pixyz.losses (Loss API)¶

Loss¶

class pixyz.losses.losses.Loss(p1, p2=None, input_var=None)[source]¶

Bases: object

input_var¶

loss_text¶

mean()[source]¶

sum()[source]¶

estimate(x={}, **kwargs)[source]¶

train(x={}, **kwargs)[source]¶: Train the implicit (adversarial) loss function.

test(x={}, **kwargs)[source]¶: Test the implicit (adversarial) loss function.

Negative expected value of log-likelihood (entropy)¶

CrossEntropy¶

class pixyz.losses.CrossEntropy(p1, p2, input_var=None)[source]¶

Bases: pixyz.losses.losses.Loss

Cross entropy, a.k.a., the negative expected value of log-likelihood (Monte Carlo approximation).

$-\mathbb{E}_{q(x)}[\log p(x)] \approx -\frac{1}{L}\sum_{l=1}^L \log p(x_l),$

where $x_l \sim q(x)$ .

loss_text¶

estimate(x={})[source]¶

Entropy¶

class pixyz.losses.Entropy(p1, input_var=None)[source]¶

Bases: pixyz.losses.losses.Loss

Entropy (Monte Carlo approximation).

$-\mathbb{E}_{p(x)}[\log p(x)] \approx -\frac{1}{L}\sum_{l=1}^L \log p(x_l),$

where $x_l \sim p(x)$ .

Note:: This class is a special case of the CrossEntropy class. You can get the same result with CrossEntropy.

loss_text¶

estimate(x={})[source]¶

StochasticReconstructionLoss¶

class pixyz.losses.StochasticReconstructionLoss(encoder, decoder, input_var=None)[source]¶

Bases: pixyz.losses.losses.Loss

Reconstruction Loss (Monte Carlo approximation).

$-\mathbb{E}_{q(z|x)}[\log p(x|z)] \approx -\frac{1}{L}\sum_{l=1}^L \log p(x|z_l),$

where $z_l \sim q(z|x)$ .

Note:: This class is a special case of the CrossEntropy class. You can get the same result with CrossEntropy.

loss_text¶

estimate(x={})[source]¶

Negative log-likelihood¶

NLL¶

class pixyz.losses.NLL(p, input_var=None)[source]¶

Bases: pixyz.losses.losses.Loss

Negative log-likelihood.

$\log p(x)$

loss_text¶

estimate(x={})[source]¶

Lower bound¶

ELBO¶

class pixyz.losses.ELBO(p, approximate_dist, input_var=None)[source]¶

Bases: pixyz.losses.losses.Loss

The evidence lower bound (Monte Carlo approximation).

$\mathbb{E}_{q(z|x)}[\log \frac{p(x,z)}{q(z|x)}] \approx \frac{1}{L}\sum_{l=1}^L \log p(x, z_l),$

where $z_l \sim q(z|x)$ .

loss_text¶

estimate(x={}, batch_size=None)[source]¶

Similarity¶

SimilarityLoss¶

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.)

estimate(x)[source]¶

MultiModalContrastivenessLoss¶

class pixyz.losses.MultiModalContrastivenessLoss(p1, p2, input_var=None, margin=0.5)[source]¶

Bases: pixyz.losses.losses.Loss

Disentangling by Partitioning: A Representation Learning Framework for Multimodal Sensory Data

estimate(x)[source]¶

Adversarial loss (GAN loss)¶

AdversarialJSDivergence¶

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).

$\mathcal{L}_{adv} = 2 \dot JS[p_{data}(x)||p(x)] + const.$

loss_text¶

estimate(x={}, discriminator=False)[source]¶

d_loss(y1, y2, batch_size)[source]¶

g_loss(y1, y2, batch_size)[source]¶

train(train_x, **kwargs)[source]¶: Train the implicit (adversarial) loss function.

test(test_x, **kwargs)[source]¶: Test the implicit (adversarial) loss function.

AdversarialWassersteinDistance¶

class pixyz.losses.AdversarialWassersteinDistance(p_data, p, discriminator, clip_value=0.01, **kwargs)[source]¶

Bases: pixyz.losses.adversarial_loss.AdversarialJSDivergence

Adversarial loss (Wasserstein Distance).

loss_text¶

d_loss(y1, y2, *args, **kwargs)[source]¶

g_loss(y1, y2, *args, **kwargs)[source]¶

train(train_x, **kwargs)[source]¶: Train the implicit (adversarial) loss function.

Loss for special purpose¶

Parameter¶

class pixyz.losses.losses.Parameter(input_var)[source]¶

Bases: pixyz.losses.losses.Loss

estimate(x={}, **kwargs)[source]¶

loss_text¶

Operators¶

LossOperator¶

class pixyz.losses.losses.LossOperator(loss1, loss2)[source]¶

Bases: pixyz.losses.losses.Loss

loss_text¶

estimate(x={}, **kwargs)[source]¶

train(x, **kwargs)[source]¶: TODO: Fix

test(x, **kwargs)[source]¶: TODO: Fix

LossSelfOperator¶

class pixyz.losses.losses.LossSelfOperator(loss1)[source]¶

Bases: pixyz.losses.losses.Loss

train(x={}, **kwargs)[source]¶: Train the implicit (adversarial) loss function.

test(x={}, **kwargs)[source]¶: Test the implicit (adversarial) loss function.

AddLoss¶

class pixyz.losses.losses.AddLoss(loss1, loss2)[source]¶

Bases: pixyz.losses.losses.LossOperator

loss_text¶

estimate(x={}, **kwargs)[source]¶

SubLoss¶

class pixyz.losses.losses.SubLoss(loss1, loss2)[source]¶

Bases: pixyz.losses.losses.LossOperator

loss_text¶

estimate(x={}, **kwargs)[source]¶

MulLoss¶

class pixyz.losses.losses.MulLoss(loss1, loss2)[source]¶

Bases: pixyz.losses.losses.LossOperator

loss_text¶

estimate(x={}, **kwargs)[source]¶

DivLoss¶

class pixyz.losses.losses.DivLoss(loss1, loss2)[source]¶

Bases: pixyz.losses.losses.LossOperator

loss_text¶

estimate(x={}, **kwargs)[source]¶

NegLoss¶

class pixyz.losses.losses.NegLoss(loss1)[source]¶

Bases: pixyz.losses.losses.LossSelfOperator

loss_text¶

estimate(x={}, **kwargs)[source]¶

BatchMean¶

class pixyz.losses.losses.BatchMean(loss1)[source]¶

Bases: pixyz.losses.losses.LossSelfOperator

Loss averaged over batch data.

$\mathbb{E}_{p_{data}(x)}[\mathcal{L}(x)] \approx \frac{1}{N}\sum_{i=1}^N \mathcal{L}(x_i),$

where $x_i \sim p_{data}(x)$ and $\mathcal{L}$ is a loss function.

loss_text¶

estimate(x={}, **kwargs)[source]¶

BatchSum¶

class pixyz.losses.losses.BatchSum(loss1)[source]¶

Bases: pixyz.losses.losses.LossSelfOperator

Loss summed over batch data.

$\sum_{i=1}^N \mathcal{L}(x_i),$

where $x_i \sim p_{data}(x)$ and $\mathcal{L}$ is a loss function.

loss_text¶

estimate(x={}, **kwargs)[source]¶