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If you are new to Rust you can install it as detailed [here][rust_download].
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We also recommend taking a look at the [official Getting Started Guide][rust_getting_started].
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### Documentation
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To learn how to build classical, deep or hybrid machine learning applications with Leaf, check out the [Leaf - Machine Learning for Hackers][leaf-book] book.
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For additional information see the [Rust API Documentation][documentation] or the [Autumn Website][autumn].
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Or start by running the **Leaf examples**.
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If you're using Cargo, just add Leaf to your `Cargo.toml`:
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We are providing a [Leaf examples repository][leaf-examples], where we and
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others publish executable machine learning models build with Leaf. It features
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a CLI for easy usage and has a detailed guide in the [project
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README.md][leaf-examples].
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Leaf comes with an examples directory as well, which features popular neural
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networks (e.g. Alexnet, Overfeat, VGG). To run them on your machine, just follow
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the install guide, clone this repoistory and then run
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```bash
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# The examples currently require CUDA support.
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cargo run --release --no-default-features --features cuda --example benchmarks alexnet
> Leaf is build in [Rust][rust]. If you are new to Rust you can install Rust as detailed [here][rust_download].
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We also recommend taking a look at the [official Rust - Getting Started Guide][rust_getting_started].
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To start building a machine learning application (Rust only for now. Wrappers are welcome) and you are using Cargo, just add Leaf to your `Cargo.toml`:
> More information on the use of feature flags in Leaf can be found in [FEATURE-FLAGS.md](./FEATURE-FLAGS.md)
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### Contributing
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## Examples
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If you want to start hacking on Leaf (e.g.
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[adding a new `Layer`](http://autumnai.com/leaf/book/create-new-layer.html))
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you should start with forking and cloning the repository.
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We are providing a [Leaf examples repository][leaf-examples], where we and
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others publish executable machine learning models build with Leaf. It features
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a CLI for easy usage and has a detailed guide in the [project
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README.md][leaf-examples].
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We have more instructions to help you get started in the [CONTRIBUTING.md][contributing].
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Leaf comes with an examples directory as well, which features popular neural
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networks (e.g. Alexnet, Overfeat, VGG). To run them on your machine, just follow
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the install guide, clone this repoistory and then run
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We also has a near real-time collaboration culture, which happens
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here on Github and on the [Leaf Gitter Channel][gitter-leaf].
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```bash
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# The examples currently require CUDA support.
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cargo run --release --no-default-features --features cuda --example benchmarks alexnet
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```
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> Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as below, without any additional terms or conditions.
Copy file name to clipboardexpand all lines: doc/book/layer-lifecycle.html
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<divid="content" class="content">
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<h1>Layer Lifecycle</h1>
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<p>In <ahref="./layers.html">2. Layers</a> we have already seen a little bit about how to
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construct a <code>Layer</code> from a <code>LayerConfig</code>. In this chapter, we take
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a closer look at what happens inside Leaf when initializing a <code>Layer</code> when
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running the <code>.forward</code> of a <code>Layer</code> and when running the <code>.backward</code>. In the
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next chapter <ahref="./building-networks.html">2.2 Create a Network</a> we then
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apply our knowledge to construct deep networks via the container layer.</p>
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<p>Initialization (<code>::from_config</code>), <code>.forward</code> and <code>.backward</code> are the three most
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important methods of a <code>Layer</code> and describe basically the entire API. Let's
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take a closer look at what happens inside Leaf, when these methods are called.</p>
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<p>In chapter <ahref="./layers.html">2. Layers</a> we saw how to
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construct a simple <code>Layer</code> from a <code>LayerConfig</code>. In this chapter, we take
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a closer look at what happens inside Leaf when initializing a <code>Layer</code> and when running its
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<code>.forward</code> and <code>.backward</code> methods. In the next chapter <ahref="./building-networks.html">2.2 Create a Network</a> we
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apply our knowledge to construct deep networks with the container layer.</p>
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<p>The most important methods of a <code>Layer</code> are initialization (<code>::from_config</code>), <code>.forward</code> and <code>.backward</code>.
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They basically describe the entire API, so let's take a closer look at what happens inside Leaf when these methods are called.</p>
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<h3>Initialization</h3>
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<p>A layer is constructed from a <code>LayerConfig</code>via the <code>Layer::from_config</code>
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<p>A layer is constructed from a <code>LayerConfig</code>with the <code>Layer::from_config</code>
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method, which returns a fully initialized <code>Layer</code>.</p>
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