Which layer should I use when I build a Neural Network with Tensorflow 2.x?

Question

I'm currently stuyind TensorFlow 2.0 and Keras. I know that the activation functions are used to calculate the output of each layer of a neural network, based on mathematical functions. However, when searching about layers, I can't find synthetic and easy-to-read information for a beginner in deep learning.

There's a keras documentation, but I would like to know synthetically:

• what are the most common layers used to create a model (Dense, Flatten, MaxPooling2D, Dropout, ...).
• In which case to use each of them? (Classification, regression, other)
• what is the appropriate way to use each layer depending on each case?

Answer 1

Depending on the problem you want to solve, there are different activation functions and loss functions that you can use.

1. Regression problem: You want to predict the price of a building. You have N features. Of course, the price of the building is a real number, therefore you need to have mean_squared_error as a loss function and a linear activation for your last node. In this case, you can have a couple of Dense() layers with relu activation, while your last layer is a Dense(1,activation='linear'). In between the Dense() layers, you can add Dropout() so as to mitigate the overfitting effect(if present).
2. Classification problem: You want to detect whether or not someone is diabetic while taking into account several factors/features. In this case, you can use again stacked Dense() layers but your last layer will be a Dense(1,activation='sigmoid'), since you want to detect whether a patient is or not diabetic. The loss function in this case is 'binary_crossentropy'. In between the Dense() layers, you can add Dropout() so as to mitigate the overfitting effect(if present).
3. Image processing problems: Here you surely have stacks of [Conv2D(),MaxPool2D(),Dropout()]. MaxPooling2D is an operation which is typical for image processing and also some natural language processing(not going to expand upon here). Sometimes, in convolutional neural network architectures, the Flatten() layer is used. Its purpose is to reduce the dimensionality of the feature maps into 1D vector whose dimension is equal to the total number of elements within the entire feature map depth. For example, if you had a matrix of [28,28], flattening it would reduce it to (1,784), where 784=28*28.

Although the question is quite broad and maybe some people will vote to close it, I tried to provide you a short overview over what you asked. I recommend that your start learning the basics behind neural networks and then delve deeper into using a framework, such as TensorFlow or PyTorch.