Category Archives: Keras
Concatenate two layers using keras.layers.concatenate() example
It is for the neural network to learn both deep patterns using the deep path and simple rules through the short path. In contrast, regular MLP forces all the data to flow through the entire stack of layers. These simple patterns in the data may end up being distorted by this sequence of transformations.
PyTorch AdamW and Adam with weight decay optimizers
Adam does not generalize as well as SGD with momentum when tested on a diverse set of deep learning tasks such as image classification, character-level language modeling, and constituency parsing. Adam lies in its dysfunctional implementation of weight decay.
Differences between Learning Rate and Weight Decay Hyperparameters in Neural networks.
The amount of regularization must be balanced for each dataset and architecture. Recognition of this principle permits the general use of super-convergence. Reducing other forms of regularization and regularizing with very large learning rates makes training significantly more efficient.
Use Image Dataset from Directory with and without Label List in Keras
All images for training are located in one folder and the target labels are in a CSV file. It should be possible to use a list of labels instead of inferring the classes from the directory structure. We have a list of labels corresponding number of files in the directory.
Advantages of ReLU vs Tanh vs Sigmoid activation function in deep neural networks.
The saturated neurons can kill gradients if we’re too positive or too negative of an input. They’re also not zero-centered and so we get these, this inefficient kind of gradient update. The third problem is an exponential function. This is a little bit computationally expensive.
How to deactivate dropout layers while evaluation and prediction mode in Keras?
Forcing your network to learn redundant representations might sound very inefficient. But in practice, it makes things more robust and prevents overfitting. It also makes your network act as if taking the consensus over an ensemble of networks.