diff options
Diffstat (limited to 'src/python/gudhi/tensorflow/cubical_layer.py')
| -rw-r--r-- | src/python/gudhi/tensorflow/cubical_layer.py | 10 |
1 files changed, 6 insertions, 4 deletions
diff --git a/src/python/gudhi/tensorflow/cubical_layer.py b/src/python/gudhi/tensorflow/cubical_layer.py index 918ff43e..16dc7d35 100644 --- a/src/python/gudhi/tensorflow/cubical_layer.py +++ b/src/python/gudhi/tensorflow/cubical_layer.py @@ -8,7 +8,7 @@ from ..cubical_complex import CubicalComplex # The parameters of the model are the pixel values. -def _Cubical(Xflat, Xdim, dimensions): +def _Cubical(Xflat, Xdim, dimensions, homology_coeff_field=11): # Parameters: Xflat (flattened image), # Xdim (shape of non-flattened image) # dimensions (homology dimensions) @@ -16,7 +16,7 @@ def _Cubical(Xflat, Xdim, dimensions): # Compute the persistence pairs with Gudhi # We reverse the dimensions because CubicalComplex uses Fortran ordering cc = CubicalComplex(dimensions=Xdim[::-1], top_dimensional_cells=Xflat) - cc.compute_persistence() + cc.compute_persistence(homology_coeff_field=homology_coeff_field) # Retrieve and ouput image indices/pixels corresponding to positive and negative simplices cof_pp = cc.cofaces_of_persistence_pairs() @@ -37,17 +37,19 @@ class CubicalLayer(tf.keras.layers.Layer): """ TensorFlow layer for computing the persistent homology of a cubical complex """ - def __init__(self, dimensions, min_persistence=None, **kwargs): + def __init__(self, dimensions, min_persistence=None, homology_coeff_field=11, **kwargs): """ Constructor for the CubicalLayer class Parameters: dimensions (List[int]): homology dimensions min_persistence (List[float]): minimum distance-to-diagonal of the points in the output persistence diagrams (default None, in which case 0. is used for all dimensions) + homology_coeff_field (int): homology field coefficient. Must be a prime number. Default value is 11. Max is 46337. """ super().__init__(dynamic=True, **kwargs) self.dimensions = dimensions self.min_persistence = min_persistence if min_persistence != None else [0.] * len(self.dimensions) + self.hcf = homology_coeff_field assert len(self.min_persistence) == len(self.dimensions) def call(self, X): @@ -64,7 +66,7 @@ class CubicalLayer(tf.keras.layers.Layer): # Don't compute gradient for this operation Xflat = tf.reshape(X, [-1]) Xdim, Xflat_numpy = X.shape, Xflat.numpy() - indices_list = _Cubical(Xflat_numpy, Xdim, self.dimensions) + indices_list = _Cubical(Xflat_numpy, Xdim, self.dimensions, self.hcf) index_essential = np.argmin(Xflat_numpy) # index of minimum pixel value for essential persistence diagram # Get persistence diagram by simply picking the corresponding entries in the image self.dgms = [] |