def instantiate(self):
"""
Instantiate this object with the supplied parameters in `self.kwds`,
or if already instantiated, return the cached instance.
"""
if self.instance is None:
self.instance = checked_call(self.cls, self.kwds)
#endif
try:
self.instance.yaml_src = self.yaml_src
except AttributeError:
pass
return self.instance
def instantiate(self):
"""
Instantiate this object with the supplied parameters in `self.kwds`,
or if already instantiated, return the cached instance.
"""
if self.instance is None:
self.instance = checked_call(self.cls, self.kwds)
#endif
try:
self.instance.yaml_src = self.yaml_src
except AttributeError:
pass
return self.instance
def instantiate(self):
"""
.. warning::
Deprecated, to be removed as of January 1, 2015.
Instantiate this object with the supplied parameters in `self.kwds`,
or if already instantiated, return the cached instance.
"""
self._warn("ObjectProxy.instantiate")
if self.instance is None:
self.instance = checked_call(self.cls, self.kwds)
try:
self.instance.yaml_src = self.yaml_src
except AttributeError:
pass
return self.instance
def instantiate(self):
"""
.. warning::
Deprecated, to be removed as of January 1, 2015.
Instantiate this object with the supplied parameters in `self.kwds`,
or if already instantiated, return the cached instance.
"""
self._warn("ObjectProxy.instantiate")
if self.instance is None:
self.instance = checked_call(self.cls, self.kwds)
try:
self.instance.yaml_src = self.yaml_src
except AttributeError:
pass
return self.instance
def _instantiate_proxy_tuple(proxy, bindings=None):
"""
Helper function for `_instantiate` that handles objects of the `Proxy`
class.
Parameters
----------
proxy : Proxy object
A `Proxy` object that.
bindings : dict, opitonal
A dictionary mapping previously instantiated `Proxy` objects
to their instantiated values.
Returns
-------
obj : object
The result object from recursively instantiating the object DAG.
"""
if proxy in bindings:
return bindings[proxy]
else:
# Respect do_not_recurse by just un-packing it (same as calling).
if proxy.callable == do_not_recurse:
obj = proxy.keywords['value']
else:
# TODO: add (requested) support for positionals (needs to be added
# to checked_call also).
if len(proxy.positionals) > 0:
raise NotImplementedError('positional arguments not yet '
'supported in proxy instantiation')
kwargs = dict((k, _instantiate(v, bindings))
for k, v in proxy.keywords.iteritems())
obj = checked_call(proxy.callable, kwargs)
try:
obj.yaml_src = proxy.yaml_src
except AttributeError: # Some classes won't allow this.
pass
bindings[proxy] = obj
return bindings[proxy]
def _instantiate_proxy_tuple(proxy, bindings=None):
"""
Helper function for `_instantiate` that handles objects of the `Proxy`
class.
Parameters
----------
proxy : Proxy object
A `Proxy` object that.
bindings : dict, opitonal
A dictionary mapping previously instantiated `Proxy` objects
to their instantiated values.
Returns
-------
obj : object
The result object from recursively instantiating the object DAG.
"""
if proxy in bindings:
return bindings[proxy]
else:
# Respect do_not_recurse by just un-packing it (same as calling).
if proxy.callable == do_not_recurse:
obj = proxy.keywords['value']
else:
# TODO: add (requested) support for positionals (needs to be added
# to checked_call also).
if len(proxy.positionals) > 0:
raise NotImplementedError('positional arguments not yet '
'supported in proxy instantiation')
kwargs = dict((k, _instantiate(v, bindings))
for k, v in six.iteritems(proxy.keywords))
obj = checked_call(proxy.callable, kwargs)
try:
obj.yaml_src = proxy.yaml_src
except AttributeError: # Some classes won't allow this.
pass
bindings[proxy] = obj
return bindings[proxy]
def package_call(to_call, ** kwargs):
return checked_call(to_call, kwargs)
def package_call(to_call, **kwargs):
return checked_call(to_call, kwargs)
def setup_detector_layer_c01b(layer, input_space, rng, irange="not specified"):
"""
.. todo::
WRITEME properly
Takes steps to set up an object for use as being some kind of convolutional
layer. This function sets up only the detector layer.
Does the following:
* raises a RuntimeError if cuda is not available
* sets layer.input_space to input_space
* sets up addition of dummy channels for compatibility with cuda-convnet:
- layer.dummy_channels: # of dummy channels that need to be added
(You might want to check this and raise an Exception if it's not 0)
- layer.dummy_space: The Conv2DSpace representing the input with dummy
channels added
* sets layer.detector_space to the space for the detector layer
* sets layer.transformer to be a Conv2D instance
* sets layer.b to the right value
Parameters
----------
layer : object
Any python object that allows the modifications described below and
has the following attributes:
* pad : int describing amount of zero padding to add
* kernel_shape : 2-element tuple or list describing spatial shape of
kernel
* fix_kernel_shape : bool, if true, will shrink the kernel shape to
make it feasible, as needed (useful for hyperparameter searchers)
* detector_channels : The number of channels in the detector layer
* init_bias : numeric constant added to a tensor of zeros to
initialize the bias
* tied_b : If true, biases are shared across all spatial locations
input_space : WRITEME
A Conv2DSpace to be used as input to the layer
rng : WRITEME
A numpy RandomState or equivalent
"""
if irange != "not specified":
raise AssertionError(
"There was a bug in setup_detector_layer_c01b."
"It uses layer.irange instead of the irange parameter to the "
"function. The irange parameter is now disabled by this "
"AssertionError, so that this error message can alert you that "
"the bug affected your code and explain why the interface is "
"changing. The irange parameter to the function and this "
"error message may be removed after April 21, 2014."
)
# Use "self" to refer to layer from now on, so we can pretend we're
# just running in the set_input_space method of the layer
self = layer
# Make sure cuda is available
check_cuda(str(type(self)))
# Validate input
if not isinstance(input_space, Conv2DSpace):
raise TypeError(
"The input to a convolutional layer should be a "
"Conv2DSpace, but layer " + self.layer_name + " got " + str(type(self.input_space))
)
if not hasattr(self, "detector_channels"):
raise ValueError(
"layer argument must have a 'detector_channels' "
"attribute specifying how many channels to put in "
"the convolution kernel stack."
)
# Store the input space
self.input_space = input_space
# Make sure number of channels is supported by cuda-convnet
# (multiple of 4 or <= 3)
# If not supported, pad the input with dummy channels
ch = self.input_space.num_channels
rem = ch % 4
if ch > 3 and rem != 0:
self.dummy_channels = 4 - rem
else:
self.dummy_channels = 0
self.dummy_space = Conv2DSpace(
shape=input_space.shape, channels=input_space.num_channels + self.dummy_channels, axes=("c", 0, 1, "b")
)
if hasattr(self, "kernel_stride"):
kernel_stride = self.kernel_stride
else:
kernel_stride = [1, 1]
output_shape = [
int(np.ceil((i_sh + 2.0 * self.pad - k_sh) / float(k_st))) + 1
for i_sh, k_sh, k_st in zip(self.input_space.shape, self.kernel_shape, kernel_stride)
]
def handle_kernel_shape(idx):
if self.kernel_shape[idx] < 1:
raise ValueError(
"kernel must have strictly positive size on all " "axes but has shape: " + str(self.kernel_shape)
)
if output_shape[idx] <= 0:
if self.fix_kernel_shape:
self.kernel_shape[idx] = self.input_space.shape[idx] + 2 * self.pad
assert self.kernel_shape[idx] != 0
output_shape[idx] = 1
warnings.warn("Had to change the kernel shape to make " "network feasible")
else:
raise ValueError("kernel too big for input " "(even with zero padding)")
map(handle_kernel_shape, [0, 1])
if self.detector_channels < 16:
raise ValueError("Cuda-convnet requires the detector layer to have " "at least 16 channels.")
self.detector_space = Conv2DSpace(shape=output_shape, num_channels=self.detector_channels, axes=("c", 0, 1, "b"))
if hasattr(self, "partial_sum"):
partial_sum = self.partial_sum
else:
partial_sum = 1
if hasattr(self, "sparse_init") and self.sparse_init is not None:
self.transformer = checked_call(
make_sparse_random_conv2D,
OrderedDict(
[
("num_nonzero", self.sparse_init),
("input_space", self.input_space),
("output_space", self.detector_space),
("kernel_shape", self.kernel_shape),
("pad", self.pad),
("partial_sum", partial_sum),
("kernel_stride", kernel_stride),
("rng", rng),
]
),
)
else:
self.transformer = make_random_conv2D(
irange=self.irange,
input_axes=self.input_space.axes,
output_axes=self.detector_space.axes,
input_channels=self.dummy_space.num_channels,
output_channels=self.detector_space.num_channels,
kernel_shape=self.kernel_shape,
pad=self.pad,
partial_sum=partial_sum,
kernel_stride=kernel_stride,
rng=rng,
)
W, = self.transformer.get_params()
W.name = self.layer_name + "_W"
if self.tied_b:
self.b = sharedX(np.zeros(self.detector_space.num_channels) + self.init_bias)
else:
self.b = sharedX(self.detector_space.get_origin() + self.init_bias)
self.b.name = self.layer_name + "_b"
logger.info("Input shape: {0}".format(self.input_space.shape))
logger.info("Detector space: {0}".format(self.detector_space.shape))
def setup_detector_layer_c01b(layer, input_space, rng):
"""
.. todo::
WRITEME properly
Takes steps to set up an object for use as being some kind of convolutional
layer. This function sets up only the detector layer.
Does the following:
* raises a RuntimeError if cuda is not available
* sets layer.input_space to input_space
* sets up addition of dummy channels for compatibility with cuda-convnet:
- layer.dummy_channels: # of dummy channels that need to be added
(You might want to check this and raise an Exception if it's not 0)
- layer.dummy_space: The Conv2DSpace representing the input with dummy
channels added
* sets layer.detector_space to the space for the detector layer
* sets layer.transformer to be a Conv2D instance
* sets layer.b to the right value
Parameters
----------
layer : object
Any python object that allows the modifications described below and
has the following attributes:
* pad : int describing amount of zero padding to add
* kernel_shape : 2-element tuple or list describing spatial shape of
kernel
* fix_kernel_shape : bool, if true, will shrink the kernel shape to
make it feasible, as needed (useful for hyperparameter searchers)
* detector_channels : The number of channels in the detector layer
* init_bias : numeric constant added to a tensor of zeros to
initialize the bias
* tied_b : If true, biases are shared across all spatial locations
input_space : WRITEME
A Conv2DSpace to be used as input to the layer
rng : WRITEME
A numpy RandomState or equivalent
"""
# Use "self" to refer to layer from now on, so we can pretend we're
# just running in the set_input_space method of the layer
self = layer
# Make sure cuda is available
check_cuda(str(type(self)))
# Validate input
if not isinstance(input_space, Conv2DSpace):
raise TypeError("The input to a convolutional layer should be a "
"Conv2DSpace, but layer " + self.layer_name + " got " +
str(type(self.input_space)))
if not hasattr(self, 'detector_channels'):
raise ValueError("layer argument must have a 'detector_channels' "
"attribute specifying how many channels to put in "
"the convolution kernel stack.")
# Store the input space
self.input_space = input_space
# Make sure number of channels is supported by cuda-convnet
# (multiple of 4 or <= 3)
# If not supported, pad the input with dummy channels
ch = self.input_space.num_channels
rem = ch % 4
if ch > 3 and rem != 0:
self.dummy_channels = 4 - rem
else:
self.dummy_channels = 0
self.dummy_space = Conv2DSpace(
shape=input_space.shape,
channels=input_space.num_channels + self.dummy_channels,
axes=('c', 0, 1, 'b')
)
if hasattr(self, 'kernel_stride'):
kernel_stride = self.kernel_stride
else:
kernel_stride = [1, 1]
output_shape = \
[int(np.ceil((i_sh + 2. * self.pad - k_sh) / float(k_st))) + 1
for i_sh, k_sh, k_st in zip(self.input_space.shape,
self.kernel_shape, kernel_stride)]
def handle_kernel_shape(idx):
if self.kernel_shape[idx] < 1:
raise ValueError("kernel must have strictly positive size on all "
"axes but has shape: " + str(self.kernel_shape))
if output_shape[idx] <= 0:
if self.fix_kernel_shape:
self.kernel_shape[idx] = \
self.input_space.shape[idx] + 2 * self.pad
assert self.kernel_shape[idx] != 0
output_shape[idx] = 1
warnings.warn("Had to change the kernel shape to make "
"network feasible")
else:
raise ValueError("kernel too big for input "
"(even with zero padding)")
map(handle_kernel_shape, [0, 1])
if self.detector_channels < 16:
raise ValueError("Cuda-convnet requires the detector layer to have "
"at least 16 channels.")
self.detector_space = Conv2DSpace(shape=output_shape,
num_channels=self.detector_channels,
axes=('c', 0, 1, 'b'))
if hasattr(self, 'partial_sum'):
partial_sum = self.partial_sum
else:
partial_sum = 1
if hasattr(self, 'sparse_init') and self.sparse_init is not None:
self.transformer = \
checked_call(make_sparse_random_conv2D,
OrderedDict([('num_nonzero', self.sparse_init),
('input_space', self.input_space),
('output_space', self.detector_space),
('kernel_shape', self.kernel_shape),
('pad', self.pad),
('partial_sum', partial_sum),
('kernel_stride', kernel_stride),
('rng', rng)]))
else:
self.transformer = make_random_conv2D(
irange=self.irange,
input_axes=self.input_space.axes,
output_axes=self.detector_space.axes,
input_channels=self.dummy_space.num_channels,
output_channels=self.detector_space.num_channels,
kernel_shape=self.kernel_shape,
pad=self.pad,
partial_sum=partial_sum,
kernel_stride=kernel_stride,
rng=rng
)
W, = self.transformer.get_params()
W.name = self.layer_name + '_W'
if self.tied_b:
self.b = sharedX(np.zeros(self.detector_space.num_channels) +
self.init_bias)
else:
self.b = sharedX(self.detector_space.get_origin() + self.init_bias)
self.b.name = self.layer_name + '_b'
logger.info('Input shape: {0}'.format(self.input_space.shape))
logger.info('Detector space: {0}'.format(self.detector_space.shape))
for i in xrange(len(means)):
W[:,i] = means[i]/norms[i]
init_mu = np.asarray(norms)
model = checked_call(S3C,
dict(nvis = X.shape[1],
nhid = len(means),
init_bias_hid = inverse_sigmoid_numpy(np.asarray([ (y==i).mean() for i in xrange(y.max()+1)])),
irange = 0.,
min_B = .1,
max_B = 1e6,
min_alpha = .1,
max_alpha = 1e6,
m_step = checked_call(Grad_M_Step, dict(learning_rate = 1.)),
init_mu = init_mu,
init_alpha = init_mu * 10.,
init_B = init_beta,
e_step = E_Step_Scan(
h_new_coeff_schedule = [ .1 ] * 50,
s_new_coeff_schedule = [ .1 ] * 50,
clip_reflections = 1,
rho = 0.5
))
)
model.W.set_value(W)
model.dataset_yaml_src = '!obj:pylearn2.datasets.mnist.MNIST { which_set : "train", center : 0 }'
serial.save('/u/goodfeli/galatea/pddbm/config/mnist/s3c_hack.pkl',model)