121 lines
3.8 KiB
Plaintext
121 lines
3.8 KiB
Plaintext
---
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title: NumPyDiscriminator
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description: API reference for qiskit.aqua.components.neural_networks.NumPyDiscriminator
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in_page_toc_min_heading_level: 1
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python_api_type: class
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python_api_name: qiskit.aqua.components.neural_networks.NumPyDiscriminator
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---
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# NumPyDiscriminator
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<Class id="qiskit.aqua.components.neural_networks.NumPyDiscriminator" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-aqua/tree/stable/0.9/qiskit/aqua/components/neural_networks/numpy_discriminator.py" signature="NumPyDiscriminator(n_features=1, n_out=1)" modifiers="class">
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Bases: `qiskit.aqua.components.neural_networks.discriminative_network.DiscriminativeNetwork`
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Discriminator based on NumPy
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**Parameters**
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* **n\_features** (`int`) – Dimension of input data vector.
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* **n\_out** (`int`) – Dimension of the discriminator’s output vector.
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## Methods
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### get\_label
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<Function id="qiskit.aqua.components.neural_networks.NumPyDiscriminator.get_label" signature="NumPyDiscriminator.get_label(x, detach=False)">
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Get data sample labels, i.e. true or fake.
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**Parameters**
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* **x** (*numpy.ndarray*) – Discriminator input, i.e. data sample.
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* **detach** (*bool*) – depreciated for numpy network
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**Returns**
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Discriminator output, i.e. data label
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**Return type**
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numpy.ndarray
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</Function>
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### load\_model
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<Function id="qiskit.aqua.components.neural_networks.NumPyDiscriminator.load_model" signature="NumPyDiscriminator.load_model(load_dir)">
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Load discriminator model
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**Parameters**
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**load\_dir** (*str*) – file with stored pytorch discriminator model to be loaded
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</Function>
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### loss
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<Function id="qiskit.aqua.components.neural_networks.NumPyDiscriminator.loss" signature="NumPyDiscriminator.loss(x, y, weights=None)">
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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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**Returns**
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loss function
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**Return type**
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float
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</Function>
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### save\_model
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<Function id="qiskit.aqua.components.neural_networks.NumPyDiscriminator.save_model" signature="NumPyDiscriminator.save_model(snapshot_dir)">
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Save discriminator model
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**Parameters**
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**snapshot\_dir** (*str*) – directory path for saving the model
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</Function>
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### set\_seed
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<Function id="qiskit.aqua.components.neural_networks.NumPyDiscriminator.set_seed" signature="NumPyDiscriminator.set_seed(seed)">
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Set seed. :param seed: seed :type seed: int
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</Function>
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### train
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<Function id="qiskit.aqua.components.neural_networks.NumPyDiscriminator.train" signature="NumPyDiscriminator.train(data, weights, penalty=False, quantum_instance=None, shots=None)">
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Perform one training step w\.r.t to the discriminator’s parameters
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**Parameters**
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* **data** (*tuple(numpy.ndarray, numpy.ndarray)*) – real\_batch: array, Training data batch. generated\_batch: array, Generated data batch.
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* **weights** (*tuple*) – real problem, generated problem
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* **penalty** (*bool*) – Depreciated for classical networks.
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* **quantum\_instance** ([*QuantumInstance*](qiskit.aqua.QuantumInstance "qiskit.aqua.QuantumInstance")) – Depreciated for classical networks.
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* **shots** (*int*) – Number of shots for hardware or qasm execution. Ignored for classical networks.
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**Returns**
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with Discriminator loss and updated parameters.
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**Return type**
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dict
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</Function>
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## Attributes
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### discriminator\_net
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<Attribute id="qiskit.aqua.components.neural_networks.NumPyDiscriminator.discriminator_net">
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Get discriminator
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**Returns**
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discriminator object
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**Return type**
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DiscriminatorNet
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</Attribute>
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</Class>
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