def find_lr():
"""Поиск максимального lr."""
train_set = (load_data.train_x(), load_data.train_y())
model = make_model()
model.compile(
optimizer=optimizers.Nadam(),
loss="mae",
metrics=[iou.intersection_over_union],
)
model.fit_generator(yield_batch(train_set), steps_per_epoch=30, epochs=1)
model.compile(optimizer=optimizers.Nadam(),
loss=iou.intersection_over_union)
lr.get_max_lr(model, yield_batch(train_set))
def train():
"""Тренировка модели."""
train_set = (load_data.train_x(), load_data.train_y())
test_set = load_data.test_x()
train_oof(train_set, test_set)
data_train = (x_train.loc[ids_train], y_train.loc[ids_train])
data_val = (x_train.loc[ids_valid], y_train.loc[ids_valid])
rez, model = train_model(data_train, data_val)
scores.append(min(rez.history["val_loss"]))
feat = yield_batch_test(data_val[0])
df = model.predict_generator(feat,
steps=len(data_val[0].index.unique()))
oof.loc[ids_valid] = df
feat = yield_batch_test(x_test)
df = model.predict_generator(feat, steps=len(x_test.index.unique()))
prediction += df / FOLDS
mean = np.mean(scores)
err = np.std(scores) / len(scores)**0.5
print(f"IOU на кроссвалидации: " + str(-np.round(sorted(scores), 5)))
print(f"IOU среднее: {-mean:0.5f} +/- {2 * err:0.5f}")
oof.to_csv(DATA_PATH / f"oof-{-mean - 2 * err:0.5f}.csv", header=False)
prediction.to_csv(DATA_PATH / f"sub-{-mean - 2 * err:0.5f}.csv",
header=False)
if __name__ == '__main__':
train_set = (load_data.train_x(), load_data.train_y())
test_set = load_data.test_x()
train_oof(train_set, test_set)