def optimizer(self, optimizer: Optional[Optimizer] = None) -> None: """ Set optimizer. Args: optimizer (Optimizer): optimizer to use with the generator. Raises: QiskitMachineLearningError: invalid input. """ if optimizer: if isinstance(optimizer, Optimizer): self._optimizer = optimizer else: raise QiskitMachineLearningError( 'Please provide an Optimizer object to use' 'as the generator optimizer.') else: self._optimizer = ADAM(maxiter=1, tol=1e-6, lr=1e-3, beta_1=0.7, beta_2=0.99, noise_factor=1e-6, eps=1e-6, amsgrad=True, snapshot_dir=self._snapshot_dir)
def __init__( self, method: str, optimizer_kwargs: Optional[Dict] = None, recorder: RecorderFactory = _recorder, ): """ Args: method: specifies optimizer to be used. Currently supports "ADAM", "AMSGRAD" and "SPSA". optimizer_kwargs: dictionary with additional optimizer_kwargs for the optimizer. recorder: recorder object which defines how to store the optimization history. """ super().__init__(recorder=recorder) self.method = method if optimizer_kwargs is None: self.optimizer_kwargs = {} else: self.optimizer_kwargs = optimizer_kwargs if self.method == "SPSA": self.optimizer = SPSA(**self.optimizer_kwargs) elif self.method == "ADAM" or self.method == "AMSGRAD": if self.method == "AMSGRAD": self.optimizer_kwargs["amsgrad"] = True self.optimizer = ADAM(**self.optimizer_kwargs)
def test_adam(self): """Test ADAM is serializable.""" adam = ADAM(maxiter=100, amsgrad=True) settings = adam.settings self.assertEqual(settings["maxiter"], 100) self.assertTrue(settings["amsgrad"])
def __init__(self, n_features: int = 1, n_out: int = 1) -> None: """ Args: n_features: Dimension of input data vector. n_out: Dimension of the discriminator's output vector. """ super().__init__() self._n_features = n_features self._n_out = n_out self._discriminator = DiscriminatorNet(self._n_features, self._n_out) self._optimizer = ADAM(maxiter=1, tol=1e-6, lr=1e-3, beta_1=0.7, beta_2=0.99, noise_factor=1e-4, eps=1e-6, amsgrad=True) self._ret = {} # type: Dict[str, Any]
def test_adam(self): """ adam test """ optimizer = ADAM(maxiter=10000, tol=1e-06) res = self._optimize(optimizer) self.assertLessEqual(res[2], 10000)
class NumPyDiscriminator(DiscriminativeNetwork): """ Discriminator based on NumPy """ def __init__(self, n_features: int = 1, n_out: int = 1) -> None: """ Args: n_features: Dimension of input data vector. n_out: Dimension of the discriminator's output vector. """ super().__init__() self._n_features = n_features self._n_out = n_out self._discriminator = DiscriminatorNet(self._n_features, self._n_out) self._optimizer = ADAM(maxiter=1, tol=1e-6, lr=1e-3, beta_1=0.7, beta_2=0.99, noise_factor=1e-4, eps=1e-6, amsgrad=True) self._ret = {} # type: Dict[str, Any] def set_seed(self, seed): """ Set seed. Args: seed (int): seed """ algorithm_globals.random_seed = seed def save_model(self, snapshot_dir): """ Save discriminator model Args: snapshot_dir (str): directory path for saving the model """ # save self._discriminator.params_values np.save( os.path.join(snapshot_dir, 'np_discriminator_architecture.csv'), self._discriminator.architecture) np.save(os.path.join(snapshot_dir, 'np_discriminator_memory.csv'), self._discriminator.memory) np.save(os.path.join(snapshot_dir, 'np_discriminator_params.csv'), self._discriminator.parameters) self._optimizer.save_params(snapshot_dir) def load_model(self, load_dir): """ Load discriminator model Args: load_dir (str): file with stored pytorch discriminator model to be loaded """ self._discriminator.architecture = \ np.load(os.path.join(load_dir, 'np_discriminator_architecture.csv')) self._discriminator.memory = np.load( os.path.join(load_dir, 'np_discriminator_memory.csv')) self._discriminator.parameters = np.load( os.path.join(load_dir, 'np_discriminator_params.csv')) self._optimizer.load_params(load_dir) @property def discriminator_net(self): """ Get discriminator Returns: DiscriminatorNet: discriminator object """ return self._discriminator @discriminator_net.setter def discriminator_net(self, net): self._discriminator = net def get_label(self, x, detach=False): # pylint: disable=arguments-differ,unused-argument """ Get data sample labels, i.e. true or fake. Args: x (numpy.ndarray): Discriminator input, i.e. data sample. detach (bool): depreciated for numpy network Returns: numpy.ndarray: Discriminator output, i.e. data label """ return self._discriminator.forward(x) def loss(self, x, y, weights=None): """ Loss function Args: x (numpy.ndarray): sample label (equivalent to discriminator output) y (numpy.ndarray): target label weights(numpy.ndarray): customized scaling for each sample (optional) Returns: float: loss function """ if weights is not None: # Use weights as scaling factors for the samples and compute the sum return (-1) * np.dot( np.multiply( y, np.log(np.maximum(np.ones(np.shape(x)) * 1e-4, x))) + np.multiply( np.ones(np.shape(y)) - y, np.log( np.maximum( np.ones(np.shape(x)) * 1e-4, np.ones(np.shape(x)) - x))), weights) else: # Compute the mean return (-1) * np.mean( np.multiply( y, np.log(np.maximum(np.ones(np.shape(x)) * 1e-4, x))) + np.multiply( np.ones(np.shape(y)) - y, np.log( np.maximum( np.ones(np.shape(x)) * 1e-4, np.ones(np.shape(x)) - x)))) def _get_objective_function(self, data, weights): """ Get the objective function Args: data (tuple): training and generated data weights (numpy.ndarray): weights corresponding to training resp. generated data Returns: objective_function: objective function for the optimization """ real_batch = data[0] real_prob = weights[0] generated_batch = data[1] generated_prob = weights[1] def objective_function(params): self._discriminator.parameters = params # Train on Real Data prediction_real = self.get_label(real_batch) loss_real = self.loss(prediction_real, np.ones(np.shape(prediction_real)), real_prob) prediction_fake = self.get_label(generated_batch) loss_fake = self.loss(prediction_fake, np.zeros(np.shape(prediction_fake)), generated_prob) return 0.5 * (loss_real[0] + loss_fake[0]) return objective_function def _get_gradient_function(self, data, weights): """ Get the gradient function Args: data (tuple): training and generated data weights (numpy.ndarray): weights corresponding to training resp. generated data Returns: gradient_function: Gradient function for the optimization """ real_batch = data[0] real_prob = weights[0] generated_batch = data[1] generated_prob = weights[1] def gradient_function(params): self._discriminator.parameters = params prediction_real = self.get_label(real_batch) grad_real = self._discriminator.backward( prediction_real, np.ones(np.shape(prediction_real)), real_prob) prediction_generated = self.get_label(generated_batch) grad_generated = self._discriminator.backward( prediction_generated, np.zeros(np.shape(prediction_generated)), generated_prob) return np.add(grad_real, grad_generated) return gradient_function def train(self, data, weights, penalty=False, quantum_instance=None, shots=None) -> Dict[str, Any]: """ Perform one training step w.r.t to the discriminator's parameters Args: data (tuple(numpy.ndarray, numpy.ndarray)): real_batch: array, Training data batch. generated_batch: array, Generated data batch. weights (tuple):real problem, generated problem penalty (bool): Depreciated for classical networks. quantum_instance (QuantumInstance): Depreciated for classical networks. shots (int): Number of shots for hardware or qasm execution. Ignored for classical networks. Returns: dict: with Discriminator loss and updated parameters. """ # Train on Generated Data # Force single optimization iteration self._optimizer._maxiter = 1 self._optimizer._t = 0 objective = self._get_objective_function(data, weights) gradient = self._get_gradient_function(data, weights) self._discriminator.parameters, loss, _ = \ self._optimizer.optimize(num_vars=len(self._discriminator.parameters), objective_function=objective, initial_point=np.array(self._discriminator.parameters), gradient_function=gradient) self._ret['loss'] = loss self._ret['params'] = self._discriminator.parameters return self._ret
class QuantumGenerator(GenerativeNetwork): """Quantum Generator. The quantum generator is a parametrized quantum circuit which can be trained with the :class:`~qiskit_machine_learning.algorithms.QGAN` algorithm to generate a quantum state which approximates the probability distribution of given training data. At the beginning of the training the parameters will be set randomly, thus, the output will is random. Throughout the training the quantum generator learns to represent the target distribution. Eventually, the trained generator can be used for state preparation e.g. in QAE. """ def __init__(self, bounds: np.ndarray, num_qubits: Union[List[int], np.ndarray], generator_circuit: Optional[QuantumCircuit] = None, init_params: Optional[Union[List[float], np.ndarray]] = None, optimizer: Optional[Optimizer] = None, gradient_function: Optional[Union[Callable, Gradient]] = None, snapshot_dir: Optional[str] = None) -> None: """ Args: bounds: k min/max data values [[min_1,max_1],...,[min_k,max_k]], given input data dim k num_qubits: k numbers of qubits to determine representation resolution, i.e. n qubits enable the representation of 2**n values [n_1,..., n_k] generator_circuit: a QuantumCircuit implementing the generator. init_params: 1D numpy array or list, Initialization for the generator's parameters. optimizer: optimizer to be used for the training of the generator gradient_function: A Gradient object, or a function returning partial derivatives of the loss function w.r.t. the generator variational params. snapshot_dir: str or None, if not None save the optimizer's parameter after every update step to the given directory Raises: QiskitMachineLearningError: Set multivariate variational distribution to represent multivariate data """ super().__init__() self._bounds = bounds self._num_qubits = num_qubits self.generator_circuit = generator_circuit if generator_circuit is None: circuit = QuantumCircuit(sum(num_qubits)) circuit.h(circuit.qubits) var_form = TwoLocal(sum(num_qubits), 'ry', 'cz', reps=1, entanglement='circular') circuit.compose(var_form, inplace=True) # Set generator circuit self.generator_circuit = circuit self._free_parameters = sorted(self.generator_circuit.parameters, key=lambda p: p.name) if init_params is None: init_params = \ algorithm_globals.random.random(self.generator_circuit.num_parameters) * 2e-2 self._bound_parameters = init_params # Set optimizer for updating the generator network self._snapshot_dir = snapshot_dir self.optimizer = optimizer self._gradient_function = gradient_function if np.ndim(self._bounds) == 1: bounds = np.reshape(self._bounds, (1, len(self._bounds))) else: bounds = self._bounds for j, prec in enumerate(self._num_qubits): # prepare data grid for dim j grid = np.linspace(bounds[j, 0], bounds[j, 1], (2**prec)) if j == 0: if len(self._num_qubits) > 1: self._data_grid = [grid] else: self._data_grid = grid # type: ignore self._grid_elements = grid elif j == 1: self._data_grid.append(grid) temp = [] for g_e in self._grid_elements: for g in grid: temp0 = [g_e] temp0.append(g) temp.append(temp0) self._grid_elements = temp # type: ignore else: self._data_grid.append(grid) temp = [] for g_e in self._grid_elements: for g in grid: temp0 = deepcopy(g_e) temp0.append(g) temp.append(temp0) self._grid_elements = deepcopy(temp) # type: ignore self._data_grid = np.array(self._data_grid, dtype=object) # type: ignore self._seed = 7 self._shots = None self._discriminator: Optional[DiscriminativeNetwork] = None self._ret: Dict[str, Any] = {} @property def parameter_values(self) -> Union[List, np.ndarray]: """ Get parameter values from the quantum generator Returns: Current parameter values """ return self._bound_parameters @parameter_values.setter def parameter_values(self, p_values: Union[List, np.ndarray]) -> None: """ Set parameter values for the quantum generator Args: p_values: Parameter values """ self._bound_parameters = p_values @property def seed(self) -> int: """ Get seed. """ return self._seed @seed.setter def seed(self, seed: int) -> None: """ Set seed. Args: seed (int): seed to use. """ self._seed = seed algorithm_globals.random_seed = seed @property def discriminator(self) -> DiscriminativeNetwork: """ Get discriminator. """ return self._discriminator @discriminator.setter def discriminator(self, discriminator: DiscriminativeNetwork) -> None: """ Set discriminator. Args: discriminator (DiscriminativeNetwork): Discriminator used to compute the loss function. """ self._discriminator = discriminator @property def optimizer(self) -> Optimizer: """ Get optimizer. """ return self._optimizer @optimizer.setter def optimizer(self, optimizer: Optional[Optimizer] = None) -> None: """ Set optimizer. Args: optimizer (Optimizer): optimizer to use with the generator. Raises: QiskitMachineLearningError: invalid input. """ if optimizer: if isinstance(optimizer, Optimizer): self._optimizer = optimizer else: raise QiskitMachineLearningError( 'Please provide an Optimizer object to use' 'as the generator optimizer.') else: self._optimizer = ADAM(maxiter=1, tol=1e-6, lr=1e-3, beta_1=0.7, beta_2=0.99, noise_factor=1e-6, eps=1e-6, amsgrad=True, snapshot_dir=self._snapshot_dir) def construct_circuit(self, params=None): """ Construct generator circuit. Args: params (list | dict): parameters which should be used to run the generator. Returns: Instruction: construct the quantum circuit and return as gate """ if params is None: return self.generator_circuit if isinstance(params, (list, np.ndarray)): params = dict(zip(self._free_parameters, params)) return self.generator_circuit.assign_parameters(params) # self.generator_circuit.build(qc=qc, q=q) # else: # generator_circuit_copy = deepcopy(self.generator_circuit) # generator_circuit_copy.params = params # generator_circuit_copy.build(qc=qc, q=q) # # return qc.copy(name='qc') # return qc.to_instruction() def get_output(self, quantum_instance: QuantumInstance, params: Optional[np.ndarray] = None, shots: Optional[int] = None) -> Tuple[List, List]: """ Get classical data samples from the generator. Running the quantum generator circuit results in a quantum state. To train this generator with a classical discriminator, we need to sample classical outputs by measuring the quantum state and mapping them to feature space defined by the training data. Args: quantum_instance: Quantum Instance, used to run the generator circuit. params: array or None, parameters which should be used to run the generator, if None use self._params shots: if not None use a number of shots that is different from the number set in quantum_instance Returns: generated samples, array: sample occurrence in percentage """ instance_shots = quantum_instance.run_config.shots q = QuantumRegister(sum(self._num_qubits), name='q') qc = QuantumCircuit(q) if params is None: params = cast(np.ndarray, self._bound_parameters) qc.append(self.construct_circuit(params), q) if quantum_instance.is_statevector: pass else: c = ClassicalRegister(sum(self._num_qubits), name='c') qc.add_register(c) qc.measure(q, c) if shots is not None: quantum_instance.set_config(shots=shots) result = quantum_instance.execute(qc) generated_samples = [] if quantum_instance.is_statevector: result = result.get_statevector(qc) values = np.multiply(result, np.conj(result)) values = list(values.real) keys = [] for j in range(len(values)): keys.append(np.binary_repr(j, int(sum(self._num_qubits)))) else: result = result.get_counts(qc) keys = list(result) values = list(result.values()) values = [float(v) / np.sum(values) for v in values] generated_samples_weights = values for i, _ in enumerate(keys): index = 0 temp = [] for k, p in enumerate(self._num_qubits): bin_rep = 0 j = 0 while j < p: bin_rep += int(keys[i][index]) * 2**(int(p) - j - 1) j += 1 index += 1 if len(self._num_qubits) > 1: temp.append(self._data_grid[k][int(bin_rep)]) else: temp.append(self._data_grid[int(bin_rep)]) generated_samples.append(temp) # self.generator_circuit._probabilities = generated_samples_weights if shots is not None: # Restore the initial quantum_instance configuration quantum_instance.set_config(shots=instance_shots) return generated_samples, generated_samples_weights def loss(self, x, weights): # pylint: disable=arguments-differ """ Loss function for training the generator's parameters. Args: x (numpy.ndarray): sample label (equivalent to discriminator output) weights (numpy.ndarray): probability for measuring the sample Returns: float: loss function """ try: # pylint: disable=no-member loss = (-1) * np.dot(np.log(x).transpose(), weights) except Exception: # pylint: disable=broad-except loss = (-1) * np.dot(np.log(x), weights) return loss.flatten() def _get_objective_function(self, quantum_instance, discriminator): """ Get objective function Args: quantum_instance (QuantumInstance): used to run the quantum circuit. discriminator (torch.nn.Module): discriminator network to compute the sample labels. Returns: objective_function: objective function for quantum generator optimization """ def objective_function(params): """ Objective function Args: params (numpy.ndarray): generator parameters Returns: self.loss: loss function """ generated_data, generated_prob = self.get_output(quantum_instance, params=params, shots=self._shots) prediction_generated = discriminator.get_label(generated_data, detach=True) return self.loss(prediction_generated, generated_prob) return objective_function def _convert_to_gradient_function(self, gradient_object, quantum_instance, discriminator): """ Convert to gradient function Args: gradient_object (Gradient): the gradient object to be used to compute analytical gradients. quantum_instance (QuantumInstance): used to run the quantum circuit. discriminator (torch.nn.Module): discriminator network to compute the sample labels. Returns: gradient_function: gradient function that takes the current parameter values and returns partial derivatives of the loss function w.r.t. the variational parameters. """ def gradient_function(current_point): """ Gradient function Args: current_point (np.ndarray): Current values for the variational parameters. Returns: np.ndarray: array of partial derivatives of the loss function w.r.t. the variational parameters. """ free_params = self._free_parameters generated_data, _ = self.get_output(quantum_instance, params=current_point, shots=self._shots) prediction_generated = discriminator.get_label(generated_data, detach=True) op = ~CircuitStateFn(primitive=self.generator_circuit) grad_object = gradient_object.convert(operator=op, params=free_params) value_dict = { free_params[i]: current_point[i] for i in range(len(free_params)) } analytical_gradients = np.array( grad_object.assign_parameters(value_dict).eval()) loss_gradients = self.loss(prediction_generated, analytical_gradients).real return loss_gradients return gradient_function def train(self, quantum_instance=None, shots=None): """ Perform one training step w.r.t to the generator's parameters Args: quantum_instance (QuantumInstance): used to run the generator circuit. shots (int): Number of shots for hardware or qasm execution. Returns: dict: generator loss(float) and updated parameters (array). """ self._shots = shots # TODO Improve access to maxiter, say via options getter, to avoid private member access # and since not all optimizers have that exact naming figure something better as well to # allow the checking below to not have to warn if it has something else and max iterations # is truly 1 anyway. try: if self._optimizer._maxiter != 1: warnings.warn( 'Please set the the optimizer maxiter argument to 1 ' 'to ensure that the generator ' 'and discriminator are updated in an alternating fashion.') except AttributeError: maxiter = self._optimizer._options.get('maxiter') if maxiter is not None and maxiter != 1: warnings.warn( 'Please set the the optimizer maxiter argument to 1 ' 'to ensure that the generator ' 'and discriminator are updated in an alternating fashion.') elif maxiter is None: warnings.warn( 'Please ensure the optimizer max iterations are set to 1 ' 'to ensure that the generator ' 'and discriminator are updated in an alternating fashion.') if isinstance(self._gradient_function, Gradient): self._gradient_function = self._convert_to_gradient_function( self._gradient_function, quantum_instance, self._discriminator) objective = self._get_objective_function(quantum_instance, self._discriminator) self._bound_parameters, loss, _ = self._optimizer.optimize( num_vars=len(self._bound_parameters), objective_function=objective, initial_point=self._bound_parameters, gradient_function=self._gradient_function) self._ret['loss'] = loss self._ret['params'] = self._bound_parameters return self._ret
class QiskitOptimizer(Optimizer): def __init__( self, method: str, optimizer_kwargs: Optional[Dict] = None, recorder: RecorderFactory = _recorder, ): """ Args: method: specifies optimizer to be used. Currently supports "ADAM", "AMSGRAD" and "SPSA". optimizer_kwargs: dictionary with additional optimizer_kwargs for the optimizer. recorder: recorder object which defines how to store the optimization history. """ super().__init__(recorder=recorder) self.method = method if optimizer_kwargs is None: self.optimizer_kwargs = {} else: self.optimizer_kwargs = optimizer_kwargs if self.method == "SPSA": self.optimizer = SPSA(**self.optimizer_kwargs) elif self.method == "ADAM" or self.method == "AMSGRAD": if self.method == "AMSGRAD": self.optimizer_kwargs["amsgrad"] = True self.optimizer = ADAM(**self.optimizer_kwargs) def _minimize( self, cost_function: CallableWithGradient, initial_params: np.ndarray = None, keep_history: bool = False, ): """ Minimizes given cost function using optimizers from Qiskit Aqua. Args: cost_function: python method which takes numpy.ndarray as input initial_params(np.ndarray): initial parameters to be used for optimization Returns: optimization_results(scipy.optimize.OptimizeResults): results of the optimization. """ history = [] number_of_variables = len(initial_params) gradient_function = None if hasattr(cost_function, "gradient") and callable( getattr(cost_function, "gradient") ): gradient_function = cost_function.gradient solution, value, nfev = self.optimizer.optimize( num_vars=number_of_variables, objective_function=cost_function, initial_point=initial_params, gradient_function=gradient_function, ) if self.method == "ADAM" or self.method == "AMSGRAD": nit = self.optimizer._t else: nit = self.optimizer.maxiter return optimization_result( opt_value=value, opt_params=solution, nit=nit, nfev=nfev, **construct_history_info(cost_function, keep_history) )
def test_adam(self): """adam test""" optimizer = ADAM(maxiter=10000, tol=1e-06) self.run_optimizer(optimizer, max_nfev=10000)