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Conv. Trans. Layer 2D
The
Conv. Trans. Layer 2D
node in AugeLab Studio is used to add a 2D transposed convolutional layer to a convolutional neural network (CNN) model. It allows you to specify the filter size, kernel size, dilation size, and activation function for the layer.The
Conv. Trans. Layer 2D
node adds a 2D transposed convolutional layer to a CNN model. It provides the following outputs:- Keras Layer: The 2D transposed convolutional layer configured with the specified parameters.
- Node Title: Conv. Trans. Layer 2D
- Node ID: OP_NODE_AI_2D_CONV_TRANS_LAYER
The
Conv. Trans. Layer 2D
node has no input sockets.The
Conv. Trans. Layer 2D
node has one output socket:- 1.Keras Layer: The 2D transposed convolutional layer configured with the specified parameters.
- 1.Drag and drop the
Conv. Trans. Layer 2D
node from the node library onto the canvas in AugeLab Studio. - 2.Set the filter size, kernel size, dilation size, and activation function in the node's properties.
- 3.The configured 2D transposed convolutional layer will be displayed in the node.
- 4.Connect the output socket of the
Conv. Trans. Layer 2D
node to other nodes in the pipeline to build a CNN model.
The
Conv. Trans. Layer 2D
node creates a 2D transposed convolutional layer using the Keras API. The layer is configured with the specified filter size, kernel size, dilation size, and activation function.The
Conv. Trans. Layer 2D
node extends the NodeCNN
base class, which provides the common functionality for CNN-related nodes.When the node is evaluated, the
getKerasLayer
method is called to create and configure the Keras layer based on the specified parameters. The getKerasLayer
method is implemented in the node's subclasses to return the appropriate Keras layer type (Conv2D
, SeparableConv2D
, or Conv2DTranspose
).The node supports various activation functions, including
relu
, sigmoid
, softmax
, softplus
, softsign
, tanh
, selu
, elu
, and exponential
. The activation function can be selected from a dropdown menu in the node's properties.The configured 2D transposed convolutional layer is outputted through the
Keras Layer
output socket, which can be connected to other nodes in the pipeline for further processing.- 1.Drag and drop the
Conv. Trans. Layer 2D
node from the node library onto the canvas in AugeLab Studio. - 2.Set the filter size, kernel size, dilation size, and activation function in the node's properties.
- 3.The configured 2D transposed convolutional layer will be displayed in the node.
- 4.Connect the output socket of the
Conv. Trans. Layer 2D
node to other nodes in the pipeline to build a CNN model.
- The
Conv. Trans. Layer 2D
node is used to add a 2D transposed convolutional layer to a CNN model. - The
Conv. Trans. Layer 2D
node requires the Keras library to be installed. - Ensure that the specified kernel size is an odd number to maintain symmetry.
- The
Conv. Trans. Layer 2D
node supports various activation functions for non-linear activation of the layer. - The configured 2D transposed convolutional layer can be connected to other nodes in the pipeline to build a CNN model.