res = [
epoch,
round(tr_loss_tot, 3),
round(tr_acc_tot * 100, 3),
round(val_loss_tot, 3),
round(val_acc_tot * 100, 3)
]
print('\t'.join(map(str, res)))
#print(epoch, val_loss_tot, val_acc_tot * 100)
# if loss is too high, convergence failure so don't bother to make predictions
if val_loss_tot > 1.0:
continue
print("Predicting with test images...")
imgs_per_batch = 99
for j in range(0, 1001, imgs_per_batch):
X_sub_part, sub_ids_part = load_test_data(j, imgs_per_batch)
y_proba_part = pred_fn(X_sub_part)
if j == 0:
y_proba = y_proba_part
ids = sub_ids_part
else:
y_proba = np.append(y_proba, y_proba_part, axis=0)
ids = np.append(ids, sub_ids_part, axis=0)
if j % 99 == 0:
print(j)
make_submission('../output/submission_vgs_' + str(i) + '.csv', y_proba,
ids)
res = [
epoch,
round(tr_loss_tot, 3),
round(tr_acc_tot * 100, 3),
round(val_loss_tot, 3),
round(val_acc_tot * 100, 3)
]
print('\t'.join(map(str, res)))
#print(epoch, val_loss_tot, val_acc_tot * 100)
# if loss is too high, convergence failure so don't bother to make predictions
if val_loss_tot > 1.0:
continue
print("Predicting with test images...")
imgs_per_batch = 99
for j in range(0, 1001, imgs_per_batch):
X_sub_part, sub_ids_part = load_test_data(j, imgs_per_batch)
y_proba_part = pred_fn(X_sub_part)
if j == 0:
y_proba = y_proba_part
ids = sub_ids_part
else:
y_proba = np.append(y_proba, y_proba_part, axis=0)
ids = np.append(ids, sub_ids_part, axis=0)
if j % 99 == 0:
print(j)
make_submission('../output/submission_vgg16_zoomed' + str(i) + '.csv',
y_proba, ids)
tr_loss_tot /= len(t_ix[:2000])
tr_acc_tot /= len(t_ix[:2000])
val_loss_tot /= len(v_ix)
val_acc_tot /= len(v_ix)
res = [epoch, round(tr_loss_tot,3), round(tr_acc_tot*100,3), round(val_loss_tot,3), round(val_acc_tot * 100,3)]
print('\t'.join(map(str,res)))
#print(epoch, val_loss_tot, val_acc_tot * 100)
# if loss is too high, convergence failure so don't bother to make predictions
if val_loss_tot > 1.0:
continue
print("Predicting with test images...")
imgs_per_batch = 99
for j in range(0, 1001, imgs_per_batch):
X_sub_part, sub_ids_part = load_test_data(j, imgs_per_batch)
y_proba_part = pred_fn(X_sub_part)
if j==0:
y_proba = y_proba_part
ids = sub_ids_part
else:
y_proba = np.append(y_proba, y_proba_part, axis=0)
ids = np.append(ids, sub_ids_part, axis=0)
if j%99==0:
print(j)
make_submission('../output/submission_resnet50_' + str(i) + '.csv', y_proba, ids)