Criterion

This module contains loss functions.

tensornet.models.loss.cross_entropy_loss()[source]

Cross Entropy Loss. The loss automatically applies the softmax activation function on the prediction input.

Returns

Cross entroy loss function

tensornet.models.loss.bce_loss()[source]

Binary Cross Entropy Loss. The loss automatically applies the sigmoid activation function on the prediction input.

Returns

Binary cross entropy loss function

tensornet.models.loss.mse_loss()[source]

Mean Squared Error Loss.

Returns

Mean squared error loss function

tensornet.models.loss.rmse_loss(smooth=1e-06)[source]

Root Mean Squared Error Loss.

Returns

Root mean squared error loss function

tensornet.models.loss.dice_loss(smooth=1)[source]

Dice Loss.

Parameters

smooth (float, optional) – Smoothing value. A larger smooth value (also known as Laplace smooth, or Additive smooth) can be used to avoid overfitting. (default: 1)

Returns

Dice loss function

tensornet.models.loss.bce_dice_loss(smooth=1e-06)[source]

BCE Dice Loss.

Parameters

smooth (float, optional) – Smoothing value.

Returns

BCE Dice loss function

tensornet.models.loss.ssim_loss(data_range=1.0, size_average=True, channel=1)[source]

SSIM Loss.

Parameters

  • data_range (float or int, optional) – Value range of input images (usually 1.0 or 255). (default: 255)

  • size_average (bool, optional) – If size_average=True, ssim of all images will be averaged as a scalar. (default: True)

  • channel (int, optional) – input channels (default: 1)

Returns

SSIM loss function

tensornet.models.loss.ms_ssim_loss(data_range=1.0, size_average=True, channel=1)[source]

MS-SSIM Loss.

Parameters

  • data_range (float or int, optional) – Value range of input images (usually 1.0 or 255). (default: 1.0)

  • size_average (bool, optional) – If size_average=True, ssim of all images will be averaged as a scalar. (default: True)

  • channel (int, optional) – input channels (default: 1)

Returns

MS-SSIM loss function