def function(self):
"""Returns the function the ``ForwardBackward``.
Returns
-------
lambda
The function.
See Also
--------
`theano.function(*args, **kwargs)`_ - How to make a graph. [**Theano Style**]
References
----------
The implementation of `ForwardBackward(net.cpp, L85)`_.
"""
if hasattr(self, '_function'): return self._function
for loss in self.losses:
for var in self.trainable_variables:
_Grad(loss, var)
self._function = _Function(
outputs=[self.blobs[key].data
for key in self.outputs])
if hasattr(self, '_model'):
_workspace.Restore(self._model, format='caffe')
return self._function
def function(self):
""" the CC Graph will create only once get this attr """
if hasattr(self, '_function'): return self._function
for cost in self._costs:
for wrt in self._wrts:
T.grad(cost, wrt)
self._function = \
theano.function(outputs=[self._blobs[name]['data']
for name in self._net_outputs], swaps=self._swap_blobs)
if hasattr(self, '_model'): ws.Restore(self._model, format=1)
return self._function
def copy_from(self, model):
"""Copy the parameters from the binary proto file. [**PyCaffe Style**]
Parameters
----------
model : str
The path of the ``.caffemodel`` file.
See Also
--------
`workspace.Restore(*args, **kwargs)`_ - How to restore tensors from a file.
References
----------
The implementation of `CopyTrainedLayersFromBinaryProto(net.cpp, L780)`_.
"""
ws.Restore(model, format='caffe')
def function(self, givens=None):
"""Returns the function the ``ForwardBackward``.
Parameters
----------
givens : None or dict
The givens to replace existing blobs.
Returns
-------
lambda
The function.
See Also
--------
`theano.function(*args, **kwargs)`_ - How to make a graph. [**Theano Style**]
References
----------
The implementation of `ForwardBackward(net.cpp, L85)`_.
"""
if hasattr(self, '_function'): return self._function
for cost in self._costs:
for wrt in self._wrts:
T.grad(cost, wrt)
if givens is not None:
if not isinstance(givens, dict):
raise TypeError('The givens should be a dict.')
for k, v in givens.items():
if not isinstance(v, Tensor):
raise ValueError('The value of givens should be a Tensor.')
self._swap_tensors[k] = v
self._function = \
theano.function(outputs=[self._blobs[name]['data']
for name in self._net_outputs], givens=self._swap_tensors)
if hasattr(self, '_model'): ws.Restore(self._model, format='caffe')
return self._function
def restore(self, sess, save_path):
ws.Restore(save_path)
def load(self):
filename = 'checkpoints/%s_%08d.bin' % (self.model_name, Config.LOAD_EPISODE)
ws.Restore(filename)
return Config.LOAD_EPISODE
def copy_from(self, model):
""" simply follow the pycaffe style """
ws.Restore(model, format=1)
