def __init__(self,
conv_configs,
in_channels,
out_channels,
split_in_channels=False,
reflective=False):
super().__init__()
assert (out_channels % len(conv_configs) == 0)
self.conv_configs = conv_configs
self.in_channels = in_channels
self.out_channels = out_channels
self.in_channels_per_conv = in_channels
self.out_channels_per_conv = out_channels / len(conv_configs)
self.split_in_channels = split_in_channels
self.reflective = reflective
if split_in_channels:
assert (in_channels % len(conv_configs) == 0)
self.in_channels_per_conv = in_channels / len(conv_configs)
self.convs = nn.ModuleList()
for config in conv_configs:
config = config.copy()
config['padding'] = 0
conv = nn.Conv1d(int(self.in_channels_per_conv),
int(self.out_channels_per_conv), **config)
fill_weights_normal(conv.weight)
fill_bias_zero(conv.bias)
self.convs.append(conv)
def __init__(self, n_features, n_time):
super().__init__()
self.weight_conv = nn.Conv1d(1, n_features, 1, bias=False)
self.n_features = n_features
self.n_time = n_time
fill_weights_normal(self.weight_conv.weight)
def __init__(self, n_classes, n_features, bias):
nn.Linear.__init__(self, n_classes, n_features, bias=bias)
fill_weights_normal(self.weight)
fill_bias_zero(self.bias)
def __init__(self, n_mappings, n_features):
nn.Linear.__init__(self, n_mappings, n_features * 2, bias=True)
self.n_mappings = n_mappings
self.n_features = n_features
fill_weights_normal(self.weight)
fill_bias_zero(self.bias)
def reset_parameters(self):
if self.affine:
fill_weights_normal(self.weight)
fill_bias_zero(self.bias)