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enable dropout rate in several computation layers.

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0.10.0rc
caoying03 8 years ago
parent 15a6252f8e
commit ce939b30ba
1 changed files with 42 additions and 32 deletions
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74
python/paddle/trainer/config_parser.py
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@ -1803,9 +1803,9 @@ class ConvTransLayer(ConvTransLayerBase):
@config_layer('norm')
class NormLayer(LayerBase):
def __init__(self, name, inputs, device=None):
def __init__(self, name, inputs, device=None, **xargs):
super(NormLayer, self).__init__(
name, 'norm', 0, inputs=inputs, device=device)
name, 'norm', 0, inputs=inputs, device=device, **xargs)
for input_index in xrange(len(self.inputs)):
input_layer = self.get_input_layer(input_index)
norm_conf = self.config.inputs[input_index].norm_conf
@ -1817,9 +1817,9 @@ class NormLayer(LayerBase):
@config_layer('pool')
class PoolLayer(LayerBase):
def __init__(self, name, inputs, device=None):
def __init__(self, name, inputs, device=None, **xargs):
super(PoolLayer, self).__init__(
name, 'pool', 0, inputs=inputs, device=device)
name, 'pool', 0, inputs=inputs, device=device, **xargs)
for input_index in xrange(len(self.inputs)):
input_layer = self.get_input_layer(input_index)
pool_conf = self.config.inputs[input_index].pool_conf
@ -1927,9 +1927,9 @@ class BatchNormLayer(LayerBase):
@config_layer('trans')
class TransLayer(LayerBase):
def __init__(self, name, inputs, device=None):
def __init__(self, name, inputs, device=None, **xargs):
super(TransLayer, self).__init__(
name, 'trans', 0, inputs=inputs, device=device)
name, 'trans', 0, inputs=inputs, device=device, **xargs)
config_assert(
len(self.inputs) == 1,
'TransLayer must have one and only one input')
@ -1938,9 +1938,9 @@ class TransLayer(LayerBase):
@config_layer('resize')
class ResizeLayer(LayerBase):
def __init__(self, name, size, inputs, device=None):
def __init__(self, name, size, inputs, device=None, **xargs):
super(ResizeLayer, self).__init__(
name, 'resize', size=size, inputs=inputs, device=device)
name, 'resize', size=size, inputs=inputs, device=device, **xargs)
config_assert(
len(self.inputs) == 1,
'ResizeLayer must have one and only one input')
@ -2270,9 +2270,10 @@ class ExpandLayer(LayerBase):
inputs,
trans_type='non-seq',
device=None,
bias=False):
bias=False,
**xargs):
super(ExpandLayer, self).__init__(
name, 'expand', 0, inputs=inputs, device=device)
name, 'expand', 0, inputs=inputs, device=device, **xargs)
config_assert(
len(self.inputs) == 2, 'ExpandLayer takes 2 and only 2 inputs')
self.config.trans_type = trans_type
@ -2356,14 +2357,16 @@ class SequenceLastInstanceLayer(LayerBase):
active_type='linear',
trans_type='non-seq',
device=None,
bias=False):
bias=False,
**xargs):
super(SequenceLastInstanceLayer, self).__init__(
name,
'seqlastins',
0,
inputs=inputs,
device=device,
active_type=active_type)
active_type=active_type,
**xargs)
config_assert(
len(inputs) == 1, 'SequenceLastInstanceLayer must have 1 input')
self.config.trans_type = trans_type
@ -2400,14 +2403,16 @@ class SequenceConcatLayer(LayerBase):
inputs,
active_type='linear',
device=None,
bias=False):
bias=False,
**xargs):
super(SequenceConcatLayer, self).__init__(
name,
'seqconcat',
0,
inputs=inputs,
device=device,
active_type=active_type)
active_type=active_type,
**xargs)
config_assert(
len(inputs) == 2, 'SequenceConcatLayer must have 2 inputs')
for input_index in xrange(len(self.inputs)):
@ -2424,14 +2429,16 @@ class SequenceReshapeLayer(LayerBase):
inputs,
active_type='linear',
device=None,
bias=False):
bias=False,
**xargs):
super(SequenceReshapeLayer, self).__init__(
name,
'seqreshape',
size,
inputs=inputs,
device=device,
active_type=active_type)
active_type=active_type,
**xargs)
config_assert(
len(inputs) == 1, 'SequenceReshapeLayer must have 1 inputs')
self.set_layer_size(size)
@ -2445,14 +2452,16 @@ class SubSequenceLayer(LayerBase):
inputs,
active_type='linear',
device=None,
bias=False):
bias=False,
**xargs):
super(SubSequenceLayer, self).__init__(
name,
'subseq',
0,
inputs=inputs,
device=device,
active_type=active_type)
active_type=active_type,
**xargs)
config_assert(len(inputs) == 3, 'SubSequenceLayer must have 3 inputs')
input_layer0 = self.get_input_layer(0)
size = input_layer0.size
@ -2462,9 +2471,9 @@ class SubSequenceLayer(LayerBase):
@config_layer('out_prod')
class OuterProdLayer(LayerBase):
def __init__(self, name, inputs, device=None):
def __init__(self, name, inputs, device=None, **xargs):
super(OuterProdLayer, self).__init__(
name, 'out_prod', 0, inputs=inputs, device=device)
name, 'out_prod', 0, inputs=inputs, device=device, **xargs)
config_assert(len(inputs) == 2, 'OuterProdLayer must have 2 inputs')
input_layer0 = self.get_input_layer(0)
input_layer1 = self.get_input_layer(1)
@ -2473,9 +2482,9 @@ class OuterProdLayer(LayerBase):
@config_layer('power')
class PowerLayer(LayerBase):
def __init__(self, name, inputs, device=None):
def __init__(self, name, inputs, device=None, **xargs):
super(PowerLayer, self).__init__(
name, 'power', 0, inputs=inputs, device=device)
name, 'power', 0, inputs=inputs, device=device, **xargs)
config_assert(len(inputs) == 2, 'PowerLayer must have 2 inputs')
input_layer1 = self.get_input_layer(1)
self.set_layer_size(input_layer1.size)
@ -2486,9 +2495,10 @@ class PowerLayer(LayerBase):
@config_layer('slope_intercept')
class SlopeInterceptLayer(LayerBase):
def __init__(self, name, inputs, slope=1.0, intercept=0.0, device=None):
def __init__(self, name, inputs, slope=1.0, intercept=0.0,
device=None, **xargs):
super(SlopeInterceptLayer, self).__init__(
name, 'slope_intercept', 0, inputs=inputs, device=device)
name, 'slope_intercept', 0, inputs=inputs, device=device, **xargs)
self.config.slope = slope
self.config.intercept = intercept
config_assert(len(inputs) == 1, 'SlopeInterceptLayer must have 1 input')
@ -2498,9 +2508,9 @@ class SlopeInterceptLayer(LayerBase):
@config_layer('scaling')
class ScalingLayer(LayerBase):
def __init__(self, name, inputs, device=None):
def __init__(self, name, inputs, device=None, **xargs):
super(ScalingLayer, self).__init__(
name, 'scaling', 0, inputs=inputs, device=device)
name, 'scaling', 0, inputs=inputs, device=device, **xargs)
config_assert(len(inputs) == 2, 'ScalingLayer must have 2 inputs')
input_layer1 = self.get_input_layer(1)
self.set_layer_size(input_layer1.size)
@ -2511,9 +2521,9 @@ class ScalingLayer(LayerBase):
@config_layer('conv_shift')
class ConvShiftLayer(LayerBase):
def __init__(self, name, inputs, device=None):
def __init__(self, name, inputs, device=None, **xargs):
super(ConvShiftLayer, self).__init__(
name, 'conv_shift', 0, inputs=inputs, device=device)
name, 'conv_shift', 0, inputs=inputs, device=device, **xargs)
config_assert(len(inputs) == 2, 'ConvShiftLayer must have 2 inputs')
input_layer0 = self.get_input_layer(0)
self.set_layer_size(input_layer0.size)
@ -2521,9 +2531,9 @@ class ConvShiftLayer(LayerBase):
@config_layer('convex_comb')
class ConvexCombinationLayer(LayerBase):
def __init__(self, name, size, inputs, device=None):
def __init__(self, name, size, inputs, device=None, **xargs):
super(ConvexCombinationLayer, self).__init__(
name, 'convex_comb', size, inputs=inputs, device=device)
name, 'convex_comb', size, inputs=inputs, device=device, **xargs)
config_assert(
len(self.inputs) == 2, 'ConvexCombinationLayer must have 2 inputs')
config_assert(
@ -2562,9 +2572,9 @@ class BilinearInterpLayer(LayerBase):
@config_layer('sum_to_one_norm')
class SumToOneNormLayer(LayerBase):
def __init__(self, name, inputs, device=None):
def __init__(self, name, inputs, device=None, **xargs):
super(SumToOneNormLayer, self).__init__(
name, 'sum_to_one_norm', 0, inputs=inputs, device=device)
name, 'sum_to_one_norm', 0, inputs=inputs, device=device, **xargs)
config_assert(
len(self.inputs) == 1, 'SumToOneNormLayer must have 1 input')
input_layer0 = self.get_input_layer(0)

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