class NeuralNetwork:
def __init__(self, shape):
self.layers = []
self.shape = shape
self.time = 0
self.input_layer = InputLayer(shape[0])
self.layers.append(self.input_layer)
prev_layer = self.input_layer
for i in range(1, len(shape)-1):
cur_layer = Layer(shape[i], prev_layer.neurons)
prev_layer.listeners.append(cur_layer)
self.layers.append(cur_layer)
prev_layer = cur_layer
self.output_layer = Layer(shape[-1], prev_layer.neurons)
prev_layer.listeners.append(self.output_layer)
self.layers.append(self.output_layer)
self._reset_layers_states()
def __len__(self):
return len(self.shape)
def __getitem__(self, i):
return self.layers[i]
def calculate(self, x):
"""
:param x: list of input values
:return: list of values which is result of network calculation
"""
self.input_layer.input_values = x
done = False
while not done:
done = True
for layer in self:
if layer.ready_to_calculate == Readiness.READY:
layer.calculate()
done = False
self._reset_layers_states()
return self.output_layer.get_output_values()
def _reset_layers_states(self):
for layer in self:
layer.reset_state()
class NeuralNetwork:
def __init__(self, shape):
self.layers = []
self.shape = shape
self.time = 0
self.input_layer = InputLayer(shape[0])
self.middle_layer = Layer(shape[1], self.input_layer.neurons)
self.input_layer.listeners.append(self.middle_layer)
self.output_layer = Layer(shape[2], self.middle_layer.neurons)
self.middle_layer.listeners.append(self.output_layer)
self.layers.append(self.input_layer)
self.layers.append(self.middle_layer)
self.layers.append(self.output_layer)
self._reset_layers_states()
def __len__(self):
return len(self.shape)
def __getitem__(self, i):
return self.layers[i]
def calculate(self, x):
"""
calculate vector x, if random value is set, add to result vector value (random()*2-1)*random_value
:param x: input vector
:param random_value: random range added to result vector
:return: result of network calculation
"""
self.input_layer.input_values = x
done = False
while not done:
done = True
for layer in self:
if layer.ready_to_calculate == Readiness.READY:
layer.calculate()
done = False
self._reset_layers_states()
return self.output_layer.get_output_values()
def _reset_layers_states(self):
for layer in self:
layer.reset_state()
def teach_by_sample(self, database, teach_value=0.5):
err = 0
for inp, out in database:
net_out = self.calculate(inp)
self.output_layer.teach_output_layer_by_sample(teach_value, out)
# teach middle layers
self.middle_layer.teach_middle_layer(teach_value)
for layer in self:
layer.commit_teach()
err += sum([abs(no-o) for no, o in zip(net_out, out)])
return err / len(database)
