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)