i have 2 ndarrays, inputs , results, both consisting of multiple arrays looking this: inputs = [ [[1,2],[2,2],[3,2]], [[2,1],[1,2],[2,3]], [[2,2],[1,1],[3,3]], ... ] results = [ [3,4,5], [3,3,5], [4,2,6], ... ] i managed split them train , test arrays, train contains 66% of arrays , test other 33%. i'd reshape them further use in lstm script fails when inputting them np.reshape() function. split = int(round(0.66 * results.shape[0])) train_results = results[:split, :] train_inputs = inputs[:split, :] test_results = results[split:, :] test_inputs = inputs[split:, :] x_train = np.reshape(train_inputs, (train_inputs.shape[0], train_inputs.shape[1], 1)) x_test = np.reshape(test_inputs, (test_inputs.shape[0], test_inputs.shape[1], 1)) please tell me how use np.reshape() correctly in case. basically loosely following tutorial: https://github.com/vict0rsch/deep_learning/tree/master/keras/recurrent you pass tuple np.reshape . for lstm layer, need sha...