pixyz.losses (Loss API)¶
Loss¶
Negative expected value of log-likelihood (entropy)¶
CrossEntropy¶
Entropy¶
StochasticReconstructionLoss¶
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class
pixyz.losses.
StochasticReconstructionLoss
(encoder, decoder, input_var=None)[source]¶ Bases:
pixyz.losses.losses.Loss
Reconstruction Loss (Monte Carlo approximation).
where
.
- Note:
- This class is a special case of the CrossEntropy class. You can get the same result with CrossEntropy.
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loss_text
¶
Negative log-likelihood¶
Lower bound¶
Similarity¶
SimilarityLoss¶
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class
pixyz.losses.
SimilarityLoss
(p1, p2, input_var=None, var=['z'], margin=0)[source]¶ Bases:
pixyz.losses.losses.Loss
Learning Modality-Invariant Representations for Speech and Images (Leidai et. al.)
Adversarial loss (GAN loss)¶
AdversarialJSDivergence¶
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class
pixyz.losses.
AdversarialJSDivergence
(p_data, p, discriminator, input_var=None, optimizer=<class 'torch.optim.adam.Adam'>, optimizer_params={}, inverse_g_loss=True)[source]¶ Bases:
pixyz.losses.losses.Loss
Adversarial loss (Jensen-Shannon divergence).
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loss_text
¶
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