def make_high_dim_model(high_dim_data, request):
# Disable the redefined-outer-name violation in this function
# pylint: disable=redefined-outer-name
data = high_dim_data
if request.param == 'DenseMPO':
model = Sequential()
model.add(
DenseMPO(data.shape[-1],
num_nodes=int(math.log(int(data.shape[-1]), 8)),
bond_dim=8,
use_bias=True,
activation='relu',
input_shape=(data.shape[-1], )))
elif request.param == 'DenseDecomp':
model = Sequential()
model.add(
DenseDecomp(512,
decomp_size=128,
use_bias=True,
activation='relu',
input_shape=(data.shape[-1], )))
elif request.param == 'DenseCondenser':
model = Sequential()
model.add(
DenseCondenser(exp_base=2,
num_nodes=3,
use_bias=True,
activation='relu',
input_shape=(data.shape[-1], )))
elif request.param == 'DenseExpander':
model = Sequential()
model.add(
DenseExpander(exp_base=2,
num_nodes=3,
use_bias=True,
activation='relu',
input_shape=(data.shape[-1], )))
elif request.param == 'DenseEntangler':
num_legs = 3
leg_dim = round(data.shape[-1]**(1. / num_legs))
assert leg_dim**num_legs == data.shape[-1]
model = Sequential()
model.add(
DenseEntangler(leg_dim**num_legs,
num_legs=num_legs,
num_levels=3,
use_bias=True,
activation='relu',
input_shape=(data.shape[-1], )))
return data, model
def test_decomp_higher_dim_input_output_shape(high_dim_data):
# pylint: disable=redefined-outer-name
data = high_dim_data
output_dim = 256
decomp_size = 128
model = Sequential()
model.add(
DenseDecomp(output_dim,
decomp_size=decomp_size,
use_bias=True,
activation='relu',
input_shape=(data.shape[-1], )))
actual_output_shape = model(data).shape
expected_output_shape = model.compute_output_shape(data.shape)
np.testing.assert_equal(expected_output_shape, actual_output_shape)
def test_decomp_num_parameters(dummy_data):
# Disable the redefined-outer-name violation in this function
# pylint: disable=redefined-outer-name
data, _ = dummy_data
output_dim = 256
decomp_size = 128
model = Sequential()
model.add(
DenseDecomp(output_dim,
decomp_size=decomp_size,
use_bias=True,
activation='relu',
input_shape=(data.shape[1],)))
# num_params = a_params + b_params + bias_params
expected_num_parameters = (data.shape[1] * decomp_size) + (
decomp_size * output_dim) + output_dim
np.testing.assert_equal(expected_num_parameters, model.count_params())
def test_config(make_model):
# Disable the redefined-outer-name violation in this function
# pylint: disable=redefined-outer-name
model = make_model
expected_num_parameters = model.layers[0].count_params()
# Serialize model and use config to create new layer
model_config = model.get_config()
layer_config = model_config['layers'][0]['config']
if 'mpo' in model.layers[0].name:
new_model = DenseMPO.from_config(layer_config)
elif 'decomp' in model.layers[0].name:
new_model = DenseDecomp.from_config(layer_config)
# Build the layer so we can count params below
new_model.build(layer_config['batch_input_shape'])
# Check that original layer had same num params as layer built from config
np.testing.assert_equal(expected_num_parameters, new_model.count_params())