def __init__(self,num_filters,activation, initW, initB, dropout, k_sparsity, l2):
NeuralNetLayerCfg.__init__(self,dropout, k_sparsity, l2)
self.type = "dense"
self.num_filters = num_filters
self.activation = activation
self.activation_prime = nn.make_activation(activation)
self.size = num_filters
self.shape = num_filters
self.initW = initW
self.initB = initB
def __init__(self, num_filters, activation, initW, initB, dropout,
k_sparsity, l2):
NeuralNetLayerCfg.__init__(self, dropout, k_sparsity, l2)
self.type = "dense"
self.num_filters = num_filters
self.activation = activation
self.activation_prime = nn.make_activation(activation)
self.size = num_filters
self.shape = num_filters
self.initW = initW
self.initB = initB
def __init__(self, num_filters, activation, filter_width, stride, padding, initW, initB, dropout, l2, k_sparsity):
NeuralNetLayerCfg.__init__(self,dropout, k_sparsity, l2)
self.type = "convolution"
self.num_filters = num_filters
self.activation = activation
self.activation_prime = nn.make_activation(activation)
self.filter_width = filter_width
self.stride = stride
self.padding = padding
self.initW = initW
self.initB = initB
def __init__(self, num_filters, activation, filter_width, stride, padding,
initW, initB, dropout, l2, k_sparsity):
NeuralNetLayerCfg.__init__(self, dropout, k_sparsity, l2)
self.type = "convolution"
self.num_filters = num_filters
self.activation = activation
self.activation_prime = nn.make_activation(activation)
self.filter_width = filter_width
self.stride = stride
self.padding = padding
self.initW = initW
self.initB = initB
