fix dice_loss, log_loss doc and example code test=document_fix (#27702)

* update 2.0 API for dice_loss and log_loss test=document_fix

python/paddle/fluid/layers/nn.py

@@ -7061,10 +7061,10 @@ def dice_loss(input, label, epsilon=0.00001, name=None):
 
 
     Parameters:
-        input (Variable): Tensor, rank>=2, shape is :math:`[N_1, N_2, ..., N_D]`, where :math:`N_1` is
+        input (Tensor): Tensor, rank>=2, shape is :math:`[N_1, N_2, ..., N_D]`, where :math:`N_1` is
                           the batch_size, :math:`N_D` is 1. It is usually the output predictions of sigmoid activation.
                           The data type can be float32 or float64.
-        label (Variable): Tensor, the groud truth with the same rank as input, shape is :math:`[N_1, N_2, ..., N_D]`.
+        label (Tensor): Tensor, the groud truth with the same rank as input, shape is :math:`[N_1, N_2, ..., N_D]`.
                           where :math:`N_1` is the batch_size, :math:`N_D` is 1. The data type can be float32 or float64.
         epsilon (float): The epsilon will be added to the numerator and denominator.
                          If both input and label are empty, it makes sure dice is 1.
@@ -7074,18 +7074,19 @@ def dice_loss(input, label, epsilon=0.00001, name=None):
                              For more information, please refer to :ref:`api_guide_Name`
 
     Returns:
-        The dice loss with shape [1], data type is the same as `input` .
-    Return Type:
-        Varaible
+        Tensor, which shape is [1], data type is the same as `input` .
 
     Example:
         .. code-block:: python
 
-            import paddle.fluid as fluid
-            x = fluid.data(name='data', shape = [3, 224, 224, 1], dtype='float32')
-            label = fluid.data(name='label', shape=[3, 224, 224, 1], dtype='float32')
-            predictions = fluid.layers.sigmoid(x)
-            loss = fluid.layers.dice_loss(input=predictions, label=label)
+            import paddle
+            import paddle.nn.functional as F
+
+            paddle.disable_static()
+            x = paddle.randn((3,224,224,2))
+            label = paddle.randint(high=2, shape=(3,224,224,1))
+            predictions = F.softmax(x)
+            loss = F.dice_loss(input=predictions, label=label)
     """
     label = one_hot(label, depth=input.shape[-1])
     reduce_dim = list(range(1, len(input.shape)))
@@ -13098,10 +13099,10 @@ def log_loss(input, label, epsilon=1e-4, name=None):
               - (1 - label) * \\log{(1 - input + \\epsilon)}
 
     Args:
-        input (Variable|list):  A 2-D tensor with shape [N x 1], where N is the
+        input (Tensor|list):  A 2-D tensor with shape [N x 1], where N is the
                                 batch size. This input is a probability computed
                                 by the previous operator. Data type float32.
-        label (Variable|list):  The ground truth which is a 2-D tensor with
+        label (Tensor|list):  The ground truth which is a 2-D tensor with
                                 shape [N x 1], where N is the batch size.
                                 Data type float32.
         epsilon (float, optional): A small number for numerical stability. Default 1e-4.
@@ -13109,15 +13110,18 @@ def log_loss(input, label, epsilon=1e-4, name=None):
             :ref:`api_guide_Name` . Usually name is no need to set and None by default.
 
     Returns:
-        Variable: A 2-D tensor with shape [N x 1], the negative log loss.
+        Tensor, which shape is [N x 1], data type is float32.
 
     Examples:
         .. code-block:: python
 
-          import paddle.fluid as fluid
-          label = fluid.data(name='label', shape=[None, 1], dtype='float32')
-          prob = fluid.data(name='prob', shape=[None, 1], dtype='float32')
-          cost = fluid.layers.log_loss(input=prob, label=label)
+          import paddle
+          import paddle.nn.functional as F
+
+          paddle.disable_static()
+          label = paddle.randn((10,1))
+          prob = paddle.randn((10,1))
+          cost = F.log_loss(input=prob, label=label)
     """
     helper = LayerHelper('log_loss', **locals())
     check_variable_and_dtype(input, 'input', ['float32'], 'log_loss')