What is a common optimal batch size for training a Convolutional Neural Network (CNN)?
Saturday, 15 June 2024
by dkarayiannakis
In the context of training Convolutional Neural Networks (CNNs) using Python and PyTorch, the concept of batch size is of paramount importance. Batch size refers to the number of training samples utilized in one forward and backward pass during the training process. It is a critical hyperparameter that significantly impacts the performance, efficiency, and generalization
- Published in Artificial Intelligence, EITC/AI/DLPP Deep Learning with Python and PyTorch, Convolution neural network (CNN), Training Convnet
Tagged under:
Artificial Intelligence, Batch Size, GPU Memory, Gradient Accumulation, Gradient Estimation, Learning Rate
What is the significance of the batch size in training a CNN? How does it affect the training process?
Sunday, 13 August 2023
by EITCA Academy
The batch size is a important parameter in training Convolutional Neural Networks (CNNs) as it directly affects the efficiency and effectiveness of the training process. In this context, the batch size refers to the number of training examples propagated through the network in a single forward and backward pass. Understanding the significance of the batch