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AugeLab Studio Manual
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Input Layer 2D

Input Layer 2D Node Documentation

The Input Layer 2D node in AugeLab Studio is used to define the input layer for a 2D convolutional neural network (CNN). It represents the initial layer of the network that takes input images.

Node Overview

The Input Layer 2D node defines the input layer for a 2D CNN. It has the following properties:
  • Node Title: Input Layer 2D
  • Node ID: OP_NODE_AI_2D_INPUT_CONV_LAYER

Inputs

The Input Layer 2D node has the following input socket:
  • Folder: A folder containing the input images for training the CNN.

Outputs

The Input Layer 2D node outputs the configured PNNModel object, which represents the CNN model architecture with the input layer defined.

Node Interaction

  1. 1.
    Drag and drop the Input Layer 2D node from the node library onto the canvas in AugeLab Studio.
  2. 2.
    Connect the Folder input socket of the Input Layer 2D node to a Choose Folder 2D block that specifies the folder containing the input images.
  3. 3.
    Configure the properties of the Input Layer 2D node, such as the input size.
  4. 4.
    Connect the output of the Input Layer 2D node to other nodes in the network to build the CNN architecture.

Implementation Details

The Input Layer 2D node is implemented using the Input layer from the Keras library. The node's evalAi method constructs the input layer based on the specified input size and color type.
  • The Input Layer 2D node expects an input folder containing the training images.
  • It determines the color type of the input images and constructs the input layer accordingly.
  • The input size defines the height and width of the input images.
  • The Input Layer 2D node configures the PNNModel object with the input layer and input size.

Usage

  1. 1.
    Drag and drop the Input Layer 2D node from the node library onto the canvas in AugeLab Studio.
  2. 2.
    Connect the Folder input socket of the Input Layer 2D node to a Choose Folder 2D block that specifies the folder containing the input images.
  3. 3.
    Configure the properties of the Input Layer 2D node, such as the input size.
  4. 4.
    Connect the output of the Input Layer 2D node to other nodes in the network to build the CNN architecture.
  5. 5.
    Train the network using the configured input layer and observe the results.

Notes

  • The Input Layer 2D node defines the input layer for a 2D CNN.
  • It takes a folder containing the input images as input.
  • The Input Layer 2D node requires the Keras library to be installed.
  • Ensure that the input folder contains the training images in the appropriate format.
  • The Input Layer 2D node outputs the configured PNNModel object, which represents the CNN model architecture with the input layer defined.
  • Connect the output of the Input Layer 2D node to other nodes in the network to build the CNN architecture.
  • The input size defines the height and width of the input images for training the CNN.