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author | MathieuCarriere <mathieu.carriere3@gmail.com> | 2022-02-02 22:11:04 +0100 |
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committer | MathieuCarriere <mathieu.carriere3@gmail.com> | 2022-02-02 22:11:04 +0100 |
commit | 5c00d2dfcf4b0e2835441533f12f195d83652e99 (patch) | |
tree | 1b938487aee9c58cd5fbc2aadbe35896b5e47005 /src/python/doc | |
parent | c07e645abc27350351af73fa9b24b3d5f881033e (diff) |
fixed bugs from the new API
Diffstat (limited to 'src/python/doc')
-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 | 2 | ||||
-rw-r--r-- | src/python/doc/rips_complex_tflow_itf_ref.rst | 2 |
3 files changed, 3 insertions, 3 deletions
diff --git a/src/python/doc/cubical_complex_tflow_itf_ref.rst b/src/python/doc/cubical_complex_tflow_itf_ref.rst index 692191ba..18b97adf 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) + dgm = cl.call(X)[0][0] loss = tf.math.reduce_sum(tf.square(.5*(dgm[:,1]-dgm[:,0]))) grads = tape.gradient(loss, [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 3200b8e5..56bb4492 100644 --- a/src/python/doc/ls_simplex_tree_tflow_itf_ref.rst +++ b/src/python/doc/ls_simplex_tree_tflow_itf_ref.rst @@ -32,7 +32,7 @@ Example of gradient computed from lower-star filtration of a simplex tree sl = LowerStarSimplexTreeLayer(simplextree=st, dimensions=[0]) with tf.GradientTape() as tape: - dgm = sl.call(F) + dgm = sl.call(F)[0][0] loss = tf.math.reduce_sum(tf.square(.5*(dgm[:,1]-dgm[:,0]))) grads = tape.gradient(loss, [F]) diff --git a/src/python/doc/rips_complex_tflow_itf_ref.rst b/src/python/doc/rips_complex_tflow_itf_ref.rst index fc42e5c9..104b0971 100644 --- a/src/python/doc/rips_complex_tflow_itf_ref.rst +++ b/src/python/doc/rips_complex_tflow_itf_ref.rst @@ -19,7 +19,7 @@ Example of gradient computed from Vietoris-Rips persistence rl = RipsLayer(maximum_edge_length=2., dimensions=[0]) with tf.GradientTape() as tape: - dgm = rl.call(X) + dgm = rl.call(X)[0][0] loss = tf.math.reduce_sum(tf.square(.5*(dgm[:,1]-dgm[:,0]))) grads = tape.gradient(loss, [X]) |