update doc and pieces of code

3 files changed, 3 insertions, 9 deletions

diff --git a/src/python/doc/cubical_complex_tflow_itf_ref.rst b/src/python/doc/cubical_complex_tflow_itf_ref.rst
index a907dfce..582e0551 100644
--- a/src/python/doc/cubical_complex_tflow_itf_ref.rst
+++ b/src/python/doc/cubical_complex_tflow_itf_ref.rst
@@ -13,11 +13,9 @@ Example of gradient computed from cubical persistence
 .. testcode::
 
     from gudhi.tensorflow import *
-    import numpy as np
     import tensorflow as tf
 
-    Xinit = np.array([[0.,2.,2.],[2.,2.,2.],[2.,2.,1.]], dtype=np.float32)
-    X = tf.Variable(initial_value=Xinit, trainable=True)
+    X = tf.Variable([[0.,2.,2.],[2.,2.,2.],[2.,2.,1.]], dtype=tf.float32, trainable=True)
     cl = CubicalLayer(dimension=0)
 
     with tf.GradientTape() as tape:
diff --git a/src/python/doc/ls_simplex_tree_tflow_itf_ref.rst b/src/python/doc/ls_simplex_tree_tflow_itf_ref.rst
index 26cf1ff2..6c8b5a08 100644
--- a/src/python/doc/ls_simplex_tree_tflow_itf_ref.rst
+++ b/src/python/doc/ls_simplex_tree_tflow_itf_ref.rst
@@ -13,7 +13,6 @@ Example of gradient computed from lower-star filtration of a simplex tree
 .. testcode::
 
     from gudhi.tensorflow import *
-    import numpy as np
     import tensorflow as tf
     import gudhi as gd
 
@@ -40,8 +39,7 @@ Example of gradient computed from lower-star filtration of a simplex tree
     st.insert([8, 9]) 
     st.insert([9, 10]) 
 
-    Finit = np.array([6.,4.,3.,4.,5.,4.,3.,2.,3.,4.,5.], dtype=np.float32)
-    F = tf.Variable(initial_value=Finit, trainable=True)
+    F = tf.Variable([6.,4.,3.,4.,5.,4.,3.,2.,3.,4.,5.], dtype=tf.float32, trainable=True)
     sl = LowerStarSimplexTreeLayer(simplextree=st, dimension=0)
 
     with tf.GradientTape() as tape:
diff --git a/src/python/doc/rips_complex_tflow_itf_ref.rst b/src/python/doc/rips_complex_tflow_itf_ref.rst
index 7300eba0..bd9c5da5 100644
--- a/src/python/doc/rips_complex_tflow_itf_ref.rst
+++ b/src/python/doc/rips_complex_tflow_itf_ref.rst
@@ -13,11 +13,9 @@ Example of gradient computed from Vietoris-Rips persistence
 .. testcode::
 
     from gudhi.tensorflow import *
-    import numpy as np
     import tensorflow as tf
 
-    Xinit = np.array([[1.,1.],[2.,2.]], dtype=np.float32)
-    X = tf.Variable(initial_value=Xinit, trainable=True)
+    X = tf.Variable([[1.,1.],[2.,2.]], dtype=tf.float32, trainable=True)
     rl = RipsLayer(maximum_edge_length=2., dimension=0)
 
     with tf.GradientTape() as tape: