minerva.losses.ntxent_loss_poly

Classes

NTXentLoss_poly

Loss function used on the pretraining of the TFC model. It is based on the NTXentLoss, but it includes a polynomial loss term.

Module Contents

class minerva.losses.ntxent_loss_poly.NTXentLoss_poly(device, batch_size, temperature, use_cosine_similarity)

Bases: torch.nn.modules.loss._Loss

Loss function used on the pretraining of the TFC model. It is based on the NTXentLoss, but it includes a polynomial loss term.

The constructor of the NTXentLoss_poly class.

Parameters

• device: str

  The device to be used in the training of the model

• batch_size: int

  The batch size of the model

• temperature: float

  The temperature of the softmax function

• use_cosine_similarity: bool

  If True, the cosine similarity is used. If False, the dot product is used

_cosine_simililarity(x, y)

Function to calculate the cosine similarity between two tensors.

Parameters

• x: torch.Tensor

  The first tensor

• y: torch.Tensor

  The second tensor

Returns

• torch.Tensor

  The cosine similarity between the two tensors

Return type:

torch.Tensor

static _dot_simililarity(x, y)

Function to calculate the dot similarity between two tensors.

Parameters

• x: torch.Tensor

  The first tensor

• y: torch.Tensor

  The second tensor

Returns

• torch.Tensor

  The dot similarity between the two tensors

Return type:

torch.Tensor

_get_correlated_mask()

Get the mask of correlated samples.

Returns

• torch.Tensor

  The mask of correlated samples

Return type:

torch.Tensor

_get_similarity_function(use_cosine_similarity)

Define the similarity function to be used in the loss calculation.

Parameters

• use_cosine_similarity: bool

  If True, the cosine similarity is used. If False, the dot product is used

Returns

• function

  The similarity function to be used in the loss calculation

Parameters:

use_cosine_similarity (bool)

batch_size

criterion

device

forward(zis, zjs)

The forward method of the NTXentLoss_poly class. It receives the samples and returns the loss of the model.

Parameters

• zis: torch.Tensor

  The positive samples

• zjs: torch.Tensor

  The negative samples

Returns

• _Loss

  The loss of the model

Parameters:

• zis (torch.Tensor)

• zjs (torch.Tensor)

Return type:

torch.nn.modules.loss._Loss

mask_samples_from_same_repr

similarity_function

softmax

temperature

Parameters:

• device (str)

• batch_size (int)

• temperature (float)

• use_cosine_similarity (bool)