Model working, testing architectures

keras.py

@@ -160,9 +160,11 @@ def Onehot(index, n, name=None):
     n is the number of different variables.
     """
     def to_onehot(x):
+        #Concat zeros to eye for indices in x equal to n (larger than those encoded by one)
+        eye = tf.concat((tf.eye(n), tf.zeros((1, n))), axis=0)
         return tf.concat((
                           x[...,:index],
-                          tf.gather(tf.eye(n), tf.cast(x[...,index], tf.int64)),
+                          tf.gather(eye, tf.cast(x[...,index], tf.int64)),
                           x[...,(index+1):],
                          ), axis=-1)
     return tf.keras.layers.Lambda(to_onehot, name=name)