Polish english apis' doc (#20198)

* refine Normal Uniform documnet

paddle/fluid/API.spec

@@ -279,7 +279,7 @@ paddle.fluid.layers.maxout (ArgSpec(args=['x', 'groups', 'name'], varargs=None,
 paddle.fluid.layers.space_to_depth (ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '26decdea9376b6b9a0d3432d82ca207b'))
 paddle.fluid.layers.affine_grid (ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '315b50c1cbd9569375b098c56f1e91c9'))
 paddle.fluid.layers.sequence_reverse (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5b32ed21ab89140a8e758002923a0da3'))
-paddle.fluid.layers.affine_channel (ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name', 'act'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None, None)), ('document', '9f303c67538e468a36c5904a0a3aa110'))
+paddle.fluid.layers.affine_channel (ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name', 'act'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None, None)), ('document', 'ecc4b1323028bde0518d666882d03515'))
 paddle.fluid.layers.similarity_focus (ArgSpec(args=['input', 'axis', 'indexes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '18ec2e3afeb90e70c8b73d2b71c40fdb'))
 paddle.fluid.layers.hash (ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', 'a0b73c21be618cec0281e7903039e5e3'))
 paddle.fluid.layers.grid_sampler (ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5d16663e096d7f04954c70ce1cc5e195'))
@@ -424,7 +424,7 @@ paddle.fluid.layers.roi_perspective_transform (ArgSpec(args=['input', 'rois', 't
 paddle.fluid.layers.generate_proposal_labels (ArgSpec(args=['rpn_rois', 'gt_classes', 'is_crowd', 'gt_boxes', 'im_info', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums', 'use_random', 'is_cls_agnostic', 'is_cascade_rcnn'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True, False, False)), ('document', 'f2342042127b536a0a16390f149f1bba'))
 paddle.fluid.layers.generate_proposals (ArgSpec(args=['scores', 'bbox_deltas', 'im_info', 'anchors', 'variances', 'pre_nms_top_n', 'post_nms_top_n', 'nms_thresh', 'min_size', 'eta', 'name'], varargs=None, keywords=None, defaults=(6000, 1000, 0.5, 0.1, 1.0, None)), ('document', '5cba014b41610431f8949e2d7336f1cc'))
 paddle.fluid.layers.generate_mask_labels (ArgSpec(args=['im_info', 'gt_classes', 'is_crowd', 'gt_segms', 'rois', 'labels_int32', 'num_classes', 'resolution'], varargs=None, keywords=None, defaults=None), ('document', 'b319b10ddaf17fb4ddf03518685a17ef'))
-paddle.fluid.layers.iou_similarity (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '72fca4a39ccf82d5c746ae62d1868a99'))
+paddle.fluid.layers.iou_similarity (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e24478fd1fcf1727d4947fe14356b3d4'))
 paddle.fluid.layers.box_coder (ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name', 'axis'], varargs=None, keywords=None, defaults=('encode_center_size', True, None, 0)), ('document', '511d7033c0cfce1a5b88c04ad6e7ed5b'))
 paddle.fluid.layers.polygon_box_transform (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2183f03c4f16712dcef6a474dbcefa24'))
 paddle.fluid.layers.yolov3_loss (ArgSpec(args=['x', 'gt_box', 'gt_label', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'gt_score', 'use_label_smooth', 'name'], varargs=None, keywords=None, defaults=(None, True, None)), ('document', '400403175718d5a632402cdae88b01b8'))
@@ -446,18 +446,18 @@ paddle.fluid.layers.piecewise_decay (ArgSpec(args=['boundaries', 'values'], vara
 paddle.fluid.layers.noam_decay (ArgSpec(args=['d_model', 'warmup_steps'], varargs=None, keywords=None, defaults=None), ('document', 'fd57228fb76195e66bbcc8d8e42c494d'))
 paddle.fluid.layers.cosine_decay (ArgSpec(args=['learning_rate', 'step_each_epoch', 'epochs'], varargs=None, keywords=None, defaults=None), ('document', '1062e487dd3b50a6e58b5703b4f594c9'))
 paddle.fluid.layers.linear_lr_warmup (ArgSpec(args=['learning_rate', 'warmup_steps', 'start_lr', 'end_lr'], varargs=None, keywords=None, defaults=None), ('document', 'dc7292c456847ba41cfd318e9f7f4363'))
-paddle.fluid.layers.Uniform ('paddle.fluid.layers.distributions.Uniform', ('document', 'af70e7003f437e7a8a9e28cded35c433'))
+paddle.fluid.layers.Uniform ('paddle.fluid.layers.distributions.Uniform', ('document', '9b1a9ebdd8ae18bf562486611ed74e59'))
 paddle.fluid.layers.Uniform.__init__ (ArgSpec(args=['self', 'low', 'high'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
-paddle.fluid.layers.Uniform.entropy (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'ba59f9ce77af3c93e2b4c8af1801a24e'))
+paddle.fluid.layers.Uniform.entropy (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'cde9f1980a2be7939798b32ec8cd59e1'))
 paddle.fluid.layers.Uniform.kl_divergence (ArgSpec(args=['self', 'other'], varargs=None, keywords=None, defaults=None), ('document', '3baee52abbed82d47e9588d9dfe2f42f'))
-paddle.fluid.layers.Uniform.log_prob (ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None), ('document', 'b79091014ceaffb6a7372a198a341c23'))
-paddle.fluid.layers.Uniform.sample (ArgSpec(args=['self', 'shape', 'seed'], varargs=None, keywords=None, defaults=(0,)), ('document', 'adac334af13f6984e991b3ecf12b8cb7'))
-paddle.fluid.layers.Normal ('paddle.fluid.layers.distributions.Normal', ('document', '3265262d0d8b3b32c6245979a5cdced9'))
+paddle.fluid.layers.Uniform.log_prob (ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None), ('document', 'ad4ed169f86c00923621504c782010b0'))
+paddle.fluid.layers.Uniform.sample (ArgSpec(args=['self', 'shape', 'seed'], varargs=None, keywords=None, defaults=(0,)), ('document', '9002ab4a80769211565b64298a770db5'))
+paddle.fluid.layers.Normal ('paddle.fluid.layers.distributions.Normal', ('document', '948f3a95ca14c952401e6a2ec30a35f9'))
 paddle.fluid.layers.Normal.__init__ (ArgSpec(args=['self', 'loc', 'scale'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
-paddle.fluid.layers.Normal.entropy (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'd2db47b1e62c037a2570fc526b93f518'))
-paddle.fluid.layers.Normal.kl_divergence (ArgSpec(args=['self', 'other'], varargs=None, keywords=None, defaults=None), ('document', '2e8845cdf1129647e6fa6e816876cd3b'))
-paddle.fluid.layers.Normal.log_prob (ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None), ('document', 'b79091014ceaffb6a7372a198a341c23'))
-paddle.fluid.layers.Normal.sample (ArgSpec(args=['self', 'shape', 'seed'], varargs=None, keywords=None, defaults=(0,)), ('document', 'adac334af13f6984e991b3ecf12b8cb7'))
+paddle.fluid.layers.Normal.entropy (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '254ff8081a9df3cb96db045411dfbcbd'))
+paddle.fluid.layers.Normal.kl_divergence (ArgSpec(args=['self', 'other'], varargs=None, keywords=None, defaults=None), ('document', '9fc9bd26e5211e2c6ad703a7fba08e65'))
+paddle.fluid.layers.Normal.log_prob (ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None), ('document', 'ad4ed169f86c00923621504c782010b0'))
+paddle.fluid.layers.Normal.sample (ArgSpec(args=['self', 'shape', 'seed'], varargs=None, keywords=None, defaults=(0,)), ('document', '9002ab4a80769211565b64298a770db5'))
 paddle.fluid.layers.Categorical ('paddle.fluid.layers.distributions.Categorical', ('document', '865c9dac8af6190e05588486ba091ee8'))
 paddle.fluid.layers.Categorical.__init__ (ArgSpec(args=['self', 'logits'], varargs=None, keywords=None, defaults=None), ('document', '933b96c9ebab8e2c1f6007a50287311e'))
 paddle.fluid.layers.Categorical.entropy (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'b360a2a7a4da07c2d268b329e09c82c1'))

python/paddle/fluid/layers/detection.py

@@ -587,20 +587,36 @@ def iou_similarity(x, y, name=None):
     ${comment}
 
     Args:
-        x(${x_type}): ${x_comment}
-        y(${y_type}): ${y_comment}
+        x (Variable): ${x_comment}.The data type is float32 or float64.
+        y (Variable): ${y_comment}.The data type is float32 or float64.
 
     Returns:
-        out(${out_type}): ${out_comment}
+        Variable: ${out_comment}.The data type is same with x.
 
     Examples:
         .. code-block:: python
 
+            import numpy as np
             import paddle.fluid as fluid
 
-            x = fluid.layers.data(name='x', shape=[4], dtype='float32')
-            y = fluid.layers.data(name='y', shape=[4], dtype='float32')
+            use_gpu = False
+            place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
+            exe = fluid.Executor(place)
+
+            x = fluid.data(name='x', shape=[None, 4], dtype='float32')
+            y = fluid.data(name='y', shape=[None, 4], dtype='float32')
             iou = fluid.layers.iou_similarity(x=x, y=y)
+
+            exe.run(fluid.default_startup_program())
+            test_program = fluid.default_main_program().clone(for_test=True)
+
+            [out_iou] = exe.run(test_program,
+                    fetch_list=iou,
+                    feed={'x': np.array([[0.5, 0.5, 2.0, 2.0],
+                                         [0., 0., 1.0, 1.0]]).astype('float32'),
+                          'y': np.array([[1.0, 1.0, 2.5, 2.5]]).astype('float32')})
+            # out_iou is [[0.2857143],
+            #             [0.       ]] with shape: [2, 1]
     """
     helper = LayerHelper("iou_similarity", **locals())
     if name is None:

python/paddle/fluid/layers/distributions.py

@@ -135,12 +135,13 @@ class Uniform(Distribution):
     broadcasting (e.g., `high - low` is a valid operation).
 
     Args:
-        low(float|list|numpy.ndarray|Variable): The lower boundary of uniform distribution.
-        high(float|list|numpy.ndarray|Variable): The higher boundary of uniform distribution.
+        low(float|list|numpy.ndarray|Variable): The lower boundary of uniform distribution.The data type is float32
+        high(float|list|numpy.ndarray|Variable): The higher boundary of uniform distribution.The data type is float32
 
     Examples:
         .. code-block:: python
 
+          import numpy as np
           from paddle.fluid import layers
           from paddle.fluid.layers import Uniform
 
@@ -158,19 +159,19 @@ class Uniform(Distribution):
           # With broadcasting:
           u4 = Uniform(low=3.0, high=[5.0, 6.0, 7.0])
 
-          # Variable as input
-          dims = 3
-
-          low = layers.data(name='low', shape=[dims], dtype='float32')
-          high = layers.data(name='high', shape=[dims], dtype='float32')
-          values = layers.data(name='values', shape=[dims], dtype='float32')
+          # Complete example
+          value_npdata = np.array([0.8], dtype="float32")
+          value_tensor = layers.create_tensor(dtype="float32")
+          layers.assign(value_npdata, value_tensor)
 
-          uniform = Uniform(low, high)
+          uniform = Uniform([0.], [2.])
 
-          sample = uniform.sample([2, 3])
+          sample = uniform.sample([2])
+          # a random tensor created by uniform distribution with shape: [2, 1]
           entropy = uniform.entropy()
-          lp = uniform.log_prob(values)
-
+          # [0.6931472] with shape: [1]
+          lp = uniform.log_prob(value_tensor)
+          # [-0.6931472] with shape: [1]
     """
 
     def __init__(self, low, high):
@@ -193,7 +194,7 @@ class Uniform(Distribution):
           seed (int): Python integer number.
 
         Returns:
-          Variable: A tensor with prepended dimensions shape.
+          Variable: A tensor with prepended dimensions shape.The data type is float32.
 
         """
         batch_shape = list((self.low + self.high).shape)
@@ -224,7 +225,7 @@ class Uniform(Distribution):
           value (Variable): The input tensor.
 
         Returns:
-          Variable: log probability.
+          Variable: log probability.The data type is same with value.
 
         """
         lb_bool = control_flow.less_than(self.low, value)
@@ -237,7 +238,7 @@ class Uniform(Distribution):
         """Shannon entropy in nats.
 
         Returns:
-          Variable: Shannon entropy of uniform distribution.
+          Variable: Shannon entropy of uniform distribution.The data type is float32.
 
         """
         return nn.log(self.high - self.low)
@@ -265,8 +266,8 @@ class Normal(Distribution):
     * :math:`Z`: is the normalization constant.
 
     Args:
-        loc(float|list|numpy.ndarray|Variable): The mean of normal distribution.
-        scale(float|list|numpy.ndarray|Variable): The std of normal distribution.
+        loc(float|list|numpy.ndarray|Variable): The mean of normal distribution.The data type is float32.
+        scale(float|list|numpy.ndarray|Variable): The std of normal distribution.The data type is float32.
 
     Examples:
         .. code-block:: python
@@ -278,36 +279,34 @@ class Normal(Distribution):
           dist = Normal(loc=0., scale=3.)
           # Define a batch of two scalar valued Normals.
           # The first has mean 1 and standard deviation 11, the second 2 and 22.
-          dist = Normal(loc=[1, 2.], scale=[11, 22.])
+          dist = Normal(loc=[1., 2.], scale=[11., 22.])
           # Get 3 samples, returning a 3 x 2 tensor.
           dist.sample([3])
 
           # Define a batch of two scalar valued Normals.
           # Both have mean 1, but different standard deviations.
-          dist = Normal(loc=1., scale=[11, 22.])
+          dist = Normal(loc=1., scale=[11., 22.])
 
           # Define a batch of two scalar valued Normals.
           # Both have mean 1, but different standard deviations.
-          dist = Normal(loc=1., scale=[11, 22.])
-
-          # Variable as input
-          dims = 3
-
-          loc = layers.data(name='loc', shape=[dims], dtype='float32')
-          scale = layers.data(name='scale', shape=[dims], dtype='float32')
-          other_loc = layers.data(
-              name='other_loc', shape=[dims], dtype='float32')
-          other_scale = layers.data(
-              name='other_scale', shape=[dims], dtype='float32')
-          values = layers.data(name='values', shape=[dims], dtype='float32')
-
-          normal = Normal(loc, scale)
-          other_normal = Normal(other_loc, other_scale)
-
-          sample = normal.sample([2, 3])
-          entropy = normal.entropy()
-          lp = normal.log_prob(values)
-          kl = normal.kl_divergence(other_normal)
+          dist = Normal(loc=1., scale=[11., 22.])
+
+          # Complete example
+          value_npdata = np.array([0.8], dtype="float32")
+          value_tensor = layers.create_tensor(dtype="float32")
+          layers.assign(value_npdata, value_tensor)
+
+          normal_a = Normal([0.], [1.])
+          normal_b = Normal([0.5], [2.])
+
+          sample = normal_a.sample([2])
+          # a random tensor created by normal distribution with shape: [2, 1]
+          entropy = normal_a.entropy()
+          # [1.4189385] with shape: [1]
+          lp = normal_a.log_prob(value_tensor)
+          # [-1.2389386] with shape: [1]
+          kl = normal_a.kl_divergence(normal_b)
+          # [0.34939718] with shape: [1]
     """
 
     def __init__(self, loc, scale):
@@ -330,7 +329,7 @@ class Normal(Distribution):
           seed (int): Python integer number.
 
         Returns:
-          Variable: A tensor with prepended dimensions shape.
+          Variable: A tensor with prepended dimensions shape.The data type is float32.
 
         """
         batch_shape = list((self.loc + self.scale).shape)
@@ -356,7 +355,7 @@ class Normal(Distribution):
         """Shannon entropy in nats.
 
         Returns:
-          Variable: Shannon entropy of normal distribution.
+          Variable: Shannon entropy of normal distribution.The data type is float32.
 
         """
         batch_shape = list((self.loc + self.scale).shape)
@@ -372,7 +371,7 @@ class Normal(Distribution):
           value (Variable): The input tensor.
 
         Returns:
-          Variable: log probability.
+          Variable: log probability.The data type is same with value.
 
         """
         var = self.scale * self.scale
@@ -387,7 +386,7 @@ class Normal(Distribution):
             other (Normal): instance of Normal.
 
         Returns:
-            Variable: kl-divergence between two normal distributions.
+            Variable: kl-divergence between two normal distributions.The data type is float32.
 
         """
         assert isinstance(other, Normal), "another distribution must be Normal"

python/paddle/fluid/layers/nn.py

@@ -13780,32 +13780,48 @@ def affine_channel(x,
     Args:
         x (Variable): Feature map input can be a 4D tensor with order NCHW
             or NHWC. It also can be a 2D tensor and the affine transformation
-            is applied in the second dimension.
+            is applied in the second dimension.The data type is float32 or float64.
         scale (Variable): 1D input of shape (C), the c-th element is the scale
             factor of the affine transformation for the c-th channel of
-            the input.
+            the input.The data type is float32 or float64.
         bias (Variable): 1D input of shape (C), the c-th element is the bias
             of the affine transformation for the c-th channel of the input.
-        data_layout (string, default NCHW): NCHW or NHWC. If input is 2D
+            The data type is float32 or float64.
+        data_layout (str, default NCHW): NCHW or NHWC. If input is 2D
             tensor, you can ignore data_layout.
-        name (str, default None): The name of this layer.
+        name (str, default None): The name of this layer. For more information,
+            please refer to :ref:`api_guide_Name` .
         act (str, default None): Activation to be applied to the output of this layer.
 
     Returns:
-        out (Variable): A tensor of the same shape and data layout with x.
+        Variable: A tensor which has the same shape, data layout and data type with x.
 
     Examples:
         .. code-block:: python
-            
+
+            import numpy as np
             import paddle.fluid as fluid
-            data = fluid.layers.data(name='data', shape=[3, 32, 32],
-                                     dtype='float32')
-            input_scale = fluid.layers.create_parameter(shape=[3],
-                                     dtype="float32")
-            input_bias = fluid.layers.create_parameter(shape=[3],
-                                     dtype="float32")
+
+            use_gpu = False
+            place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
+            exe = fluid.Executor(place)
+
+            data = fluid.data(name='data', shape=[None, 1, 2, 2], dtype='float32')
+            input_scale = fluid.layers.create_parameter(shape=[1], dtype="float32",
+                                    default_initializer=fluid.initializer.Constant(2.0))
+            input_bias = fluid.layers.create_parameter(shape=[1],dtype="float32",
+                                    default_initializer=fluid.initializer.Constant(0.5))
             out = fluid.layers.affine_channel(data,scale=input_scale,
-                                     bias=input_bias)
+                                    bias=input_bias)
+
+            exe.run(fluid.default_startup_program())
+            test_program = fluid.default_main_program().clone(for_test=True)
+
+            [out_array] = exe.run(test_program,
+                                  fetch_list=out,
+                                  feed={'data': np.ones([1,1,2,2]).astype('float32')})
+            # out_array is [[[[2.5, 2.5],
+            #                [2.5, 2.5]]]] with shape: [1, 1, 2, 2]
 
     """
     helper = LayerHelper("affine_channel", **locals())