def setup(self):
self.n_symm = np.asarray(self.symmetries).shape[0]
self.dense_symm = DenseSymmFFT(
space_group=self.symmetries,
shape=self.shape,
features=self.features[0],
dtype=self.dtype,
use_bias=self.use_bias,
kernel_init=self.kernel_init,
bias_init=self.bias_init,
precision=self.precision,
)
self.equivariant_layers = [
DenseEquivariantFFT(
product_table=self.product_table,
shape=self.shape,
features=self.features[layer + 1],
use_bias=self.use_bias,
dtype=self.dtype,
precision=self.precision,
kernel_init=self.kernel_init,
bias_init=self.bias_init,
)
for layer in range(self.layers - 1)
]
def setup(self):
# TODO: evenutally remove this warning
# supports a deprecated attribute
if self.extra_bias:
warn_deprecation(
(
"`extra_bias` is detrimental for performance and is deprecated. "
"Please switch to the default `extra_bias=False`. Previously saved "
"parameters can be migrated using `nk.models.update_GCNN_parity`."
)
)
self.n_symm = np.asarray(self.symmetries).shape[0]
self.dense_symm = DenseSymmFFT(
space_group=self.symmetries,
shape=self.shape,
features=self.features[0],
dtype=self.dtype,
use_bias=self.use_bias,
kernel_init=self.kernel_init,
bias_init=self.bias_init,
precision=self.precision,
)
self.equivariant_layers = [
DenseEquivariantFFT(
product_table=self.product_table,
shape=self.shape,
features=self.features[layer + 1],
use_bias=self.use_bias,
dtype=self.dtype,
precision=self.precision,
kernel_init=self.kernel_init,
bias_init=self.bias_init,
)
for layer in range(self.layers - 1)
]
self.equivariant_layers_flip = [
DenseEquivariantFFT(
product_table=self.product_table,
shape=self.shape,
features=self.features[layer + 1],
# this would bias the same outputs as self.equivariant
use_bias=self.extra_bias and self.use_bias,
dtype=self.dtype,
precision=self.precision,
kernel_init=self.kernel_init,
bias_init=self.bias_init,
)
for layer in range(self.layers - 1)
]