Product
Introducing License Enforcement in Socket
Ensure open-source compliance with Socket’s License Enforcement Beta. Set up your License Policy and secure your software!
Keras style progressbar for pytorch (PK Bar)
pkbar.Pbar
(progress bar)loading and processing dataset
10/10 [==============================] - 1.0s
pkbar.Kbar
(keras bar)Epoch: 1/3
100/100 [========] - 10s 102ms/step - loss: 3.7782 - rmse: 1.1650 - val_loss: 0.1823 - val_rmse: 0.4269
Epoch: 2/3
100/100 [========] - 10s 101ms/step - loss: 0.1819 - rmse: 0.4265 - val_loss: 0.1816 - val_rmse: 0.4261
Epoch: 3/3
100/100 [========] - 10s 101ms/step - loss: 0.1813 - rmse: 0.4258 - val_loss: 0.1810 - val_rmse: 0.4254
pip install pkbar
pkbar.Pbar
(progress bar)import pkbar
import time
pbar = pkbar.Pbar(name='loading and processing dataset', target=10)
for i in range(10):
time.sleep(0.1)
pbar.update(i)
loading and processing dataset
10/10 [==============================] - 1.0s
pkbar.Kbar
(keras bar) for a concreate exampleimport pkbar
import torch
# training loop
train_per_epoch = num_of_batches_per_epoch
for epoch in range(num_epochs):
################################### Initialization ########################################
kbar = pkbar.Kbar(target=train_per_epoch, epoch=epoch, num_epochs=num_epochs, width=8, always_stateful=False)
# By default, all metrics are averaged over time. If you don't want this behavior, you could either:
# 1. Set always_stateful to True, or
# 2. Set stateful_metrics=["loss", "rmse", "val_loss", "val_rmse"], Metrics in this list will be displayed as-is.
# All others will be averaged by the progbar before display.
###########################################################################################
# training
for i in range(train_per_epoch):
outputs = model(inputs)
train_loss = criterion(outputs, targets)
train_rmse = torch.sqrt(train_loss)
optimizer.zero_grad()
train_loss.backward()
optimizer.step()
############################# Update after each batch ##################################
kbar.update(i, values=[("loss", train_loss), ("rmse", train_rmse)])
########################################################################################
# validation
outputs = model(inputs)
val_loss = criterion(outputs, targets)
val_rmse = torch.sqrt(val_loss)
################################ Add validation metrics ###################################
kbar.add(1, values=[("val_loss", val_loss), ("val_rmse", val_rmse)])
###########################################################################################
Epoch: 1/3
100/100 [========] - 10s 102ms/step - loss: 3.7782 - rmse: 1.1650 - val_loss: 0.1823 - val_rmse: 0.4269
Epoch: 2/3
100/100 [========] - 10s 101ms/step - loss: 0.1819 - rmse: 0.4265 - val_loss: 0.1816 - val_rmse: 0.4261
Epoch: 3/3
100/100 [========] - 10s 101ms/step - loss: 0.1813 - rmse: 0.4258 - val_loss: 0.1810 - val_rmse: 0.4254
Keras progbar's code from tf.keras.utils.Progbar
FAQs
Keras Progress Bar for PyTorch
We found that pkbar demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Product
Ensure open-source compliance with Socket’s License Enforcement Beta. Set up your License Policy and secure your software!
Product
We're launching a new set of license analysis and compliance features for analyzing, managing, and complying with licenses across a range of supported languages and ecosystems.
Product
We're excited to introduce Socket Optimize, a powerful CLI command to secure open source dependencies with tested, optimized package overrides.