PerceptiLabs
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      • A Guide to Using U-Nets for Image Segmentation
      • A Voice Recognition model using Image Recognition
    • Environmental
      • Automated Weather Analysis Using Image Recognition
      • Wildfire Detection
    • Healthcare & Medical
      • Brain Tumor Detection
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      • Classifying Chest X-Rays to Detect Pneumonia
      • Classifying Ways to Wear a Face Mask
      • Detecting Defective Pills
      • Highlighting Blood Cells in Dark-field Microscopy Using Image Segmentation
      • Ocular Disease Recognition
      • Retinal OCT
      • Skin Cancer Classification
    • Industrial IoT & Manufacturing
      • Air Conditioner Piston Check
      • Classifying Fruit
      • Classifying Wood Veneers Into Dry and Wet
      • Defect Detection in Metal Surfaces
      • Fabric Stain Classification
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  1. Advanced
  2. Components

Operations

The Operations Components provide common math operations that can be applied to layers in a neural network, and more generally, to tensors and matrices.

Argmax

Performs an Argmax function that computes the maximum values for a set of inputs that would result in a maximum output.

This is typically the last layer of the model in cases where you only want a single answer rather than a distribution (e.g., for classification).

Parameters:

  • Dimension: specifies which axis contains the inputs on which to compute Argmax.

Merge

Combines two or more layers within a neural network such as when building skip connections.

Parameters:

  • Number of inputs: specifies the number of input Components to merge. Adjusting this, sets the number of input sockets available in the Component to which other Components can be dragged and connected to.

  • Operation: specifies the type of merge to perform. Can be set to:

    • Concatenate: concatenates the values from each input. You can also set which dimension you want to concatenate on, where -1 defaults to the last dimension.

    • Subtraction: subtracts the values from each input.

    • Addition: adds the values from each input.

    • Multiplication: multiples the values from each input.

    • Division: divides the values from each input.

Switch

Switches between two different inputs and sends only the output from one of them at a time. This Component swaps every other time (i.e., first Component, second Component, first Component etc.).

This can be useful for models involving multiple inputs such as GANs, where image data from two sources are connected to the Component's input sockets, and the output is connected to a Dense layer.

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Last updated 3 years ago

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  • Argmax
  • Merge
  • Switch