with open('data/meta.json', 'r') as f:

meta = json.load(f)

cnn = build_cnn(227, meta['n_subjects'])

cnn.load_weights('data/weights/weights.best.h5')

bottleneck = Bottleneck(cnn, ~1)

train_x, train_y = np.load('data/train_x.npy'), np.load('data/train_y.npy')

test_x, test_y = np.load('data/test_x.npy'), np.load('data/test_y.npy')

train_x = np.vstack([train_x, test_x])

train_y = np.hstack([train_y, test_y])

dev_x = np.load('data/dev_x.npy')

dev_protocol = np.load('data/dev_protocol.npy')

train_emb = bottleneck.predict(train_x, batch_size=256)

dev_emb = bottleneck.predict(dev_x, batch_size=256)

del train_x

pca = PCA(N_OUT)

pca.fit(train_emb)

W_pca = pca.components_

np.save('data/w_pca', W_pca)

tpe, tpe_pred = build_tpe(N_IN, N_OUT, W_pca.T)

train_y = np.array(train_y)

subjects = list(set(train_y))

anchors_inds = []

positives_inds = []

labels = []

for subj in subjects:

mask = train_y == subj

inds = np.where(mask)[0]

for a, p in itertools.permutations(inds, 2):

anchors_inds.append(a)

positives_inds.append(p)

labels.append(subj)

anchors = train_emb[anchors_inds]

positives = train_emb[positives_inds]

n_anchors = len(anchors_inds)

inds = np.arange(n_anchors)

def get_batch(hard=False):

batch_inds = np.random.choice(inds, size=BIG_BATCH_SIZE, replace=False)

train_emb2 = tpe_pred.predict(train_emb, batch_size=1024)

scores = train_emb2 @ train_emb2.T

negative_inds = []

for i in batch_inds:

label = labels[i]

mask = train_y == label

if hard:

negative_inds.append(np.ma.array(scores[label], mask=mask).argmax())

else:

negative_inds.append(np.random.choice(np.where(np.logical_not(mask))[0], size=1)[0])

return anchors[batch_inds], positives[batch_inds], train_emb[negative_inds]

def test():

dev_emb2 = tpe_pred.predict(dev_emb)

tsc, isc = get_scores(dev_emb2, dev_protocol)

eer, _, _, _ = calc_metrics(tsc, isc)

return eer

z = np.zeros((BIG_BATCH_SIZE,))

min_eer = float('inf')

for e in range(NB_EPOCH):

print('epoch: {}'.format(e))

a, p, n = get_batch(e > COLD_START)

tpe.fit([a, p, n], z, batch_size=BATCH_SIZE, nb_epoch=1)

eer = test()

print('EER: {:.2f}'.format(eer * 100))

if eer < min_eer:

min_eer = eer