How to write a backward pass in MXNet

This recipe helps you write a backward pass in MXNet

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

This recipe explains how to write backward pass in MXNet.

FastText and Word2Vec Word Embeddings Python Implementation

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 pass 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()

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