Initializing a Network

In nimblenet, a neural network is configured according to a dict of parameters specified upon initialization.

from nimblenet.neuralnet import NeuralNet
network = NeuralNet({
    "n_inputs" : 2,
    "layers"   : [ (1, sigmoid_function) ],
})

Important

The final tuple in the layers list always describe the number of output signals.

Parameters

The two dict keys n_inputs and layers are required. However, the network is further customizable through specifying any of the following dict parameters:

• n_inputs the number of input signals
• layers the topology of the network
• initial_bias_value the input signal from the bias node will be initialized to this value
• weights_low the lower bound on weight value during the random initialization
• weights_high the upper bound on weight value during the random initialization

Example

from nimblenet.neuralnet import NeuralNet
settings            = {
    # Required settings
    "n_inputs"              : 2,       # Number of network input signals
    "layers"                : [  (3, sigmoid_function), (1, sigmoid_function) ],
                                        # [ (number_of_neurons, activation_function) ]
                                        # The last pair in the list dictate the number of output signals

    # Optional settings
    "initial_bias_value"    : 0.0,
    "weights_low"           : -0.1,     # Lower bound on the initial weight value
    "weights_high"          : 0.1,      # Upper bound on the initial weight value
}
network = NeuralNet( settings )