# Path: dltk/core/activations.py
# def leaky_relu(inputs, alpha=0.1):
#     """Leaky ReLu activation function
# 
#     Args:
#         inputs (tf.Tensor): input Tensor
#         alpha (float): leakiness parameter
# 
#     Returns:
#         tf.Tensor: a leaky ReLu activated tensor
#     """
#     return tf.maximum(inputs, alpha * inputs)


import tensorflow as tf
import numpy as np
from dltk.core.activations import leaky_relu


def test_leaky_relu():
    test_alpha = tf.constant(0.1)
    test_inp_1 = tf.constant(1.)
    test_inp_2 = tf.constant(-1.)

    test_relu_1 = leaky_relu(test_inp_1, test_alpha)

# Path: dltk/core/activations.py
# def leaky_relu(inputs, alpha=0.1):
#     """Leaky ReLu activation function
# 
#     Args:
#         inputs (tf.Tensor): input Tensor
#         alpha (float): leakiness parameter
# 
#     Returns:
#         tf.Tensor: a leaky ReLu activated tensor
#     """
#     return tf.maximum(inputs, alpha * inputs)


# Path: tests/test_activations.py
import tensorflow as tf
import numpy as np
from dltk.core.activations import leaky_relu



def test_leaky_relu():
    test_alpha = tf.constant(0.1)
    test_inp_1 = tf.constant(1.)
    test_inp_2 = tf.constant(-1.)

    test_relu_1 = leaky_relu(test_inp_1, test_alpha)

    test_relu_2 = leaky_relu(test_inp_2, test_alpha)