def build_hparams(cell_name='amoeba_net_d'):
"""Build tf.Hparams for training Amoeba Net.
Args:
cell_name: Which of the cells in model_specs.py to use to build the
amoebanet neural network; the cell names defined in that
module correspond to architectures discovered by an
evolutionary search described in
https://arxiv.org/abs/1802.01548.
Returns:
A set of tf.HParams suitable for Amoeba Net training.
"""
hparams = imagenet_hparams()
operations, hiddenstate_indices, used_hiddenstates = (
model_specs.get_normal_cell(cell_name))
hparams.add_hparam('normal_cell_operations', operations)
hparams.add_hparam('normal_cell_hiddenstate_indices',
hiddenstate_indices)
hparams.add_hparam('normal_cell_used_hiddenstates',
used_hiddenstates)
operations, hiddenstate_indices, used_hiddenstates = (
model_specs.get_reduction_cell(cell_name))
hparams.add_hparam('reduction_cell_operations',
operations)
hparams.add_hparam('reduction_cell_hiddenstate_indices',
hiddenstate_indices)
hparams.add_hparam('reduction_cell_used_hiddenstates',
used_hiddenstates)
hparams.set_hparam('data_format', 'NHWC')
return hparams
def build_hparams():
"""Build tf.Hparams for training Amoeba Net."""
hparams = model_lib.imagenet_hparams()
hparams.add_hparam('reduction_size', FLAGS.reduction_size)
operations, hiddenstate_indices, used_hiddenstates = (
model_specs.get_normal_cell(FLAGS.cell_name))
hparams.add_hparam('normal_cell_operations', operations)
hparams.add_hparam('normal_cell_hiddenstate_indices',
hiddenstate_indices)
hparams.add_hparam('normal_cell_used_hiddenstates',
used_hiddenstates)
operations, hiddenstate_indices, used_hiddenstates = (
model_specs.get_reduction_cell(FLAGS.cell_name))
hparams.add_hparam('reduction_cell_operations',
operations)
hparams.add_hparam('reduction_cell_hiddenstate_indices',
hiddenstate_indices)
hparams.add_hparam('reduction_cell_used_hiddenstates',
used_hiddenstates)
hparams.add_hparam('stem_reduction_size', FLAGS.stem_reduction_size)
hparams.set_hparam('data_format', 'NHWC')
override_with_flags(hparams)
return hparams
