AugeLab Studio Manual
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  • 👋Welcome to AugeLab Studio User Manual
  • 📘Introduction
    • AugeLab Studio
    • Key Features
    • Use Cases
    • System Requirements
  • 🚀Getting Started
    • Signing up
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    • First Look
    • Simple Tour
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      • Detection
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    • More Local Examples
    • Further Reading
  • đŸ–Ĩī¸AugeLab Studio Interface
    • Detailed Look
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    • Managing Projects
    • Installing AI and much more
      • Leverage AI with Module Downloader
  • 🧱Function Blocks
    • Block Structures
    • Sockets
    • Blocks Column
    • Connections
    • All Function Blocks
      • AI Blocks
        • Face Detection
        • Mask Detection
        • Object Detection - Custom
        • Object Detection
        • Pose Estimation
        • Safety Equipment Detection
        • Social Distance Detector
        • Super Resolution
        • Text Detection
        • OCR
      • CNN Blocks
        • Average Pooling 2D
        • Batch Normalization
        • Choose Folder 2D
        • Compile Model
        • Conv. Sep. Layer 2D
        • Conv. Trans. Layer 2D
        • Convolutional Layer 2D
        • Dropout Layer
        • Flatten Layer 2D
        • Fully Connected
        • Global Average Pooling 2D
        • Global Max Pooling 2D
        • Input Layer 2D
        • Loss CCE
        • Max Pooling 2D
        • Metrics Accuracy
        • Model EfficientNet
        • Model MobileNet
        • Model ResNet
        • Model VGG
        • Optimizer Adadelta
        • Optimizer Adagrad
        • Optimizer Adam
        • Optimizer Adamax
        • Optimizer FTRL
        • Optimizer Nadam
        • Optimizer RMSProp
        • Optimizer SGD
        • ReLU Layer
        • Softmax Layer
        • Training Parameters
      • Data/Logic
        • Flow Control
          • Batch Concatenation
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          • HMI Background
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        • logic
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        • Data Operations
          • Data Memory
          • Data to JSON
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        • Referencing
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          • Data Write Local
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        • Signal Operators
          • Delay Step
          • Edge Falling
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          • Multi Port Switch
          • OFF Delay
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      • Image/Transformations
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          • Color Density Percentage
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          • Histogram On Curve
          • Histogram On Line
          • Image Color Match
          • Image Memory
          • Image Resolution and Channel Value
          • Maximum Images
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          • Measure Position Distance
          • Minimum Images
          • Non-zero of Image
          • Std. of Image
          • Structural Similarity
        • Transformation Filters
          • Auto Alignment
          • Auto Contrast
          • Color Quantizer and Clustering
          • Color Space
          • Contrast-Brightness-Gamma
          • Contrast Optimization
          • Deconvolution
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          • FloodFill
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        • Color Filters
          • 2D Filter
          • Apply Mask
          • Bilateral Filter
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          • HSV Filter
          • Image Adaptive Threshold
          • Image Threshold
          • Invert Image
          • Morphological Transformations
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          • RGB Mask
          • RGB Set
          • Sobel Filter
        • Operations
          • Add Images Weighted
          • Add Images
          • Collage Images
          • Divide Images
          • Flip Image
          • Image AutoRotator
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          • Image Resizer
          • Merge Channels
          • Multiply Images
          • Polar Transform
          • Rotate Image Angle
          • Slice Image
          • Split Image
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      • Detections/Shapes
        • Detectors
          • Barcode Reader
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          • Circle Detector
          • Corner Detector
          • Custom CNN Model
          • Data Matrix Reader
          • Detect Reference
          • Feature Detector
          • Find Object - Multiple Image
          • Find Object
          • Find Reference
          • Harris Corner Filter
          • Line Detector
          • Match Shapes
          • Measure Object Distance
          • Shape Detector
        • Draw
          • Draw Detections
          • Draw Line
          • Draw Point
          • Draw Rectangle
          • Draw Result On Image
          • Write Date On Image
          • Write Text On Image
        • Roi Processing
          • Check Area (Polygon)
          • Check Area
          • Get Pixel Mouse
          • Get Pixel
          • Get ROI
          • Image ROI Center
          • Image ROI Polygon
          • Image ROI Select Multi
          • Image ROI Select
          • Image ROI
          • Perspective Transform
          • Rectangles in Rectangle
        • Shape Analysis
          • Approximate Contour
          • Choose Line
          • Contour to Image
          • Fill Contour
          • Find Contour
          • Hull Convex
          • Minimum Circle
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          • Minimum Rectangle
          • Minimum Rotated Rectangle
          • Most Similar Shape
          • Point Polygon Test
      • Input/Output
        • Communication
          • Modbus Connect
          • Modbus Read
          • Modbus Write
          • MQTT Publish
          • MQTT Subscribe
          • OPC UA Client
          • OPC UA Read
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          • REST API - Get
          • REST API - Post
          • Send Mail
          • Siemens S7 Connect
          • Siemens S7 Read
          • Siemens S7 Write
        • Data Inputs
          • Date-Time List
          • Date-Time
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          • Keyboard/Barcode Reader
          • Logic Input
          • Number Input
          • Number Range
          • PWM (Pulse Width Modulation)
          • Rising Edge
          • String Input
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        • Image Inputs
          • Camera IP (ONVIF)
          • Camera IP
          • Camera USB External
          • Camera USB Vidgear
          • Camera USB
          • Load Image From Path
          • Load Image
          • Make Image
          • Pixel
          • Video
        • Outputs/Exports
          • CSV Export
          • Cycle Timer
          • File/Folder Operations
          • GPU Statistics
          • Image Logger
          • Image Write
          • Led Output
          • Multi Image Write
          • Output
          • Scope
          • Show Image
          • Stop
  • 📡Devices and Communication
    • Camera Usage
    • Communication Protocols
    • Further Reading
  • 🧩Example Projects
    • Demo Projects
    • Circumference Measurement
    • Object Counting
    • Tile Width Measurement
    • Human Detection
    • Object Detection
  • 🔑Key Features
    • Deploy Custom HMI Applications
    • Annotate Data for Object Detection
    • Train Custom AI Models
      • Choosing the Right Database
      • When to Stop Training
    • Create Plugins
      • Components
      • Coding Reference
    • Share Your Solutions with Community
    • Instal Python Packages
  • 📑FAQ
    • Contact Us
    • FAQ
    • Setting up a full project
  • Additional Resources
    • Training Schedule
    • Training Materials
    • AugeLab Experts
  • Appendix
    • Dictionary
    • References
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  • đŸ“Ĩ Inputs
  • 📤 Outputs
  • đŸ•šī¸ Controls
  • 🎨 Features
  • 📝 Usage Instructions
  • 📊 Evaluation
  • đŸ› ī¸ Troubleshooting

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  1. Function Blocks
  2. All Function Blocks
  3. CNN Blocks

Optimizer Adagrad

This function block serves as an interface for the Adagrad optimization algorithm, commonly used in machine learning tasks. It allows users to configure specific parameters that influence the optimization process.

đŸ“Ĩ Inputs

This function block does not have any inputs.

📤 Outputs

This block outputs the configured Adagrad optimizer, ready to be integrated into machine learning workflows.

đŸ•šī¸ Controls

Learning rate A control to set the learning rate, which determines the step size at each iteration of the optimization. Default value is set to 0.001.

Initial Accumulator A control to initialize the accumulator which is used for scaling the learning rate. The default is 0.1.

Epsilon A control for the small constant that prevents division by zero errors during optimization. The default value is set to 1e-07.

🎨 Features

Customizable Parameters Provides easy access to adjust learning rate, initial accumulator, and epsilon, allowing users to fine-tune the optimization process according to their needs.

Integration with Keras This block outputs an optimizer that can be seamlessly utilized within Keras models for training.

📝 Usage Instructions

  1. Set Parameters: Configure the Learning rate, Initial Accumulator, and Epsilon fields to your desired values.

  2. Run the Block: Execute the block to produce the Adagrad optimizer configured with the specified parameters.

  3. Use in AI Workflows: Integrate the output optimizer within your machine learning model training processes.

📊 Evaluation

When executed, this function block outputs a configured Adagrad optimizer which can then be used with Keras for training models, enhancing the learning efficacy during the training phase.

đŸ› ī¸ Troubleshooting

Invalid Parameter Value

Ensure that all parameters are set to valid floating-point numbers. If a parameter shows an error, adjust accordingly based on the expected value range.

No Output on Execution

If the block does not output an optimizer, ensure the parameters are filled correctly. The absence of parameters can lead to undefined behavior.

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Last updated 8 months ago

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