SmartPredict
  • Documentation
  • OVERVIEW
    • Presentation
      • Key features
      • Who may benefit from its use
      • The SmartPredict Graphical User Interface (GUI)
        • The SmartPredict Modules
        • Focus on the Notebooks
        • Practical shortcuts
    • Prerequisites
  • Getting started
    • Getting started (Part1) : Iris classification project
      • Project description
      • Step 1. Create the project
      • Step 2. Upload the dataset
      • Step 3. Preprocess the dataset
      • Step 4. Build the flowchart
        • Set up the flowchart
        • Configure the modules
      • Step 5. Run the build
      • Step 6. Deploy the project
      • Step 7. Make inferences with our model
      • Conclusion
  • Getting started (Part 2): Predicting the passengers' survival in the Titanic shipwreck
    • Project description
    • Step 1. Create the project
    • Step 2. Upload the dataset
    • Step 3. Preprocess the dataset
    • Step 4. Build the flowchart
    • Step 5. Run the build
    • Step 6. Deploy the pipeline
    • Step 7. Make inferences with our pipeline
  • MODULE REFERENCE
    • CORE MODULES
      • Introduction
      • Basic Operations
        • Item Saver
      • Web Services
        • Web Service IN and OUT
      • Data retrieval
        • Data fetcher
        • Data frame loader/converter
        • Image data loader
      • Data preprocessing
        • Introduction
        • Array Reshaper
        • Generic Data Preprocessor
        • Missing data handler
        • Normalizer
        • One Hot Encoder
        • Ordinal Encoder
      • Data selection
        • Features selector
        • Generic data splitter
        • Labeled data splitter
      • Training and Prediction
        • Predictor DL models
        • Predictor ML models
        • Predictor ML models (Probabilistic models)
        • Trainer ML models
        • Trainer/Evaluator DL models
      • Evaluation and fine-tuning
        • Cross Validator for ML
        • Evaluator for ML models
      • Machine Learning algorithms
        • ML modules in SmartPredict
        • Decision Tree Regressor
        • KNeighbors Classifier
        • KNeighbors Regressors
        • Linear Regressor
        • Logistic Regressor
        • MLP Regressor
        • Naive Bayes Classifier
        • Random Forest Classifier
        • Random Forest Regressor
        • Support Vector Classifier
        • Support Vector Regressor
        • XGBoost Classifier
        • XGBoost Regressor
      • Deep learning algorithms
        • Dense Neural Network
        • Recurrent Neural Networks
      • Computer Vision
        • Convolutional Recurrent Networks
        • Fully Convolutional Neural Networks
        • Face detector
        • Image IO
        • Image matcher
        • Yolo
      • Natural Language Processing
        • Introduction
        • Text cleaner
        • Text vectorizer
      • Times Series processing
        • TS features selector
      • TensorFlow API
        • LSTM Layer
        • Dense Layer
      • Helpers
        • Data/Object Logger
        • Object Selector (5 ports)
      • Conclusion
  • CUSTOM MODULES
    • Function
    • Class
    • Use cases
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  • Description
  • Parameters
  • Activation function
  • Input
  • Initialization

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  1. MODULE REFERENCE
  2. CORE MODULES
  3. TensorFlow API

Dense Layer

This module belongs the 'TensorFlow API' category .

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Last updated 5 years ago

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Description

The Dense Layer module in a neural network is a layer that performs the integration of the input along with an activation.

Parameters

The Dense Layer module contains an enriched set of functions to configure at will.

We can , for instance, set the Tensorflow Keras API as either:

  • Automatic,

  • Sequential

  • or Functional.

Activation function

The Activation function, in turn, could be :

  • Sigmoid

  • Softmax

  • Exponential linear unit (ELU)

  • Scaled Exponential Linear Unit (SELU)

  • Softplus

  • Softsign

  • Rectified Linear Unit(RELU)

  • Hyperbolic tangent

  • Sigmoid Hard

  • Sigmoid Exponential (base e)

  • Identity function (Linear)

Input

As an input we can choose among : dimension, shape and batch input shape.

Initialization

  • Zeros

  • Ones

  • Constant

  • Random

  • Normal

  • Random Uniform

  • Truncated Normal

  • Variance scaling

  • Orthogonal

  • Identity

  • Le Cun Uniform

  • Glorot Normal

  • He Normal

  • Le Cun Normal

  • He Uniform

Regularizers are available: bias , activity , kernel .

Activity regularizer: None, L1 regularizer, L2 regularizer

Both Kernel and Bias can be attributed an based on :

initialization
There are many types of activation functions in the Dense Layer module.