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What does the final layer of a deep neural network return?
The final layer of the neural network will have one neuron for each of the classes and they will return a value between 0 and 1, which can be inferred as a probably. The output then results in a probability distribution as it sums to 1.
Which of the following can be used to reduce the overfitting of the neural network?
Dropout is a regularization technique that prevents neural networks from overfitting. Regularization methods like L1 and L2 reduce overfitting by modifying the cost function. Dropout on the other hand, modify the network itself. It randomly drops neurons from the neural network during training in each iteration.
How to prevent overfitting in a neural network?
In addition, to prevent overfitting overall you should use regularization some techniques include l1 or l2 regularization on the weights and/or dropout. It is better to have a neural network with more capacity than necessary and use regularization to prevent overfitting than trying to perfectly adjust the number of hidden units and layers.
When to use dropout in a neural network?
Third point, dropout is useful in large networks with lots of data and lots of iterations, in my network I applied dropout on the final fully connected layers only, convolution layers did not get dropout applied.
Increasing/Decreasing number of neurons in first hidden layer (or other layers). My guess is that increasing the number of neurons and layers can lead to overfitting. Increasing the number of epochs doesn’t, while increasing the batchsize could lead to overfitting. Is this correct?
How is the architecture of a neural network inspired?
The architecture of my network is loosely inspired by VGG-16, more specifically my images are resized to 128 x 128 x 3, and then I run: