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'][1]['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) elif 'condenser' in model.layers[0].name: new_model = DenseCondenser.from_config(layer_config) elif 'expander' in model.layers[0].name: new_model = DenseExpander.from_config(layer_config) elif 'entangler' in model.layers[0].name: new_model = DenseEntangler.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())
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_expander_num_parameters(dummy_data): # Disable the redefined-outer-name violation in this function # pylint: disable=redefined-outer-name data, _ = dummy_data exp_base = 2 num_nodes = 3 model = Sequential() model.add( DenseExpander(exp_base=exp_base, num_nodes=num_nodes, use_bias=True, activation='relu', input_shape=(data.shape[-1], ))) output_dim = data.shape[-1] * (exp_base**num_nodes) # num_params = (num_nodes * num_node_params) + num_bias_params expected_num_parameters = (num_nodes * data.shape[-1] * data.shape[-1] * exp_base) + output_dim np.testing.assert_equal(expected_num_parameters, model.count_params())