update doc of allclose (#2401)

* rename the inputs and update code example, test=develop

* update doc

doc/fluid/api/tensor.rst

@@ -52,6 +52,7 @@ paddle.tensor
     tensor/isfinite.rst
     tensor/less_equal.rst
     tensor/less_than.rst
+    tensor/logic.rst
     tensor/linalg.rst
     tensor/linspace.rst
     tensor/load.rst

doc/fluid/api/tensor/logic.rst

+======
+logic
+======
+
+..  toctree::
+    :maxdepth: 1
+
+    logic/allclose.rst
\ No newline at end of file

doc/fluid/api/tensor/logic/allclose.rst

+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
+
+.. _api_tensor_logic_allclose:
+
+allclose
+--------
+
+..  autofunction:: paddle.tensor.logic.allclose
+    :noindex:
\ No newline at end of file

doc/fluid/api_cn/tensor_cn/allclose_cn.rst

@@ -3,23 +3,18 @@
 allclose
 -------------------------------
 
-.. py:function:: paddle.allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False, name=None)
+.. py:function:: paddle.allclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name=None)
 
-:alias_main: paddle.allclose
-:alias: paddle.allclose,paddle.tensor.allclose,paddle.tensor.logic.allclose
-
-
-
-逐个检查input和other的所有元素是否均满足如下条件：
+逐个检查x和y的所有元素是否均满足如下条件：
 
 ..  math::
-    \left| input - other \right| \leq atol + rtol \times \left| other \right|
+    \left| x - y \right| \leq atol + rtol \times \left| y \right|
 
 该API的行为类似于 :math:`numpy.allclose` ，即当两个待比较Tensor的所有元素均在一定容忍误差范围内视为相等则该API返回True值。
 
 参数:
-    - **input** (Variable) - 第一个输入待比较Tensor input。
-    - **other** (Variable) - 第二个输入待比较Tensor other。
+    - **x** (Tensor) - 输入的 `Tensor` ，数据类型为：float32、float64。
+    - **y** (Tensor) - 输入的 `Tensor` ，数据类型为：float32、float64。
     - **rtol** (float，可选) - 相对容忍误差，默认值为1e-5。
     - **atol** (float，可选) - 绝对容忍误差，默认值为1e-8。
     - **equal_nan** (bool，可选) - 如果设置为True，则两个NaN数值将被视为相等，默认值为False。
@@ -27,43 +22,37 @@ allclose
 
 返回：计算得到的布尔类型单值Tensor。
 
-返回类型：变量（Variable）
-
 **代码示例**:
 
 .. code-block:: python
 
     import paddle
-    import paddle.fluid as fluid
     import numpy as np
-    use_cuda = fluid.core.is_compiled_with_cuda()
-    a = fluid.data(name="a", shape=[2], dtype='float32')
-    b = fluid.data(name="b", shape=[2], dtype='float32')
-    result = paddle.allclose(a, b, rtol=1e-05, atol=1e-08,
+
+    paddle.disable_static()
+
+    np_x = np.array([10000., 1e-07]).astype("float32")
+    np_y = np.array([10000.1, 1e-08]).astype("float32")
+    x = paddle.to_tensor (np_x)
+    y = paddle.to_tensor (np_y)
+    result1 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08,
+                            equal_nan=False, name="ignore_nan")
+    np_result1 = result1.numpy()
+    # [False]
+    result2 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08,
+                                equal_nan=True, name="equal_nan")
+    np_result2 = result2.numpy()
+    # [False]
+
+    np_x = np.array([1.0, float('nan')]).astype("float32")
+    np_y = np.array([1.0, float('nan')]).astype("float32")
+    x = paddle.to_tensor (np_x)
+    y = paddle.to_tensor (np_y)
+    result1 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08,
                             equal_nan=False, name="ignore_nan")
-    result_nan = paddle.allclose(a, b, rtol=1e-05, atol=1e-08,
+    np_result1 = result1.numpy()
+    # [False]
+    result2 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08,
                                 equal_nan=True, name="equal_nan")
-    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-    exe = fluid.Executor(place)
-    exe.run(fluid.default_startup_program())
-    x = np.array([10000., 1e-07]).astype("float32")
-    y = np.array([10000.1, 1e-08]).astype("float32")
-    result_v, result_nan_v = exe.run(
-        feed={'a': x, 'b': y},
-        fetch_list=[result, result_nan])
-    print(result_v, result_nan_v)
-    # Output: (array([False]), array([False]))
-    x = np.array([10000., 1e-08]).astype("float32")
-    y = np.array([10000.1, 1e-09]).astype("float32")
-    result_v, result_nan_v = exe.run(
-        feed={'a': x, 'b': y},
-        fetch_list=[result, result_nan])
-    print(result_v, result_nan_v)
-    # Output: (array([ True]), array([ True]))
-    x = np.array([1.0, float('nan')]).astype("float32")
-    y = np.array([1.0, float('nan')]).astype("float32")
-    result_v, result_nan_v = exe.run(
-        feed={'a': x, 'b': y},
-        fetch_list=[result, result_nan])
-    print(result_v, result_nan_v)
-    # Output: (array([False]), array([ True]))
+    np_result2 = result2.numpy()
+    # [True]