def test_shape_sanity_check(in_dim_base, dim1, dim2, num_nodes, bond_dim): model = Sequential([ Input(in_dim_base**num_nodes), DenseMPO(dim1**num_nodes, num_nodes=num_nodes, bond_dim=bond_dim), DenseMPO(dim2**num_nodes, num_nodes=num_nodes, bond_dim=bond_dim), ]) # Hard code batch size. result = model.predict(np.ones((32, in_dim_base**num_nodes))) assert result.shape == (32, dim2**num_nodes)
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 test_mpo_num_parameters(dummy_data): # Disable the redefined-outer-name violation in this function # pylint: disable=redefined-outer-name data, _ = dummy_data output_dim = data.shape[1] num_nodes = int(math.log(data.shape[1], 8)) bond_dim = 8 model = Sequential() model.add( DenseMPO(output_dim, num_nodes=num_nodes, bond_dim=bond_dim, use_bias=True, activation='relu', input_shape=(data.shape[1], ))) in_leg_dim = math.ceil(data.shape[1]**(1. / num_nodes)) out_leg_dim = math.ceil(output_dim**(1. / num_nodes)) # num_params = num_edge_node_params + num_middle_node_params + bias_params expected_num_parameters = (2 * in_leg_dim * bond_dim * out_leg_dim) + ( (num_nodes - 2) * in_leg_dim * bond_dim * bond_dim * out_leg_dim) + output_dim np.testing.assert_equal(expected_num_parameters, 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