# machine learning – Implement Relu derivative in python numpy

## machine learning – Implement Relu derivative in python numpy

Thats an exercise in vectorization.

This code

``````if x > 0:
y = 1
elif xi <= 0:
y = 0
``````

Can be reformulated into

``````y = (x > 0) * 1
``````

This is something that will work for numpy arrays, since boolean expressions involving them are turned into arrays of values of these expressions for elements in said array.

I guess this is what you are looking for:

``````>>> def reluDerivative(x):
...     x[x<=0] = 0
...     x[x>0] = 1
...     return x

>>> z = np.random.uniform(-1, 1, (3,3))
>>> z
array([[ 0.41287266, -0.73082379,  0.78215209],
[ 0.76983443,  0.46052273,  0.4283139 ],
[-0.18905708,  0.57197116,  0.53226954]])
>>> reluDerivative(z)
array([[ 1.,  0.,  1.],
[ 1.,  1.,  1.],
[ 0.,  1.,  1.]])
``````

#### machine learning – Implement Relu derivative in python numpy

Basic function to return derivative of relu could be summarized as follows:

``````f(x) = x > 0
``````

So, with numpy that would be:

``````def relu_derivative(z):
return np.greater(z, 0).astype(int)
``````