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| author | Oleksii Kachaiev <kachayev@gmail.com> | 2023-05-03 10:36:09 +0200 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2023-05-03 10:36:09 +0200 |
| commit | 2aeb591be6b19a93f187516495ed15f1a47be925 (patch) | |
| tree | 9a6f759856a3f6b2d7c6db3514927ba3e5af10b5 /examples/backends/plot_wass2_gan_torch.py | |
| parent | 8a7035bdaa5bb164d1c16febbd83650d1fb6d393 (diff) | |
[DOC] Corrected spelling errors (#467)
* Fix typos in docstrings and examples
* A few more fixes
* Fix ref for `center_ot_dual` function
* Another typo
* Fix titles formatting
* Explicit empty line after math blocks
* Typo: asymmetric
* Fix code cell formatting for 1D barycenters
* Empirical
* Fix indentation for references
* Fixed all WARNINGs about title formatting
* Fix empty lines after math blocks
* Fix whitespace line
* Update changelog
* Consistent Gromov-Wasserstein
* More Gromov-Wasserstein consistency
---------
Co-authored-by: Rémi Flamary <remi.flamary@gmail.com>
Diffstat (limited to 'examples/backends/plot_wass2_gan_torch.py')
| -rw-r--r-- | examples/backends/plot_wass2_gan_torch.py | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/examples/backends/plot_wass2_gan_torch.py b/examples/backends/plot_wass2_gan_torch.py index cc82f4f..f39d186 100644 --- a/examples/backends/plot_wass2_gan_torch.py +++ b/examples/backends/plot_wass2_gan_torch.py @@ -19,13 +19,13 @@ optimization problem: In practice we do not have access to the full distribution :math:`\mu_d` but -samples and we cannot compute the Wasserstein distance for lare dataset. +samples and we cannot compute the Wasserstein distance for large dataset. [Arjovsky2017] proposed to approximate the dual potential of Wasserstein 1 with a neural network recovering an optimization problem similar to GAN. In this example we will optimize the expectation of the Wasserstein distance over minibatches at each iterations as proposed in [Genevay2018]. Optimizing the Minibatches -of the Wasserstein distance has been studied in[Fatras2019]. +of the Wasserstein distance has been studied in [Fatras2019]. [Arjovsky2017] Arjovsky, M., Chintala, S., & Bottou, L. (2017, July). Wasserstein generative adversarial networks. In International conference @@ -183,7 +183,7 @@ for i in range(9): # %% # Animate trajectories of generated samples along iteration -# ------------------------------------------------------- +# --------------------------------------------------------- pl.figure(4, (8, 8)) |