Making predictions from keras with SciKit
This seems about right.

You can use SciKit learn quite easily, as the predictions and test results you have should all be in NumPy arrays anyway. Take a look at the regression metrics. The metrics you have named are shown in the documentation to take lists, but here is an example showing they work just as well with NumPy arrays:

In [1]: from sklearn.metrics import r2_score

In [2]: y_true = [3, -0.5, 2, 7]

In [3]: y_pred = [2.5, 0.0, 2, 8]

In [4]: r2_score(y_true, y_pred)
Out[4]: 0.94860813704496794

In [6]: import numpy as np

In [13]: y_true = np.array([3, -0.5, 2, 7])

In [14]: y_pred = np.array([2.5, 0.0, 2, 8])

In [15]: r2_score(y_true, y_pred)
Out[15]: 0.94860813704496794            # identical result




One last comment:

17 examples doesn't sound like a lot. Look at model.summary() after compilation to see how many parameters your model has (spoiler - it's 1360). I would expect that you model (with your number of epochs and batch size etc.) will overfit, just memorising the dataset, and probably score 100%.

While this is a good sanity check to make sure your model can indeed learn, it might be a good idea to split a larger dataset (if available) into train, validation and test datasets. The simply use train and val as you have above, but in the prediction line, us the test set, which the model has never seen before. Unless your data is extremely homogenous, I wouldn't expect a accuracy metric near 100%.