explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2024-09-21 19:39:13 +03:00
Code Issues Packages Projects Releases Wiki Activity

PyTorch spelling

Browse Source
This commit is contained in:
svlandeg 2020-09-09 16:27:21 +02:00
parent c89e07927e
commit aa27e3f1f2
1 changed files with 3 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
website/docs/usage/layers-architectures.md
View File

@ -228,7 +228,7 @@ Thinc allows you to [wrap models](https://thinc.ai/docs/usage-frameworks)
written in other machine learning frameworks like PyTorch, TensorFlow and MXNet written in other machine learning frameworks like PyTorch, TensorFlow and MXNet
using a unified [`Model`](https://thinc.ai/docs/api-model) API. using a unified [`Model`](https://thinc.ai/docs/api-model) API.
For example, let's use Pytorch to define a very simple Neural network consisting For example, let's use PyTorch to define a very simple Neural network consisting
of two hidden `Linear` layers with `ReLU` activation and dropout, and a of two hidden `Linear` layers with `ReLU` activation and dropout, and a
softmax-activated output layer. softmax-activated output layer.
@ -274,7 +274,7 @@ In the above example, we have combined our custom PyTorch model with a character
embedding layer defined by spaCy. embedding layer defined by spaCy.
[CharacterEmbed](/api/architectures#CharacterEmbed) returns a `Model` that takes [CharacterEmbed](/api/architectures#CharacterEmbed) returns a `Model` that takes
a `List[Doc]` as input, and outputs a `List[Floats2d]`. To make sure that the a `List[Doc]` as input, and outputs a `List[Floats2d]`. To make sure that the
wrapped Pytorch model receives valid inputs, we use Thinc's wrapped PyTorch model receives valid inputs, we use Thinc's
[`with_array`](https://thinc.ai/docs/api-layers#with_array) helper. [`with_array`](https://thinc.ai/docs/api-layers#with_array) helper.
As another example, you could have a model where you use PyTorch just for the As another example, you could have a model where you use PyTorch just for the
@ -284,7 +284,7 @@ consideration for those parts of the network.
## Custom models for trainable components {#custom-models} ## Custom models for trainable components {#custom-models}
To use our custom model including the Pytorch subnetwork, all we need to do is To use our custom model including the PyTorch subnetwork, all we need to do is
register the architecture. The full example then becomes: register the architecture. The full example then becomes:
```python ```python