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:orphan:
.. To get rid of WARNING: document isn't included in any toctree
TensorFlow layer for lower-star persistence on simplex trees
############################################################
.. include:: differentiation_sum.inc
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
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])
st.insert([3, 4])
st.insert([4, 5])
st.insert([5, 6])
st.insert([6, 7])
st.insert([7, 8])
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)
sl = LowerStarSimplexTreeLayer(simplextree=st, dimension=0)
with tf.GradientTape() as tape:
dgm = sl.call(F)
loss = tf.math.reduce_sum(tf.square(.5*(dgm[:,1]-dgm[:,0])))
grads = tape.gradient(loss, [F])
print(grads[0].indices.numpy())
print(grads[0].values.numpy())
.. testoutput::
[2 4]
[-1. 1.]
Documentation for LowerStarSimplexTreeLayer
-------------------------------------------
.. autoclass:: gudhi.tensorflow.LowerStarSimplexTreeLayer
:members:
:special-members: __init__
:show-inheritance:
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