def solve(self, decaf_net, previous_net=None):
"""Solves the net."""
# first, run a pass to initialize all the parameters.
logging.info('StochasticSolver: precomputing.')
self._iter_idx = 0
self._decaf_net = decaf_net
self._previous_net = previous_net
initial_loss = decaf_net.forward_backward(self._previous_net)
logging.info('StochasticSolver: initial loss: %f.', initial_loss)
logging.info('(Under mpirun, the given loss will just be an estimate'
' on the root node.)')
self.initialize_status()
# the main iteration
timer = Timer()
logging.info('StochasticSolver: started.')
for _ in range(self._max_iter):
if mpi.SIZE > 1:
loss = mpi.COMM.allreduce(
decaf_net.forward_backward(self._previous_net)) / mpi.SIZE
# we need to broadcast and average the parameters
params = decaf_net.params()
for param in params:
diff = param.diff()
diff_cache = diff.copy()
mpi.COMM.Allreduce(diff_cache, diff)
diff /= mpi.SIZE
else:
loss = decaf_net.forward_backward(self._previous_net)
self.compute_update_value()
decaf_net.update()
if (mpi.is_root() and
self._snapshot_interval > 0 and self._iter_idx > 0 and
self._iter_idx % self._snapshot_interval == 0):
# perform snapshot.
self.snapshot()
if (self.spec.get('disp', 0) and
self._iter_idx % self.spec['disp']):
logging.info('Iter %d, loss %f, elapsed %s', self._iter_idx,
loss, timer.lap())
self.iter_callback(loss)
self._iter_idx += 1
# perform last snapshot.
if mpi.is_root() and 'folder' in self.spec:
self.snapshot(True)
mpi.barrier()
logging.info('StochasticSolver: finished. Total time %s.',
timer.total())
def solve(self, decaf_net, previous_net=None):
"""Solves the net."""
# first, run a pass to initialize all the parameters.
logging.info('StochasticSolver: precomputing.')
self._iter_idx = 0
self._decaf_net = decaf_net
self._previous_net = previous_net
initial_loss = decaf_net.forward_backward(self._previous_net)
logging.info('StochasticSolver: initial loss: %f.', initial_loss)
logging.info('(Under mpirun, the given loss will just be an estimate'
' on the root node.)')
self.initialize_status()
# the main iteration
timer = Timer()
logging.info('StochasticSolver: started.')
for _ in range(self._max_iter):
if mpi.SIZE > 1:
loss = mpi.COMM.allreduce(
decaf_net.forward_backward(self._previous_net)) / mpi.SIZE
# we need to broadcast and average the parameters
params = decaf_net.params()
for param in params:
diff = param.diff()
diff_cache = diff.copy()
mpi.COMM.Allreduce(diff_cache, diff)
diff /= mpi.SIZE
else:
loss = decaf_net.forward_backward(self._previous_net)
self.compute_update_value()
decaf_net.update()
if (mpi.is_root() and self._snapshot_interval > 0
and self._iter_idx > 0
and self._iter_idx % self._snapshot_interval == 0):
# perform snapshot.
self.snapshot()
if (self.spec.get('disp', 0)
and self._iter_idx % self.spec['disp']):
logging.info('Iter %d, loss %f, elapsed %s', self._iter_idx,
loss, timer.lap())
self.iter_callback(loss)
self._iter_idx += 1
# perform last snapshot.
if mpi.is_root() and 'folder' in self.spec:
self.snapshot(True)
mpi.barrier()
logging.info('StochasticSolver: finished. Total time %s.',
timer.total())
def testIsRoot(self):
if mpi.RANK == 0:
self.assertTrue(mpi.is_root())
else:
self.assertFalse(mpi.is_root())
def testIsRoot(self):
if mpi.RANK == 0:
self.assertTrue(mpi.is_root())
else:
self.assertFalse(mpi.is_root())
