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提交 f331d567 authored 作者: Frederic Bastien's avatar Frederic Bastien
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white space fix.

上级 bc0aee06
隐藏空白字符变更
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正在显示 包含 91 行增加 和 91 行删除
theano/scan.py
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...@@ -956,14 +956,14 @@ def scan( fn ...@@ -956,14 +956,14 @@ def scan( fn
for input in dummy_f.maker.expanded_inputs[fromIdx:] : for input in dummy_f.maker.expanded_inputs[fromIdx:] :
# make sure that we do not add the same shared variable twice # make sure that we do not add the same shared variable twice
if isinstance(input.variable, SharedVariable) and not input.update: if isinstance(input.variable, SharedVariable) and not input.update:
shared_non_seqs += [input.variable] shared_non_seqs += [input.variable]
new_var = input.variable.type() new_var = input.variable.type()
if input.variable.name: if input.variable.name:
new_var.name = input.variable.name + '_copy' new_var.name = input.variable.name + '_copy'
inner_fn_inputs += [new_var] inner_fn_inputs += [new_var]
slice_to_seqs += [ n_extended_outs] slice_to_seqs += [ n_extended_outs]
givens[input.variable] = inner_fn_inputs[-1] givens[input.variable] = inner_fn_inputs[-1]
copy_map[inner_fn_inputs[-1]] = input.variable copy_map[inner_fn_inputs[-1]] = input.variable
elif not isinstance(input.variable, SharedVariable): elif not isinstance(input.variable, SharedVariable):
# also add the normal tensor that are non sequences at the # also add the normal tensor that are non sequences at the
# end of the inputs intertwingled with the shared variables # end of the inputs intertwingled with the shared variables
...@@ -998,7 +998,7 @@ def scan( fn ...@@ -998,7 +998,7 @@ def scan( fn
# and non sequences # and non sequences
for seq in seqs : for seq in seqs :
if not seq.get('input', None): if not seq.get('input', None):
raiseValue('All input sequences should provide') raiseValue('All input sequences should provide')
unwrapped_seqs = [ seq.get('input',tensor.as_tensor(0.)) for seq in seqs ] unwrapped_seqs = [ seq.get('input',tensor.as_tensor(0.)) for seq in seqs ]
unwrapped_outs = [ out.get('initial',tensor.as_tensor(0.)) for out in outs_info ] unwrapped_outs = [ out.get('initial',tensor.as_tensor(0.)) for out in outs_info ]
...@@ -1644,16 +1644,16 @@ class ScanGrad(Op): ...@@ -1644,16 +1644,16 @@ class ScanGrad(Op):
def __eq__(self,other): def __eq__(self,other):
rval = type(self) == type(other) rval = type(self) == type(other)
if rval: if rval:
rval = (self.inputs == other.inputs) and \ rval = (self.inputs == other.inputs) and \
(self.outputs == other.outputs) and \ (self.outputs == other.outputs) and \
(self.n_seqs == other.n_seqs) and \ (self.n_seqs == other.n_seqs) and \
(self.n_outs == other.n_outs) and \ (self.n_outs == other.n_outs) and \
(self.go_backwards == other.go_backwards) and \ (self.go_backwards == other.go_backwards) and \
(self.n_outs_not_shared == other.n_outs_not_shared) and\ (self.n_outs_not_shared == other.n_outs_not_shared) and\
(self.truncate_gradient == other.truncate_gradient) and\ (self.truncate_gradient == other.truncate_gradient) and\
(self.mode == other.mode) and \ (self.mode == other.mode) and \
(self.seqs_taps == other.seqs_taps) and \ (self.seqs_taps == other.seqs_taps) and \
(self.outs_taps == other.outs_taps) (self.outs_taps == other.outs_taps)
return rval return rval
def __hash__(self): def __hash__(self):
...@@ -1786,79 +1786,79 @@ class ScanGrad(Op): ...@@ -1786,79 +1786,79 @@ class ScanGrad(Op):
initOuts_size.update({j:0}) initOuts_size.update({j:0})
for i in the_range: for i in the_range:
# time slice of inputs # time slice of inputs
_ins = [] _ins = []
_i = i _i = i
if go_backwards: if go_backwards:
_i = n_steps -1 -i _i = n_steps -1 -i
for j in xrange(self.n_seqs): for j in xrange(self.n_seqs):
if self.seqs_taps.has_key(j): if self.seqs_taps.has_key(j):
ls_taps = self.seqs_taps[j] ls_taps = self.seqs_taps[j]
min_tap = seqs_mins[j] min_tap = seqs_mins[j]
for tap_value in ls_taps: for tap_value in ls_taps:
k = _i - min_tap + tap_value k = _i - min_tap + tap_value
_ins += [seqs[j][k]] _ins += [seqs[j][k]]
# time slice of outputs + taps # time slice of outputs + taps
_outs = [] _outs = []
for j in xrange(self.n_outs): for j in xrange(self.n_outs):
if self.outs_taps.has_key(j): if self.outs_taps.has_key(j):
ls_taps = self.outs_taps[j] ls_taps = self.outs_taps[j]
min_tap = outs_mins[j] min_tap = outs_mins[j]
seed_sz = initOuts_size[j] seed_sz = initOuts_size[j]
for tap_value in ls_taps: for tap_value in ls_taps:
if i + tap_value < 0: if i + tap_value < 0:
if seed_sz < 1: if seed_sz < 1:
_outs += [outInfo[j]] _outs += [outInfo[j]]
else: else:
k = i + seed_sz + tap_value k = i + seed_sz + tap_value
if k < 0 : if k < 0 :
#past value not provided .. issue a warning and use 0 #past value not provided .. issue a warning and use 0
_outs += [numpy.zeros(outInfo[j][0].shape)] _outs += [numpy.zeros(outInfo[j][0].shape)]
warning('Past value %d for output $d not given' \ warning('Past value %d for output $d not given' \
%(j,tap_value)) %(j,tap_value))
else: else:
_outs += [outInfo[j][k]] _outs += [outInfo[j][k]]
else: else:
if j>= self.n_outs_not_shared: if j>= self.n_outs_not_shared:
_outs += [outs[j] ] _outs += [outs[j] ]
else: else:
_outs += [outs[j][i + tap_value]] _outs += [outs[j][i + tap_value]]
g_out = [] g_out = []
g_out = [ arg[i] for arg in g_outs] g_out = [ arg[i] for arg in g_outs]
grad_args = g_out + _ins + _outs + non_seqs grad_args = g_out + _ins + _outs + non_seqs
grads=self.grad_fn(*grad_args) grads=self.grad_fn(*grad_args)
# get gradient for inputs # get gradient for inputs
pos = 0 pos = 0
for j in xrange(self.n_seqs): for j in xrange(self.n_seqs):
if self.seqs_taps.has_key(j): if self.seqs_taps.has_key(j):
ls_taps = self.seqs_taps[j] ls_taps = self.seqs_taps[j]
min_tap = seqs_mins[j] min_tap = seqs_mins[j]
for tap_value in ls_taps : for tap_value in ls_taps :
k = _i - min_tap + tap_value k = _i - min_tap + tap_value
g_seqs[j][k-lower_limit] += grads[pos] g_seqs[j][k-lower_limit] += grads[pos]
pos += 1 pos += 1
# get gradient for outputs
for j in xrange(self.n_outs_not_shared):
if self.outs_taps.has_key(j):
ls_taps = self.outs_taps[j]
min_tap = outs_mins[j]
seed_sz = initOuts_size[j]
for tap_value in ls_taps:
if i+tap_value < 0 :
k = i + seed_sz + tap_value
if k >= 0 :
g_outInfo[j][k] += grads[pos]
else:
g_outInfo[j] += grads[pos]
else:
g_outs[j][i+tap_value] += grads[pos] # get gradient for outputs
pos += 1 for j in xrange(self.n_outs_not_shared):
for j in xrange(len(g_non_seqs)): if self.outs_taps.has_key(j):
g_non_seqs[j] += grads[j+pos] ls_taps = self.outs_taps[j]
min_tap = outs_mins[j]
seed_sz = initOuts_size[j]
for tap_value in ls_taps:
if i+tap_value < 0 :
k = i + seed_sz + tap_value
if k >= 0 :
g_outInfo[j][k] += grads[pos]
else:
g_outInfo[j] += grads[pos]
else:
g_outs[j][i+tap_value] += grads[pos]
pos += 1
for j in xrange(len(g_non_seqs)):
g_non_seqs[j] += grads[j+pos]
# return the gradient # return the gradient
...@@ -1886,7 +1886,7 @@ class ScanSpaceOptimizer(Optimizer): ...@@ -1886,7 +1886,7 @@ class ScanSpaceOptimizer(Optimizer):
# check the outputs # check the outputs
for i,out in enumerate(node.outputs): for i,out in enumerate(node.outputs):
if op.store_steps[i] == 0 : if op.store_steps[i] == 0 :
# if we do not have a range for this output # if we do not have a range for this output
req_steps = numpy.max(numpy.abs(op.outs_taps.get(i,1))) req_steps = numpy.max(numpy.abs(op.outs_taps.get(i,1)))
# look at all its clients # look at all its clients
for cl,_dx in out.clients: for cl,_dx in out.clients:
...@@ -1906,7 +1906,7 @@ class ScanSpaceOptimizer(Optimizer): ...@@ -1906,7 +1906,7 @@ class ScanSpaceOptimizer(Optimizer):
# if it is a tensor, and the first # if it is a tensor, and the first
# dimension is just -1 # dimension is just -1
if cl.op.idx_list[0] == -1 and req_steps != None: if cl.op.idx_list[0] == -1 and req_steps != None:
req_steps = numpy.max([1, req_steps]) req_steps = numpy.max([1, req_steps])
else: else:
# or a constant that evaluates to # or a constant that evaluates to
# -1 # -1
......
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