def __init__(self): NeuralNetwork.__init__(self, 1, 1) # Get the layer and modify the values for the layer to suit the AND operation # The threshold for True or False is 0.5 self.layer = NeuralNetwork.getlayer(self, 0) self.layer[0] = 5 self.layer[1] = -6 return
def __init__(self): NeuralNetwork.__init__(self, [2, 2, 1]) layer = [] layer.append(self.getLayer(0)) layer.append(self.getLayer(1)) self.network[0] = torch.from_numpy( np.array([[20.0, -20.0], [20.0, -20.0], [-10.0, 30.0]])) self.network[1] = torch.from_numpy(np.array([20.0, 20.0, -30.0]))
def __init__(self, data_set_name, layer_sizes, P2_algorithm_basis=None): NeuralNetwork.__init__(self, data_set_name, layer_sizes) self.network_name = 'RBF' self.logger = Logger('DEMO') self.P2_algorithm_basis = P2_algorithm_basis # instance of Project 2 API - a single class for accessing all things needed from P2 self.P2API = P2API(data_set_name, self.P2_algorithm_basis) # list of tuples for RBF neuron values, each tuple is (beta, mu) # beta at spot 1 and vector is at spot 0 self.rbf_neurons = []
def __init__(self): NeuralNetwork.__init__(self, 2, 2, 1) # Get the layer and modify the values for the layer to suit the AND operation # The threshold for True or False is 0.5 self.layer = NeuralNetwork.getlayer(self, 0) self.layer[0, :] = -5 self.layer[1, 0] = 4 self.layer[2, 0] = 4 self.layer[1, 1] = 6 self.layer[2, 1] = 6 self.layer = NeuralNetwork.getlayer(self, 1) self.layer[0] = -1 self.layer[1] = -2 self.layer[2] = 2.5 return
def __init__(self, data_set_name, layer_sizes): NeuralNetwork.__init__(self, data_set_name, layer_sizes) self.logger = Logger('DEMO') # configure class-level log level here self.network_name = 'MLP'
def __init__(self): NeuralNetwork.__init__(self, [2, 1]) layer = self.getLayer(0) self.network[0] = torch.from_numpy(np.array([20.0, 20.0, -10.0]))
def __init__(self): self.layersizes = (784, 128, 10) NeuralNetwork.__init__(self, self.layersizes)