# Layers
A layer performs complex mathematical computations to extract features from input data. It then outputs the transformed data and passes them to the next layer in the network.
Layers encapsulate weights, or states, which are actively tracked and continuously updated during model training.
## Add a Layer
Layers are added to a network after the [**Dataset Block**](/user-interface/project/network/network-mapping/create-a-mapping-deprecated/input-node.md) has been set up.
To add a layer to your network:
1. On the **Network** view, right-click anywhere to open a menu, from which you can select the type of layer to be added to your project**.**
2. Click the layer block when it appears on the **Network** view, then set the [**Layer Properties**](#layer-properties) panel.
{% hint style="info" %}
Add a [**Loss and Optimizer**](/user-interface/project/network/network-mapping/create-a-mapping-deprecated/loss-node.md) to a network after the last layer. After saving, the network becomes available for training and evaluation (see [**Evaluate / Train Model**](/user-interface/project/menu-bar/evaluate-a-model.md)).
{% endhint %}
## Layer Properties
The **Layer Properties** panel appears to the right when you click a Layer on the Network view. From here, you can set the properties associated with a layer.
For example, for a **Conv2D** layer, you may want to change the number of filters, kernel size, and activation type.
## Connections
Once layers have been added to your project, you can start connecting them to each other.
Starting from the **Dataset Block**, grab the layer's right handle and drag it to the next layer's left handle. Perform the same procedure for the rest of the layers.
When connecting layers, each block shows the calculated output shape affected by the preceding layers.
{% hint style="info" %}
Set up the Dataset Block first before connecting it to the first layer on the network. For more information, see [Input Node](/user-interface/project/network/network-mapping/create-a-mapping-deprecated/input-node.md).
{% endhint %}
### Remove Connections
If you make a mistake when connecting layers, remove the connection by grabbing the layer's left handle and dragging it away from the layer.
## Layer Operations
Layers can be duplicated, deleted, and spawned. You can also copy a layer's properties and apply them to another layer.
{% hint style="info" %}
This same set of operations is also applicable to Loss and Optimizer blocks, which are also layers. See [**Loss and Optimizer**](/user-interface/project/network/network-mapping/create-a-mapping-deprecated/loss-node.md) for more information.
{% endhint %}
### Duplicate a Layer
When duplicating a layer, the layer, including its properties, are copied into the Network view. However, the connections between the layer and adjacent layers are not copied.
This is helpful when laying out two or more of the same type of layer onto the network. If the layer properties are different, you can just edit them later.
To duplicate a layer:
1. From the **Network** view, position your mouse cursor over the layer to be duplicated, then make a right click.
2. On the popup menu, click 
to duplicate the layer onto the network.
{% hint style="info" %}
If your layers are already connected, you may have to remove the connections, insert the duplicated layer in the desired position, then reconnect the layers together. See [**Connections**](#connections) for more information.
{% endhint %}
### Remove a Layer
If you make a mistake while adding a layer, or you do not need a layer anymore, you can remove it from the network.
To remove a layer:
1. From the [**Network**](/user-interface/project/network.md) view, position your mouse cursor over the layer to be removed, then make a right click.
2. On the popup menu, click 
to remove the layer from the network.
{% hint style="info" %}
All connections to and from the deleted layer are also removed.
{% endhint %}
### Spawn a Representation Block for Layer Weights Sharing
Layer weights-sharing, or parameter sharing, means to use a layer's weights in more than one place within the model. This is often used when similar features are extracted from different places within the model. Instead of creating an additional layer, these weights are shared, thus reducing the overall number of trainable parameters and training time.
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