LeakyRelu #13488

ivy/functional/frontends/tensorflow/nn.py

@@ -489,6 +489,12 @@ def softmax(logits, axis=None, name=None):
     return ivy.softmax(logits, axis=axis)
 
 
+@with_unsupported_dtypes({"2.9.0 and below": "float16"}, "tensorflow")
+@to_ivy_arrays_and_back
+def leaky_relu(features, alpha, name=None):
+    return ivy.leaky_relu(features, alpha=alpha)
+
+
 @to_ivy_arrays_and_back
 def crelu(features, axis=-1, name=None):
     c = ivy.concat([features, -features], axis=axis)

ivy/functional/frontends/tensorflow/raw_ops.py

@@ -132,11 +132,13 @@ def Concat(*, concat_dim, values, name="Concat"):
 
 Cos = to_ivy_arrays_and_back(map_raw_ops_alias(tf_frontend.math.cos))
 
+
 @to_ivy_arrays_and_back
 def Cross(*, a, b, name='Cross'):
     a, b = check_tensorflow_casting(a, b)
     return ivy.cross(a, b)
 
+
 @to_ivy_arrays_and_back
 def Cosh(*, x, name="Cosh"):
     return ivy.cosh(x)
@@ -750,6 +752,34 @@ def BatchMatMulV3(x, y, Tout=ivy.Dtype, adj_x=False, adj_y=False, name="BatchMat
 
 Slice = to_ivy_arrays_and_back(map_raw_ops_alias(tf_frontend.slice))
 
+LeakyRelu = to_ivy_arrays_and_back(
+    map_raw_ops_alias(
+        tf_frontend.nn.leaky_relu,
+    ))
+
+LeakyRelu.supported_dtypes = {
+    "numpy": (
+        "float32",
+        "float64",
+    ),
+    "tensorflow": (
+        "bfloat16",
+        "float16",
+        "float32",
+        "float64",
+    ),
+    "torch": (
+        "float32",
+        "float64",
+    ),
+    "jax": (
+        "bfloat16",
+        "float16",
+        "float32",
+        "float64",
+    ),
+}
+
 
 @to_ivy_arrays_and_back
 def Prod(*, input, axis, keep_dims=False, name="Prod"):

ivy_tests/test_ivy/test_frontends/test_tensorflow/test_nn.py

@@ -15,6 +15,36 @@
 )
 
 
+@handle_frontend_test(
+    fn_tree="tensorflow.nn.leaky_relu",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("float"),
+        min_num_dims=1,
+    ),
+    test_with_out=st.just(False),
+    alpha=helpers.floats(min_value=0, max_value=1)
+)
+def test_tensorflow_leaky_relu(
+        *,
+        dtype_and_x,
+        alpha,
+        frontend,
+        test_flags,
+        fn_tree,
+        on_device,
+):
+    dtype, x = dtype_and_x
+    return helpers.test_frontend_function(
+        input_dtypes=dtype,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        features=x[0],
+        alpha=alpha
+    )
+
+
 @st.composite
 def _x_and_filters(
     draw,

ivy_tests/test_ivy/test_frontends/test_tensorflow/test_raw_ops.py

@@ -314,8 +314,8 @@ def test_tensorflow_Cos(  # NOQA
 
 # Cross
 @handle_frontend_test(
-        fn_tree='tensorflow.raw_ops.Cross',
-        dtype_and_x=helpers.dtype_and_values(
+    fn_tree='tensorflow.raw_ops.Cross',
+    dtype_and_x=helpers.dtype_and_values(
         available_dtypes=helpers.get_dtypes("float"),
         min_num_dims=1,
         max_num_dims=5,
@@ -324,11 +324,11 @@ def test_tensorflow_Cos(  # NOQA
         safety_factor_scale="log",
         num_arrays=2,
         shared_dtype=True,
-        ),
-        test_with_out=st.just(False),
+    ),
+    test_with_out=st.just(False),
 )
 def test_tensorflow_Cross(  # NOQA
-     *,
+    *,
     dtype_and_x,
     frontend,
     test_flags,
@@ -346,6 +346,7 @@ def test_tensorflow_Cross(  # NOQA
         b=xs[1],
     )
 
+
 # Rsqrt
 @handle_frontend_test(
     fn_tree="tensorflow.raw_ops.Rsqrt",
@@ -381,7 +382,7 @@ def test_tensorflow_Rsqrt(
     ),
     test_with_out=st.just(False),
 )
-def test_tensorflow_Cosh(  # NOQA
+def test_tensorflow_Cosh(
     *,
     dtype_and_x,
     frontend,
@@ -3691,3 +3692,33 @@ def test_tensorflow_Prod(  # NOQA
         axis=axis,
         keep_dims=keep_dims,
     )
+
+
+@handle_frontend_test(
+    fn_tree="tensorflow.raw_ops.LeakyRelu",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("float"),
+        min_num_dims=1,
+    ),
+    test_with_out=st.just(False),
+    alpha=helpers.floats(min_value=0, max_value=1)
+)
+def test_tensorflow_LeakyReLU(
+        *,
+        dtype_and_x,
+        alpha,
+        frontend,
+        test_flags,
+        fn_tree,
+        on_device,
+):
+    dtype, x = dtype_and_x
+    return helpers.test_frontend_function(
+        input_dtypes=dtype,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        features=x[0],
+        alpha=alpha
+    )