Add the parameter checking for softplus and fix the doc string (#26530)

python/paddle/fluid/tests/unittests/test_activation_op.py

@@ -717,6 +717,9 @@ class TestSoftshrinkAPI(unittest.TestCase):
             # The input dtype must be float16, float32, float64.
             x_int32 = paddle.data(name='x_int32', shape=[12, 10], dtype='int32')
             self.assertRaises(TypeError, F.softshrink, x_int32)
+            # The threshold must be no less than zero
+            x_fp32 = paddle.data(name='x_fp32', shape=[12, 10], dtype='float32')
+            self.assertRaises(ValueError, F.softshrink, x_fp32, -1.0)
             # support the input dtype is float16
             x_fp16 = paddle.data(name='x_fp16', shape=[12, 10], dtype='float16')
             F.softshrink(x_fp16)

python/paddle/nn/functional/activation.py

@@ -225,7 +225,7 @@ def hardtanh(x, min=-1.0, max=1.0, name=None):
                         x,  \\text{otherwise}
                       \\end{cases}
 
-    Args:
+    Parameters:
         x (Tensor): The input Tensor with data type float32, float64.
         min (float, optional): The minimum value of the linear region range. Default is -1.
         max (float, optional): The maximum value of the linear region range. Default is 1.
@@ -598,9 +598,9 @@ def relu6(x, name=None):
 
     .. math::
 
-        \text{relu6}(x) = \min(\max(0,x), 6)
+        relu6(x) = min(max(0,x), 6)
 
-    Args:
+    Parameters:
         x (Tensor): The input Tensor with data type float32, float64.
         name (str, optional): Name for the operation (optional, default is None).
             For more information, please refer to :ref:`api_guide_Name`.
@@ -609,18 +609,16 @@ def relu6(x, name=None):
         A Tensor with the same data type and shape as ``x`` .
 
     Examples:
-
         .. code-block:: python
 
-        import paddle
-        import paddle.nn.functional as F
-        import numpy as np
-
-        paddle.disable_static()
+            import paddle
+            import paddle.nn.functional as F
+            import numpy as np
 
-        x = paddle.to_tensor(np.array([-1, 0.3, 6.5]))
-        out = F.relu6(x) # [0, 0.3, 6]
+            paddle.disable_static()
 
+            x = paddle.to_tensor(np.array([-1, 0.3, 6.5]))
+            out = F.relu6(x) # [0, 0.3, 6]
     """
     threshold = 6.0
     if in_dygraph_mode():
@@ -646,11 +644,9 @@ def selu(x,
 
     .. math::
 
-        \text{selu}(x) = scale * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1))), \\
-        with\,alpha=1.6732632423543772848170429916717 and \\
-        scale=1.0507009873554804934193349852946
+        selu(x) = scale * (max(0,x) + min(0, alpha * (e^{x} - 1)))
 
-    Args:
+    Parameters:
         x (Tensor): The input Tensor with data type float32, float64.
         scale (float, optional): The value of scale for selu. Default is 1.0507009873554804934193349852946
         alpha (float, optional): The value of alpha for selu. Default is 1.6732632423543772848170429916717
@@ -661,18 +657,16 @@ def selu(x,
         A Tensor with the same data type and shape as ``x`` .
 
     Examples:
-
         .. code-block:: python
 
-        import paddle
-        import paddle.nn.functional as F
-        import numpy as np
-
-        paddle.disable_static()
+            import paddle
+            import paddle.nn.functional as F
+            import numpy as np
 
-        x = paddle.to_tensor(np.array([[0, 1],[2, 3]]))
-        out = F.selu(x) # [[0, 1.050701],[2.101402, 3.152103]]
+            paddle.disable_static()
 
+            x = paddle.to_tensor(np.array([[0, 1],[2, 3]]))
+            out = F.selu(x) # [[0, 1.050701],[2.101402, 3.152103]]
     """
     if in_dygraph_mode():
         return core.ops.selu(x, 'scale', scale, 'alpha', alpha)
@@ -856,10 +850,10 @@ def softplus(x, beta=1, threshold=20, name=None):
 
     .. math::
 
-        \text{softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x)) \\
-        \text{For numerical stability, the implementation reverts to the linear function when :}\,x \times \beta > threshold.
+        softplus(x) = \\frac{1}{beta} * \\log(1 + e^{beta * x}) \\\\
+        \\text{For numerical stability, the implementation reverts to the linear function when: beta * x > threshold.}
 
-    Args:
+    Parameters:
         x (Tensor): The input Tensor with data type float32, float64.
         beta (float, optional): The value of beta for softplus. Default is 1
         threshold (float, optional): The value of threshold for softplus. Default is 20
@@ -870,18 +864,16 @@ def softplus(x, beta=1, threshold=20, name=None):
         A Tensor with the same data type and shape as ``x`` .
 
     Examples:
-
         .. code-block:: python
 
-        import paddle
-        import paddle.nn.functional as F
-        import numpy as np
-
-        paddle.disable_static()
+            import paddle
+            import paddle.nn.functional as F
+            import numpy as np
 
-        x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
-        out = F.softplus(x) # [0.513015, 0.598139, 0.744397, 0.854355]
+            paddle.disable_static()
 
+            x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
+            out = F.softplus(x) # [0.513015, 0.598139, 0.744397, 0.854355]
     """
     if in_dygraph_mode():
         return core.ops.softplus(x, 'beta', beta, 'threshold', threshold)
@@ -905,14 +897,13 @@ def softshrink(x, threshold=0.5, name=None):
 
     .. math::
 
-        \text{softshrink}(x) =
-        \begin{cases}
-        x - threshold, & \text{ if } x > threshold \\
-        x + threshold, & \text{ if } x < -threshold \\
-        0, & \text{ otherwise }
-        \end{cases}
+        softshrink(x)= \\begin{cases}
+                        x - threshold, \\text{if } x > threshold \\\\
+                        x + threshold, \\text{if } x < -threshold \\\\
+                        0,  \\text{otherwise}
+                      \\end{cases}
 
-    Args:
+    Parameters:
         x (Tensor): The input Tensor with data type float32, float64.
         threshold (float, optional): The value of threshold(must be no less than zero) for softplus. Default is 0.5
         name (str, optional): Name for the operation (optional, default is None).
@@ -922,19 +913,22 @@ def softshrink(x, threshold=0.5, name=None):
         A Tensor with the same data type and shape as ``x`` .
 
     Examples:
-
         .. code-block:: python
 
-        import paddle
-        import paddle.nn.functional as F
-        import numpy as np
-
-        paddle.disable_static()
+            import paddle
+            import paddle.nn.functional as F
+            import numpy as np
 
-        x = paddle.to_tensor(np.array([-0.9, -0.2, 0.1, 0.8]))
-        out = F.softshrink(x) # [-0.4, 0, 0, 0.3]
+            paddle.disable_static()
 
+            x = paddle.to_tensor(np.array([-0.9, -0.2, 0.1, 0.8]))
+            out = F.softshrink(x) # [-0.4, 0, 0, 0.3]
     """
+    if threshold < 0:
+        raise ValueError(
+            "The threshold must be no less than zero. Received: {}.".format(
+                threshold))
+
     if in_dygraph_mode():
         return core.ops.softshrink(x, 'lambda', threshold)
 
@@ -956,9 +950,9 @@ def softsign(x, name=None):
 
     .. math::
 
-        \text{softsign}(x) = \frac{x}{1 + |x|}
+        softsign(x) = \\frac{x}{1 + |x|}
 
-    Args:
+    Parameters:
         x (Tensor): The input Tensor with data type float32, float64.
         name (str, optional): Name for the operation (optional, default is None).
             For more information, please refer to :ref:`api_guide_Name`.
@@ -967,18 +961,16 @@ def softsign(x, name=None):
         A Tensor with the same data type and shape as ``x`` .
 
     Examples:
-
         .. code-block:: python
 
-        import paddle
-        import paddle.nn.functional as F
-        import numpy as np
-
-        paddle.disable_static()
+            import paddle
+            import paddle.nn.functional as F
+            import numpy as np
 
-        x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
-        out = F.softsign(x) # [-0.285714, -0.166667, 0.0909091, 0.230769]
+            paddle.disable_static()
 
+            x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
+            out = F.softsign(x) # [-0.285714, -0.166667, 0.0909091, 0.230769]
     """
     if in_dygraph_mode():
         return core.ops.softsign(x)
@@ -997,7 +989,7 @@ def tanhshrink(x, name=None):
 
     .. math::
 
-        \text{tanhshrink}(x) = x - \text{tanh}(x)
+        tanhshrink(x) = x - tanh(x)
 
     Args:
         x (Tensor): The input Tensor with data type float32, float64.
@@ -1008,18 +1000,16 @@ def tanhshrink(x, name=None):
         A Tensor with the same data type and shape as ``x`` .
 
     Examples:
-
         .. code-block:: python
 
-        import paddle
-        import paddle.nn.functional as F
-        import numpy as np
-
-        paddle.disable_static()
+            import paddle
+            import paddle.nn.functional as F
+            import numpy as np
 
-        x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
-        out = F.tanhshrink(x) # [-0.020051, -0.00262468, 0.000332005, 0.00868739]
+            paddle.disable_static()
 
+            x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
+            out = F.tanhshrink(x) # [-0.020051, -0.00262468, 0.000332005, 0.00868739]
     """
     if in_dygraph_mode():
         return core.ops.tanh_shrink(x)

python/paddle/nn/layer/activation.py

@@ -513,7 +513,7 @@ class ReLU6(layers.Layer):
 
     .. math::
 
-        \text{ReLU6}(x) = \min(\max(0,x), 6)
+        ReLU6(x) = min(max(0,x), 6)
 
     Parameters:
         name (str, optional): Name for the operation (optional, default is None).
@@ -524,17 +524,16 @@ class ReLU6(layers.Layer):
         - output: Tensor with the same shape as input.
 
     Examples:
-
         .. code-block:: python
 
-        import paddle
-        import numpy as np
+            import paddle
+            import numpy as np
 
-        paddle.disable_static()
+            paddle.disable_static()
 
-        x = paddle.to_tensor(np.array([-1, 0.3, 6.5]))
-        m = paddle.nn.ReLU6()
-        out = m(x) # [0, 0.3, 6]
+            x = paddle.to_tensor(np.array([-1, 0.3, 6.5]))
+            m = paddle.nn.ReLU6()
+            out = m(x) # [0, 0.3, 6]
     """
 
     def __init__(self, name=None):
@@ -551,9 +550,7 @@ class SELU(layers.Layer):
 
     .. math::
 
-        \text{SELU}(x) = scale * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1))), \\
-        with\,alpha=1.6732632423543772848170429916717 and \\
-        scale=1.0507009873554804934193349852946
+        SELU(x) = scale * (max(0,x) + min(0, alpha * (e^{x} - 1)))
 
     Parameters:
         scale (float, optional): The value of scale for SELU. Default is 1.0507009873554804934193349852946
@@ -566,17 +563,16 @@ class SELU(layers.Layer):
         - output: Tensor with the same shape as input.
 
     Examples:
-
         .. code-block:: python
 
-        import paddle
-        import numpy as np
+            import paddle
+            import numpy as np
 
-        paddle.disable_static()
+            paddle.disable_static()
 
-        x = paddle.to_tensor(np.array([[0, 1],[2, 3]]))
-        m = paddle.nn.SELU()
-        out = m(x) # [[0, 1.050701],[2.101402, 3.152103]]
+            x = paddle.to_tensor(np.array([[0, 1],[2, 3]]))
+            m = paddle.nn.SELU()
+            out = m(x) # [[0, 1.050701],[2.101402, 3.152103]]
     """
 
     def __init__(self,
@@ -684,10 +680,12 @@ class Softplus(layers.Layer):
 
     .. math::
 
-        \text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x)) \\
-        \text{For numerical stability, the implementation reverts to the linear function when :}\,x \times \beta > threshold.
+        Softplus(x) = \\frac{1}{beta} * \\log(1 + e^{beta * x}) \\\\
+        \\text{For numerical stability, the implementation reverts to the linear function when: beta * x > threshold.}
 
     Parameters:
+        beta (float, optional): The value of beta for Softplus. Default is 1
+        threshold (float, optional): The value of threshold for Softplus. Default is 20
         name (str, optional): Name for the operation (optional, default is None).
             For more information, please refer to :ref:`api_guide_Name`.
 
@@ -696,18 +694,16 @@ class Softplus(layers.Layer):
         - output: Tensor with the same shape as input.
 
     Examples:
-
         .. code-block:: python
 
-        import paddle
-        import numpy as np
-
-        paddle.disable_static()
+            import paddle
+            import numpy as np
 
-        x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
-        m = paddle.nn.Softplus()
-        out = m(x) # [0.513015, 0.598139, 0.744397, 0.854355]
+            paddle.disable_static()
 
+            x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
+            m = paddle.nn.Softplus()
+            out = m(x) # [0.513015, 0.598139, 0.744397, 0.854355]
     """
 
     def __init__(self, beta=1, threshold=20, name=None):
@@ -726,14 +722,14 @@ class Softshrink(layers.Layer):
 
     .. math::
 
-        \text{Softshrink}(x) =
-        \begin{cases}
-        x - threshold, & \text{ if } x > threshold \\
-        x + threshold, & \text{ if } x < -threshold \\
-        0, & \text{ otherwise }
-        \end{cases}
+        Softshrink(x)= \\begin{cases}
+                        x - threshold, \\text{if } x > threshold \\\\
+                        x + threshold, \\text{if } x < -threshold \\\\
+                        0,  \\text{otherwise}
+                      \\end{cases}
 
     Parameters:
+        threshold (float, optional): The value of threshold(must be no less than zero) for softplus. Default is 0.5
         name (str, optional): Name for the operation (optional, default is None).
             For more information, please refer to :ref:`api_guide_Name`.
 
@@ -742,17 +738,16 @@ class Softshrink(layers.Layer):
         - output: Tensor with the same shape as input.
 
     Examples:
-
         .. code-block:: python
 
-        import paddle
-        import numpy as np
+            import paddle
+            import numpy as np
 
-        paddle.disable_static()
+            paddle.disable_static()
 
-        x = paddle.to_tensor(np.array([-0.9, -0.2, 0.1, 0.8]))
-        m = paddle.nn.Softshrink()
-        out = m(x) # [-0.4, 0, 0, 0.3]
+            x = paddle.to_tensor(np.array([-0.9, -0.2, 0.1, 0.8]))
+            m = paddle.nn.Softshrink()
+            out = m(x) # [-0.4, 0, 0, 0.3]
     """
 
     def __init__(self, threshold=0.5, name=None):
@@ -770,7 +765,7 @@ class Softsign(layers.Layer):
 
     .. math::
 
-        \text{Softsign}(x) = \frac{x}{1 + |x|}
+        Softsign(x) = \\frac{x}{1 + |x|}
 
     Parameters:
         name (str, optional): Name for the operation (optional, default is None).
@@ -781,17 +776,16 @@ class Softsign(layers.Layer):
         - output: Tensor with the same shape as input.
 
     Examples:
-
         .. code-block:: python
 
-        import paddle
-        import numpy as np
+            import paddle
+            import numpy as np
 
-        paddle.disable_static()
+            paddle.disable_static()
 
-        x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
-        m = paddle.nn.Softsign()
-        out = m(x) # [-0.285714, -0.166667, 0.0909091, 0.230769]
+            x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
+            m = paddle.nn.Softsign()
+            out = m(x) # [-0.285714, -0.166667, 0.0909091, 0.230769]
     """
 
     def __init__(self, name=None):
@@ -808,7 +802,7 @@ class Tanhshrink(layers.Layer):
 
     .. math::
 
-        \text{Tanhshrink}(x) = x - \text{Tanh}(x)
+        Tanhshrink(x) = x - tanh(x)
 
     Parameters:
         name (str, optional): Name for the operation (optional, default is None).
@@ -821,14 +815,14 @@ class Tanhshrink(layers.Layer):
     Examples:
         .. code-block:: python
 
-        import paddle
-        import numpy as np
+            import paddle
+            import numpy as np
 
-        paddle.disable_static()
+            paddle.disable_static()
 
-        x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
-        m = paddle.nn.Tanhshrink()
-        out = m(x) # [-0.020051, -0.00262468, 0.000332005, 0.00868739]
+            x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
+            m = paddle.nn.Tanhshrink()
+            out = m(x) # [-0.020051, -0.00262468, 0.000332005, 0.00868739]
     """
 
     def __init__(self, name=None):