fixup! squash! Add trainable theta and discretization options

nengo · Jun 22, 2021 · ed73d4b · ed73d4b
1 parent 357044f
commit ed73d4b
Showing 1 changed file with 2 additions and 3 deletions.
diff --git a/keras_lmu/layers.py b/keras_lmu/layers.py
@@ -199,7 +199,7 @@ def _cont2discrete_zoh(A, B):
 
         # slice A/B back out of combined matrix
         discrt_A = ms[: A.shape[0], : A.shape[1]]
-        discrt_B = ms[A.shape[0] :, : A.shape[1] :]
+        discrt_B = ms[A.shape[0] :, : A.shape[1]]
 
         return discrt_A, discrt_B
 
@@ -623,8 +623,7 @@ class LMUFFT(tf.keras.layers.Layer):
         step. If this value is smaller than the size of the input sequence, only that
         number of steps will be represented at the time of prediction, however the
         entire sequence will still be processed in order for information to be
-        projected to and from the hidden layer. If ``trainable_theta`` is enabled, then
-        theta will be updated during the course of training.
+        projected to and from the hidden layer.
     hidden_cell : ``tf.keras.layers.Layer``
         Keras Layer implementing the hidden component.
     input_to_hidden : bool