Implement the rectified linear unit used throughout AlexNet.
ReLU(x)=max(0,x).Apply the function elementwise and return a float64 NumPy array with the same shape as x.
Examples
Input: x = [-2,-0.5,0,3]
Output: [0,0,0,3]
Explanation: Negative values become zero while nonnegative values are unchanged.
Input: x = [[-1,2],[3,-4]]
Output: [[0,2],[3,0]]
Input: x = [0,1,5]
Output: [0,1,5]
Hint 1
np.maximum can compare the whole array with zero elementwise.
Requirements
- Replace every negative element with zero.
- Keep zero and positive elements unchanged.
- Return a new float64 NumPy array with the same shape as x.
Constraints
- x is a nonempty float64 NumPy array of any rank.
- Every input value is finite.
All steps in AlexNet
Accepts: array
ReLU Activation
Implement the rectified linear unit used throughout AlexNet.
ReLU(x)=max(0,x).Apply the function elementwise and return a float64 NumPy array with the same shape as x.
Examples
Input: x = [-2,-0.5,0,3]
Output: [0,0,0,3]
Explanation: Negative values become zero while nonnegative values are unchanged.
Input: x = [[-1,2],[3,-4]]
Output: [[0,2],[3,0]]
Input: x = [0,1,5]
Output: [0,1,5]
Hint 1
np.maximum can compare the whole array with zero elementwise.
Requirements
- Replace every negative element with zero.
- Keep zero and positive elements unchanged.
- Return a new float64 NumPy array with the same shape as x.
Constraints
- x is a nonempty float64 NumPy array of any rank.
- Every input value is finite.
All steps in AlexNet
Accepts: array