add api mindspore.ops.tanhshrink

docs/api/api_python/mindspore.ops.rst

@@ -275,6 +275,7 @@ mindspore.ops
     mindspore.ops.svd
     mindspore.ops.t
     mindspore.ops.tan
+    mindspore.ops.tanhshrink
     mindspore.ops.true_divide
     mindspore.ops.trunc
     mindspore.ops.truncate_div

docs/api/api_python/ops/mindspore.ops.func_tanhshrink.rst

@@ -0,0 +1,8 @@
+mindspore.ops.tanhshrink
+=========================
+
+.. py:function:: mindspore.ops.tanhshrink(x)
+
+    按元素计算Tanhshrink函数。
+
+    详情请查看 :class:`mindspore.nn.Tanhshrink` 。

docs/api/api_python_en/mindspore.ops.rst

@@ -275,6 +275,7 @@ Element-by-Element Operations
     mindspore.ops.svd
     mindspore.ops.t
     mindspore.ops.tan
+    mindspore.ops.tanhshrink
     mindspore.ops.true_divide
     mindspore.ops.trunc
     mindspore.ops.truncate_div

mindspore/python/mindspore/nn/layer/activation.py

@@ -744,10 +744,9 @@ class Tanhshrink(Cell):
     def __init__(self):
         """Initialize Tanhshrink."""
         super(Tanhshrink, self).__init__()
-        self.tanh = P.Tanh()
 
     def construct(self, x):
-        return x - self.tanh(x)
+        return F.tanhshrink(x)
 
 
 @constexpr

mindspore/python/mindspore/ops/function/__init__.py

@@ -272,6 +272,7 @@ from .math_func import (
     sinh,
     cosh,
     tanh,
+    tanhshrink,
     asinh,
     arcsinh,
     acosh,

mindspore/python/mindspore/ops/function/math_func.py

@@ -9612,6 +9612,28 @@ def sum(x, dim=None, keepdim=False, *, dtype=None):
     return out
 
 
+def tanhshrink(x):
+    '''
+    Applies element-wise, :math:`Tanhshrink(x)=x-Tanh(x)` .
+    see :class:`mindspore.nn.Tanhshrink` for more details.
+
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
+    Examples:
+        >>> import mindspore as ms
+        >>> import mindspore.ops as ops
+        >>> from mindspore import Tensor
+        >>> import numpy as np
+        >>> x = Tensor(np.array([1, 2, 3, 2, 1]), ms.float16)
+        >>> output = ops.tanhshrink(x)
+        >>> print(output)
+        [0.2383 1.036  2.004  1.036  0.2383]
+    '''
+    tanh_op = _get_cache_prim(P.Tanh)()
+    return x - tanh_op(x)
+
+
 __all__ = [
     'addn',
     'absolute',
@@ -9730,6 +9752,7 @@ __all__ = [
     'sinh',
     'cosh',
     'tanh',
+    'tanhshrink',
     'asinh',
     'arcsinh',
     'acosh',

tests/st/ops/test_ops_tanhshrink.py

@@ -0,0 +1,92 @@
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+import numpy as np
+import pytest
+
+import mindspore as ms
+import mindspore.nn as nn
+import mindspore.ops as ops
+from mindspore import Tensor
+
+
+class Net(nn.Cell):
+    def construct(self, x):
+        return ops.tanhshrink(x)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_arm_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
+def test_tanhshrink_normal(mode):
+    """
+    Feature: Tanhshrink
+    Description: Verify the result of Tanhshrink with normal input
+    Expectation: success
+    """
+    ms.set_context(mode=mode)
+    net = Net()
+    a = Tensor(np.array([1, 2, 3, 2, 1]).astype(np.float16))
+    output = net(a).asnumpy()
+    expected_output = np.array([0.2383, 1.036, 2.004, 1.036, 0.2383]).astype(np.float16)
+    assert np.allclose(output, expected_output, 1e-3, 1e-3)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_arm_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
+def test_tanhshrink_negative(mode):
+    """
+    Feature: Tanhshrink
+    Description: Verify the result of Tanhshrink with negative input
+    Expectation: success
+    """
+    ms.set_context(mode=mode)
+    net = Net()
+    a = Tensor(np.array([-1, -2, -3, -2, -1]).astype(np.float16))
+    output = net(a).asnumpy()
+    expected_output = np.array([-0.2383, -1.036, -2.004, -1.036, -0.2383]).astype(np.float16)
+    assert np.allclose(output, expected_output, 1e-3, 1e-3)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_arm_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
+def test_tanhshrink_zeros(mode):
+    """
+    Feature: Tanhshrink
+    Description: Verify the result of Tanhshrink with zeros
+    Expectation: success
+    """
+    ms.set_context(mode=mode)
+    net = Net()
+    a = Tensor(np.array([0, 0, 0, 0, 0]).astype(np.float16))
+    output = net(a).asnumpy()
+    expected_output = np.array([0, 0, 0, 0, 0]).astype(np.float16)
+    assert np.allclose(output, expected_output, 1e-3, 1e-3)