add tanhshrink_py (#1146)

* add tanhshrink_py

* format code

* remove useless test cases

Author: fsx950223

tensorflow_addons/activations/tanhshrink.py

@@ -38,3 +38,7 @@ def tanhshrink(x: types.TensorLike) -> tf.Tensor:
 @tf.RegisterGradient("Addons>Tanhshrink")
 def _tanhshrink_grad(op, grad):
     return _activation_so.ops.addons_tanhshrink_grad(grad, op.inputs[0])
+
+
+def _tanhshrink_py(x):
+    return x - tf.math.tanh(x)

tensorflow_addons/activations/tanhshrink_test.py

@@ -18,6 +18,7 @@
 import numpy as np
 import tensorflow as tf
 from tensorflow_addons.activations import tanhshrink
+from tensorflow_addons.activations.tanhshrink import _tanhshrink_py
 from tensorflow_addons.utils import test_utils
 
 
@@ -26,22 +27,22 @@ class TanhshrinkTest(tf.test.TestCase, parameterized.TestCase):
     @parameterized.named_parameters(
         ("float16", np.float16), ("float32", np.float32), ("float64", np.float64)
     )
-    def test_tanhshrink(self, dtype):
-        x = tf.constant([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=dtype)
-        expected_result = tf.constant(
-            [-1.0359724, -0.23840582, 0.0, 0.23840582, 1.0359724], dtype=dtype
-        )
-
-        self.assertAllCloseAccordingToType(tanhshrink(x), expected_result)
-
-    @parameterized.named_parameters(("float32", np.float32), ("float64", np.float64))
-    def test_theoretical_gradients(self, dtype):
-        # Only test theoretical gradients for float32 and float64
-        # because of the instability of float16 while computing jacobian
-        x = tf.constant([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=dtype)
-
-        theoretical, numerical = tf.test.compute_gradient(tanhshrink, [x])
-        self.assertAllCloseAccordingToType(theoretical, numerical, atol=1e-4)
+    def test_same_as_py_func(self, dtype):
+        np.random.seed(1234)
+        for _ in range(20):
+            self.verify_funcs_are_equivalent(dtype)
+
+    def verify_funcs_are_equivalent(self, dtype):
+        x_np = np.random.uniform(-10, 10, size=(4, 4)).astype(dtype)
+        x = tf.convert_to_tensor(x_np)
+        with tf.GradientTape(persistent=True) as t:
+            t.watch(x)
+            y_native = tanhshrink(x)
+            y_py = _tanhshrink_py(x)
+        self.assertAllCloseAccordingToType(y_native, y_py, atol=1e-4)
+        grad_native = t.gradient(y_native, x)
+        grad_py = t.gradient(y_py, x)
+        self.assertAllCloseAccordingToType(grad_native, grad_py, atol=1e-4)
 
 
 if __name__ == "__main__":