fix doc + reshape in cubical

3 files changed, 2 insertions, 2 deletions

diff --git a/ext/gudhi-deploy b/ext/gudhi-deploy
-Subproject 290ade1086bedbc96a35df886cadecabbf4072e
+Subproject 975d1bffb317f3b84bf1a3d576cdfdbf7b45861
diff --git a/src/python/doc/cubical_complex_tflow_itf_ref.rst b/src/python/doc/cubical_complex_tflow_itf_ref.rst
index 18b97adf..881a2950 100644
--- a/src/python/doc/cubical_complex_tflow_itf_ref.rst
+++ b/src/python/doc/cubical_complex_tflow_itf_ref.rst
@@ -19,7 +19,7 @@ Example of gradient computed from cubical persistence
     cl = CubicalLayer(dimensions=[0])
 
     with tf.GradientTape() as tape:
-        dgm = cl.call(X)[0][0]
+        dgm = cl.call(X)[0]
         loss = tf.math.reduce_sum(tf.square(.5*(dgm[:,1]-dgm[:,0])))
 
     grads = tape.gradient(loss, [X])
diff --git a/src/python/gudhi/tensorflow/cubical_layer.py b/src/python/gudhi/tensorflow/cubical_layer.py
index 369b0e54..31c44205 100644
--- a/src/python/gudhi/tensorflow/cubical_layer.py
+++ b/src/python/gudhi/tensorflow/cubical_layer.py
@@ -72,5 +72,5 @@ class CubicalLayer(tf.keras.layers.Layer):
             if min_pers >= 0:
                 finite_dgm = self.dgms[idx_dim]
                 persistent_indices = tf.where(tf.math.abs(finite_dgm[:,1]-finite_dgm[:,0]) > min_pers)
-                self.dgms[idx_dim] = tf.gather(finite_dgm, indices=persistent_indices)
+                self.dgms[idx_dim] = tf.reshape(tf.gather(finite_dgm, indices=persistent_indices), [-1,2])
         return self.dgms