Make training work

n3fit/src/n3fit/backends/keras_backend/operations.py

@@ -400,5 +400,8 @@ def add_target_dependence(xgrid, a_value):
     # in the input. Now, as opposed to the free-proton fit the input
     # (still called `xgrid`) is a two-dimensional array.
     # TODO: Find a better (again) to propagate the A-dependence
+    if xgrid.ndim == 1:
+        xgrid = np.expand_dims(xgrid, axis=-1)
+
     a_value_expand = np.full(xgrid.shape, a_value)
-    return np.stack((xgrid, a_value_expand), axis=-1)
+    return np.concatenate((xgrid, a_value_expand), axis=-1)

n3fit/src/n3fit/io/writer.py

@@ -11,6 +11,7 @@
 import validphys
 import n3fit
 from n3fit import vpinterface
+from n3fit.backends import operations as op
 
 XGRID = np.array(
     [
@@ -326,6 +327,8 @@ def jsonfit(replica_status, pdf_object, tr_chi2, vl_chi2, true_chi2, stop_epoch,
     all_info["erf_vl"] = vl_chi2
     all_info["chi2"] = true_chi2
     all_info["pos_state"] = replica_status.positivity_status
+    # all_info["arc_lengths"] = None
+    # all_info["integrability"] = None
     all_info["arc_lengths"] = vpinterface.compute_arclength(pdf_object).tolist()
     all_info["integrability"] = vpinterface.integrability_numbers(pdf_object).tolist()
     all_info["timing"] = timing

n3fit/src/n3fit/vpinterface.py

@@ -22,6 +22,7 @@
 from collections.abc import Iterable
 import numpy as np
 import numpy.linalg as la
+from n3fit.backends import operations as op
 from validphys.core import PDF, MCStats
 from validphys.pdfbases import ALL_FLAVOURS, check_basis
 from validphys.lhapdfset import LHAPDFSet
@@ -102,15 +103,17 @@ def __call__(self, xarr, flavours=None, replica=None):
         # Ensures that the input has the shape the model expect, no matter the input
         # as the scaling is done by the model itself
         mod_xgrid = xarr.reshape(1, -1, 1)
+        # TODO: Make different `A` predictions depending on some inputs.
+        nn_input = op.add_target_dependence(mod_xgrid, a_value=1)
 
         if replica is None or replica == 0:
             # We need generate output values for all replicas
-            result = np.concatenate([m.predict({"pdf_input": mod_xgrid}) for m in self._lhapdf_set], axis=0)
+            result = np.concatenate([m.predict({"pdf_input": nn_input}) for m in self._lhapdf_set], axis=0)
             if replica == 0:
                 # We want _only_ the central value
                 result = np.mean(result, axis=0, keepdims=True)
         else:
-            result = self._lhapdf_set[replica - 1].predict({"pdf_input": mod_xgrid})
+            result = self._lhapdf_set[replica - 1].predict({"pdf_input": nn_input})
 
         if flavours != "n3fit":
             # Ensure that the result has its flavour in the basis-defined order