diff options
| -rw-r--r-- | src/python/doc/cubical_complex_tflow_itf_ref.rst | 2 | ||||
| -rw-r--r-- | src/python/doc/ls_simplex_tree_tflow_itf_ref.rst | 13 | ||||
| -rw-r--r-- | src/python/doc/rips_complex_tflow_itf_ref.rst | 2 | ||||
| -rw-r--r-- | src/python/gudhi/tensorflow/cubical_layer.py | 2 | ||||
| -rw-r--r-- | src/python/gudhi/tensorflow/lower_star_simplex_tree_layer.py | 2 | ||||
| -rw-r--r-- | src/python/gudhi/tensorflow/rips_layer.py | 2 |
6 files changed, 6 insertions, 17 deletions
diff --git a/src/python/doc/cubical_complex_tflow_itf_ref.rst b/src/python/doc/cubical_complex_tflow_itf_ref.rst index 92727a75..692191ba 100644 --- a/src/python/doc/cubical_complex_tflow_itf_ref.rst +++ b/src/python/doc/cubical_complex_tflow_itf_ref.rst @@ -16,7 +16,7 @@ Example of gradient computed from cubical persistence import tensorflow as tf X = tf.Variable([[0.,2.,2.],[2.,2.,2.],[2.,2.,1.]], dtype=tf.float32, trainable=True) - cl = CubicalLayer(dimension=0) + cl = CubicalLayer(dimensions=[0]) with tf.GradientTape() as tape: dgm = cl.call(X) 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 0a6764fa..3200b8e5 100644 --- a/src/python/doc/ls_simplex_tree_tflow_itf_ref.rst +++ b/src/python/doc/ls_simplex_tree_tflow_itf_ref.rst @@ -17,17 +17,6 @@ Example of gradient computed from lower-star filtration of a simplex tree import gudhi as gd st = gd.SimplexTree() - st.insert([0]) - st.insert([1]) - st.insert([2]) - st.insert([3]) - st.insert([4]) - st.insert([5]) - st.insert([6]) - st.insert([7]) - st.insert([8]) - st.insert([9]) - st.insert([10]) st.insert([0, 1]) st.insert([1, 2]) st.insert([2, 3]) @@ -40,7 +29,7 @@ Example of gradient computed from lower-star filtration of a simplex tree st.insert([9, 10]) 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) + sl = LowerStarSimplexTreeLayer(simplextree=st, dimensions=[0]) with tf.GradientTape() as tape: dgm = sl.call(F) diff --git a/src/python/doc/rips_complex_tflow_itf_ref.rst b/src/python/doc/rips_complex_tflow_itf_ref.rst index 7aa77da6..fc42e5c9 100644 --- a/src/python/doc/rips_complex_tflow_itf_ref.rst +++ b/src/python/doc/rips_complex_tflow_itf_ref.rst @@ -16,7 +16,7 @@ Example of gradient computed from Vietoris-Rips persistence import tensorflow as tf X = tf.Variable([[1.,1.],[2.,2.]], dtype=tf.float32, trainable=True) - rl = RipsLayer(maximum_edge_length=2., dimension=0) + rl = RipsLayer(maximum_edge_length=2., dimensions=[0]) with tf.GradientTape() as tape: dgm = rl.call(X) diff --git a/src/python/gudhi/tensorflow/cubical_layer.py b/src/python/gudhi/tensorflow/cubical_layer.py index 0971a446..d07a4cd8 100644 --- a/src/python/gudhi/tensorflow/cubical_layer.py +++ b/src/python/gudhi/tensorflow/cubical_layer.py @@ -42,7 +42,7 @@ class CubicalLayer(tf.keras.layers.Layer): Constructor for the CubicalLayer class Parameters: - dimensions (list of int): homology dimensions + dimensions (List[int]): homology dimensions """ super().__init__(dynamic=True, **kwargs) self.dimensions = dimensions diff --git a/src/python/gudhi/tensorflow/lower_star_simplex_tree_layer.py b/src/python/gudhi/tensorflow/lower_star_simplex_tree_layer.py index fd1698ea..aa55604a 100644 --- a/src/python/gudhi/tensorflow/lower_star_simplex_tree_layer.py +++ b/src/python/gudhi/tensorflow/lower_star_simplex_tree_layer.py @@ -49,7 +49,7 @@ class LowerStarSimplexTreeLayer(tf.keras.layers.Layer): Parameters: simplextree (gudhi.SimplexTree): underlying simplex tree. Its vertices MUST be named with integers from 0 to n = number of vertices - dimensions (int): homology dimensions + dimensions (List[int]): homology dimensions """ super().__init__(dynamic=True, **kwargs) self.dimensions = dimensions diff --git a/src/python/gudhi/tensorflow/rips_layer.py b/src/python/gudhi/tensorflow/rips_layer.py index da7087f6..472a418b 100644 --- a/src/python/gudhi/tensorflow/rips_layer.py +++ b/src/python/gudhi/tensorflow/rips_layer.py @@ -46,7 +46,7 @@ class RipsLayer(tf.keras.layers.Layer): Parameters: maximum_edge_length (float): maximum edge length for the Rips complex - dimensions (int): homology dimensions + dimensions (List[int]): homology dimensions """ super().__init__(dynamic=True, **kwargs) self.max_edge = maximum_edge_length |