Convolution has a number of properties that are very interesting in the field of deep learning:
- It can successfully encode and decode spatial properties of the data.
- It can be calculated relatively quickly with the latest developments.
- It can be used to address several computer vision problems.
- It can be combined with other types of layers for maximum performance.
Keras has wrapper functions for TensorFlow that involve the most popular dimensions, that is, one, two, and three dimensions: Conv1D, Conv2D, and Conv3D. In this chapter, we will continue to focus on two-dimensional convolutions, but be sure that if you have understood the concept, you can easily go ahead and use the others.
Conv2D
The two-dimensional convolution method has the following signature: tensorflow.keras.layers.Conv2D. The most common arguments used in a convolutional layer are the following:
- filters refers to the number of filters to be learned in this particular layer and affects the dimension...
