# Source code for qiskit_machine_learning.algorithms.distribution_learners.qgan.generative_network

```
# This code is part of Qiskit.
#
# (C) Copyright IBM 2019, 2021.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
""" Generative Quantum and Classical Neural Networks."""
from typing import Optional, List, Tuple
from abc import ABC, abstractmethod
import numpy as np
from qiskit.utils import QuantumInstance
[docs]class GenerativeNetwork(ABC):
"""
Base class for generative Quantum and Classical Neural Networks.
This method should initialize the module, but
raise an exception if a required component of the module is not available.
"""
@abstractmethod
def __init__(self):
super().__init__()
self._num_parameters = 0
self._num_qubits = 0
self._bounds = []
@property
@abstractmethod
def parameter_values(self):
"""
Get parameter values from the generator
Raises:
NotImplementedError: not implemented
"""
raise NotImplementedError()
[docs] @abstractmethod
def get_output(
self,
quantum_instance: QuantumInstance,
params: Optional[np.ndarray] = None,
shots: Optional[int] = None,
) -> Tuple[List, List]:
"""
Apply quantum/classical neural network to given input and get the respective output
Args:
quantum_instance: Quantum Instance, used to run the generator circuit.
params: 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:
Neural network output
Raises:
NotImplementedError: not implemented
"""
raise NotImplementedError()
[docs] @abstractmethod
def loss(self):
"""
Loss function used for optimization
"""
raise NotImplementedError()
[docs] @abstractmethod
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 generator network.
Ignored for a classical network.
shots (int): Number of shots for hardware or qasm execution.
Ignored for classical network
Returns:
dict: generator loss and updated parameters.
Raises:
NotImplementedError: not implemented
"""
raise NotImplementedError()
```