Multidimensional input fails

Thanks for the great repo.

My impression was that n-beats could handle multidimensional input, but when I try to increase the input_dim in the example to a value greater than 1 the code fails. Is there anything simple that I'm missing to get the code working with multidimensional input?

Failing code example:

import warnings

import numpy as np

from nbeats_pytorch.model import NBeatsNet as NBeatsPytorch

warnings.filterwarnings(action="ignore", message="Setting attributes")

def main():
    num_samples, time_steps, input_dim, output_dim = 50_000, 10, 2, 1

    for BackendType in [NBeatsPytorch]:
        backend = BackendType(
            backcast_length=time_steps, forecast_length=output_dim,
            stack_types=(NBeatsPytorch.GENERIC_BLOCK, NBeatsPytorch.GENERIC_BLOCK),
            nb_blocks_per_stack=2, thetas_dim=(4, 4), share_weights_in_stack=True,
            hidden_layer_units=64
        )

        # Definition of the objective function and the optimizer.
        backend.compile(loss="mae", optimizer="adam")

        # Definition of the data. The problem to solve is to find f such as | f(x) - y | -> 0.
        # where f = np.mean.
        x = np.random.uniform(size=(num_samples, time_steps, input_dim))
        y = np.mean(x, axis=1, keepdims=True)
        print(f"Shape(x,y): {x.shape}, {y.shape}")

        # Split data into training and testing datasets.
        c = num_samples // 10
        x_train, y_train, x_test, y_test = x[c:], y[c:], x[:c], y[:c]
        test_size = len(x_test)

        # Train the model.
        print("Training...")
        backend.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=20, batch_size=128)

        # Save the model for later.
        backend.save("n_beats_model.h5")

        # Predict on the testing set (forecast).
        predictions_forecast = backend.predict(x_test)
        np.testing.assert_equal(predictions_forecast.shape, (test_size, backend.forecast_length, output_dim))

        # Predict on the testing set (backcast).
        predictions_backcast = backend.predict(x_test, return_backcast=True)
        np.testing.assert_equal(predictions_backcast.shape, (test_size, backend.backcast_length, output_dim))

        # Load the model.
        model_2 = BackendType.load("n_beats_model.h5")

        np.testing.assert_almost_equal(predictions_forecast, model_2.predict(x_test))

if __name__ == "__main__":
    main()

philipperemy / n-beats

Multidimensional input fails #50