A layer with parameters

Baseline class for any Neural Network used as a layer in a deep network *

LossLayer is a flexible output "layer" that performs a loss function on an input without MLP logic.

RepeatVector layer.

Convolutional Neural Network Loss Layer.

3D convolution layer implementation.

Zero cropping layer for 1D convolutional neural networks.

Cropping layer for 3D convolutional neural networks.

2D depth-wise convolution layer configuration.

Space to batch utility layer for convolutional input types.

Zero padding 1D layer for convolutional neural networks.

Zero padding layer for convolutional neural networks.

Subsampling 3D layer, used for downsampling a 3D convolution

2D Upsampling layer.

Embedding layer for sequences: feed-forward layer that expects fixed-length number (inputLength) of integers/indices per example as input, ranged from 0 to numClasses - 1.

Deep neural net normalization approach normalizes activations between layers "brightness normalization" Used for nets like AlexNet For a^i_{x,y} the activity of a neuron computed by applying kernel i at position (x,y) and applying ReLU nonlinearity, the response normalized activation b^i_{x,y} is given by: x^2 = (a^j_{x,y})^2 unitScale = (k + alpha * sum_{j=max(0, i - n/2)}^{max(N-1, i + n/2)} (a^j_{x,y})^2 ) y = b^i_{x,y} = x * unitScale**-beta gy = epsilon (aka deltas from previous layer) sumPart = sum(a^j_{x,y} * gb^j_{x,y}) gx = gy * unitScale**-beta - 2 * alpha * beta * sumPart/unitScale * a^i_{x,y} Reference:
http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf
https://github.com/vlfeat/matconvnet/issues/10
Created by nyghtowl on 10/29/15.

RNN tutorial: https://deeplearning4j.org/docs/latest/deeplearning4j-nn-recurrent READ THIS FIRST Bdirectional LSTM layer implementation.

LSTM layer implementation.

Recurrent Neural Network Output Layer.