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How to Develop a Neural Net for Predicting Disturbances in the Ionosphere

It can be challenging to develop a neural network predictive model for a new dataset.

One approach is to first inspect the dataset and develop ideas for what models might work, then explore the learning dynamics of simple models on the dataset, then finally develop and tune a model for the dataset with a robust test harness.

This process can be used to develop effective neural network models for classification and regression predictive modeling problems.

In this tutorial, you will discover how to develop a Multilayer Perceptron neural network model for the ionosphere binary classification dataset.

After completing this tutorial, you will know:

  • How to load and summarize the ionosphere dataset and use the results to suggest data preparations and model configurations to use.
  • How to explore the learning dynamics of simple MLP models on the dataset.
  • How to develop robust estimates of model performance, tune model performance, and make predictions on new data.

Let’s get started.

How to Develop a Neural Net for Predicting Disturbances in the Ionosphere

How to Develop a Neural Net for Predicting Disturbances in the Ionosphere
Photo by Sergey Pesterev, some rights reserved.

Tutorial Overview

This tutorial is divided into four parts; they are:

  1. Ionosphere Binary Classification Dataset
  2. Neural Network Learning Dynamics
  3. Evaluating and Tuning MLP Models
  4. Final Model and Make Predictions

Ionosphere Binary Classification Dataset

The first step is to define and explore the dataset.

We will be working with the “Ionosphere” standard binary classification dataset.

This dataset involves predicting whether a structure is in the atmosphere or not given radar returns.

You can learn more about the dataset here:

You can see the first few rows of the dataset below.

We can see that the values are all numeric and perhaps in the range [-1, 1]. This suggests some type of scaling would probably not be needed.

We can also see that the label is a string (“g” and “b“), suggesting that the values will need to be encoded to 0 and 1 prior to fitting a model.

We can load the dataset as a pandas DataFrame directly from the URL; for example:

Running the example loads the dataset directly from the URL and reports the shape of the dataset.

In this case, we can see that the dataset has 35 variables (34 input and one output) and that the dataset has 351 rows of data.

This is not many rows of data for a neural network and suggests that a small network, perhaps with regularization, would be appropriate.

It also suggests that using k-fold cross-validation would be a good idea given that it will give a more reliable estimate of model performance than a train/test split and because a single model will fit in seconds instead of hours or days with the largest datasets.

Next, we can learn more about the dataset by looking at summary statistics and a plot of the data.

Running the example first loads the data before and then prints summary statistics for each variable.

We can see that the mean values for each variable are in the tens, with values ranging from -1 to 1. This confirms that scaling the data is probably not required.

A histogram plot is then created for each variable.

We can see that many variables have a Gaussian or Gaussian-like distribution.

We may have some benefit in using a power transform on each variable in order to make the probability distribution less skewed which will likely improve model performance.

Histograms of the Ionosphere Classification Dataset

Histograms of the Ionosphere Classification Dataset

Now that we are familiar with the dataset, let’s explore how we might develop a neural network model.

Neural Network Learning Dynamics

We will develop a Multilayer Perceptron (MLP) model for the dataset using TensorFlow.

We cannot know what model architecture of learning hyperparameters would be good or best for this dataset, so we must experiment and discover what works well.

Given that the dataset is small, a small batch size is probably a good idea, e.g. 16 or 32 rows. Using the Adam version of stochastic gradient descent is a good idea when getting started as it will automatically adapts the learning rate and works well on most datasets.

Before we evaluate models in earnest, it is a good idea to review the learning dynamics and tune the model architecture and learning configuration until we have stable learning dynamics, then look at getting the most out of the model.

We can do this by using a simple train/test split of the data and review plots of the learning curves. This will help us see if we are over-learning or under-learning; then we can adapt the configuration accordingly.

First, we must ensure all input variables are floating-point values and encode the target label as integer values 0 and 1.

Next, we can split the dataset into input and output variables, then into 67/33 train and test sets.

We can define a minimal MLP model. In this case, we will use one hidden layer with 10 nodes and one output layer (chosen arbitrarily). We will use the ReLU activation function in the hidden layer and the “he_normal” weight initialization, as together, they are a good practice.

The output of the model is a sigmoid activation for binary classification and we will minimize binary cross-entropy loss.

We will fit the model for 200 training epochs (chosen arbitrarily) with a batch size of 32 because it is a small dataset.

We are fitting the model on raw data, which we think might be a good idea, but it is an important starting point.

At the end of training, we will evaluate the model’s performance on the test dataset and report performance as the classification accuracy.

Finally, we will plot learning curves of the cross-entropy loss on the train and test sets during training.

Tying this all together, the complete example of evaluating our first MLP on the ionosphere dataset is listed below.

Running the example first fits the model on the training dataset, then reports the classification accuracy on the test dataset.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

In this case, we can see that the model achieved an accuracy of about 88 percent, which is a good baseline in performance that we might be able to improve upon.

Line plots of the loss on the train and test sets are then created.

We can see that the model appears to converge but has overfit the training dataset.

Learning Curves of Simple MLP on Ionosphere Dataset

Learning Curves of Simple MLP on Ionosphere Dataset

Let’s try increasing the capacity of the model.

This will slow down learning for the same learning hyperparameters and may offer better accuracy.

We will add a second hidden layer with eight nodes, chosen arbitrarily.

The complete example is listed below.

Running the example first fits the model on the training dataset, then reports the accuracy on the test dataset.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

In this case, we can see a slight improvement in accuracy to about 93 percent, although the high variance of the train/test split means that this evaluation is not reliable.

Learning curves for the loss on the train and test sets are then plotted. We can see that the model still appears to show an overfitting behavior.

Learning Curves of Deeper MLP on the Ionosphere Dataset

Learning Curves of Deeper MLP on the Ionosphere Dataset

Finally, we can try a wider network.

We will increase the number of nodes in the first hidden layer from 10 to 50, and in the second hidden layer from 8 to 10.

This will add more capacity to the model, slow down learning, and may further improve results.

We will also reduce the number of training epochs from 200 to 100.

The complete example is listed below.

Running the example first fits the model on the training dataset, then reports the accuracy on the test dataset.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

In this case, the model achieves a better accuracy score, with a value of about 94 percent. We will ignore model performance for now.

Line plots of the learning curves are created showing that the model achieved a reasonable fit and had more than enough time to converge.

Learning Curves of Wider MLP on the Ionosphere Dataset

Learning Curves of Wider MLP on the Ionosphere Dataset

Now that we have some idea of the learning dynamics for simple MLP models on the dataset, we can look at evaluating the performance of the models as well as tuning the configuration of the models.

Evaluating and Tuning MLP Models

The k-fold cross-validation procedure can provide a more reliable estimate of MLP performance, although it can be very slow.

This is because k models must be fit and evaluated. This is not a problem when the dataset size is small, such as the ionosphere dataset.

We can use the StratifiedKFold class and enumerate each fold manually, fit the model, evaluate it, and then report the mean of the evaluation scores at the end of the procedure.

We can use this framework to develop a reliable estimate of MLP model performance with a range of different data preparations, model architectures, and learning configurations.

It is important that we first developed an understanding of the learning dynamics of the model on the dataset in the previous section before using k-fold cross-validation to estimate the performance. If we started to tune the model directly, we might get good results, but if not, we might have no idea of why, e.g. that the model was over or under fitting.

If we make large changes to the model again, it is a good idea to go back and confirm that the model is converging appropriately.

The complete example of this framework to evaluate the base MLP model from the previous section is listed below.

Running the example reports the model performance each iteration of the evaluation procedure and reports the mean and standard deviation of classification accuracy at the end of the run.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

In this case, we can see that the MLP model achieved a mean accuracy of about 93.4 percent.

We will use this result as our baseline to see if we can achieve better performance.

Next, let’s try adding regularization to reduce overfitting of the model.

In this case, we can add dropout layers between the hidden layers of the network. For example:

The complete example of the MLP model with dropout is listed below.

Running reports the mean and standard deviation of the classification accuracy at the end of the run.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

In this case, we can see that the MLP model with dropout achieves better results with an accuracy of about 94.6 percent compared to 93.4 percent without dropout

Finally, we will try reducing the batch size from 32 down to 8.

This will result in more noisy gradients and may also slow down the speed at which the model is learning the problem.

The complete example is listed below.

Running reports the mean and standard deviation of the classification accuracy at the end of the run.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

In this case, we can see that the MLP model with dropout achieves slightly better results with an accuracy of about 94.9 percent.

We will use this configuration as our final model.

We could continue to test alternate configurations to the model architecture (more or fewer nodes or layers), learning hyperparameters (more or fewer batches), and data transforms.

I leave this as an exercise; let me know what you discover. Can you get better results?
Post your results in the comments below, I’d love to see what you get.

Next, let’s look at how we might fit a final model and use it to make predictions.

Final Model and Make Predictions

Once we choose a model configuration, we can train a final model on all available data and use it to make predictions on new data.

In this case, we will use the model with dropout and a small batch size as our final model.

We can prepare the data and fit the model as before, although on the entire dataset instead of a training subset of the dataset.

We can then use this model to make predictions on new data.

First, we can define a row of new data.

Note: I took this row from the first row of the dataset and the expected label is a ‘g‘.

We can then make a prediction.

Then invert the transform on the prediction, so we can use or interpret the result in the correct label.

And in this case, we will simply report the prediction.

Tying this all together, the complete example of fitting a final model for the ionosphere dataset and using it to make a prediction on new data is listed below.

Running the example fits the model on the entire dataset and makes a prediction for a single row of new data.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

In this case, we can see that the model predicted a “g” label for the input row.

Further Reading

This section provides more resources on the topic if you are looking to go deeper.

Tutorials

Summary

In this tutorial, you discovered how to develop a Multilayer Perceptron neural network model for the ionosphere binary classification dataset.

Specifically, you learned:

  • How to load and summarize the ionosphere dataset and use the results to suggest data preparations and model configurations to use.
  • How to explore the learning dynamics of simple MLP models on the dataset.
  • How to develop robust estimates of model performance, tune model performance and make predictions on new data.

Do you have any questions?
Ask your questions in the comments below and I will do my best to answer.

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12 Responses to How to Develop a Neural Net for Predicting Disturbances in the Ionosphere

  1. Brent Janaky February 12, 2021 at 5:28 am #

    Wow, excellent tutorial and it’s fresh off the press! I just started DL so perfect timing. There’s a lot to study here so I have my homework cut out for me.

    Thank you, Jason.

  2. Ankur Kumar February 12, 2021 at 6:05 am #

    Awesome tutorial.
    Thanks for sharing, Jason Brownlee

  3. Sachin Dev Duggal CEO Builder AI February 12, 2021 at 4:34 pm #

    Awesome ! Good technical stuff. Its really hard to fit vast content. Highly appreciate your effort @Jason Brownlee.

  4. Alia Sharma February 12, 2021 at 4:47 pm #

    Amazing presentation and the way of explaining concepts in layman language is commendable. Kindly tell me how to get access to the collab notebook. I read this type of concept from article of sachin dev duggal which (CEO & Founder of Builder AI).

  5. Sonia Ahlawat February 13, 2021 at 2:46 am #

    Great article!! More understandable

  6. kamal May 27, 2021 at 1:42 am #

    sir as i want to apply horizontal ensembling to ionosphere data it gives me error

    File “C:\Users\LENOVO\untitled4.py”, line 79, in
    members = load_all_models(950, 1000)

    File “C:\Users\LENOVO\untitled4.py”, line 38, in load_all_models
    model = load_model(filename)

    File “C:\Users\LENOVO\anaconda3\envs\tensorflow\lib\site-packages\keras\engine\saving.py”, line 492, in load_wrapper
    return load_function(*args, **kwargs)

    File “C:\Users\LENOVO\anaconda3\envs\tensorflow\lib\site-packages\keras\engine\saving.py”, line 584, in load_model
    model = _deserialize_model(h5dict, custom_objects, compile)

    File “C:\Users\LENOVO\anaconda3\envs\tensorflow\lib\site-packages\keras\engine\saving.py”, line 274, in _deserialize_model
    model = model_from_config(model_config, custom_objects=custom_objects)

    File “C:\Users\LENOVO\anaconda3\envs\tensorflow\lib\site-packages\keras\engine\saving.py”, line 627, in model_from_config
    return deserialize(config, custom_objects=custom_objects)

    File “C:\Users\LENOVO\anaconda3\envs\tensorflow\lib\site-packages\keras\layers\__init__.py”, line 168, in deserialize
    printable_module_name=’layer’)

    File “C:\Users\LENOVO\anaconda3\envs\tensorflow\lib\site-packages\keras\utils\generic_utils.py”, line 147, in deserialize_keras_object
    list(custom_objects.items())))

    File “C:\Users\LENOVO\anaconda3\envs\tensorflow\lib\site-packages\keras\engine\sequential.py”, line 301, in from_config
    custom_objects=custom_objects)

    File “C:\Users\LENOVO\anaconda3\envs\tensorflow\lib\site-packages\keras\layers\__init__.py”, line 168, in deserialize
    printable_module_name=’layer’)

    File “C:\Users\LENOVO\anaconda3\envs\tensorflow\lib\site-packages\keras\utils\generic_utils.py”, line 149, in deserialize_keras_object
    return cls.from_config(config[‘config’])

    File “C:\Users\LENOVO\anaconda3\envs\tensorflow\lib\site-packages\keras\engine\base_layer.py”, line 1179, in from_config
    return cls(**config)

    File “C:\Users\LENOVO\anaconda3\envs\tensorflow\lib\site-packages\keras\legacy\interfaces.py”, line 91, in wrapper
    return func(*args, **kwargs)

    File “C:\Users\LENOVO\anaconda3\envs\tensorflow\lib\site-packages\keras\layers\core.py”, line 877, in __init__
    self.kernel_initializer = initializers.get(kernel_initializer)

    File “C:\Users\LENOVO\anaconda3\envs\tensorflow\lib\site-packages\keras\initializers.py”, line 515, in get
    return deserialize(identifier)

    File “C:\Users\LENOVO\anaconda3\envs\tensorflow\lib\site-packages\keras\initializers.py”, line 510, in deserialize
    printable_module_name=’initializer’)

    File “C:\Users\LENOVO\anaconda3\envs\tensorflow\lib\site-packages\keras\utils\generic_utils.py”, line 149, in deserialize_keras_object
    return cls.from_config(config[‘config’])

    File “C:\Users\LENOVO\anaconda3\envs\tensorflow\lib\site-packages\keras\initializers.py”, line 30, in from_config
    return cls(**config)

    File “C:\Users\LENOVO\anaconda3\envs\tensorflow\lib\site-packages\keras\initializers.py”, line 204, in __init__
    ‘but got’, distribution)

    ValueError: (‘Invalid distribution argument: expected one of {“normal”, “uniform”} but got’, ‘truncated_normal’)

    kindly help me to solve this

    • Jason Brownlee May 27, 2021 at 5:39 am #

      Sorry I don’t know the cause of your error.

      Perhaps you can post your code, data and error on stackoverflow.com

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