class NeuralNetworkLibrary:
def create_neuron(self, numberOfInputs, activationFuncion):
self._neuron = Neuron(int(numberOfInputs), str(activationFuncion))
def set_neuron_inputs(self, val):
value = ast.literal_eval(val)
self._neuron.inputs = value
assert self._neuron.inputs[:-1] == value, "Neuron's inputs was not set: "
def randomize_neuron_weights(self):
self._neuron.randomizeWeights()
assert self._neuron.weights, "Weights were not initialized"
def calculate_neuron_output(self):
beforeOutput = self._neuron.output
self._neuron.calcOutput()
afterOutput = self._neuron.output
assert beforeOutput is not afterOutput, "Neuron's aoutput was not updated"
def update_neuron_weights(self):
beforeWeights = self._neuron.weights
self._neuron.updateWeights()
afterWeights = self._neuron.weights
assert beforeWeights is not afterWeights, "Weights were not updated"
def initialize_network(self, data, target, numberOfNeurons, activationFunctions):
self._network = Network(ast.literal_eval(data), ast.literal_eval(target), ast.literal_eval(numberOfNeurons),
ast.literal_eval(activationFunctions))
def initialize_random_weights(self):
self._network.initWeights()
def are_weights_initialized(self):
for layer in self._network.net:
for neuron in layer:
if not neuron.weights:
raise AssertionError("Network weights were not initialized")
def train_until_error(self, error):
self._network.train(error=eval(error))
assert self._network.totalError <= eval(error), "Desired error was not achieved"
def train_until_epoch(self, epoch):
self._network.train(epochs=eval(epoch))
assert self._network.learningEpochs == eval(epoch), "Desired error was not achieved"
def run_network(self, inputs):
self._network.run(ast.literal_eval(inputs))
def is_net_output_within_deviation(self, target, permissibleDeviation):
permDev = float(permissibleDeviation)
targetSet = ast.literal_eval(target)
for targetOutputs,netOutputs in zip(targetSet, self._network.netOutputs):
for targetOutput,netOutput in zip(targetOutputs,netOutputs):
assert targetOutput-permDev < netOutput < targetOutput+permDev, \
"Net output is : " + str(netOutput) + " but should be: " + str(targetOutput)
from src.network import Network n = Network([[0,0],[0,1],[1,0],[1,1]], [[0],[1],[1],[0]], [10,5,1], ['sigmoid']*3) n.initWeights() n.train(error=pow(10, -5), epochs=pow(10, 3), eta=0.7, repeatSet=10, plot=True, plotInterval=5) n.run([[0.1,0.1],[0.1,0.9],[1,0],[1,1]])
