Neural Networks are a family of computer learning models, based on biological neural networks (in particular the structures found in the brain). While learning by example, Neural Networks are capable of estimating any function, even extremely complex ones.
Typically, a Neural Network consists of several layers connected in succession. There are different types of layers which each have unique properties. Depending on the task at hand, one should determine the optimal network structure by changing the layer types, their count or their various parameters.
The work flow of a Neural Network is bidirectional. Prediction is usually flowing forward, while learning is flowing backward. In Brain Simulator, this meticulous task planning is handled by a group planners like
RBMGroup, which encapsulate the layers.
IMPORTANT: Each network of layers need to be placed in immediate succession and placed inside an appropriate group eg.
NeuralNetworkGroup. This is to ensure that the forward/backward flow planning is executed correctly. If a layer is placed outside the group or if other nodes are placed in between layers, the planning can fail with unexpected results following. At the moment there is no automatic validation of this, so please take care to place layers in succession inside the appropriate group.
The following Nodes (layers) should be put inside
- Hidden Layer - most commonly used layer within neural networks. It takes an input and feeds another layer.
- Output Layer - output layer takes a target as input, and automatically scales it's neurons to fit the target.
- Partial output Layer - output layer as above but only part of the output will be used for update/delta.
- Q Learning output Layer - Q learning inside NN.
- LSTM Layer - fully recurrent Long Short Term Memory (LSTM)  hidden layer with forget gates and peephole. connections trained by truncated Real-Time Recurrent Learning (RTRL) algorithm.
- Stack Layer - Joins two inputs to a single output. Acts as join but allows propagation of deltas.
- Gaussian Layer - Hidden layer where each pair of neurons is interpreted as parameters of Gaussian distribution.
- Convolutional Layer - Standard convolutional layer that is most often used for processing images. Its weights correspond to filters that are applied to the input (images). Wonderful tutorial on convolution can be found on Andrej Karpathy's course website.
- Pooling Layer - Pooling layer is usually used alongside convolutional layer. Pooling layer simply reduces the input dimension.
RBMGroup (Node group used for Restricted Boltzmann Machines and deep learning. Derived from
NeuralNetworkGroup whose functionality it inherits):
- RBM input Layer - Input layer of RBM network. Can only act as a visible layer.
- RBM Layer - One layer of RBM network. Inherited from classical neural hidden layer. Can act as both visible and hidden layer.
Check out the examples for a variety of implementations of Neural Networks.
LSTM  are one of the units that supports the recurrent connections in the network. So far, there are two methods that can be used for training:
- Real-Time Recurrent Learning (RTRL) 
- Back-Propagation Through Time (BPTT) 
- RTRL is default and it takes into account previous cell-state of the LSTM, while BPTT is method that remambers all states and wht it updates, it unfold the network in time.
When one wants to use BPTT, the method is following:
- Set the LSTM to perform BPTT.
- Set the Network number of time steps to unfold. It will do update in the last time step.
- When you use observer, it has
TimeStep. -1 will show the actual value, other will show the corresponidng values for the the desired time-step.
Batch learning allows training of a network on multiple data samples - the batch at once, which often produces better results than online learning. To prevent the network from overfitting current data one can use replay buffer node that creates mini-batches from randomly selected data samples.
Controlling flow of the network
In order to control the way data flows inside the network, you can play around with the tasks inside the layers. For example, if you only want to train a certain layer, you can simply disable the
UpdateWeights tasks in all the layers that you don't want to train (and enable it in the layer whose weights you'd like to update).
Additionally, you can easily disable learning of the whole network if you so desire (e.g. if your model is already trained successfully and you only need to run the forward pass and get the output of the network).
To do so, simply enable the
DisableLearning parameter of whichever gradient descent method (
Adadelta) is currently selected. This setting overrides the un/checking of the
UpdateWeights tasks inside the layers if (and only if) set to Enabled.
This means that if you only ever want to train either all layers (the whole network) or none of them, just keep the
UpdateWeights task enabled inside all the layers and only use the
DisableLearning network parameter to turn learning on and off.