def __init__(self, layers, activation_function, activation_args):
"""
Base class providing the skeleton of a network
"""
self.layers = len(layers)
if (self.layers < 2):
raise ValueError('Network must contain at least 2 layers, {count} given'.format(count = self.layers))
self.layer_counts = layers
for index, layer_size in enumerate(self.layer_counts):
if (not (layer_size > 0)):
raise ValueError('Network layers must contain at least 1 node, layer {bad_layer} specifies {bad_count}'.format(
bad_layer = index, bad_count = layer_size))
self.inputs = int(self.layer_counts[0])
self.outputs = int(self.layer_counts[-1])
self.edge_weights = GenerateDefaultEdgeWeights(self)
self.edge_update_deltas = []
self.update_edge_weights(self.edge_weights)
self.bias_weights = GenerateDefaultBiasWeights(self)
self.bias_update_deltas = []
self.update_bias_weights(self.bias_weights)
try:
self.activator = ActivationFunctions.construct(activation_function, **activation_args)
except Exception as e:
raise ValueError('Failed to construct activation function, \'{af}\' with args \'{args}\''.format(af = activation_function, args = str(activation_args)))
def test_returns_same_size(self):
np.random.seed(0)
elements = 30
for r in range(1,elements):
for c in range(1,elements):
Q = np.random.rand(r,c)
F = ActivationFunctions.construct('sigmoidal', beta = 1)
A = F.call(Q)
self.assertTrue(A.shape == Q.shape)
