From e29a33449dc66192868a8290c564f622ee2d79b5 Mon Sep 17 00:00:00 2001 From: svlandeg Date: Wed, 2 Sep 2020 13:41:18 +0200 Subject: [PATCH] rewrite intro, simpel Model example --- website/docs/usage/layers-architectures.md | 38 +++++++++++++++------- 1 file changed, 26 insertions(+), 12 deletions(-) diff --git a/website/docs/usage/layers-architectures.md b/website/docs/usage/layers-architectures.md index 3ef28acaf..ac91ca0ad 100644 --- a/website/docs/usage/layers-architectures.md +++ b/website/docs/usage/layers-architectures.md @@ -10,18 +10,32 @@ menu: next: /usage/projects --- -​A **model architecture** is a function that wires up a -[Thinc `Model`](https://thinc.ai/docs/api-model) instance, which you can then -use in a component or as a layer of a larger network. You can use Thinc as a -thin wrapper around frameworks such as PyTorch, TensorFlow or MXNet, or you can -implement your logic in Thinc directly. ​ spaCy's built-in components will never -construct their `Model` instances themselves, so you won't have to subclass the -component to change its model architecture. You can just **update the config** -so that it refers to a different registered function. Once the component has -been created, its model instance has already been assigned, so you cannot change -its model architecture. The architecture is like a recipe for the network, and -you can't change the recipe once the dish has already been prepared. You have to -make a new one. +> #### Example +> +> ````python +> from thinc.api import Model, chain +> +> def build_model(width: int, classes: int) -> Model: +> tok2vec = build_tok2vec(width) +> output_layer = build_output_layer(width, classes) +> model = chain(tok2vec, output_layer) +> return model +> ```` + +A **model architecture** is a function that wires up a +[Thinc `Model`](https://thinc.ai/docs/api-model) instance. It describes the +neural network that is run internally as part of a component in a spaCy +pipeline. To define the actual architecture, you can implement your logic in +Thinc directly, but you can also use Thinc as a thin wrapper around frameworks +such as PyTorch, TensorFlow or MXNet. + +spaCy's built-in components require a `Model` instance to be passed to them via +the config system. To change the model architecture of an existing component, +you just need to **update the config** so that it refers to a different +registered function. Once the component has been created from this config, you +won't be able to change it anymore. The architecture is like a recipe for the +network, and you can't change the recipe once the dish has already been +prepared. You have to make a new one. ## Type signatures {#type-sigs}