def evaluate_with_no_cv(emb_array, actual_issame):
thresholds = np.arange(0, 4, 0.01)
embeddings1 = emb_array[0::2]
embeddings2 = emb_array[1::2]
nrof_thresholds = len(thresholds)
accuracys = np.zeros((nrof_thresholds))
diff = np.subtract(embeddings1, embeddings2)
dist = np.sum(np.square(diff),1)
for threshold_idx, threshold in enumerate(thresholds):
_, _, accuracys[threshold_idx] = facenet.calculate_accuracy(threshold, dist, actual_issame)
best_acc = np.max(accuracys)
best_thre = thresholds[np.argmax(accuracys)]
return best_acc,best_thre
def evaluate_with_no_cv(emb_array, actual_issame):
thresholds = np.arange(0, 4, 0.01)
embeddings1 = emb_array[0::2]
embeddings2 = emb_array[1::2]
nrof_thresholds = len(thresholds)
accuracys = np.zeros((nrof_thresholds))
diff = np.subtract(embeddings1, embeddings2)
dist = np.sum(np.square(diff),1)
for threshold_idx, threshold in enumerate(thresholds):
_, _, accuracys[threshold_idx] = facenet.calculate_accuracy(threshold, dist, actual_issame)
best_acc = np.max(accuracys)
best_thre = thresholds[np.argmax(accuracys)]
return best_acc,best_thre