qiskit.aqua.components.neural_networks.DiscriminativeNetwork
class DiscriminativeNetwork
Base class for discriminative Quantum or Classical Neural Networks.
This method should initialize the module but raise an exception if a required component of the module is not available.
__init__
abstract __init__()
Initialize self. See help(type(self)) for accurate signature.
Methods
__init__() | Initialize self. |
get_label(x) | Apply quantum/classical neural network to the given input sample and compute the respective data label |
loss(x, y[, weights]) | Loss function used for optimization |
set_seed(seed) | Set seed. |
train(data, weights[, penalty, …]) | Perform one training step w.r.t to the discriminator’s parameters |
get_label
abstract get_label(x)
Apply quantum/classical neural network to the given input sample and compute the respective data label
Parameters
x (Discriminator) – input, i.e. data sample.
Raises
NotImplementedError – not implemented
loss
abstract loss(x, y, weights=None)
Loss function used for optimization
Parameters
- x (Discriminator) – output.
- y (Label) – the data point
- weights (numpy.ndarray) – Data weights.
Returns
Loss w.r.t to the generated data points.
Raises
NotImplementedError – not implemented
set_seed
abstract set_seed(seed)
Set seed.
Parameters
seed (int) – seed
Raises
NotImplementedError – not implemented
train
abstract train(data, weights, penalty=False, quantum_instance=None, shots=None)
Perform one training step w.r.t to the discriminator’s parameters
Parameters
- data (numpy.ndarray) – Data batch.
- weights (numpy.ndarray) – Data sample weights.
- penalty (bool) – Indicate whether or not penalty function is applied to the loss function. Ignored if no penalty function defined.
- quantum_instance (QuantumInstance) – used to run Quantum network. Ignored for a classical network.
- shots (int) – Number of shots for hardware or qasm execution. Ignored for classical network
Returns
with Discriminator loss and updated parameters.
Return type
dict
Raises
NotImplementedError – not implemented