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# Dependencies | ||
/node_modules | ||
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# Production | ||
/build | ||
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# Generated files | ||
.docusaurus | ||
.cache-loader | ||
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# Misc | ||
.DS_Store | ||
.env.local | ||
.env.development.local | ||
.env.test.local | ||
.env.production.local | ||
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npm-debug.log* | ||
yarn-debug.log* | ||
yarn-error.log* |
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# Website | ||
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This website is built using [Docusaurus 2](https://docusaurus.io/), a modern static website generator. | ||
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### Installation | ||
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``` | ||
$ yarn | ||
``` | ||
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### Local Development | ||
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``` | ||
$ yarn start | ||
``` | ||
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This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server. | ||
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### Build | ||
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``` | ||
$ yarn build | ||
``` | ||
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This command generates static content into the `build` directory and can be served using any static contents hosting service. | ||
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### Deployment | ||
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Using SSH: | ||
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``` | ||
$ USE_SSH=true yarn deploy | ||
``` | ||
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Not using SSH: | ||
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``` | ||
$ GIT_USER=<Your GitHub username> yarn deploy | ||
``` | ||
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If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch. |
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module.exports = { | ||
presets: [require.resolve('@docusaurus/core/lib/babel/preset')], | ||
}; |
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--- | ||
slug: first-blog-post | ||
title: First Blog Post | ||
authors: | ||
name: Gao Wei | ||
title: Docusaurus Core Team | ||
url: https://github.com/wgao19 | ||
image_url: https://github.com/wgao19.png | ||
tags: [hola, docusaurus] | ||
--- | ||
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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Pellentesque elementum dignissim ultricies. Fusce rhoncus ipsum tempor eros aliquam consequat. Lorem ipsum dolor sit amet |
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--- | ||
slug: long-blog-post | ||
title: Long Blog Post | ||
authors: endi | ||
tags: [hello, docusaurus] | ||
--- | ||
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This is the summary of a very long blog post, | ||
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Use a `<!--` `truncate` `-->` comment to limit blog post size in the list view. | ||
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<!--truncate--> | ||
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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Pellentesque elementum dignissim ultricies. Fusce rhoncus ipsum tempor eros aliquam consequat. Lorem ipsum dolor sit amet |
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--- | ||
slug: mdx-blog-post | ||
title: MDX Blog Post | ||
authors: [slorber] | ||
tags: [docusaurus] | ||
--- | ||
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Blog posts support [Docusaurus Markdown features](https://docusaurus.io/docs/markdown-features), such as [MDX](https://mdxjs.com/). | ||
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:::tip | ||
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Use the power of React to create interactive blog posts. | ||
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```js | ||
<button onClick={() => alert('button clicked!')}>Click me!</button> | ||
``` | ||
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<button onClick={() => alert('button clicked!')}>Click me!</button> | ||
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::: |
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--- | ||
slug: welcome | ||
title: Welcome | ||
authors: [slorber, yangshun] | ||
tags: [facebook, hello, docusaurus] | ||
--- | ||
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[Docusaurus blogging features](https://docusaurus.io/docs/blog) are powered by the [blog plugin](https://docusaurus.io/docs/api/plugins/@docusaurus/plugin-content-blog). | ||
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Simply add Markdown files (or folders) to the `blog` directory. | ||
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Regular blog authors can be added to `authors.yml`. | ||
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The blog post date can be extracted from filenames, such as: | ||
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- `2019-05-30-welcome.md` | ||
- `2019-05-30-welcome/index.md` | ||
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A blog post folder can be convenient to co-locate blog post images: | ||
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 | ||
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The blog supports tags as well! | ||
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**And if you don't want a blog**: just delete this directory, and use `blog: false` in your Docusaurus config. |
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endi: | ||
name: Endilie Yacop Sucipto | ||
title: Maintainer of Docusaurus | ||
url: https://github.com/endiliey | ||
image_url: https://github.com/endiliey.png | ||
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yangshun: | ||
name: Yangshun Tay | ||
title: Front End Engineer @ Facebook | ||
url: https://github.com/yangshun | ||
image_url: https://github.com/yangshun.png | ||
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slorber: | ||
name: Sébastien Lorber | ||
title: Docusaurus maintainer | ||
url: https://sebastienlorber.com | ||
image_url: https://github.com/slorber.png |
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{ | ||
"label": "Guides", | ||
"position": 2, | ||
"link": { | ||
"type": "generated-index", | ||
"description": "5 minutes to learn the most important Docusaurus concepts." | ||
} | ||
} |
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--- | ||
sidebar_position: 1 | ||
--- | ||
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# Main class | ||
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This library has a maser class called `IAFlow`, that has functions to management model creation and training. In this section, we will see the CRUD for models. | ||
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## Constructor | ||
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The constructor of `IAFlow` class has the following parameters: | ||
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- **models_folder**: Folder to save the models. | ||
- **params_notifier**: Parameters for notifier, see documentation [here](https://pypi.org/project/notify-function/#description). | ||
- **builder_function**: Function to build the model. | ||
- **checkpoint_params** (optional): Parameters for checkpoint, see documentation [here](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ModelCheckpoint). | ||
- **tensorboard_params** (optional): Parameters for tensorboard, see documentation [here](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/TensorBoard). | ||
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```python title="IAFlow constructor" | ||
from iaflow import IAFlow | ||
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def custom_builder(input_shape): | ||
model = Sequential([ | ||
Dense(units=512, activation='relu'), | ||
Dense(units=512, activation='relu'), | ||
Dense(units=1, activation='sigmoid') | ||
]) | ||
return model | ||
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# Parameters for notifier, see documentation | ||
# https://pypi.org/project/notify-function/#description | ||
params_notifier = { | ||
'title': 'Training update', | ||
'webhook_url': os.environ.get('WEBHOOK_URL'), | ||
'frequency_epoch': 20 # Send a notification every 20 epochs, by default it is every epoch | ||
} | ||
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ia_maker = IAFlow( | ||
models_folder='./models', | ||
params_notifier=params_notifier, | ||
builder_function=custom_builder, | ||
checkpoint_params={ | ||
'monitor': 'val_loss', | ||
'save_best_only': True, | ||
'save_weights_only': True | ||
}, | ||
tensorboard_params={ | ||
'histogram_freq': 1, | ||
'write_graph': True, | ||
'write_images': True | ||
} | ||
) | ||
``` | ||
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## Update builder function | ||
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The `update_builder_function` method is used to update the builder function. Has the following parameters: | ||
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- **builder_function**: New function to build the model. | ||
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```python title=Example | ||
ia_maker.update_builder_function(custom_builder_2) | ||
``` | ||
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## Update notifier parameters | ||
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The `update_notifier_parameters` method is used to update the notifier parameters. Has the following parameters: | ||
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- **params_notifier**: New parameters for notifier, see documentation [here](https://pypi.org/project/notify-function/#description). | ||
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```python title=Example | ||
ia_maker.update_notifier_parameters(params_notifier_2) | ||
``` |
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--- | ||
sidebar_position: 3 | ||
--- | ||
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# Management datasets | ||
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The instance created by the [Main class](./main-class) allow to manage the datasets, in this section we will see the CRUD for datasets. | ||
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## Create dataset | ||
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The `add_dataset` method is used to define a dataset. Has the following parameters: | ||
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- **name** (string): Name of the dataset. | ||
- **epochs** (int): Number of epochs to train the model. | ||
- **train_ds** (tf.data.Dataset | List): Dataset to train the model. | ||
- **val_ds** (tf.data.Dataset | List, optional): Dataset to validate the model. | ||
- **test_ds** (tf.data.Dataset | List, optional): Dataset to test the model. | ||
- **batch_size** (int, optional): Batch size to train the model. | ||
- **shuffle_buffer** (int, optional): Buffer size to shuffle the dataset. | ||
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```python title=Example | ||
all_data = tf.data.Dataset.from_tensor_slices(( | ||
tf.random.uniform([1000, 2]), | ||
tf.random.uniform([1000, 1]) | ||
)) | ||
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train_ds = all_data.take(5) | ||
val_ds = all_data.skip(5) | ||
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ia_maker.add_dataset( | ||
name='dataset_1', | ||
epochs=10, | ||
batch_size=32, | ||
shuffle_buffer=512, | ||
train_ds=train_ds, | ||
val_ds=val_ds | ||
) | ||
``` | ||
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```bash title=Output | ||
Dataset dataset_1 was added | ||
``` | ||
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## Update dataset | ||
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The `update_dataset` method is used to update a dataset. Has the following parameters: | ||
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- **name** (string): Name of the dataset to update. | ||
- **epochs** (int): New number of epochs to train the model. | ||
- **train_ds** (tf.data.Dataset | List): New dataset to train the model. | ||
- **val_ds** (tf.data.Dataset | List): New dataset to validate the model. | ||
- **test_ds** (tf.data.Dataset | List): New dataset to test the model. | ||
- **batch_size** (int): New batch size to train the model. | ||
- **shuffle_buffer** (int): New buffer size to shuffle the dataset. | ||
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```python title=Example | ||
ia_maker.update_dataset( | ||
name='dataset_1', | ||
epochs=50, | ||
batch_size=128, | ||
shuffle_buffer=1024, | ||
train_ds=train_ds_2, | ||
val_ds=val_ds_2 | ||
) | ||
``` | ||
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```bash title=Output | ||
Dataset dataset_1 was updated | ||
``` | ||
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## Delete dataset | ||
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The `delete_dataset` method is used to delete a dataset. Has the following parameters: | ||
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- **name** (string): Name of the dataset to delete. | ||
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```python title=Example | ||
ia_maker.delete_dataset(name='dataset_1') | ||
``` | ||
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```bash title=Output | ||
Dataset dataset_1 was deleted | ||
``` |
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