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