What are the random functions in lasagne?
This recipe explains what are the random functions in lasagne.
Recipe Objective - What are the random functions in lasagne?
Lasagne provides the "lasagne.random" class.
This module has a wide random number generator and is used for weight initialization and seeding noise layers.
Access Face Recognition Project Code using Facenet in Python
For more related projects:-
/projects/data-science-projects/keras-deep-learning-projects
/projects/data-science-projects/neural-network-projects
Functions:-
1. lasagne.random.get_rng() - Get the package-level random number generator.
2. lasagne.random.set_rng(new_rng) - Set the package-level random number generator.
What Users are saying..
Anand Kumpatla
Sr Data Scientist @ Doubleslash Software Solutions Pvt Ltd
ProjectPro is a unique platform and helps many people in the industry to solve real-life problems with a step-by-step walkthrough of projects. A platform with some fantastic resources to gain... 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
Many-to-One LSTM for Sentiment Analysis and Text Generation
In this LSTM Project , you will build develop a sentiment detection model using many-to-one LSTMs for accurate prediction of sentiment labels in airline text reviews. Additionally, we will also train many-to-one LSTMs on 'Alice's Adventures in Wonderland' to generate contextually relevant text.
PyTorch Project to Build a GAN Model on MNIST Dataset
In this deep learning project, you will learn how to build a GAN Model on MNIST Dataset for generating new images of handwritten digits.
Build a Multi Class Image Classification Model Python using CNN
This project explains How to build a Sequential Model that can perform Multi Class Image Classification in Python using CNN
Image Segmentation using Mask R-CNN with Tensorflow
In this Deep Learning Project on Image Segmentation Python, you will learn how to implement the Mask R-CNN model for early fire detection.
AWS MLOps Project for ARCH and GARCH Time Series Models
Build and deploy ARCH and GARCH time series forecasting models in Python on AWS .
Build an Image Classifier for Plant Species Identification
In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.
Build an optimal End-to-End MLOps Pipeline and Deploy on GCP
Learn how to build and deploy an end-to-end optimal MLOps Pipeline for Loan Eligibility Prediction Model in Python on GCP
Build a Customer Churn Prediction Model using Decision Trees
Develop a customer churn prediction model using decision tree machine learning algorithms and data science on streaming service data.
Learn to Build a Neural network from Scratch using NumPy
In this deep learning project, you will learn to build a neural network from scratch using NumPy
Customer Market Basket Analysis using Apriori and Fpgrowth algorithms
In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.