From ed73d4b75bf7a5be343fc4d8882fb0259f0af01e Mon Sep 17 00:00:00 2001 From: Daniel Rasmussen Date: Tue, 22 Jun 2021 12:51:59 -0300 Subject: [PATCH] fixup! squash! Add trainable theta and discretization options --- keras_lmu/layers.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/keras_lmu/layers.py b/keras_lmu/layers.py index 3248889e..fefff4ee 100644 --- 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