def __repr__(self):
""" Representing the class object """
if self.structure is None:
super(NonLinearCoxPHModel, self).__repr__()
return self.name
else:
S = len(self.structure)
self.name = self.__class__.__name__
empty = len(self.name)
self.name += '( '
for i, s in enumerate(self.structure):
n = 'Layer({}): '.format(i + 1)
activation = nn.activation_function(s['activation'],
return_text=True)
n += 'activation = {}, '.format(s['activation'])
n += 'num_units = {} '.format(s['num_units'])
if i != S - 1:
self.name += n + '; \n'
self.name += empty * ' ' + ' '
else:
self.name += n
self.name += ')'
return self.name
def __repr__(self):
""" Representing the class object """
if self.structure is None:
super(NeuralMultiTaskModel, self).__repr__()
return self.name
else:
S = len(self.structure)
self.name = self.__class__.__name__
empty = len(self.name)
self.name += '( '
for i, s in enumerate(self.structure):
if isinstance(s, list):
for s_ in s:
n = 'Layer({}): '.format(i + 1)
activation = nn.activation_function(s_['activation'],
return_text=True)
n += 'activation = {}, '.format(s_['activation'])
if 'num_units' in s_.keys():
n += 'units = {} '.format(s_['num_units'])
if i != S - 1:
self.name += n + '; \n'
self.name += empty * ' ' + ' '
else:
self.name += n
self.name = self.name + ')'
else:
n = 'Layer({}): '.format(i + 1)
activation = nn.activation_function(s['activation'],
return_text=True)
n += 'activation = {}, '.format(s['activation'])
if 'num_units' in s.keys():
n += 'units = {} '.format(s['num_units'])
if i != S - 1:
self.name += n + '; \n'
self.name += empty * ' ' + ' '
else:
self.name += n
self.name = self.name + ')'
return self.name
