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
Powered by GitBook
On this page
  • Description
  • Parameters
  • Optimization parameters
  • Features extractor

Was this helpful?

  1. MODULE REFERENCE
  2. CORE MODULES
  3. Computer Vision

Convolutional Recurrent Networks

This module belongs to the " Computer Vision" category .

Description

The Convolutional Recurrent Network (CRNN or ConVNet) module is used to initiate a RNN estimator based on SmartPredict library.

This is a particularly popular function for treating visual data.

Parameters

As parameters, we can input shape ourselves right from the beginning. Then , we can adjust the optimization parameters and finally set the features selector.

Optimization parameters

The CRNN parameters are as follows:

  • Learning rate

  • Learning rate Reduction Factor

  • Beta 1 and 2

  • Gradient Clipping by Norm/Value

  • Epsilon

  • Momentum

  • Rho

Optimizers

As optimizers :

  • adam

  • rmsprop

  • adagrad

  • adamax

  • sgd (stochastic gradient descent)

Features extractor

The CRNN module parameters allow to implement different kinds of features extractor:

  • fcn

  • darknet

  • mobilenet_v2

  • resnet50_v2

  • resnet101_v2

  • resnet152_v2

  • nasnet

  • xception

The output decoder can be ctc or simple and imagenet is available as a features extractor weight.

PreviousComputer VisionNextFully Convolutional Neural Networks

Last updated 5 years ago

Was this helpful?

The CRNN.
We can select one from the various features extractors.