184 lines
8.3 KiB
Plaintext
184 lines
8.3 KiB
Plaintext
---
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title: PyTorchDiscriminator
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description: API reference for qiskit.aqua.components.neural_networks.PyTorchDiscriminator
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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.PyTorchDiscriminator
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---
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# qiskit.aqua.components.neural\_networks.PyTorchDiscriminator
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<Class id="qiskit.aqua.components.neural_networks.PyTorchDiscriminator" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-aqua/tree/stable/0.9/qiskit/aqua/components/neural_networks/pytorch_discriminator.py" signature="PyTorchDiscriminator(n_features=1, n_out=1)" modifiers="class">
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Discriminator based on PyTorch
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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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**Raises**
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[**MissingOptionalLibraryError**](qiskit.aqua.MissingOptionalLibraryError "qiskit.aqua.MissingOptionalLibraryError") – Pytorch not installed
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### \_\_init\_\_
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<Function id="qiskit.aqua.components.neural_networks.PyTorchDiscriminator.__init__" signature="__init__(n_features=1, n_out=1)">
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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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**Raises**
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[**MissingOptionalLibraryError**](qiskit.aqua.MissingOptionalLibraryError "qiskit.aqua.MissingOptionalLibraryError") – Pytorch not installed
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</Function>
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## Methods
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| -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------ |
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| [`__init__`](#qiskit.aqua.components.neural_networks.PyTorchDiscriminator.__init__ "qiskit.aqua.components.neural_networks.PyTorchDiscriminator.__init__")(\[n\_features, n\_out]) | **type n\_features**`int` |
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| [`get_label`](#qiskit.aqua.components.neural_networks.PyTorchDiscriminator.get_label "qiskit.aqua.components.neural_networks.PyTorchDiscriminator.get_label")(x\[, detach]) | Get data sample labels, i.e. true or fake. |
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| [`gradient_penalty`](#qiskit.aqua.components.neural_networks.PyTorchDiscriminator.gradient_penalty "qiskit.aqua.components.neural_networks.PyTorchDiscriminator.gradient_penalty")(x\[, lambda\_, k, c]) | Compute gradient penalty for discriminator optimization |
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| [`load_model`](#qiskit.aqua.components.neural_networks.PyTorchDiscriminator.load_model "qiskit.aqua.components.neural_networks.PyTorchDiscriminator.load_model")(load\_dir) | Load discriminator model |
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| [`loss`](#qiskit.aqua.components.neural_networks.PyTorchDiscriminator.loss "qiskit.aqua.components.neural_networks.PyTorchDiscriminator.loss")(x, y\[, weights]) | Loss function |
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| [`save_model`](#qiskit.aqua.components.neural_networks.PyTorchDiscriminator.save_model "qiskit.aqua.components.neural_networks.PyTorchDiscriminator.save_model")(snapshot\_dir) | Save discriminator model |
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| [`set_seed`](#qiskit.aqua.components.neural_networks.PyTorchDiscriminator.set_seed "qiskit.aqua.components.neural_networks.PyTorchDiscriminator.set_seed")(seed) | Set seed. |
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| [`train`](#qiskit.aqua.components.neural_networks.PyTorchDiscriminator.train "qiskit.aqua.components.neural_networks.PyTorchDiscriminator.train")(data, weights\[, penalty, …]) | Perform one training step w\.r.t to the discriminator’s parameters |
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## Attributes
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| | |
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| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------- |
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| [`discriminator_net`](#qiskit.aqua.components.neural_networks.PyTorchDiscriminator.discriminator_net "qiskit.aqua.components.neural_networks.PyTorchDiscriminator.discriminator_net") | Get discriminator |
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### discriminator\_net
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<Attribute id="qiskit.aqua.components.neural_networks.PyTorchDiscriminator.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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object
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</Attribute>
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### get\_label
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<Function id="qiskit.aqua.components.neural_networks.PyTorchDiscriminator.get_label" signature="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** (*Union(numpy.ndarray, torch.Tensor)*) – Discriminator input, i.e. data sample.
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* **detach** (*bool*) – if None detach from torch tensor variable (optional)
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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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torch.Tensor
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</Function>
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### gradient\_penalty
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<Function id="qiskit.aqua.components.neural_networks.PyTorchDiscriminator.gradient_penalty" signature="gradient_penalty(x, lambda_=5.0, k=0.01, c=1.0)">
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Compute gradient penalty for discriminator optimization
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**Parameters**
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* **x** (*numpy.ndarray*) – Generated data sample.
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* **lambda** (*float*) – Gradient penalty coefficient 1.
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* **k** (*float*) – Gradient penalty coefficient 2.
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* **c** (*float*) – Gradient penalty coefficient 3.
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**Returns**
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Gradient penalty.
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**Return type**
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torch.Tensor
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</Function>
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### load\_model
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<Function id="qiskit.aqua.components.neural_networks.PyTorchDiscriminator.load_model" signature="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.PyTorchDiscriminator.loss" signature="loss(x, y, weights=None)">
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Loss function
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**Parameters**
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* **x** (*torch.Tensor*) – Discriminator output.
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* **y** (*torch.Tensor*) – Label of the data point
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* **weights** (*torch.Tensor*) – Data weights.
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**Returns**
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Loss w\.r.t to the generated data points.
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**Return type**
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torch.Tensor
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</Function>
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### save\_model
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<Function id="qiskit.aqua.components.neural_networks.PyTorchDiscriminator.save_model" signature="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.PyTorchDiscriminator.set_seed" signature="set_seed(seed)">
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Set seed.
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**Parameters**
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**seed** (`int`) – seed
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</Function>
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### train
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<Function id="qiskit.aqua.components.neural_networks.PyTorchDiscriminator.train" signature="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** (`Iterable`) – Data batch.
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* **weights** (`Iterable`) – Data sample weights.
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* **penalty** (`bool`) – Indicate whether or not penalty function is applied to the loss function. Ignored if no penalty function defined.
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* **quantum\_instance** ([*QuantumInstance*](qiskit.aqua.QuantumInstance "qiskit.aqua.QuantumInstance")) – used to run Quantum network. Ignored for a classical network.
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* **shots** (`Optional`\[`int`]) – Number of shots for hardware or qasm execution. Ignored for classical network
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**Returns**
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**with discriminator loss and updated parameters.data, weights, penalty=True,**
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quantum\_instance=None, shots=None) -> Dict\[str, Any]:
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**Return type**
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dict
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</Function>
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</Class>
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