Ниже приведены коды для расчета функции стоимости и градиента для классификации изображения на основе курса Coursera Deep Learning.
После расчета стоимости, как показано ниже
cost = -np.sum(Y*np.log(A) + (1-Y)*np.log(1-A)) / m
cost.shape равно
()
Тогда какова цель операции ниже
cost = np.squeeze(cost)
В функции
def propagate(w, b, X, Y):
"""
Implement the cost function and its gradient for the propagation
Arguments:
w -- weights, a numpy array of size (num_px * num_px * 3, 1)
b -- bias, a scalar
X -- data of size (num_px * num_px * 3, number of examples)
Y -- true "label" vector (containing 0 if non-cat, 1 if cat) of size (1, number of examples)
Return:
cost -- negative log-likelihood cost for logistic regression
dw -- gradient of the loss with respect to w, thus same shape as w
db -- gradient of the loss with respect to b, thus same shape as b
"""
m = X.shape[1]
# FORWARD PROPAGATION (FROM X TO COST)
A = sigmoid(np.dot(w.T, X) + b) # compute activation
cost = -np.sum(Y*np.log(A) + (1-Y)*np.log(1-A)) / m # compute cost
# BACKWARD PROPAGATION (TO FIND GRAD)
dw = np.dot(X, (A-Y).T) / m
db = np.sum(A-Y) / m
assert(dw.shape == w.shape)
assert(db.dtype == float)
cost = np.squeeze(cost)
assert(cost.shape == ())
grads = {"dw": dw,
"db": db}
return grads, cost