How to save a deep learning model in R
It is very important to be able to save a well trained model for future usage. In this recipe, we shall learn how to save a deep learning model in R.
Recipe Objective: How to save a deep learning model in R?
You might want to save your trained model for future usage at times. This saved model can be easily re-used anytime. To save a deep learning model in R, you can use the save_model hdf5() function as follows:
#saving the model
model <- save_model_hdf5("modelname")
#or
save_model_hdf5(model, "modelname")
What Users are saying..
Ed Godalle
Director Data Analytics at EY / EY Tech
I am the Director of Data Analytics with over 10+ years of IT experience. I have a background in SQL, Python, and Big Data working with Accenture, IBM, and Infosys. I am looking to enhance my skills... Read More
Relevant Projects
Machine Learning Projects
Data Science Projects
Python Projects for Data Science
Data Science Projects in R
Machine Learning Projects for Beginners
Deep Learning Projects
Neural Network Projects
Tensorflow Projects
NLP Projects
Kaggle Projects
IoT Projects
Big Data Projects
Hadoop Real-Time Projects Examples
Spark Projects
Data Analytics Projects for Students
You might also like
Data Science Tutorial
Data Scientist Salary
How to Become a Data Scientist
Data Analyst vs Data Scientist
Data Scientist Resume
Data Science Projects for Beginners
Machine Learning Engineer
Machine Learning Projects for Beginners
Datasets
Pandas Dataframe
Machine Learning Algorithms
Regression Analysis
MNIST Dataset
Data Science Interview Questions
Python Data Science Interview Questions
Spark Interview Questions
Hadoop Interview Questions
Data Analyst Interview Questions
Machine Learning Interview Questions
AWS vs Azure
Hadoop Architecture
Spark Architecture
Relevant Projects
PyCaret Project to Build and Deploy an ML App using Streamlit
In this PyCaret Project, you will build a customer segmentation model with PyCaret and deploy the machine learning application using Streamlit.
Build Classification Algorithms for Digital Transformation[Banking]
Implement a machine learning approach using various classification techniques in Python to examine the digitalisation process of bank customers.
Build a Hybrid Recommender System in Python using LightFM
In this Recommender System project, you will build a hybrid recommender system in Python using LightFM .
Learn to Build a Polynomial Regression Model from Scratch
In this Machine Learning Regression project, you will learn to build a polynomial regression model to predict points scored by the sports team.
NLP Project on LDA Topic Modelling Python using RACE Dataset
Use the RACE dataset to extract a dominant topic from each document and perform LDA topic modeling in python.
Learn to Build an End-to-End Machine Learning Pipeline - Part 1
In this Machine Learning Project, you will learn how to build an end-to-end machine learning pipeline for predicting truck delays, addressing a major challenge in the logistics industry.
Text Classification with Transformers-RoBERTa and XLNet Model
In this machine learning project, you will learn how to load, fine tune and evaluate various transformer models for text classification tasks.
Azure Deep Learning-Deploy RNN CNN models for TimeSeries
In this Azure MLOps Project, you will learn to perform docker-based deployment of RNN and CNN Models for Time Series Forecasting on Azure Cloud.
Model Deployment on GCP using Streamlit for Resume Parsing
Perform model deployment on GCP for resume parsing model using Streamlit App.
Build CNN for Image Colorization using Deep Transfer Learning
Image Processing Project -Train a model for colorization to make grayscale images colorful using convolutional autoencoders.