delete unnecessary parameters and modifications for some mathmatical

layers.

python/paddle/trainer/config_parser.py

@@ -1803,9 +1803,8 @@ class ConvTransLayer(ConvTransLayerBase):
 
 @config_layer('norm')
 class NormLayer(LayerBase):
-    def __init__(self, name, inputs, device=None, **xargs):
-        super(NormLayer, self).__init__(
-            name, 'norm', 0, inputs=inputs, device=device, **xargs)
+    def __init__(self, name, inputs, **xargs):
+        super(NormLayer, self).__init__(name, 'norm', 0, inputs=inputs, **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 +1816,8 @@ class NormLayer(LayerBase):
 
 @config_layer('pool')
 class PoolLayer(LayerBase):
-    def __init__(self, name, inputs, device=None, **xargs):
-        super(PoolLayer, self).__init__(
-            name, 'pool', 0, inputs=inputs, device=device, **xargs)
+    def __init__(self, name, inputs, **xargs):
+        super(PoolLayer, self).__init__(name, 'pool', 0, inputs=inputs, **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
@@ -1851,7 +1849,6 @@ class BatchNormLayer(LayerBase):
                  inputs,
                  active_type="linear",
                  bias=True,
-                 device=None,
                  use_global_stats=True,
                  moving_average_fraction=0.9,
                  batch_norm_type=None,
@@ -1893,7 +1890,6 @@ class BatchNormLayer(LayerBase):
             0,
             active_type=active_type,
             inputs=inputs,
-            device=device,
             **xargs)
 
         if use_global_stats is not None:
@@ -1927,9 +1923,9 @@ class BatchNormLayer(LayerBase):
 
 @config_layer('trans')
 class TransLayer(LayerBase):
-    def __init__(self, name, inputs, device=None, **xargs):
+    def __init__(self, name, inputs, **xargs):
         super(TransLayer, self).__init__(
-            name, 'trans', 0, inputs=inputs, device=device, **xargs)
+            name, 'trans', 0, inputs=inputs, **xargs)
         config_assert(
             len(self.inputs) == 1,
             'TransLayer must have one and only one input')
@@ -1938,9 +1934,9 @@ class TransLayer(LayerBase):
 
 @config_layer('resize')
 class ResizeLayer(LayerBase):
-    def __init__(self, name, size, inputs, device=None, **xargs):
+    def __init__(self, name, size, inputs, **xargs):
         super(ResizeLayer, self).__init__(
-            name, 'resize', size=size, inputs=inputs, device=device, **xargs)
+            name, 'resize', size=size, inputs=inputs, **xargs)
         config_assert(
             len(self.inputs) == 1,
             'ResizeLayer must have one and only one input')
@@ -2265,15 +2261,9 @@ def Generator(
 
 @config_layer('expand')
 class ExpandLayer(LayerBase):
-    def __init__(self,
-                 name,
-                 inputs,
-                 trans_type='non-seq',
-                 device=None,
-                 bias=False,
-                 **xargs):
+    def __init__(self, name, inputs, trans_type='non-seq', bias=False, **xargs):
         super(ExpandLayer, self).__init__(
-            name, 'expand', 0, inputs=inputs, device=device, **xargs)
+            name, 'expand', 0, inputs=inputs, **xargs)
         config_assert(
             len(self.inputs) == 2, 'ExpandLayer takes 2 and only 2 inputs')
         self.config.trans_type = trans_type
@@ -2304,12 +2294,10 @@ class MaxLayer(LayerBase):
                  inputs,
                  trans_type='non-seq',
                  active_type='linear',
-                 device=None,
                  bias=False,
                  output_max_index=None,
                  **xargs):
-        super(MaxLayer, self).__init__(
-            name, 'max', 0, inputs=inputs, device=device, **xargs)
+        super(MaxLayer, self).__init__(name, 'max', 0, inputs=inputs, **xargs)
         config_assert(len(self.inputs) == 1, 'MaxLayer must have 1 input')
         self.config.trans_type = trans_type
         self.config.active_type = active_type
@@ -2356,7 +2344,6 @@ class SequenceLastInstanceLayer(LayerBase):
                  inputs,
                  active_type='linear',
                  trans_type='non-seq',
-                 device=None,
                  bias=False,
                  **xargs):
         super(SequenceLastInstanceLayer, self).__init__(
@@ -2364,7 +2351,6 @@ class SequenceLastInstanceLayer(LayerBase):
             'seqlastins',
             0,
             inputs=inputs,
-            device=device,
             active_type=active_type,
             **xargs)
         config_assert(
@@ -2378,39 +2364,32 @@ class SequenceLastInstanceLayer(LayerBase):
 
 @config_layer('seqfirstins')
 class SequenceFirstInstanceLayer(SequenceLastInstanceLayer):
-    def __init__(
-            self,
+    def __init__(self,
                  name,
                  inputs,
                  active_type='linear',
                  trans_type='non-seq',
-            device=None,
-            bias=False, ):
+                 bias=False,
+                 **xargs):
         super(SequenceFirstInstanceLayer, self).__init__(
             name,
             inputs=inputs,
             active_type=active_type,
             device=device,
-            bias=bias)
+            bias=bias,
+            **xargs)
         self.config.trans_type = trans_type
         self.config.select_first = True
 
 
 @config_layer('seqconcat')
 class SequenceConcatLayer(LayerBase):
-    def __init__(self,
-                 name,
-                 inputs,
-                 active_type='linear',
-                 device=None,
-                 bias=False,
-                 **xargs):
+    def __init__(self, name, inputs, active_type='linear', bias=False, **xargs):
         super(SequenceConcatLayer, self).__init__(
             name,
             'seqconcat',
             0,
             inputs=inputs,
-            device=device,
             active_type=active_type,
             **xargs)
         config_assert(
@@ -2428,7 +2407,6 @@ class SequenceReshapeLayer(LayerBase):
                  size,
                  inputs,
                  active_type='linear',
-                 device=None,
                  bias=False,
                  **xargs):
         super(SequenceReshapeLayer, self).__init__(
@@ -2436,7 +2414,6 @@ class SequenceReshapeLayer(LayerBase):
             'seqreshape',
             size,
             inputs=inputs,
-            device=device,
             active_type=active_type,
             **xargs)
         config_assert(
@@ -2447,21 +2424,9 @@ class SequenceReshapeLayer(LayerBase):
 
 @config_layer('subseq')
 class SubSequenceLayer(LayerBase):
-    def __init__(self,
-                 name,
-                 inputs,
-                 active_type='linear',
-                 device=None,
-                 bias=False,
-                 **xargs):
+    def __init__(self, name, inputs, active_type='linear', bias=False, **xargs):
         super(SubSequenceLayer, self).__init__(
-            name,
-            'subseq',
-            0,
-            inputs=inputs,
-            device=device,
-            active_type=active_type,
-            **xargs)
+            name, 'subseq', 0, inputs=inputs, 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
@@ -2471,9 +2436,9 @@ class SubSequenceLayer(LayerBase):
 
 @config_layer('out_prod')
 class OuterProdLayer(LayerBase):
-    def __init__(self, name, inputs, device=None, **xargs):
+    def __init__(self, name, inputs, device=None):
         super(OuterProdLayer, self).__init__(
-            name, 'out_prod', 0, inputs=inputs, device=device, **xargs)
+            name, 'out_prod', 0, inputs=inputs, device=device)
         config_assert(len(inputs) == 2, 'OuterProdLayer must have 2 inputs')
         input_layer0 = self.get_input_layer(0)
         input_layer1 = self.get_input_layer(1)
@@ -2482,9 +2447,9 @@ class OuterProdLayer(LayerBase):
 
 @config_layer('power')
 class PowerLayer(LayerBase):
-    def __init__(self, name, inputs, device=None, **xargs):
+    def __init__(self, name, inputs, device=None):
         super(PowerLayer, self).__init__(
-            name, 'power', 0, inputs=inputs, device=device, **xargs)
+            name, 'power', 0, inputs=inputs, device=device)
         config_assert(len(inputs) == 2, 'PowerLayer must have 2 inputs')
         input_layer1 = self.get_input_layer(1)
         self.set_layer_size(input_layer1.size)
@@ -2495,8 +2460,13 @@ class PowerLayer(LayerBase):
 
 @config_layer('slope_intercept')
 class SlopeInterceptLayer(LayerBase):
-    def __init__(self, name, inputs, slope=1.0, intercept=0.0,
-                 device=None, **xargs):
+    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, **xargs)
         self.config.slope = slope
@@ -2508,9 +2478,9 @@ class SlopeInterceptLayer(LayerBase):
 
 @config_layer('scaling')
 class ScalingLayer(LayerBase):
-    def __init__(self, name, inputs, device=None, **xargs):
+    def __init__(self, name, inputs, device=None):
         super(ScalingLayer, self).__init__(
-            name, 'scaling', 0, inputs=inputs, device=device, **xargs)
+            name, 'scaling', 0, inputs=inputs, device=device)
         config_assert(len(inputs) == 2, 'ScalingLayer must have 2 inputs')
         input_layer1 = self.get_input_layer(1)
         self.set_layer_size(input_layer1.size)
@@ -2521,9 +2491,9 @@ class ScalingLayer(LayerBase):
 
 @config_layer('conv_shift')
 class ConvShiftLayer(LayerBase):
-    def __init__(self, name, inputs, device=None, **xargs):
+    def __init__(self, name, inputs, device=None):
         super(ConvShiftLayer, self).__init__(
-            name, 'conv_shift', 0, inputs=inputs, device=device, **xargs)
+            name, 'conv_shift', 0, inputs=inputs, device=device)
         config_assert(len(inputs) == 2, 'ConvShiftLayer must have 2 inputs')
         input_layer0 = self.get_input_layer(0)
         self.set_layer_size(input_layer0.size)
@@ -2531,9 +2501,9 @@ class ConvShiftLayer(LayerBase):
 
 @config_layer('convex_comb')
 class ConvexCombinationLayer(LayerBase):
-    def __init__(self, name, size, inputs, device=None, **xargs):
+    def __init__(self, name, size, inputs, device=None):
         super(ConvexCombinationLayer, self).__init__(
-            name, 'convex_comb', size, inputs=inputs, device=device, **xargs)
+            name, 'convex_comb', size, inputs=inputs, device=device)
         config_assert(
             len(self.inputs) == 2, 'ConvexCombinationLayer must have 2 inputs')
         config_assert(
@@ -2572,9 +2542,9 @@ class BilinearInterpLayer(LayerBase):
 
 @config_layer('sum_to_one_norm')
 class SumToOneNormLayer(LayerBase):
-    def __init__(self, name, inputs, device=None, **xargs):
+    def __init__(self, name, inputs, device=None):
         super(SumToOneNormLayer, self).__init__(
-            name, 'sum_to_one_norm', 0, inputs=inputs, device=device, **xargs)
+            name, 'sum_to_one_norm', 0, inputs=inputs, device=device)
         config_assert(
             len(self.inputs) == 1, 'SumToOneNormLayer must have 1 input')
         input_layer0 = self.get_input_layer(0)
@@ -2619,17 +2589,10 @@ class AverageLayer(LayerBase):
                  average_strategy='average',
                  trans_type='non-seq',
                  active_type='linear',
-                 device=None,
                  bias=False,
                  **xargs):
         super(AverageLayer, self).__init__(
-            name,
-            'average',
-            0,
-            inputs=inputs,
-            device=device,
-            active_type=active_type,
-            **xargs)
+            name, 'average', 0, inputs=inputs, active_type=active_type, **xargs)
         self.config.average_strategy = average_strategy
         self.config.trans_type = trans_type
         config_assert(len(inputs) == 1, 'AverageLayer must have 1 input')
@@ -2653,9 +2616,9 @@ class CosSimLayer(LayerBase):
 
 @config_layer('tensor')
 class TensorLayer(LayerBase):
-    def __init__(self, name, size, inputs, device=None, bias=True, **xargs):
+    def __init__(self, name, size, inputs, bias=True, **xargs):
         super(TensorLayer, self).__init__(
-            name, 'tensor', size, inputs=inputs, device=device, **xargs)
+            name, 'tensor', size, inputs=inputs, **xargs)
         config_assert(len(self.inputs) == 2, 'TensorLayer must have 2 inputs')
         config_assert(size > 0, 'size must be positive')
         config_assert(inputs[1].parameter_name == None,