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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
内嵌 并排
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
for input in dummy_f.maker.expanded_inputs[fromIdx:] :
# make sure that we do not add the same shared variable twice
if isinstance(input.variable, SharedVariable) and not input.update:
shared_non_seqs += [input.variable]
new_var = input.variable.type()
if input.variable.name:
new_var.name = input.variable.name + '_copy'
inner_fn_inputs += [new_var]
slice_to_seqs += [ n_extended_outs]
givens[input.variable] = inner_fn_inputs[-1]
copy_map[inner_fn_inputs[-1]] = input.variable
shared_non_seqs += [input.variable]
new_var = input.variable.type()
if input.variable.name:
new_var.name = input.variable.name + '_copy'
inner_fn_inputs += [new_var]
slice_to_seqs += [ n_extended_outs]
givens[input.variable] = inner_fn_inputs[-1]
copy_map[inner_fn_inputs[-1]] = input.variable
elif not isinstance(input.variable, SharedVariable):
# also add the normal tensor that are non sequences at the
# end of the inputs intertwingled with the shared variables
......@@ -998,7 +998,7 @@ def scan( fn
# and non sequences
for seq in seqs :
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_outs = [ out.get('initial',tensor.as_tensor(0.)) for out in outs_info ]
......@@ -1644,16 +1644,16 @@ class ScanGrad(Op):
def __eq__(self,other):
rval = type(self) == type(other)
if rval:
rval = (self.inputs == other.inputs) and \
(self.outputs == other.outputs) and \
(self.n_seqs == other.n_seqs) and \
(self.n_outs == other.n_outs) and \
(self.go_backwards == other.go_backwards) and \
(self.n_outs_not_shared == other.n_outs_not_shared) and\
(self.truncate_gradient == other.truncate_gradient) and\
(self.mode == other.mode) and \
(self.seqs_taps == other.seqs_taps) and \
(self.outs_taps == other.outs_taps)
rval = (self.inputs == other.inputs) and \
(self.outputs == other.outputs) and \
(self.n_seqs == other.n_seqs) and \
(self.n_outs == other.n_outs) and \
(self.go_backwards == other.go_backwards) and \
(self.n_outs_not_shared == other.n_outs_not_shared) and\
(self.truncate_gradient == other.truncate_gradient) and\
(self.mode == other.mode) and \
(self.seqs_taps == other.seqs_taps) and \
(self.outs_taps == other.outs_taps)
return rval
def __hash__(self):
......@@ -1786,79 +1786,79 @@ class ScanGrad(Op):
initOuts_size.update({j:0})
for i in the_range:
# time slice of inputs
_ins = []
_i = i
if go_backwards:
_i = n_steps -1 -i
# time slice of inputs
_ins = []
_i = i
if go_backwards:
_i = n_steps -1 -i
for j in xrange(self.n_seqs):
if self.seqs_taps.has_key(j):
ls_taps = self.seqs_taps[j]
min_tap = seqs_mins[j]
for tap_value in ls_taps:
k = _i - min_tap + tap_value
_ins += [seqs[j][k]]
# time slice of outputs + taps
_outs = []
for j in xrange(self.n_outs):
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:
if seed_sz < 1:
_outs += [outInfo[j]]
else:
k = i + seed_sz + tap_value
if k < 0 :
for j in xrange(self.n_seqs):
if self.seqs_taps.has_key(j):
ls_taps = self.seqs_taps[j]
min_tap = seqs_mins[j]
for tap_value in ls_taps:
k = _i - min_tap + tap_value
_ins += [seqs[j][k]]
# time slice of outputs + taps
_outs = []
for j in xrange(self.n_outs):
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:
if seed_sz < 1:
_outs += [outInfo[j]]
else:
k = i + seed_sz + tap_value
if k < 0 :
#past value not provided .. issue a warning and use 0
_outs += [numpy.zeros(outInfo[j][0].shape)]
warning('Past value %d for output $d not given' \
%(j,tap_value))
else:
_outs += [outInfo[j][k]]
else:
if j>= self.n_outs_not_shared:
_outs += [outs[j] ]
else:
_outs += [outs[j][i + tap_value]]
g_out = []
g_out = [ arg[i] for arg in g_outs]
grad_args = g_out + _ins + _outs + non_seqs
grads=self.grad_fn(*grad_args)
# get gradient for inputs
pos = 0
for j in xrange(self.n_seqs):
if self.seqs_taps.has_key(j):
ls_taps = self.seqs_taps[j]
min_tap = seqs_mins[j]
for tap_value in ls_taps :
k = _i - min_tap + tap_value
g_seqs[j][k-lower_limit] += grads[pos]
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]
_outs += [numpy.zeros(outInfo[j][0].shape)]
warning('Past value %d for output $d not given' \
%(j,tap_value))
else:
_outs += [outInfo[j][k]]
else:
if j>= self.n_outs_not_shared:
_outs += [outs[j] ]
else:
_outs += [outs[j][i + tap_value]]
g_out = []
g_out = [ arg[i] for arg in g_outs]
grad_args = g_out + _ins + _outs + non_seqs
grads=self.grad_fn(*grad_args)
# get gradient for inputs
pos = 0
for j in xrange(self.n_seqs):
if self.seqs_taps.has_key(j):
ls_taps = self.seqs_taps[j]
min_tap = seqs_mins[j]
for tap_value in ls_taps :
k = _i - min_tap + tap_value
g_seqs[j][k-lower_limit] += grads[pos]
pos += 1
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]
# 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]
pos += 1
for j in xrange(len(g_non_seqs)):
g_non_seqs[j] += grads[j+pos]
# return the gradient
......@@ -1886,7 +1886,7 @@ class ScanSpaceOptimizer(Optimizer):
# check the outputs
for i,out in enumerate(node.outputs):
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)))
# look at all its clients
for cl,_dx in out.clients:
......@@ -1906,7 +1906,7 @@ class ScanSpaceOptimizer(Optimizer):
# if it is a tensor, and the first
# dimension is just -1
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:
# or a constant that evaluates to
# -1
......
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