Instructions to use JoshuaPeddle/MonetGenerator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use JoshuaPeddle/MonetGenerator with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy), and from_pretrained_keras was removed in huggingface_hub 1.0. # See https://github.com/keras-team/tf-keras for more details. # !pip install "huggingface_hub<1.0" tf_keras from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("JoshuaPeddle/MonetGenerator") - Notebooks
- Google Colab
- Kaggle
Commit ·
51af0f8
1
Parent(s): 2bebb38
Update README.md
Browse files
README.md
CHANGED
|
@@ -6,3 +6,15 @@ widget:
|
|
| 6 |
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
|
| 7 |
example_title: Tiger
|
| 8 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 6 |
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
|
| 7 |
example_title: Tiger
|
| 8 |
---
|
| 9 |
+
|
| 10 |
+
## Model description
|
| 11 |
+
|
| 12 |
+
The aim of this work is to map a simple distribution - which is easy to sample and whose density is simple to estimate - to a more complex one learned from the data. This kind of generative model is also known as "normalizing flow". The latent distribution we wish to map to in this example is Gaussian.
|
| 13 |
+
|
| 14 |
+
## Training and evaluation data
|
| 15 |
+
|
| 16 |
+
This model is trained on a toy dataset, the make_moons from sklearn.datasets.
|
| 17 |
+
|
| 18 |
+
### Training hyperparameters
|
| 19 |
+
|
| 20 |
+
|