prevision_quantum_nn.models.qnn¶
Quantum Neural Network module provides with the base class of Quantum Neural Networks from which all models should inherit
Module Contents¶
Classes¶
QuantumNeuralNetwork |
Quantum Neural Network |
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class
prevision_quantum_nn.models.qnn.QuantumNeuralNetwork(params)¶ - Quantum Neural Network
- base class for all quantum neural networks
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params¶ contains the parameters of the model
Type: dictionnary
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use_early_stopper¶ if True, early stopping will be activated, default: True
Type: bool
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early_stopper¶ early stopper that stops the run when the validation loss increases
Type: EarlyStopper
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early_stopper_patience¶ number of iterations during which the early stopper module will save the weights and restore them when triggered
Type: int
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postprocessor¶ postprocessor to be called as a callback during the run
Type: Postprocessor
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built¶ True if the model has been built, it False, fit won’t start
Type: bool
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running_mode¶ default, “simulation”, but could also be “computation”once the access to quantum computers will be effective
Type: str
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architecture¶ architecture of the quantum computer - can be: 1. qubit 2. cv
Type: str
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num_q¶ number of qubits/qumodes
Type: int
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num_categories¶ number of categories/classes/labels of the problem only the first num_categories qubits/qumodes will be measured
Type: int
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num_actions¶ number of actions in the case of a reinforcement learning mode
Type: int
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max_iterations¶ maximum number of iteration that the fitting phase needs to perform
Type: int
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num_layers¶ number of layers of the quantum neural network
Type: int
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snapshot_frequency¶ frequency in number of iterations at which the model needs to snapshot
Type: int
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type_problem¶ problem that is being solved, can be: 1. classifiation 2. multiclassificaiton 3. regression 4. reinforcement_learning
Type: str
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batch_size¶ batch size to be used for one fitting iteration
Type: int
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save¶ if True, the model will save at the end of fit
Type: bool
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prefix¶ name of the file to which the output should go to
Type: str
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build(self)¶ build the model: initializes the weights
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check_model(self)¶ Checks the model’s parameters consistency.
Raises: ValueError when needed
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build_early_stopper(self)¶ Builds the early stoppe.
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get_random_batch(self, features, labels, batch_size)¶ Get random batch. :param features: features to be randomly selected :type features: numpy array :param labels: labels to be randomly selected :type labels: numpy array
Returns: - numpy array
- randomized batched features
- labels: numpy array
- randomized batched labels
Return type: features
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logging_iteration(self, val_features, val_labels, train_loss, val_loss)¶ Dumps information during training.
Parameters: - val_features (array) – validation features
- val_labels (array) – validation labels
- train_loss (float) – loss of the current iteration
- val_loss (float) – loss of the current iteration