def get_layer_monitoring_channels(self, state_below=None,
state=None, targets=None):
rval = OrderedDict()
state = state_below
for layer in self.layers:
# We don't go through all the inner layers recursively
state_below = state
state = layer.fprop(state)
args = [state_below, state]
if layer is self.layers[-1] and targets is not None:
args.append(targets)
ch = layer.get_layer_monitoring_channels(*args)
if not isinstance(ch, OrderedDict):
raise TypeError(str((type(ch), layer.layer_name)))
for key in ch:
value = ch[key]
doc = get_monitor_doc(value)
if doc is None:
doc = str(type(layer)) + \
".get_monitoring_channels_from_state did" + \
" not provide any further documentation for" + \
" this channel."
doc = 'This channel came from a layer called "' + \
layer.layer_name + '" of an MLP.\n' + doc
value.__doc__ = doc
rval[layer.layer_name+'_'+key] = value
return rval
def get_monitoring_channels(self, data):
state_below = data
targets = None
rval = OrderedDict()
if self.encoder is not None:
rval = OrderedDict()
"""
state = state_below
for layer in self.layers:
state_below = state
state = layer.fprop(state)
args = [state_below, state]
if layer is self.layers[-1] and targets is not None:
args.append(targets)
ch = layer.get_layer_monitoring_channels(*args)
if not isinstance(ch, OrderedDict):
raise TypeError(str((type(ch), layer.layer_name)))
for key in ch:
value = ch[key]
doc = get_monitor_doc(value)
if doc is None:
doc = str(type(layer)) + \
".get_monitoring_channels_from_state did" + \
" not provide any further documentation for" + \
" this channel."
doc = 'This channel came from a layer called "' + \
layer.layer_name + '" of an MLP.\n' + doc
value.__doc__ = doc
rval[layer.layer_name + '_' + key] = value
"""
top_layer = self.layers[-1]
ch = top_layer.get_layer_monitoring_channels()
for key, value in ch.iteritems():
doc = get_monitor_doc(value)
if doc is None:
doc = str(type(top_layer)) + \
".get_monitoring_channels_from_state did" + \
" not provide any further documentation for" + \
" this channel."
doc = 'This channel came from a layer called "' + \
top_layer.layer_name + '" of an MLP.\n' + doc
value.__doc__ = doc
rval[top_layer.layer_name + '_' + key] = value
Z, log_det_jac = self.get_fprop_and_log_det_jacobian(data)
prior = self.log_p_z(Z)
rval['ave_output'] = Z.mean()
rval['ave_log_det_jac'] = log_det_jac.mean()
rval['ave_prior'] = prior.mean()
rval['cumulative_sum'] = self.prior.get_cumulative(Z).sum(axis=1).mean(axis=0)
return rval
def get_layer_monitoring_channels(self,
state_below=None,
state=None,
targets=None):
"""
Block monitoring channels if not necessary
Parameters
---------
: todo
"""
rval = OrderedDict()
if self.use_monitoring_channels:
state = state_below
x = state
state_conc = None
for layer in self.layers:
# We don't go through all the inner layers recursively
state_below = state
if ((self.x_shortcut and layer is not self.layers[0]
and layer is not self.layers[-1])):
state = self.create_shortcut_batch(state, x, 2, 1)
if self.y_shortcut and layer is self.layers[-1]:
state = layer.fprop(state_conc)
else:
state = layer.fprop(state)
if self.y_shortcut and layer is not self.layers[-1]:
if layer is self.layers[0]:
state_conc = state
else:
state_conc = self.create_shortcut_batch(
state_conc, state, 2)
args = [state_below, state]
if layer is self.layers[-1] and targets is not None:
args.append(targets)
ch = layer.get_layer_monitoring_channels(*args)
if not isinstance(ch, OrderedDict):
raise TypeError(str((type(ch), layer.layer_name)))
for key in ch:
value = ch[key]
doc = get_monitor_doc(value)
if doc is None:
doc = str(type(layer)) + \
".get_monitoring_channels_from_state did" + \
" not provide any further documentation for" + \
" this channel."
doc = 'This channel came from a layer called "' + \
layer.layer_name + '" of an MLP.\n' + doc
value.__doc__ = doc
rval[layer.layer_name + '_' + key] = value
return rval
def get_layer_monitoring_channels(self, state_below=None,
state=None, targets=None):
"""
Block monitoring channels if not necessary
Parameters
---------
: todo
"""
rval = OrderedDict()
if self.use_monitoring_channels:
state = state_below
x = state
state_conc = None
for layer in self.layers:
# We don't go through all the inner layers recursively
state_below = state
if ((self.x_shortcut and
layer is not self.layers[0] and
layer is not self.layers[-1])):
state = self.create_shortcut_batch(state, x, 2, 1)
if self.y_shortcut and layer is self.layers[-1]:
state = layer.fprop(state_conc)
else:
state = layer.fprop(state)
if self.y_shortcut and layer is not self.layers[-1]:
if layer is self.layers[0]:
state_conc = state
else:
state_conc = self.create_shortcut_batch(state_conc,
state, 2)
args = [state_below, state]
if layer is self.layers[-1] and targets is not None:
args.append(targets)
ch = layer.get_layer_monitoring_channels(*args)
if not isinstance(ch, OrderedDict):
raise TypeError(str((type(ch), layer.layer_name)))
for key in ch:
value = ch[key]
doc = get_monitor_doc(value)
if doc is None:
doc = str(type(layer)) + \
".get_monitoring_channels_from_state did" + \
" not provide any further documentation for" + \
" this channel."
doc = 'This channel came from a layer called "' + \
layer.layer_name + '" of an MLP.\n' + doc
value.__doc__ = doc
rval[layer.layer_name + '_' + key] = value
return rval