fix document for swish, thresholded_relu, gaussian_randdom

paddle/fluid/API.spec

@@ -233,7 +233,7 @@ paddle.fluid.layers.relu6 (ArgSpec(args=['x', 'threshold', 'name'], varargs=None
 paddle.fluid.layers.pow (ArgSpec(args=['x', 'factor', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', '00d437d1e0d9450ea75a0495b93b54a7'))
 paddle.fluid.layers.stanh (ArgSpec(args=['x', 'scale_a', 'scale_b', 'name'], varargs=None, keywords=None, defaults=(0.67, 1.7159, None)), ('document', 'd3f742178a7263adf5929153d104883d'))
 paddle.fluid.layers.hard_sigmoid (ArgSpec(args=['x', 'slope', 'offset', 'name'], varargs=None, keywords=None, defaults=(0.2, 0.5, None)), ('document', '607d79ca873bee40eed1c79a96611591'))
-paddle.fluid.layers.swish (ArgSpec(args=['x', 'beta', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', 'e0dc7bc66cba939033bc028d7a62c5f4'))
+paddle.fluid.layers.swish (ArgSpec(args=['x', 'beta', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', '60b4dbe35f2b47f7290e79907a4eacec'))
 paddle.fluid.layers.prelu (ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'cb417a61f701c937f33d057fe85203ab'))
 paddle.fluid.layers.brelu (ArgSpec(args=['x', 't_min', 't_max', 'name'], varargs=None, keywords=None, defaults=(0.0, 24.0, None)), ('document', '49580538249a52c857fce75c94ad8af7'))
 paddle.fluid.layers.leaky_relu (ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(0.02, None)), ('document', '1eb3009c69060299ec87949ee0d4b9ae'))
@@ -259,7 +259,7 @@ paddle.fluid.layers.elementwise_pow (ArgSpec(args=['x', 'y', 'axis', 'act', 'nam
 paddle.fluid.layers.elementwise_mod (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '4101ee1f9280f00dce54054ccc434890'))
 paddle.fluid.layers.elementwise_floordiv (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '67e6101c31314d4082621e8e443cfb68'))
 paddle.fluid.layers.uniform_random_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0)), ('document', 'cfa120e583cd4a5bfa120c8a26f98a28'))
-paddle.fluid.layers.gaussian_random (ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', 'ebbf399d4e03190ce5dc9488f05c92f4'))
+paddle.fluid.layers.gaussian_random (ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', 'dd4ddb66c78a2564e5d1e0e345d8286f'))
 paddle.fluid.layers.sampling_id (ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', 'c39b647b6cf08e058d96ee503d5284fe'))
 paddle.fluid.layers.gaussian_random_batch_size_like (ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32')), ('document', 'b24d0b21361c4bb8ef2cec8c26fb12b2'))
 paddle.fluid.layers.sum (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', 'f4b60847cb0f1ae00823ba6fb1b11310'))
@@ -410,7 +410,7 @@ paddle.fluid.layers.softsign (ArgSpec(args=['x', 'name'], varargs=None, keywords
 paddle.fluid.layers.softshrink (ArgSpec(args=['x', 'alpha'], varargs=None, keywords=None, defaults=(None,)), ('document', '958c7bfdfb0b5e92af6ca4a90d24e5ef'))
 paddle.fluid.layers.hard_shrink (ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,)), ('document', '386a4103d2884b2f1312ebc1e8ee6486'))
 paddle.fluid.layers.cumsum (ArgSpec(args=['x', 'axis', 'exclusive', 'reverse'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '5ab9d5721a6734fe127069e4314e1309'))
-paddle.fluid.layers.thresholded_relu (ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,)), ('document', '9a0464425426a9b9c1b7500ede2836c1'))
+paddle.fluid.layers.thresholded_relu (ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,)), ('document', '94c71025bf11ab8172fd455350274138'))
 paddle.fluid.layers.prior_box (ArgSpec(args=['input', 'image', 'min_sizes', 'max_sizes', 'aspect_ratios', 'variance', 'flip', 'clip', 'steps', 'offset', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, [1.0], [0.1, 0.1, 0.2, 0.2], False, False, [0.0, 0.0], 0.5, None, False)), ('document', '0fdf82762fd0a5acb2578a72771b5b44'))
 paddle.fluid.layers.density_prior_box (ArgSpec(args=['input', 'image', 'densities', 'fixed_sizes', 'fixed_ratios', 'variance', 'clip', 'steps', 'offset', 'flatten_to_2d', 'name'], varargs=None, keywords=None, defaults=(None, None, None, [0.1, 0.1, 0.2, 0.2], False, [0.0, 0.0], 0.5, False, None)), ('document', '7a484a0da5e993a7734867a3dfa86571'))
 paddle.fluid.layers.multi_box_head (ArgSpec(args=['inputs', 'image', 'base_size', 'num_classes', 'aspect_ratios', 'min_ratio', 'max_ratio', 'min_sizes', 'max_sizes', 'steps', 'step_w', 'step_h', 'offset', 'variance', 'flip', 'clip', 'kernel_size', 'pad', 'stride', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None, 0.5, [0.1, 0.1, 0.2, 0.2], True, False, 1, 0, 1, None, False)), ('document', 'fd58078fdfffd899b91f992ba224628f'))

python/paddle/fluid/layers/nn.py

@@ -11397,23 +11397,71 @@ def hard_sigmoid(x, slope=0.2, offset=0.5, name=None):
 @templatedoc()
 def swish(x, beta=1.0, name=None):
     """
-    ${comment}
+    Elementwise swish activation function. See `Searching for Activation Functions <https://arxiv.org/abs/1710.05941>`_ for more details.
+    
+    Equation:
+
+    .. math::
+        out = \\frac{x}{1 + e^{- beta * x}}
+    
     Args:
-        x(${x_type}): ${x_comment}
-        beta(${beta_type}|1.0): ${beta_comment}
-        name(str|None): A name for this layer(optional). If set None, the layer
-                        will be named automatically.
+        x(Variable): Tensor or LoDTensor, dtype: float32 or float64, the input of swish activation.
+        
+        beta(float): Constant beta of swish operator, default 1.0.
+        
+        name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
-        output(${out_type}): ${out_comment}
+
+        Variable: Output of the swish activation, Tensor or LoDTensor, with the same dtype and shape with the input x.
 
     Examples:
 
         .. code-block:: python
             
-            import paddle.fluid as fluid
-            x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
+            # declarative mode
+            import numpy as np
+            from paddle import fluid
+            
+            x = fluid.data(name="x", shape=(-1, 3), dtype="float32")
             y = fluid.layers.swish(x, beta=2.0)
+            
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            start = fluid.default_startup_program()
+            main = fluid.default_main_program()
+            
+            data = np.random.randn(2, 3).astype("float32")
+            exe.run(start)
+            y_np, = exe.run(main, feed={"x": data}, fetch_list=[y])
+            
+            data
+            # array([[-1.1239197 ,  1.3391294 ,  0.03921051],
+            #        [ 1.1970421 ,  0.02440812,  1.2055548 ]], dtype=float32)
+            y_np
+            # array([[-0.2756806 ,  1.0610548 ,  0.01998957],
+            #        [ 0.9193261 ,  0.01235299,  0.9276883 ]], dtype=float32)
+
+
+        .. code-block:: python
+
+            # imperative mode
+            import numpy as np
+            from paddle import fluid
+            import paddle.fluid.dygraph as dg
+            
+            data = np.random.randn(2, 3).astype("float32")
+            place = fluid.CPUPlace()
+            with dg.guard(place) as g:
+                x = dg.to_variable(data)
+                y = fluid.layers.swish(x)
+                y_np = y.numpy()
+            data
+            # array([[-0.0816701 ,  1.1603649 , -0.88325626],
+            #        [ 0.7522361 ,  1.0978601 ,  0.12987892]], dtype=float32)
+            y_np
+            # array([[-0.03916847,  0.8835007 , -0.25835553],
+            #        [ 0.51126915,  0.82324016,  0.06915068]], dtype=float32)
     """
     helper = LayerHelper('swish', **locals())
     out = helper.create_variable_for_type_inference(dtype=x.dtype)
@@ -12158,24 +12206,57 @@ def uniform_random_batch_size_like(input,
 @templatedoc()
 def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
     """
-    ${comment}
+    Generate a random tensor whose data is drawn from a Gaussian distribution.
 
     Args:
-        shape (tuple|list): ${shape_comment}
-        mean (Float): ${mean_comment}
-        std (Float): ${std_comment}
-        seed (Int): ${seed_comment}
-        dtype(np.dtype|core.VarDesc.VarType|str): Output data type.
+        shape (Tuple[int] | List[int]): Shape of the generated random tensor.
+        
+        mean (float): Mean of the random tensor, defaults to 0.0.
+            
+        std (float): Standard deviation of the random tensor, defaults to 1.0.
+        
+        seed (int): ${seed_comment}
+        
+        dtype(np.dtype | core.VarDesc.VarType | str): Output data type, float32 or float64.
 
     Returns:
-        out (Variable): ${out_comment}
+        Variable: Random tensor whose data is drawn from a Gaussian distribution, dtype: flaot32 or float64 as specified.
 
     Examples:
        .. code-block:: python
        
-            import paddle.fluid as fluid
-            import paddle.fluid.layers as layers
-            out = layers.gaussian_random(shape=[20, 30])
+           # declarative mode 
+           import numpy as np
+           from paddle import fluid
+   
+           x = fluid.layers.gaussian_random((2, 3), std=2., seed=10)
+   
+           place = fluid.CPUPlace()
+           exe = fluid.Executor(place)
+           start = fluid.default_startup_program()
+           main = fluid.default_main_program()
+   
+           exe.run(start)
+           x_np, = exe.run(main, feed={}, fetch_list=[x])
+
+           x_np
+           # array([[2.3060477, 2.676496 , 3.9911983],
+           #        [0.9990833, 2.8675377, 2.2279181]], dtype=float32)
+
+       .. code-block:: python
+
+           # imperative mode
+           import numpy as np
+           from paddle import fluid
+           import paddle.fluid.dygraph as dg
+    
+           place = fluid.CPUPlace()
+           with dg.guard(place) as g:
+               x = fluid.layers.gaussian_random((2, 4), mean=2., dtype="float32", seed=10)
+               x_np = x.numpy()       
+           x_np
+           # array([[2.3060477 , 2.676496  , 3.9911983 , 0.9990833 ],
+           #        [2.8675377 , 2.2279181 , 0.79029655, 2.8447366 ]], dtype=float32)
     """
 
     helper = LayerHelper('gaussian_random', **locals())

python/paddle/fluid/layers/ops.py

@@ -160,10 +160,70 @@ def thresholded_relu(x, threshold=None):
     return _thresholded_relu_(**kwargs)
 
 
-thresholded_relu.__doc__ = _thresholded_relu_.__doc__ + """
+thresholded_relu.__doc__ = """
+:strong:`Thresholded ReLU Activation Operator`
+
+Equation:
+    ..  math::
+        out = \\begin{cases}
+            x, &if x > threshold \\\\
+            0, &otherwise
+            \\end{cases}
+
+Args:
+    x(Variable): The input of Thresholded ReLU op, Tensor or LoDTensor, dtype: float32 or float64.
+        
+    threshold(float, optional): The threshold value. Note that if the arg `threshold` is not set, the threshold in the equation is 1.0.
+
+Returns:
+
+    Variable: The output of Thresholded ReLU op, Tensor or LoDTensor, dtype: float32 or float64, the same as the input, shape: the same as the input.
+
 Examples:
     
-    >>> import paddle.fluid as fluid
-    >>> data = fluid.layers.data(name="input", shape=[1])
-    >>> result = fluid.layers.thresholded_relu(data, threshold=0.4)
+    .. code-block:: python
+    
+        # declarative mode
+        import numpy as np
+        from paddle import fluid
+        
+        x = fluid.data(name="x", shape=(-1, 3), dtype="float32")
+        y = fluid.layers.thresholded_relu(x, threshold=0.1)
+        
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        start = fluid.default_startup_program()
+        main = fluid.default_main_program()
+        
+        data = np.random.randn(2, 3).astype("float32")
+        exe.run(start)
+        
+        y_np, = exe.run(main, feed={"x": data}, fetch_list=[y])
+        
+        data
+        # array([[ 0.21134382, -1.1805999 ,  0.32876605],
+        #        [-1.2210793 , -0.7365624 ,  1.0013918 ]], dtype=float32)
+        y_np
+        # array([[ 0.21134382, -0.        ,  0.32876605],
+        #        [-0.        , -0.        ,  1.0013918 ]], dtype=float32)
+
+    .. code-block:: python
+    
+        # imperative mode
+        import numpy as np
+        from paddle import fluid
+        import paddle.fluid.dygraph as dg
+        
+        data = np.random.randn(2, 3).astype("float32")
+        place = fluid.CPUPlace()
+        with dg.guard(place) as g:
+            x = dg.to_variable(data)
+            y = fluid.layers.thresholded_relu(x, threshold=0.1)
+            y_np = y.numpy()
+        data
+        # array([[ 0.21134382, -1.1805999 ,  0.32876605],
+        #        [-1.2210793 , -0.7365624 ,  1.0013918 ]], dtype=float32)
+        y_np
+        # array([[ 0.21134382, -0.        ,  0.32876605],
+        #        [-0.        , -0.        ,  1.0013918 ]], dtype=float32)
 """