INNER CODE UNIT · Python
compute_cost
amanchadha/coursera-deep-learning-specialization · C2 - Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Week 2/opt_utils.py:82
def compute_cost(a3, Y):
"""
Implement the cost function
Arguments:
a3 -- post-activation, output of forward propagation
Y -- "true" labels vector, same shape as a3
Returns:
cost - value of the cost function
"""
m = Y.shape[1]
logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)
cost = 1./m * np.sum(logprobs)
return cost