qiskit.aqua.components.neural_networks.NumPyDiscriminator

class NumPyDiscriminator(n_features=1, n_out=1)

Discriminator based on NumPy

Parameter

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

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

__init__(n_features=1, n_out=1)

Parameter

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

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

Methods

__init__([n_features, n_out])	type n_features int
get_label(x[, detach])	Get data sample labels, i.e. true or fake.
load_model(load_dir)	Load discriminator model
loss(x, y[, weights])	Loss function :param x: sample label (equivalent to discriminator output) :type x: numpy.ndarray :param y: target label :type y: numpy.ndarray :param weights: customized scaling for each sample (optional) :type weights: numpy.ndarray
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

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discriminator object

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DiscriminatorNet

get_label(x, detach=False)

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

Parameter

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

• detach (bool) – depreciated for numpy network

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Discriminator output, i.e. data label

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numpy.ndarray

load_model(load_dir)

Load discriminator model

Parameter

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

loss(x, y, weights=None)

Loss function :param x: sample label (equivalent to discriminator output) :type x: numpy.ndarray :param y: target label :type y: numpy.ndarray :param weights: customized scaling for each sample (optional) :type weights: numpy.ndarray

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loss function

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float

save_model(snapshot_dir)

Save discriminator model

Parameter

snapshot_dir (str) – directory path for saving the model

set_seed(seed)

Set seed. :param seed: seed :type seed: int

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

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

Parameter

• 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.

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with Discriminator loss and updated parameters.

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dict