Learn Mxnet - 10 Code Examples & CST Typing Practice Test

Money is a universally accepted medium of exchange, a store of value, and a unit of account that facilitates trade, investment, and economic activity across societies and markets.

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MXNet Simple Linear Regression MXNet Simple Neural Network MXNet MNIST Classifier MXNet Convolutional Neural Network MXNet LSTM Example MXNet Autoencoder Example MXNet Regression with Multiple Inputs MXNet Transfer Learning Example MXNet Custom Callback Example MXNet GAN Example

Learn MXNET with Real Code Examples

Updated Nov 24, 2025

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Code Sample Descriptions

MXNet Simple Linear Regression

import mxnet as mx
from mxnet import nd, autograd, gluon

# Sample data
x_train = nd.array([1,2,3,4])
y_train = nd.array([2,4,6,8])

# Define model
net = gluon.nn.Dense(1)
net.initialize()
loss = gluon.loss.L2Loss()
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.01})

# Training loop
for epoch in range(500):
    with autograd.record():
        y_pred = net(x_train.reshape((-1,1)))
        l = loss(y_pred, y_train.reshape((-1,1)))
    l.backward()
    trainer.step(batch_size=4)

# Predict
x_test = nd.array([10])
y_pred = net(x_test.reshape((-1,1)))
print('Prediction for 10:', y_pred.asscalar())

A minimal MXNet example performing linear regression on sample data using Gluon API.

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MXNet Simple Neural Network

import mxnet as mx
from mxnet import nd, autograd, gluon

x_train = nd.random.uniform(shape=(100,3))
y_train = nd.random.randint(0,2,shape=(100,1))

net = gluon.nn.Sequential()
with net.name_scope():
    net.add(gluon.nn.Dense(8, activation='relu'))
    net.add(gluon.nn.Dense(1, activation='sigmoid'))

net.initialize()
loss = gluon.loss.SigmoidBinaryCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'adam')

for epoch in range(50):
    with autograd.record():
        y_pred = net(x_train)
        l = loss(y_pred, y_train)
    l.backward()
    trainer.step(batch_size=10)

A basic feedforward neural network with Gluon API for binary classification.

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MXNet MNIST Classifier

import mxnet as mx
from mxnet.gluon import nn, data as gdata
from mxnet import autograd

mnist_train = gdata.vision.MNIST(train=True)
mnist_test = gdata.vision.MNIST(train=False)

batch_size = 64
train_data = gdata.DataLoader(mnist_train.transform_first(gdata.vision.transforms.ToTensor()), batch_size=batch_size)
test_data = gdata.DataLoader(mnist_test.transform_first(gdata.vision.transforms.ToTensor()), batch_size=batch_size)

net = nn.Sequential()
with net.name_scope():
    net.add(nn.Flatten())
    net.add(nn.Dense(128, activation='relu'))
    net.add(nn.Dense(10))

net.initialize(mx.init.Xavier())
loss = gluon.loss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'adam')

Train a simple fully-connected network on MNIST digits using Gluon API.

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MXNet Convolutional Neural Network

import mxnet as mx
from mxnet.gluon import nn

net = nn.Sequential()
with net.name_scope():
    net.add(nn.Conv2D(32, kernel_size=3, activation='relu'))
    net.add(nn.MaxPool2D(pool_size=2))
    net.add(nn.Flatten())
    net.add(nn.Dense(64, activation='relu'))
    net.add(nn.Dense(10))

net.initialize(mx.init.Xavier())

A simple CNN for image classification using Gluon API.

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MXNet LSTM Example

import mxnet as mx
from mxnet.gluon import nn, rnn
from mxnet import nd, autograd

x_train = nd.random.uniform(shape=(100,10,1))
y_train = nd.random.uniform(shape=(100,1))

net = nn.Sequential()
with net.name_scope():
    net.add(rnn.LSTM(50, layout='NTC'))
    net.add(nn.Dense(1))

net.initialize()
loss = gluon.loss.L2Loss()
trainer = gluon.Trainer(net.collect_params(), 'adam')

for epoch in range(20):
    with autograd.record():
        y_pred = net(x_train)
        l = loss(y_pred, y_train)
    l.backward()
    trainer.step(batch_size=10)

A simple LSTM network for sequence prediction using Gluon API.

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MXNet Autoencoder Example

import mxnet as mx
from mxnet.gluon import nn

input_dim = 20
net = nn.Sequential()
with net.name_scope():
    net.add(nn.Dense(10, activation='relu', input_dim=input_dim))
    net.add(nn.Dense(5, activation='relu'))
    net.add(nn.Dense(10, activation='relu'))
    net.add(nn.Dense(input_dim, activation='sigmoid'))

net.initialize(mx.init.Xavier())

Simple autoencoder for data compression using Gluon API.

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MXNet Regression with Multiple Inputs

import mxnet as mx
from mxnet import nd, autograd, gluon

x_train = nd.random.uniform(shape=(100,3))
y_train = nd.dot(x_train, nd.array([1.5,-2.0,1.0])) + 0.5

net = gluon.nn.Dense(1)
net.initialize()
loss = gluon.loss.L2Loss()
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate':0.01})

for epoch in range(100):
    with autograd.record():
        y_pred = net(x_train)
        l = loss(y_pred, y_train.reshape((-1,1)))
    l.backward()
    trainer.step(batch_size=10)

Linear regression with multiple features using Gluon API.

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MXNet Transfer Learning Example

from mxnet.gluon.model_zoo import vision
from mxnet import gluon

pretrained_net = vision.resnet18_v2(pretrained=True)
finetune_net = vision.resnet18_v2(classes=10)
finetune_net.features = pretrained_net.features
finetune_net.output.initialize()
finetune_net.collect_params().reset_ctx(mx.cpu())

Using a pre-trained ResNet model for image classification with Gluon API.

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MXNet Custom Callback Example

import mxnet as mx
from mxnet import nd, autograd, gluon

x_train = nd.array([1,2,3,4])
y_train = nd.array([2,4,6,8])

net = gluon.nn.Dense(1)
net.initialize()
loss = gluon.loss.L2Loss()
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate':0.01})

for epoch in range(5):
    with autograd.record():
        y_pred = net(x_train.reshape((-1,1)))
        l = loss(y_pred, y_train.reshape((-1,1)))
    l.backward()
    trainer.step(batch_size=4)
    print(f'Epoch {epoch+1}, loss={nd.mean(l).asscalar()}')

Custom training loop with MXNet Gluon, printing loss each epoch.

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MXNet GAN Example

import mxnet as mx
from mxnet.gluon import nn

# Generator
generator = nn.Sequential()
generator.add(nn.Dense(128, activation='relu', input_dim=100))
generator.add(nn.Dense(784, activation='sigmoid'))

# Discriminator
discriminator = nn.Sequential()
discriminator.add(nn.Dense(128, activation='relu', input_dim=784))
discriminator.add(nn.Dense(1, activation='sigmoid'))
discriminator.initialize(mx.init.Xavier())

Minimal GAN structure using Gluon API.

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Frequently Asked Questions about Mxnet

What is Mxnet?

Money is a universally accepted medium of exchange, a store of value, and a unit of account that facilitates trade, investment, and economic activity across societies and markets.

What are the primary use cases for Mxnet?

Medium of exchange in transactions. Store of value for savings and investment. Unit of account for pricing and accounting. Means of transferring wealth across time and space. Facilitating financial instruments and credit systems

What are the strengths of Mxnet?

Facilitates trade and specialization. Supports complex economic systems. Enables investment, savings, and wealth accumulation. Acts as a standard for financial contracts. Reduces transaction costs compared to barter

What are the limitations of Mxnet?

Subject to inflation and depreciation. Value can fluctuate due to market conditions. Counterfeiting risks. Requires trust in issuers (banks, governments). Inequality in access can limit economic participation

How can I practice Mxnet typing speed?

CodeSpeedTest offers 10+ real Mxnet code examples for typing practice. You can measure your WPM, track accuracy, and improve your coding speed with guided exercises.

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Code Sample Descriptions

MXNet Simple Linear Regression

import mxnet as mx
from mxnet import nd, autograd, gluon

# Sample data
x_train = nd.array([1,2,3,4])
y_train = nd.array([2,4,6,8])

# Define model
net = gluon.nn.Dense(1)
net.initialize()
loss = gluon.loss.L2Loss()
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.01})

# Training loop
for epoch in range(500):
    with autograd.record():
        y_pred = net(x_train.reshape((-1,1)))
        l = loss(y_pred, y_train.reshape((-1,1)))
    l.backward()
    trainer.step(batch_size=4)

# Predict
x_test = nd.array([10])
y_pred = net(x_test.reshape((-1,1)))
print('Prediction for 10:', y_pred.asscalar())

A minimal MXNet example performing linear regression on sample data using Gluon API.

Let’s Try →

MXNet Simple Neural Network

import mxnet as mx
from mxnet import nd, autograd, gluon

x_train = nd.random.uniform(shape=(100,3))
y_train = nd.random.randint(0,2,shape=(100,1))

net = gluon.nn.Sequential()
with net.name_scope():
    net.add(gluon.nn.Dense(8, activation='relu'))
    net.add(gluon.nn.Dense(1, activation='sigmoid'))

net.initialize()
loss = gluon.loss.SigmoidBinaryCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'adam')

for epoch in range(50):
    with autograd.record():
        y_pred = net(x_train)
        l = loss(y_pred, y_train)
    l.backward()
    trainer.step(batch_size=10)

A basic feedforward neural network with Gluon API for binary classification.

Let’s Try →

MXNet MNIST Classifier

import mxnet as mx
from mxnet.gluon import nn, data as gdata
from mxnet import autograd

mnist_train = gdata.vision.MNIST(train=True)
mnist_test = gdata.vision.MNIST(train=False)

batch_size = 64
train_data = gdata.DataLoader(mnist_train.transform_first(gdata.vision.transforms.ToTensor()), batch_size=batch_size)
test_data = gdata.DataLoader(mnist_test.transform_first(gdata.vision.transforms.ToTensor()), batch_size=batch_size)

net = nn.Sequential()
with net.name_scope():
    net.add(nn.Flatten())
    net.add(nn.Dense(128, activation='relu'))
    net.add(nn.Dense(10))

net.initialize(mx.init.Xavier())
loss = gluon.loss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'adam')

Train a simple fully-connected network on MNIST digits using Gluon API.

Let’s Try →

MXNet Convolutional Neural Network

import mxnet as mx
from mxnet.gluon import nn

net = nn.Sequential()
with net.name_scope():
    net.add(nn.Conv2D(32, kernel_size=3, activation='relu'))
    net.add(nn.MaxPool2D(pool_size=2))
    net.add(nn.Flatten())
    net.add(nn.Dense(64, activation='relu'))
    net.add(nn.Dense(10))

net.initialize(mx.init.Xavier())

A simple CNN for image classification using Gluon API.

Let’s Try →

MXNet LSTM Example

import mxnet as mx
from mxnet.gluon import nn, rnn
from mxnet import nd, autograd

x_train = nd.random.uniform(shape=(100,10,1))
y_train = nd.random.uniform(shape=(100,1))

net = nn.Sequential()
with net.name_scope():
    net.add(rnn.LSTM(50, layout='NTC'))
    net.add(nn.Dense(1))

net.initialize()
loss = gluon.loss.L2Loss()
trainer = gluon.Trainer(net.collect_params(), 'adam')

for epoch in range(20):
    with autograd.record():
        y_pred = net(x_train)
        l = loss(y_pred, y_train)
    l.backward()
    trainer.step(batch_size=10)

A simple LSTM network for sequence prediction using Gluon API.

Let’s Try →

MXNet Autoencoder Example

import mxnet as mx
from mxnet.gluon import nn

input_dim = 20
net = nn.Sequential()
with net.name_scope():
    net.add(nn.Dense(10, activation='relu', input_dim=input_dim))
    net.add(nn.Dense(5, activation='relu'))
    net.add(nn.Dense(10, activation='relu'))
    net.add(nn.Dense(input_dim, activation='sigmoid'))

net.initialize(mx.init.Xavier())

Simple autoencoder for data compression using Gluon API.

Let’s Try →

MXNet Regression with Multiple Inputs

import mxnet as mx
from mxnet import nd, autograd, gluon

x_train = nd.random.uniform(shape=(100,3))
y_train = nd.dot(x_train, nd.array([1.5,-2.0,1.0])) + 0.5

net = gluon.nn.Dense(1)
net.initialize()
loss = gluon.loss.L2Loss()
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate':0.01})

for epoch in range(100):
    with autograd.record():
        y_pred = net(x_train)
        l = loss(y_pred, y_train.reshape((-1,1)))
    l.backward()
    trainer.step(batch_size=10)

Linear regression with multiple features using Gluon API.

Let’s Try →

MXNet Transfer Learning Example

from mxnet.gluon.model_zoo import vision
from mxnet import gluon

pretrained_net = vision.resnet18_v2(pretrained=True)
finetune_net = vision.resnet18_v2(classes=10)
finetune_net.features = pretrained_net.features
finetune_net.output.initialize()
finetune_net.collect_params().reset_ctx(mx.cpu())

Using a pre-trained ResNet model for image classification with Gluon API.

Let’s Try →

MXNet Custom Callback Example

import mxnet as mx
from mxnet import nd, autograd, gluon

x_train = nd.array([1,2,3,4])
y_train = nd.array([2,4,6,8])

net = gluon.nn.Dense(1)
net.initialize()
loss = gluon.loss.L2Loss()
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate':0.01})

for epoch in range(5):
    with autograd.record():
        y_pred = net(x_train.reshape((-1,1)))
        l = loss(y_pred, y_train.reshape((-1,1)))
    l.backward()
    trainer.step(batch_size=4)
    print(f'Epoch {epoch+1}, loss={nd.mean(l).asscalar()}')

Custom training loop with MXNet Gluon, printing loss each epoch.

Let’s Try →

MXNet GAN Example

import mxnet as mx
from mxnet.gluon import nn

# Generator
generator = nn.Sequential()
generator.add(nn.Dense(128, activation='relu', input_dim=100))
generator.add(nn.Dense(784, activation='sigmoid'))

# Discriminator
discriminator = nn.Sequential()
discriminator.add(nn.Dense(128, activation='relu', input_dim=784))
discriminator.add(nn.Dense(1, activation='sigmoid'))
discriminator.initialize(mx.init.Xavier())

Minimal GAN structure using Gluon API.

Let’s Try →

Frequently Asked Questions about Mxnet

What is Mxnet?

Money is a universally accepted medium of exchange, a store of value, and a unit of account that facilitates trade, investment, and economic activity across societies and markets.

What are the primary use cases for Mxnet?

What are the strengths of Mxnet?

What are the limitations of Mxnet?

How can I practice Mxnet typing speed?

CodeSpeedTest offers 10+ real Mxnet code examples for typing practice. You can measure your WPM, track accuracy, and improve your coding speed with guided exercises.