD5 7843c4bc55b513c793a28d1decfddde6
BLAKE2b-256 881729faff6a2f49468850415ea4313df75958355b2bb5fd1b2d697b07ec5223

See more details on using hashes here.

File details

Details for the file tinygraphs-2.0.3-py3-none-any.whl.

File metadata

  • Download URL: tinygraphs-2.0.3-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-2.0.3-py3-none-any.whl
Algorithm Hash digest
SHA256 89ea32fb46f335d90924f3429f585f50a34f2da624d86fb4bdb8241ce8be5d50
MD5 f75e3ae95552b2b62fe33125bfb0956a
BLAKE2b-256 d3d61d836b0b3066e616a6e43784196a84b5e17ec0975f99963cb5099bc0402c

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