reformatting activations sigmoid ivy-llc#754 ivy-llc#10446 (ivy-llc#1…

…0485)

* reformatting activations sigmoid ivy-llc#754 ivy-llc#10446

* more modification to sigmoid function

ivy/array/activations.py

@@ -110,12 +110,33 @@ def gelu(
         """
         return ivy.gelu(self._data, approximate=approximate, out=out)
 
-    def sigmoid(self: ivy.Array, /, *, out: Optional[ivy.Array] = None) -> ivy.Array:
+    def sigmoid(
+        self: ivy.Array,
+        /, 
+        *, 
+        out: Optional[ivy.Array] = None
+    ) -> ivy.Array:
+
         """
-        ivy.Array instance method variant of ivy.sigmoid. This method simply wraps the
-        function, and so the docstring for ivy.sigmoid also applies to this method
+        ivy.Array instance method variant of ivy.sigmoid.
+
+        This method simply wraps the function, and so the docstring for ivy.sigmoid also applies to this method
         with minimal changes.
 
+        Parameters
+        ----------
+        self
+            Input array
+        out
+            optional output array for writing the result to. It must have the same shape the input broadcast to
+            default: None
+
+        Returns
+        -------
+        ret
+            an array with the sigmoid activation function applied element-wise.
+
+
         Examples
         --------
         >>> x = ivy.array([-1., 1., 2.])

ivy/functional/backends/jax/activations.py

@@ -35,7 +35,12 @@ def relu(x: JaxArray, /, *, out: Optional[JaxArray] = None) -> JaxArray:
     return jnp.maximum(x, 0)
 
 
-def sigmoid(x: JaxArray, /, *, out: Optional[JaxArray] = None) -> JaxArray:
+def sigmoid(
+    x: JaxArray,
+    /,
+    *,
+    out: Optional[JaxArray] = None
+) -> JaxArray:
     return 1 / (1 + jnp.exp(-x))
 
 

ivy/functional/backends/numpy/activations.py

@@ -37,7 +37,12 @@ def gelu(
     return ivy.astype(ret, x.dtype, copy=False)
 
 
-def sigmoid(x: np.ndarray, /, *, out: Optional[np.ndarray] = None) -> np.ndarray:
+def sigmoid(
+    x: np.ndarray,
+    /,
+    *,
+    out: Optional[np.ndarray] = None
+) -> np.ndarray:
     if not ivy.is_array(x):
         return np.asarray(1 / (1 + np.exp(-x)))
     return np.asarray(1 / (1 + np.exp(-x))).astype(x.dtype)

ivy/functional/backends/tensorflow/activations.py

@@ -33,7 +33,12 @@ def relu(x: Tensor, /, *, out: Optional[Tensor] = None) -> Tensor:
     return tf.nn.relu(x)
 
 
-def sigmoid(x: Tensor, /, *, out: Optional[Tensor] = None) -> Tensor:
+def sigmoid(
+    x: Tensor,
+    /,
+    *,
+    out: Optional[Tensor] = None
+) -> Tensor:
     if not ivy.is_array(x):
         x = float(x)
     return tf.nn.sigmoid(x)

ivy/functional/backends/torch/activations.py

@@ -46,7 +46,12 @@ def gelu(
 
 
 @with_unsupported_dtypes({"1.11.0 and below": ("float16",)}, backend_version)
-def sigmoid(x: torch.Tensor, /, *, out: Optional[torch.Tensor] = None) -> torch.Tensor:
+def sigmoid(
+    x: torch.Tensor,
+    /,
+    *,
+    out: Optional[torch.Tensor] = None
+) -> torch.Tensor:
     if not ivy.is_array(x):
         x = torch.tensor(x)
     return torch.sigmoid(x, out=out)

ivy/functional/ivy/activations.py

@@ -401,17 +401,23 @@ def relu(
 @handle_array_like_without_promotion
 @handle_array_function
 def sigmoid(
-    x: Union[ivy.Array, ivy.NativeArray], /, *, out: Optional[ivy.Array] = None
+    x: Union[ivy.Array, ivy.NativeArray],
+    /,
+    *,
+    out: Optional[ivy.Array] = None
 ) -> ivy.Array:
-    """Applies the sigmoid function element-wise.
+
+    """
+    Applies the sigmoid function element-wise.
 
     Parameters
     ----------
     x
         input array.
     out
         optional output array, for writing the result to. It must have a shape that the
-        inputs broadcast to.
+        input broadcast to.
+        default: None
 
     Returns
     -------
@@ -422,16 +428,23 @@ def sigmoid(
     --------
     With :class:`ivy.Array` input:
 
-    >>> x = ivy.array([-1., 1., 2.])
+    >>> x = ivy.array([-1.0, 1.0, 2.0])
     >>> y = ivy.sigmoid(x)
     >>> print(y)
     ivy.array([0.269, 0.731, 0.881])
+    
+    or
+    
+    >>> x = ivy.array([-1.0, 1.0, 2.0])
+    >>> y = x.sigmoid()
+    >>> print(y)
+    ivy.array([0.269, 0.731, 0.881])
 
 
-    >>> x = ivy.array([-1.3, 3.8, 2.1])
+    >>> x = ivy.array([[-1.3, 3.8, 2.1], [1.7, 4.2, -6.6]])
     >>> y = ivy.sigmoid(x)
     >>> print(y)
-    ivy.array([0.214, 0.978, 0.891])
+    ivy.array([[0.214, 0.978, 0.891], [0.846,0.985,0.001]] )
     """
     return current_backend(x).sigmoid(x, out=out)