How to write a forward pass in MXNet

This recipe helps you write a forward pass in MXNet

Recipe Objective: How to write a forward pass in MXNet?

This recipe explains how to write forward pass in MXNet.

Step 1: Importing library

Let us first import the necessary libraries. We'll import mxnet as mx and ndarray(nd alias) and gluon from mxnet.

import mxnet as mx
from mxnet import nd, gluon

Step 2: Forward And Backward Pass

There are three significant steps in training a neural network. The first is forward pass, and the second is backward pass. Forward pass calculates loss and backward pass calculates the gradient of the loss concerning parameters. We have implemented forward and backward pass on a small data set. We can implement forward bass by calling net.forward(a) or net(a). We can implement a backward pass by calling l.backward().

a = nd.random.uniform(shape=(4, 4))
b = nd.random.uniform(shape=(4, ))
loss = gluon.loss.L2Loss()
with autograd.record():
    l = loss(net.forward(a), b)
l.backward()

What Users are saying..

profile image

Savvy Sahai

Data Science Intern, Capgemini
linkedin profile url

As a student looking to break into the field of data engineering and data science, one can get really confused as to which path to take. Very few ways to do it are Google, YouTube, etc. I was one of... Read More

Relevant Projects

House Price Prediction Project using Machine Learning in Python
Use the Zillow Zestimate Dataset to build a machine learning model for house price prediction.

Hands-On Approach to Regression Discontinuity Design Python
In this machine learning project, you will learn to implement Regression Discontinuity Design Example in Python to determine the effect of age on Mortality Rate in Python.

Locality Sensitive Hashing Python Code for Look-Alike Modelling
In this deep learning project, you will find similar images (lookalikes) using deep learning and locality sensitive hashing to find customers who are most likely to click on an ad.

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.

LLM Project to Build and Fine Tune a Large Language Model
In this LLM project for beginners, you will learn to build a knowledge-grounded chatbot using LLM's and learn how to fine tune it.

Machine Learning project for Retail Price Optimization
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.

Hands-On Approach to Causal Inference in Machine Learning
In this Machine Learning Project, you will learn to implement various causal inference techniques in Python to determine, how effective the sprinkler is in making the grass wet.

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.

Build a Credit Default Risk Prediction Model with LightGBM
In this Machine Learning Project, you will build a classification model for default prediction with LightGBM.

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.