def __init__(self,
filters,
pool_size,
conv_size,
pool_strides=None,
conv_strides=None,
conv_padding='valid',
connectivity_type='procedural'):
super(AvePool2DConv2DSynapses, self).__init__()
self.filters = filters
self.pool_size = _get_param_2d('pool_size', pool_size)
self.conv_size = _get_param_2d('conv_size', conv_size)
self.pool_strides = _get_param_2d('pool_strides',
pool_strides,
default=self.pool_size)
self.conv_strides = _get_param_2d('conv_strides',
conv_strides,
default=(1, 1))
self.conv_padding = PadMode(conv_padding)
self.pool_output_shape = None
self.connectivity_type = ConnectivityType(connectivity_type)
if self.pool_strides[0] < self.pool_size[0] or self.pool_strides[
1] < self.pool_size[1]:
raise NotImplementedError(
'pool stride < pool size is not supported')
if self.conv_strides[0] != 1 or self.conv_strides[1] != 1:
raise NotImplementedError('conv stride != 1 is not supported')
if self.connectivity_type is ConnectivityType.TOEPLITZ:
if self.conv_padding is PadMode.SAME:
if (self.conv_size[0] % 2) == 0 or (self.conv_size[1] %
2) == 0:
raise NotImplementedError(
'Toeplitz with same conv_padding and even conv_size is not supported'
)
def __init__(self, pool_size, pool_strides=None,
connectivity_type='procedural'):
super(AvePool2DSynapses, self).__init__()
self.pool_size = _get_param_2d('pool_size', pool_size)
self.pool_strides = _get_param_2d('pool_strides', pool_strides, default=self.pool_size)
self.connectivity_type = ConnectivityType(connectivity_type)
if self.pool_strides[0] < self.pool_size[0] or self.pool_strides[1] < self.pool_size[1]:
raise NotImplementedError('pool stride < pool size is not supported')
def __init__(self, name, filters, conv_size, conv_strides=None,
conv_padding='valid', connectivity_type='procedural', neurons=IFNeurons()):
super(Conv2D, self).__init__(name, neurons)
self.filters = filters
self.conv_size = _get_param_2d('conv_size', conv_size)
self.conv_strides = _get_param_2d('conv_strides', conv_strides, default=(1, 1))
self.conv_padding = PadMode(conv_padding)
self.connectivity_type = ConnectivityType(connectivity_type)
def __init__(self,
name,
pool_size,
pool_strides=None,
pool_padding='valid',
connectivity_type='procedural',
neurons=IFNeurons()):
super(AvePool2D, self).__init__(name, neurons)
self.pool_size = _get_param_2d('pool_size', pool_size)
self.pool_strides = _get_param_2d('pool_strides',
pool_strides,
default=self.pool_size)
self.pool_padding = PadMode(pool_padding)
self.connectivity_type = ConnectivityType(connectivity_type)
def __init__(self,
filters,
conv_size,
conv_strides=None,
conv_padding='valid',
connectivity_type='procedural'):
super(ConvTranspose2DSynapses, self).__init__()
self.filters = filters
self.conv_size = _get_param_2d('conv_size', conv_size)
self.conv_strides = _get_param_2d('conv_strides',
conv_strides,
default=(1, 1))
self.conv_padding = PadMode(conv_padding)
self.connectivity_type = ConnectivityType(connectivity_type)
def __init__(self,
name,
connectivity_type='procedural',
neurons=IFNeurons()):
super(Identity, self).__init__(name, neurons)
self.connectivity_type = ConnectivityType(connectivity_type)
def __init__(self, connectivity_type='procedural'):
super(IdentitySynapses, self).__init__()
self.connectivity_type = ConnectivityType(connectivity_type)