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