def make_sparse_random_conv2D(num_nonzero, input_space, output_space,
kernel_shape, batch_size, \
kernel_stride = (1,1), border_mode = 'valid', message = "", rng=None):
raise NotImplementedError("Not yet modified after copy-paste from "
"pylearn2.linear.conv2d")
""" Creates a Conv2D with random kernels, where the randomly initialized
values are sparse"""
if rng is None:
rng = default_sparse_rng()
W = np.zeros(( output_space.num_channels, input_space.num_channels, \
kernel_shape[0], kernel_shape[1]))
def random_coord():
return [rng.randint(dim) for dim in W.shape]
for i in xrange(num_nonzero):
o, ch, r, c = random_coord()
while W[o, ch, r, c] != 0:
o, ch, r, c = random_coord()
W[o, ch, r, c] = rng.randn()
W = sharedX(W)
return Conv2D(filters=W,
batch_size=batch_size,
input_space=input_space,
output_axes=output_space.axes,
kernel_stride=kernel_stride,
border_mode=border_mode,
filters_shape=W.get_value(borrow=True).shape,
message=message)
def make_sparse_random_local(num_nonzero, input_space, output_space,
kernel_shape, batch_size, \
kernel_stride = (1,1), border_mode = 'valid', message = "", rng=None):
"""
.. todo::
WRITEME
"""
raise NotImplementedError("Not yet modified after copy-paste from "
"pylearn2.linear.conv2d_c01b")
""" Creates a Conv2D with random kernels, where the randomly initialized
values are sparse"""
if rng is None:
rng = default_sparse_rng()
W = np.zeros(( output_space.num_channels, input_space.num_channels, \
kernel_shape[0], kernel_shape[1]))
def random_coord():
return [ rng.randint(dim) for dim in W.shape ]
for i in xrange(num_nonzero):
o, ch, r, c = random_coord()
while W[o, ch, r, c] != 0:
o, ch, r, c = random_coord()
W[o, ch, r, c] = rng.randn()
W = sharedX( W)
def make_sparse_random_local(num_nonzero, input_space, output_space,
kernel_shape, batch_size, \
kernel_stride = (1,1), border_mode = 'valid', message = "", rng=None):
"""
.. todo::
WRITEME
"""
raise NotImplementedError("Not yet modified after copy-paste from "
"pylearn2.linear.conv2d_c01b")
""" Creates a Conv2D with random kernels, where the randomly initialized
values are sparse"""
if rng is None:
rng = default_sparse_rng()
W = np.zeros(( output_space.num_channels, input_space.num_channels, \
kernel_shape[0], kernel_shape[1]))
def random_coord():
return [ rng.randint(dim) for dim in W.shape ]
for i in xrange(num_nonzero):
o, ch, r, c = random_coord()
while W[o, ch, r, c] != 0:
o, ch, r, c = random_coord()
W[o, ch, r, c] = rng.randn()
W = sharedX( W)
def make_sparse_random_conv2D(num_nonzero, input_space, output_space,
kernel_shape, batch_size, \
kernel_stride = (1,1), border_mode = 'valid', message = "", rng=None):
raise NotImplementedError("Not yet modified after copy-paste from "
"pylearn2.linear.conv2d")
""" Creates a Conv2D with random kernels, where the randomly initialized
values are sparse"""
if rng is None:
rng = default_sparse_rng()
W = np.zeros(( output_space.num_channels, input_space.num_channels, \
kernel_shape[0], kernel_shape[1]))
def random_coord():
return [ rng.randint(dim) for dim in W.shape ]
for i in xrange(num_nonzero):
o, ch, r, c = random_coord()
while W[o, ch, r, c] != 0:
o, ch, r, c = random_coord()
W[o, ch, r, c] = rng.randn()
W = sharedX( W)
return Conv2D(filters = W,
batch_size = batch_size,
input_space = input_space,
output_axes = output_space.axes,
kernel_stride = kernel_stride, border_mode = border_mode,
filters_shape = W.get_value(borrow=True).shape, message = message)
def make_sparse_random_conv2D(num_nonzero, input_space, output_space,
kernel_shape,
pad = 0,
kernel_stride = (1,1), border_mode = 'valid', message = "", rng=None,
partial_sum = None):
""" Creates a Conv2D with random kernels, where the randomly initialized
values are sparse"""
if rng is None:
rng = default_sparse_rng()
W = np.zeros((input_space.num_channels, \
kernel_shape[0], kernel_shape[1],
output_space.num_channels))
def random_coord():
return [rng.randint(dim) for dim in W.shape[0:3] ]
for o in xrange(output_space.num_channels):
for i in xrange(num_nonzero):
ch, r, c = random_coord()
while W[ch, r, c, o] != 0:
ch, r, c = random_coord()
W[ch, r, c, o] = rng.randn()
W = sharedX(W)
return Conv2D(filters = W,
input_axes = input_space.axes,
output_axes = output_space.axes,
kernel_stride = kernel_stride, pad=pad,
message = message, partial_sum=partial_sum)
def make_sparse_random_conv2D(num_nonzero,
input_space,
output_space,
kernel_shape,
pad=0,
kernel_stride=(1, 1),
border_mode='valid',
message="",
rng=None,
partial_sum=None):
"""
.. todo::
WRITEME properly
Creates a Conv2D with random kernels, where the randomly initialized
values are sparse
"""
if rng is None:
rng = default_sparse_rng()
W = np.zeros((input_space.num_channels, \
kernel_shape[0], kernel_shape[1],
output_space.num_channels))
def random_coord():
return [rng.randint(dim) for dim in W.shape[0:3]]
for o in xrange(output_space.num_channels):
for i in xrange(num_nonzero):
ch, r, c = random_coord()
while W[ch, r, c, o] != 0:
ch, r, c = random_coord()
W[ch, r, c, o] = rng.randn()
W = sharedX(W)
return Conv2D(filters=W,
input_axes=input_space.axes,
output_axes=output_space.axes,
kernel_stride=kernel_stride,
pad=pad,
message=message,
partial_sum=partial_sum)
