def make_random_conv2D(irange, input_channels, input_axes, output_axes,
output_channels,
kernel_shape,
kernel_stride = (1,1), pad=0, message = "", rng = None,
partial_sum = None, sparse_init = None):
"""
.. todo::
WRITEME properly
Creates a Conv2D with random kernels.
Should be functionally equivalent to
pylearn2.linear.conv2d.make_random_conv2D
"""
if rng is None:
rng = default_rng()
W = sharedX( rng.uniform(-irange,irange,(input_channels, \
kernel_shape[0], kernel_shape[1], output_channels)))
return Conv2D(filters = W,
input_axes = input_axes,
output_axes = output_axes,
kernel_stride = kernel_stride, pad=pad,
message = message, partial_sum=partial_sum)
def make_random_conv2D(irange,
input_channels,
input_axes,
output_axes,
output_channels,
kernel_shape,
kernel_stride=(1, 1),
pad=0,
message="",
rng=None,
partial_sum=None):
""" Creates a Conv2D with random kernels.
Should be functionally equivalent to
pylearn2.linear.conv2d.make_random_conv2D
"""
if rng is None:
rng = default_rng()
W = sharedX( rng.uniform(-irange,irange,(input_channels, \
kernel_shape[0], kernel_shape[1], output_channels)))
return Conv2D(filters=W,
input_axes=input_axes,
output_axes=output_axes,
kernel_stride=kernel_stride,
pad=pad,
message=message,
partial_sum=partial_sum)
def make_random_conv3D(
irange,
input_axes,
output_axes,
signal_shape,
filter_shape,
kernel_stride=(1, 1),
pad=0,
message="",
rng=None,
partial_sum=None,
):
""" Creates a Conv3D with random kernels.
"""
if rng is None:
rng = default_rng()
_filter_4d_shape = (filter_shape[0], filter_shape[1], filter_shape[2], filter_shape[3] * filter_shape[4])
W = sharedX(rng.uniform(-irange, irange, (_filter_4d_shape)))
return Conv3DC01TB(
filters=W,
input_axes=input_axes,
output_axes=output_axes,
signal_shape=signal_shape,
filter_shape=filter_shape,
kernel_stride=kernel_stride,
pad=pad,
message=message,
partial_sum=partial_sum,
)
def make_random_conv3D(irange,
input_axes,
output_axes,
signal_shape,
filter_shape,
kernel_stride=(1, 1),
pad=0,
message="",
rng=None,
partial_sum=None):
""" Creates a Conv3D with random kernels.
"""
if rng is None:
rng = default_rng()
_filter_4d_shape = (filter_shape[0], filter_shape[1], filter_shape[2],
filter_shape[3] * filter_shape[4])
W = sharedX(rng.uniform(-irange, irange, (_filter_4d_shape)))
return Conv3DC01TB(filters=W,
input_axes=input_axes,
output_axes=output_axes,
signal_shape=signal_shape,
filter_shape=filter_shape,
kernel_stride=kernel_stride,
pad=pad,
message=message,
partial_sum=partial_sum)
def make_random_local(
irange,
input_channels,
input_axes,
input_groups,
image_shape,
output_channels,
output_axes,
kernel_shape,
subsample=(1, 1),
pad=0,
message="",
rng=None,
partial_sum=None,
):
""" Creates a Local with random weights.
"""
if rng is None:
rng = default_rng()
def num_pos(img, stride, kwidth):
img = img + 2 * pad
return (img - kwidth) // stride + 1
num_row_pos = num_pos(image_shape[0], subsample[0], kernel_shape[0])
num_col_pos = num_pos(image_shape[1], subsample[1], kernel_shape[1])
assert input_channels % input_groups == 0
colors_per_group = input_channels // input_groups
assert output_channels % input_groups == 0
filters_per_group = output_channels // input_groups
W = sharedX(
rng.uniform(
-irange,
irange,
(
num_row_pos,
num_col_pos,
colors_per_group,
kernel_shape[0],
kernel_shape[1],
input_groups,
filters_per_group,
),
)
)
return Local(
filters=W,
image_shape=image_shape,
input_groups=input_groups,
input_axes=input_axes,
output_axes=output_axes,
subsample=subsample,
pad=pad,
message=message,
partial_sum=partial_sum,
)
def make_random_conv2D(
irange,
input_channels,
input_axes,
output_axes,
output_channels,
kernel_shape,
subsample=(1, 1),
border_mode="valid",
message="",
rng=None,
):
""" Creates a Conv2D with random kernels.
Should be functionally equivalent to
pylearn2.linear.conv2d.make_random_conv2D
"""
if rng is None:
rng = default_rng()
W = sharedX(rng.uniform(-irange, irange, (input_channels, kernel_shape[0], kernel_shape[1], output_channels)))
return Conv2D(
filters=W,
input_axes=input_axes,
output_axes=output_axes,
subsample=subsample,
border_mode=border_mode,
message=message,
)
def make_random_local(irange,
input_channels,
input_axes,
input_groups,
image_shape,
output_channels,
output_axes,
kernel_shape,
kernel_stride=(1, 1),
pad=0,
message="",
rng=None,
partial_sum=None):
"""
.. todo::
WRITEME properly
Creates a Local with random weights.
"""
if rng is None:
rng = default_rng()
def num_pos(img, stride, kwidth):
img = img + 2 * pad
return (img - kwidth) // stride + 1
num_row_pos = num_pos(image_shape[0], kernel_stride[0], kernel_shape[0])
num_col_pos = num_pos(image_shape[1], kernel_stride[1], kernel_shape[1])
assert input_channels % input_groups == 0
colors_per_group = input_channels // input_groups
assert output_channels % input_groups == 0
filters_per_group = output_channels // input_groups
W = sharedX(
rng.uniform(
-irange, irange,
(num_row_pos, num_col_pos, colors_per_group, kernel_shape[0],
kernel_shape[1], input_groups, filters_per_group)))
return Local(filters=W,
image_shape=image_shape,
input_groups=input_groups,
input_axes=input_axes,
output_axes=output_axes,
kernel_stride=kernel_stride,
pad=pad,
message=message,
partial_sum=partial_sum)
