Neural network models module¶

Some basis of neural network models with PyTorch package.

`fynance.models.neural_network.BaseNeuralNet`()	Base object for neural network model with PyTorch.
`fynance.models.neural_network.MultiLayerPerceptron`(X, y)	Neural network with MultiLayer Perceptron architecture.

class fynance.models.neural_network.BaseNeuralNet¶

Bases: torch.nn.modules.module.Module

Base object for neural network model with PyTorch.

Inherits of torch.nn.Module object with some higher level methods.

See also

Attributes:	criterion : torch.nn.modules.loss A loss function. optimizer : torch.optim An optimizer algorithm. N, M : int Respectively input and output dimension.

Methods

set_optimizer(criterion, optimizer, kwargs)**	Set optimizer object with specified criterion (loss function) and any optional parameters.
train_on(X, y)	Trains the neural network on X as inputs and y as ouputs.
predict(X)	Predicts the outputs of neural network model for X as inputs.

predict(self, X)¶

Predicts outputs of neural network model.

Parameters:	X : torch.Tensor Inputs to compute prediction.
Returns:	torch.Tensor Outputs prediction.

set_data(self, X, y, x_type=None, y_type=None)¶

Set data inputs and outputs.

Parameters:	X, y : array-like Respectively input and output data. x_type, y_type : torch.dtype Respectively input and ouput data types. Default is None.

set_optimizer(self, criterion, optimizer, **kwargs)¶

Set the optimizer object.

Set optimizer object with specified criterion as loss function and any kwargs as optional parameters.

Parameters:	criterion : torch.nn.modules.loss A loss function. optimizer : torch.optim An optimizer algorithm. kwargs : dict Keyword arguments of optimizer, cf pytorch documentation [1].
Returns:	NeuralNetwork Self object model.

References

[1]	https://pytorch.org/docs/stable/optim.html

train_on(self, X, y)¶

Trains the neural network model.

Parameters:	X, y : torch.Tensor Respectively inputs and outputs to train model.
Returns:	torch.nn.modules.loss Loss outputs.

class fynance.models.neural_network.MultiLayerPerceptron(X, y, layers=[], activation=None, drop=None)¶

Neural network with MultiLayer Perceptron architecture.

Refered as vanilla neural network model, with n hidden layers s.t n \(\geq\) 1, with each one a specified number of neurons.

See also

Attributes:	criterion : torch.nn.modules.loss A loss function. optimizer : torch.optim An optimizer algorithm. n : int Number of hidden layers. layers : list of int List with the number of neurons for each hidden layer. f : torch.nn.Module Activation function.

Methods

set_optimizer(criterion, optimizer, kwargs)**	Set optimizer object with specified criterion (loss function) and any optional parameters.
train_on(X, y)	Trains the neural network on X as inputs and y as ouputs.
predict(X)	Predicts the outputs of neural network model for X as inputs.
set_data(X, y)	Set respectively input and ouputs data tensor.

__init__(self, X, y, layers=[], activation=None, drop=None)¶

Initialize.

Parameters:	X, y : array-like Respectively inputs and outputs data. layers : list of int List of number of neurons in each hidden layer. activation : torch.nn.Module Activation function of layers. drop : float, optional Probability of an element to be zeroed.