- Flattening is converting the data into a 1-dimensional array for inputting it to the next layer. In Neural Networks, flatten is not a layer of neurons. It is an input layer specification.
- All of the mentioned
- Keras.layers.flatten function flattens the multi-dimensional input tensors into a single dimension.
- "flatten" function flattens the multi-dimensional input tensors into a single dimension, so you can model your input layer and build your neural network model, then pass those data into every single neuron of the model effectively.
In Artificial Intelligence (Machine Learning/Neural Networks/Deep Learning) we follow different types of processes on data before upcoming steps. We, humans, understand data in different ways, machines understand the same data in their own way. If we consider arrays, they've arrays for one as well as multi-dimensions, etc.
So, when we provide machines with data we have to process initial data in such a way that they can grasp it and use it in further processing.
Flattening is one such process that converts your two-dimensional array data into a single or one dimension. Suppose our images are sets of 28 x 28 in dimensions, and we want to treat them as a series of numeric values we can perform flattening.
Refer AI and Machine Learning for Coders by Laurence Moroney for more information.