GatedRecurrentUnit¶

Defined in fynance.models.gru

class GatedRecurrentUnit(X, y, drop=None, x_type=None, y_type=None, bias=True, forward_activation=nn.Softmax, hidden_activation=nn.Tanh, hidden_state_size=None, reset_activation=nn.Sigmoid, update_activation=nn.Sigmoid)[source]

Bases: _OutputLayerMixin, GRUCell

Gated Recurrent Unit neural network.

Full GRU model: _GRUCell gating logic followed by a forward output projection. Mitigates vanishing gradients compared to RecurrentNeuralNetwork via reset and update gates. Use LongShortTermMemory when you need an explicit memory cell state (longer dependencies).

Parameters:

X, yarray-like or int

If it’s an array-like, respectively inputs and outputs data.
If it’s an integer, respectively dimension of inputs and outputs.

dropfloat, optional

Probability of an element to be zeroed.

forward_activation, hidden_activationtorch.nn.Module, optional

Activation functions, default is respectively Softmax and Tanh function.

hidden_state_sizeint, optional

Size of hidden states, default is the same size than input.

reset_activation, update_activationtorch.nn.Module, optional

Activation functions for reset and update gate, default are both Sigmoid function.

Attributes:

criteriontorch.nn.modules.loss: A loss function.
optimizertorch.optim: An optimizer algorithm.
W_h, W_r, W_u, W_ytorch.nn.Linear: Respectively recurrent (candidate), reset, update and forward weights.
f_h, f_r, f_u, f_ytorch.nn.Module: Respectively candidate, reset, update and forward activation functions.

See also

fynance.models.rnn.RecurrentNeuralNetwork
fynance.models.lstm.LongShortTermMemory

forward(X, H)[source]

Forward method.

Parameters:

X, Htorch.Tensor: Respectively input data and hidden state.

Returns:

torch.Tensor: Output data.
torch.Tensor: Hidden state.

load_model(path, load_optimizer=False)

Save the model with this weights and parameters.

Parameters:

pathstr or os.PathLike object: Path to load the model.
load_optimizerbool, optional: If True, then load also the optimizer.

predict(X, H)

Predicts outputs of neural network model.

Parameters:

Xtorch.Tensor: Inputs to compute prediction.
Htorch.Tensor: States of the model.

Returns:

torch.Tensor: Outputs prediction.
torch.Tensor: Updated states of the model.

save_model(path, save_optimizer=False)

Save the model with this weights and parameters.

Parameters:

pathstr or os.PathLike object: Path to save the model.
save_optimizerbool, optional: If True, then save also the optimizer.

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

Set data inputs and outputs.

Coerces X and y to torch.Tensor and caches them as self.X / self.y. After the call the attributes self.T (number of observations), self.N (input columns) and self.M (output columns) are set.

Parameters:

X, yarray-like: Respectively input and output data. Accepted types: numpy.ndarray, torch.Tensor, pandas.DataFrame. Shapes must be (T, N) and (T, M) respectively.
x_type, y_typetorch.dtype, optional: Target dtypes for the resulting tensors. Default is None, which preserves the input dtype.

Returns:

BaseNeuralNet: self, to allow chaining.

Raises:

ValueError: If self.N / self.M were already set and X / y do not match, or if X and y have different lengths.

set_lr_scheduler(lr_scheduler, **kwargs)

Set dynamic learning rate.

Parameters:

lr_schedulertorch.optim.lr_scheduler._LRScheduler: Method from torch.optim.lr_scheduler to wrap self.optimizer, cf module torch.optim.lr_scheduler in PyTorch documentation [2].
**kwargs: Keyword arguments to pass to the learning rate scheduler.

References

[2]

https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate

set_optimizer(criterion, optimizer, params=None, **kwargs)

Set the optimizer object.

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

Parameters:

criterionCallabletorch.nn.modules.loss: A loss function.
optimizertorch.optim.Optimizer: An optimizer algorithm.
paramsobject or iterable object: Layer of parameters to optimize or dicts defining parameter groups. If set to None then all parameters of model will be optimized. Default is None.
**kwargs: Keyword arguments of optimizer, cf PyTorch documentation [1].

Returns:

BaseNeuralNet: Self object model.

References

[1]

https://pytorch.org/docs/stable/optim.html

set_seed(seed_torch=None, seed_numpy=None)

Set seed for PyTorch and NumPy random number generator.

Parameters:

seed_torch, seed_numpybool or int, optional: If seed is an int \(0 < seed < 2^32\) set respectively PyTorch and NumPy seed with the number. Otherwise if is True then choose a random number, else doesn’t set seed.

train_on(X, y, H)

Trains the neural network model.

Parameters:

X, y, Htorch.Tensor: Respectively inputs, outputs and states to train model.

Returns:

torch.nn.modules.loss: Loss outputs.
torch.Tensor: Updated states of the model.