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Modern Computer Vision with PyTorch

You're reading from  Modern Computer Vision with PyTorch

Product type Book
Published in Nov 2020
Publisher Packt
ISBN-13 9781839213472
Pages 824 pages
Edition 1st Edition
Languages
Authors (2):
V Kishore Ayyadevara V Kishore Ayyadevara
Profile icon V Kishore Ayyadevara
Yeshwanth Reddy Yeshwanth Reddy
Profile icon Yeshwanth Reddy
View More author details

Table of Contents (25) Chapters

Preface Section 1 - Fundamentals of Deep Learning for Computer Vision
Artificial Neural Network Fundamentals PyTorch Fundamentals Building a Deep Neural Network with PyTorch Section 2 - Object Classification and Detection
Introducing Convolutional Neural Networks Transfer Learning for Image Classification Practical Aspects of Image Classification Basics of Object Detection Advanced Object Detection Image Segmentation Applications of Object Detection and Segmentation Section 3 - Image Manipulation
Autoencoders and Image Manipulation Image Generation Using GANs Advanced GANs to Manipulate Images Section 4 - Combining Computer Vision with Other Techniques
Training with Minimal Data Points Combining Computer Vision and NLP Techniques Combining Computer Vision and Reinforcement Learning Moving a Model to Production Using OpenCV Utilities for Image Analysis Other Books You May Enjoy Appendix

Understanding the impact of varying the batch size

In the previous section, 32 data points were considered per batch in the training dataset. This resulted in a greater number of weight updates per epoch as there were 1,875 weight updates per epoch (60,000/32 is nearly equal to 1,875, where 60,000 is the number of training images).

Furthermore, we did not consider the model's performance on an unseen dataset (validation dataset). We will explore this in this section.

In this section, we will compare the following:

  • The loss and accuracy values of the training and validation data when the training batch size is 32.
  • The loss and accuracy values of the training and validation data when the training batch size is 10,000.

Now that we have brought validation data into the picture, let's rerun the code provided in the Building a neural network section with additional code to generate validation data, as well as to calculate the loss and accuracy values of the validation dataset.

The...
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