qiskit.aqua.components.neural_networks.PyTorchDiscriminator

class PyTorchDiscriminator(n_features=1, n_out=1)

Discriminator based on PyTorch

Parâmetros

• n_features (int) – Dimension of input data vector.

• n_out (int) – Dimension of the discriminator’s output vector.

Levanta

MissingOptionalLibraryError – Pytorch not installed

__init__(n_features=1, n_out=1)

Parâmetros

• n_features (int) – Dimension of input data vector.

• n_out (int) – Dimension of the discriminator’s output vector.

Levanta

MissingOptionalLibraryError – Pytorch not installed

Methods

__init__([n_features, n_out])	type n_features int
get_label(x[, detach])	Get data sample labels, i.e. true or fake.
gradient_penalty(x[, lambda_, k, c])	Compute gradient penalty for discriminator optimization
load_model(load_dir)	Load discriminator model
loss(x, y[, weights])	Loss function
save_model(snapshot_dir)	Save discriminator model
set_seed(seed)	Set seed.
train(data, weights[, penalty, …])	Perform one training step w.r.t to the discriminator’s parameters

Attributes

discriminator_net

Get discriminator

property discriminator_net

Get discriminator

Retorna

discriminator object

Tipo de retorno

object

get_label(x, detach=False)

Get data sample labels, i.e. true or fake.

Parâmetros

• x (Union(numpy.ndarray, torch.Tensor)) – Discriminator input, i.e. data sample.

• detach (bool) – if None detach from torch tensor variable (optional)

Retorna

Discriminator output, i.e. data label

Tipo de retorno

torch.Tensor

gradient_penalty(x, lambda_=5.0, k=0.01, c=1.0)

Compute gradient penalty for discriminator optimization

Parâmetros

• x (numpy.ndarray) – Generated data sample.

• lambda (float) – Gradient penalty coefficient 1.

• k (float) – Gradient penalty coefficient 2.

• c (float) – Gradient penalty coefficient 3.

Retorna

Gradient penalty.

Tipo de retorno

torch.Tensor

load_model(load_dir)

Load discriminator model

Parâmetros

load_dir (str) – file with stored pytorch discriminator model to be loaded

loss(x, y, weights=None)

Loss function

Parâmetros

• x (torch.Tensor) – Discriminator output.

• y (torch.Tensor) – Label of the data point

• weights (torch.Tensor) – Data weights.

Retorna

Loss w.r.t to the generated data points.

Tipo de retorno

torch.Tensor

save_model(snapshot_dir)

Save discriminator model

Parâmetros

snapshot_dir (str) – directory path for saving the model

set_seed(seed)

Set seed.

Parâmetros

seed (int) – seed

train(data, weights, penalty=False, quantum_instance=None, shots=None)

Perform one training step w.r.t to the discriminator’s parameters

Parâmetros

• data (Iterable) – Data batch.

• weights (Iterable) – 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 (Optional[int]) – Number of shots for hardware or qasm execution. Ignored for classical network

Retorna

with discriminator loss and updated parameters.data, weights, penalty=True,

quantum_instance=None, shots=None) -> Dict[str, Any]:

Tipo de retorno

dict