network

Neural network for MALA.

class FeedForwardNet(params: Parameters | None = None)[source]

Bases: Network

Initialize this network as a feed-forward network.

forward(inputs)[source]

Perform a forward pass through the network.

Parameters:: inputs (torch.Tensor) – Input array for which the forward pass is to be performed.
Returns:: predicted_array – Predicted outputs of array.
Return type:: torch.Tensor

class Network(params: Parameters | None = None)[source]

Bases: Module

Central network class for this framework, based on pytorch.nn.Module.

The correct type of neural network will automatically be instantiated by this class if possible. You can also instantiate the desired network directly by calling upon the subclass.

Parameters:: params (mala.common.parametes.Parameters) – Parameters used to create this neural network.

loss_func

Loss function.

Type:: function

mini_batch_size

Size of mini batches propagated through network.

Type:: int

number_of_layers

Number of NN layers.

Type:: int

params

MALA neural network parameters.

Type:: mala.common.parametes.ParametersNetwork

use_ddp

If True, the torch distributed data parallel formalism will be used.

Type:: bool

calculate_loss(output, target)[source]

Calculate the loss for a predicted output and target.

Parameters:

output (torch.Tensor) – Predicted output.
target (torch.Tensor.) – Actual output.

Returns:

loss_val – Loss value for output and target.

Return type:

float

do_prediction(array)[source]

Predict the output values for an input array.

Interface to do predictions. The data put in here is assumed to be a scaled torch.Tensor and in the right units. Be aware that this will pass the entire array through the network, which might be very demanding in terms of RAM.

Parameters:: array (torch.Tensor) – Input array for which the prediction is to be performed.
Returns:: predicted_array – Predicted outputs of array.
Return type:: torch.Tensor

abstract forward(inputs)[source]

Abstract method. To be implemented by the derived class.

Parameters:: inputs (torch.Tensor) – Torch tensor to be propagated.

classmethod load_from_file(params, file)[source]

Load a network from a file.

Parameters:

params (mala.common.parameters.Parameters) – Parameters object with which the network should be created. Has to be compatible to the network architecture. This is usually enforced by using the same Parameters object (and saving/loading it to)
file (string or ZipExtFile) – Path to the file from which the network should be loaded.

Returns:

loaded_network – The network that was loaded from the file.

Return type:

Network

save_network(path_to_file)[source]

Save the network.

This function serves as an interfaces to pytorchs own saving functionalities AND possibly own saving needs.

Parameters:: path_to_file (string) – Path to the file in which the network should be saved.