Fix doc for transpose, conv3d and batch_norm. (#18035)

* update some op doc, test=develop

paddle/fluid/API.spec

@@ -87,7 +87,7 @@ paddle.fluid.layers.square_error_cost (ArgSpec(args=['input', 'label'], varargs=
 paddle.fluid.layers.chunk_eval (ArgSpec(args=['input', 'label', 'chunk_scheme', 'num_chunk_types', 'excluded_chunk_types'], varargs=None, keywords=None, defaults=(None,)), ('document', '5aa25d023acea1fb49a0de56be86990b'))
 paddle.fluid.layers.sequence_conv (ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'bias_attr', 'param_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(3, 1, None, None, None, None, None)), ('document', '3d8e8f3e0e1cf520156be37605e83ccd'))
 paddle.fluid.layers.conv2d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None)), ('document', '8ca6121acd6d23cd8806a93f493c2e17'))
-paddle.fluid.layers.conv3d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None)), ('document', '37042620f9bd3a2da6e5d3138b2f724b'))
+paddle.fluid.layers.conv3d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None)), ('document', 'd2990494eaf531fb584321b7edfb5104'))
 paddle.fluid.layers.sequence_pool (ArgSpec(args=['input', 'pool_type', 'is_test', 'pad_value'], varargs=None, keywords=None, defaults=(False, 0.0)), ('document', 'e90a93251c52dc4e6fb34fb3991b3f82'))
 paddle.fluid.layers.sequence_softmax (ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', '19ef6f9cdd27feac8a1ae060f19c10b4'))
 paddle.fluid.layers.softmax (ArgSpec(args=['input', 'use_cudnn', 'name', 'axis'], varargs=None, keywords=None, defaults=(False, None, -1)), ('document', 'cee673c79e3ff4582656a24e04f841e5'))
@@ -95,7 +95,7 @@ paddle.fluid.layers.pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'po
 paddle.fluid.layers.pool3d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True)), ('document', '043de7333b79ee0ac55053c14ed81625'))
 paddle.fluid.layers.adaptive_pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None)), ('document', '859b887174d06f361658f69cb7c06d95'))
 paddle.fluid.layers.adaptive_pool3d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None)), ('document', '55db6ae7275fb9678a6814aebab81a9c'))
-paddle.fluid.layers.batch_norm (ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu', 'use_global_stats'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False, False)), ('document', '581f9f99cd7f4b0cab9e0aad5fa0ea24'))
+paddle.fluid.layers.batch_norm (ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu', 'use_global_stats'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False, False)), ('document', '9cf79315d3423dddba0404e8f85a89b8'))
 paddle.fluid.layers.data_norm (ArgSpec(args=['input', 'act', 'epsilon', 'param_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var'], varargs=None, keywords=None, defaults=(None, 1e-05, None, 'NCHW', False, None, None, None, False)), ('document', '2460b30fb87037555208fa8ac6fc1787'))
 paddle.fluid.layers.beam_search_decode (ArgSpec(args=['ids', 'scores', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '83e08f21af41ac8bac37aeab1f86fdd0'))
 paddle.fluid.layers.conv2d_transpose (ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None)), ('document', '03993955ab1e6d3044c44e6f17fc85e9'))

python/paddle/fluid/layers/nn.py

@@ -2237,7 +2237,7 @@ def conv3d(input,
 
     Args:
         input (Variable): The input image with [N, C, D, H, W] format.
-            num_filters(int): The number of filter. It is as same as the output
+        num_filters(int): The number of filter. It is as same as the output
             image channel.
         filter_size (int|tuple|None): The filter size. If filter_size is a tuple,
             it must contain three integers, (filter_size_D, filter_size_H, filter_size_W).
@@ -3098,18 +3098,24 @@ def batch_norm(input,
             numerical stability. Default is 1e-5.
         param_attr(ParamAttr|None): The parameter attribute for Parameter `scale`
              of batch_norm. If it is set to None or one attribute of ParamAttr, batch_norm
-             will create ParamAttr as param_attr. If the Initializer of the param_attr
-             is not set, the parameter is initialized with Xavier. Default: None.
+	     will create ParamAttr as param_attr, the name of scale can be set in ParamAttr.
+	     If the Initializer of the param_attr is not set, the parameter is initialized 
+	     with Xavier. Default: None.
         bias_attr(ParamAttr|None): The parameter attribute for the bias of batch_norm.
              If it is set to None or one attribute of ParamAttr, batch_norm
-             will create ParamAttr as bias_attr. If the Initializer of the bias_attr
-             is not set, the bias is initialized zero. Default: None.
+	     will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr. 
+	     If the Initializer of the bias_attr is not set, the bias is initialized zero. 
+	     Default: None.
         data_layout(string, default NCHW): NCHW|NHWC
         in_place(bool, Default False): Make the input and output of batch norm reuse memory.
         name(string, Default None): A name for this layer(optional). If set None, the layer
             will be named automatically.
-        moving_mean_name(string, Default None): The name of moving_mean which store the global Mean.
+        moving_mean_name(string, Default None): The name of moving_mean which store the global Mean. If it 
+            is set to None, batch_norm will save global mean with a random name, otherwise, batch_norm 
+            will save global mean with the string.
         moving_variance_name(string, Default None): The name of the moving_variance which store the global Variance.
+            If it is set to None, batch_norm will save global variance with a random name, otherwise, batch_norm 
+            will save global variance with the string.
         do_model_average_for_mean_and_var(bool, Default False): Do model average for mean and variance or not.
         fuse_with_relu (bool): if True, this OP performs relu after batch norm.
         use_global_stats(bool, Default False): Whether to use global mean and
@@ -5998,7 +6004,7 @@ def transpose(x, perm, name=None):
     if len(perm) != len(x.shape):
         raise ValueError(
             "Input(perm) is the permutation of dimensions of Input(input). "
-            "It's length shoud be equal to Input(input)'s rank.")
+            "Its length should be equal to Input(input)'s rank.")
     for idx, dim in enumerate(perm):
         if dim >= len(x.shape):
             raise ValueError(