def test_shape_kernel3(self):
for padding in ['WRAP', 'SAME']:
layer = tl.LocallyConnected1d(6, 3, padding=padding)
x = np.array([[0, 1], [2, 3], [4, 5]])
layer.init(shapes.signature(x))
y = layer(x)
self.assertEqual(y.shape, (3, 6))
for padding in ['VALID']:
layer = tl.LocallyConnected1d(6, 3, padding=padding)
x = np.array([[0, 1], [2, 3], [4, 5]])
layer.init(shapes.signature(x))
y = layer(x)
self.assertEqual(y.shape, (1, 6))
def LocallyConnectedDense(
n_modules,
n_units,
kernel_size=1, # pylint: disable=invalid-name
kernel_initializer=init.GlorotUniformInitializer(),
bias_initializer=init.RandomNormalInitializer(1e-6),
use_bias=True):
"""Layer using LocallyConnected1d for approximation of Dense layer.
The layer splits the last axis of a tensor into `n_modules`, then runs
LocallyConnected1d (grouped convolution) on all those modules, and
concatenates their results. It is essentially a locally-sensitive
approximation of Dense layer, with number of parameters smaller by the factor
of `n_modules / kernel_size`.
Args:
n_modules: Indicates how many modules (pixels) should be input and output
split into for processing.
n_units: how many outputs (filters) should each module generate.
kernel_size: The size of the kernel to be used.
kernel_initializer: Function that creates a matrix of (random) initial
connection weights `W` for the layer.
bias_initializer: Function that creates a vector of (random) initial
bias weights `b` for the layer.
use_bias: If `True`, compute an affine map `y = Wx + b`; else compute
a linear map `y = Wx`.
Returns:
LocallyConnectedDense base.Layer.
"""
if n_modules == 1:
return tl.Dense(n_units,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
use_bias=use_bias)
return tl.Serial(
tl.SplitLastAxis(n_modules),
tl.LocallyConnected1d(n_units,
kernel_size,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
use_bias=use_bias,
padding='WRAP'), tl.MergeLastTwoAxes())
