Creating a 3-Input, 1-Output Regression Network with MXNET for Image Classification Tasks Using Deep Learning Techniques.

Understanding MXNET and Creating a 3-Input, 1-Output Regression Network with 3x3 Hidden Layers

In this article, we will explore how to create a 3-input, 1-output regression network using MXNET. We will go through the process of building such a network, including the use of fully connected layers, activation functions, and linear regression outputs.

Introduction to MXNET

MXNET is an open-source deep learning framework developed by Facebook’s Artificial Intelligence Research Lab (FAIR). It provides a wide range of features for building and training neural networks, including support for CPU and GPU acceleration, automatic differentiation, and dynamic computation graphs. MXNET is widely used in industry and academia for various applications, including computer vision, natural language processing, and recommender systems.

Building the Network

To build a 3-input, 1-output regression network with 3x3 hidden layers using MXNET, we need to follow these steps:

Import the necessary libraries and load the data.
Create the input and output variables.
Build the first fully connected layer (fc1) with 3 nodes and activation function sigmoid.
Build the second fully connected layer (fc2) with 3 nodes and activation function sigmoid.
Build the third fully connected layer (fc3) with 3 nodes and activation function sigmoid.
Build the fourth fully connected layer (fc4) with 1 node, which will be the output of the network.

Building the Network in MXNET

Here is an example code snippet that shows how to build a 3-input, 1-output regression network with 3x3 hidden layers using MXNET:

data <- mx.symbol.Variable("data")

# First fully connected layer (fc1) with 3 nodes and activation function sigmoid
fc1 <- mx.symbol.FullyConnected(data, name="fc1", num_hidden=3)
act1 <- mx.symbol.Activation(fc1, name="sigm1", act_type="sigmoid")

# Second fully connected layer (fc2) with 3 nodes and activation function sigmoid
fc2 <- mx.symbol.FullyConnected(act1, name="fc2", num_hidden=3)
act2 <- mx.symbol.Activation(fc2, name="sigm2", act_type="sigmoid")

# Third fully connected layer (fc3) with 3 nodes and activation function sigmoid
fc3 <- mx.symbol.FullyConnected(act2, name="fc3", num_hidden=3)
act3 <- mx.symbol.Activation(fc3, name="sigm3", act_type="sigmoid")

# Fourth fully connected layer (fc4) with 1 node, which will be the output of the network
fc4 <- mx.symbol.FullyConnected(act3, name="fc4", num_hidden=1)

# Linear regression output for the final layer
linear_regression_output <- mx.symbol.LinearRegressionOutput(fc4, name="output")

Note that in this example, we use the LinearRegressionOutput function to specify the loss function and optimization algorithm for the network.

Optimizing the Network

To optimize the network, we need to specify the learning rate, regularization strength, and other hyperparameters. In this example, we will use a fixed learning rate of 0.07 and no regularization.

# Optimization parameters
learning_rate <- 0.07
regularization_strength <- 0

# Create the model
mxn <- mx.model.FeedForward.create(
    array.layout = "rowmajor",
    linear_regression_output,
    X = lcinm,  # Input data
    y = lcouta,  # Output data
    learning.rate = learning_rate,
    eval.metric = mx.metric.rmse
)

Training the Network

To train the network, we need to call the mx.model.FeedForward.create function and pass in the training data, output data, and optimization parameters.

# Train the model
mxn <- mx.model.FeedForward.train(
    mxn,
    X = lcinm,
    y = lcouta,
    learning.rate = 0.07,
    eval.metric = mx.metric.rmse
)

Evaluating the Network

To evaluate the network, we need to call the mx.metric.rmse function and pass in the predicted output data.

# Evaluate the model
rmse <- mx.metric.rmse(mxn, X = lcinm, y = lcouta)
print(rmse)

Conclusion

In this article, we have explored how to create a 3-input, 1-output regression network with 3x3 hidden layers using MXNET. We have gone through the process of building such a network, including the use of fully connected layers, activation functions, and linear regression outputs. We have also optimized the network using a fixed learning rate and no regularization. Finally, we have trained and evaluated the network using sample data.

Additional Resources

MXNET documentation: https://mxnet.apache.org/
MXNET tutorials: https://mxnet.apache.org/tutorials.html
MXNET GitHub repository: https://github.com/apache/incubator-mxnet

Last modified on 2023-10-11