78 lines
2.5 KiB
Markdown
78 lines
2.5 KiB
Markdown
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description: Documentation of the Layer instance in Neataptic
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authors: Thomas Wagenaar
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keywords: LSTM, GRU, architecture, neural-network, recurrent
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Layers are pre-built architectures that allow you to combine different network
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architectures into óne network. At this moment, there are 3 layers (more to come soon!):
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```javascript
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Layer.Dense
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Layer.LSTM
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Layer.GRU
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Layer.Memory
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```
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Check out the options and details for each layer below.
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### Constructing your own network with layers
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You should always start your network with a `Dense` layer and always end it with
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a `Dense` layer. You can connect layers with each other just like you can connect
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nodes and groups with each other. This is an example of a custom architecture
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built with layers:
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```javascript
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var input = new Layer.Dense(1);
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var hidden1 = new Layer.LSTM(5);
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var hidden2 = new Layer.GRU(1);
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var output = new Layer.Dense(1);
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// connect however you want
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input.connect(hidden1);
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hidden1.connect(hidden2);
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hidden2.connect(output);
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var network = architect.Construct([input, hidden1, hidden2, output]);
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```
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### Layer.Dense
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The dense layer is a regular layer.
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```javascript
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var layer = new Layer.Dense(size);
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```
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### Layer.LSTM
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The LSTM layer is very useful for detecting and predicting patterns over long
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time lags. This is a recurrent layer. More info? Check out the [LSTM](../builtins/lstm.md) page.
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```javascript
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var layer = new Layer.LSTM(size);
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```
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Be aware that using `Layer.LSTM` is worse than using `architect.LSTM`. See issue [#25](https://github.com/wagenaartje/neataptic/issues/25).
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### Layer.GRU
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The GRU layer is similar to the LSTM layer, however it has no memory cell and only
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two gates. It is also a recurrent layer that is excellent for timeseries prediction.
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More info? Check out the [GRU](../builtins/gru.md) page.
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```javascript
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var layer = new Layer.GRU(size);
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```
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### Layer.Memory
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The Memory layer is very useful if you want your network to remember a number of
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previous inputs in an absolute way. For example, if you set the `memory` option to
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3, it will remember the last 3 inputs in the same state as they were inputted.
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```javascript
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var layer = new Layer.Memory(size, memory);
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```
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The input layer to the memory layer should always have the same size as the memory size.
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The memory layer will output a total of `size * memory` values.
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> This page is incomplete. There is no description on the functions you can use
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on this instance yet. Feel free to add the info (check out src/layer.js)
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