def __init__(self):
super(column, self).__init__()
self.fc1 = nn.Linear(in_features=1,
out_features=firstLayerSize,
bias=True)
self.echo = etnn.LiESNCell(1,
False,
firstLayerSize,
n_hidden,
spectral_radius=0.9,
seed=123456789)
self.out = etnn.RRCell(n_hidden, 1)
def __init__(self, preTrainedModel):
super(column, self).__init__()
self.frontEnd = preTrainedModel
self.echo = etnn.LiESNCell(1,
False,
60,
n_hidden,
spectral_radius=0.9,
seed=123456789)
self.out = etnn.RRCell(n_hidden,
16,
softmax_output=True,
ridge_param=0.05)
def __init__(self,
preTrainedModel,
leaky_rate,
spectral_radius=0.9,
n_hidden=n_hidden,
numOfClasses=numOfClasses,
lastLayerSize=lastLayerSize):
super(column, self).__init__()
self.frontEnd = preTrainedModel
self.echo = etnn.LiESNCell(leaky_rate,
train_leaky_rate,
lastLayerSize,
n_hidden,
spectral_radius=0.5,
nonlin_func=torch.nn.functional.relu,
seed=123456)
self.outLinear = nn.Linear(n_hidden, numOfClasses, bias=True)
def __init__(self,
preTrainedModel,
leaky_rate,
spectral_radius=spectral_radius,
n_hidden=n_hidden,
numOfClasses=numOfClasses,
lastLayerSize=60):
super(column, self).__init__()
self.frontEnd = preTrainedModel
self.echo = etnn.LiESNCell(leaky_rate,
train_leaky_rate,
lastLayerSize,
n_hidden,
spectral_radius=spectral_radius,
nonlin_func=F.relu,
seed=seed)
self.outLinear = nn.Linear(n_hidden, numOfClasses, bias=True)
def __init__(self,
preTrainedModel,
forgetRate,
spectral_radius=spectral_radius,
n_hidden=n_hidden,
numOfClasses=numOfClasses,
lastLayerSize=lastLayerSize):
super(column, self).__init__()
self.frontEnd = preTrainedModel
self.echo = etnn.LiESNCell(
forgetRate,
train_forgetRate,
inputRate,
train_inputRate,
lastLayerSize,
n_hidden,
spectral_radius=spectral_radius,
seed=seed) #,nonlin_func=torch.nn.functional.relu
self.outLinear = nn.Linear(n_hidden, numOfClasses, bias=True)
