Category Archives: Keras
Filters, kernel size, input shape in Conv2d layer
We don’t explicitly define the filters that our convolutional layer will use, instead parameterize the filters and let the network learn the best filters to use during training. We need to define “how many filters we’ll use at each layer”.
How to save Keras training History object to File using Callback?
You can learn a lot about Keras models by observing their History objects after training. In this post, you will discover how you can save the history object into a CSV file of deep learning models training metrics over time during training.
How to calculate the number of parameters for a Convolutional and Dense layer in Keras?
we need to understand whether or not the layer contains biases for each layer. If it is, then we simply add the number of biases. The number of biases will be equal to the number of nodes(filters) in the layer.
Change the Learning Rate using Schedules API in Keras
The intuition behind slowly reducing the learning rate is that maybe during the initial steps of learning you can afford to take much bigger steps but then as learning approaches convergence then having a slower learning rate allows you to take smaller steps.