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CNN (convolutional neural network) models are very useful when we use them to train multidimensional types of data, such as images. We can also implement CNN model for regression data analysis. We previously used Python for CNN model regression, and in this article, we implement the same approach in R.

Video: R language implements CNN (Convolutional Neural Network) model for regression data analysis

 

We use a one-dimensional convolution function to apply the CNN model. We need the Keras R interface to use the Keras neural network API in R. If it is not available in the development environment, you need to install it first. This tutorial covers:

1. To prepare data
2. Define and fit the model
3. Predict and visualize results
4. The source code

Let’s start with the libraries we need to load this tutorial.

library(keras)
library(caret)
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To prepare

Data In this tutorial, we use the Boston housing data set as target regression data. First, we’ll load the data set and divide it into training and test sets.

set.seed(123)
boston = MASS::Boston
indexes = createDataPartition(boston$medv, p = .85, list = F)
 
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train = boston[indexes,]
test = boston[-indexes,]
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Next, we separate the X input and Y output portions of the training and test data and convert them to matrix type. As you may know, “MEDV” is the y data output of the Boston housing data set, which is the last column. The remaining columns are x input data. Check dimensions.

dim(xtrain)
[1] 432  13
 
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dim(ytrain)
[1] 432   1
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Next, we will redefine the shape of the X input data by adding another dimension.

dim(xtrain)
[1] 432  13   1
 
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dim(xtest)
[1] 74 13  1
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Here, we can extract the input dimension of the Keras model.

print(in_dim)
[1] 13  1
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Define and fit the model

We define the Keras model and add a one-dimensional convolution layer. The input shape changes to (13,1) as defined above. We added Flatten and Dense layers and compiled them using the “Adam” optimizer.

model %>% summary() ________________________________________________________________________ Layer (type) Output Shape Param # ======================================================================== conv1d_2 (Conv1D) (None, 12, 64) 192 ________________________________________________________________________ flatten_2 (Flatten) (None, 768) 0 ________________________________________________________________________ dense_3 (Dense) (None, 32) 24608 ________________________________________________________________________ dense_4 (Dense) (None, 1) 33 = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = Total params: 24833 Trainable params: 24833 Non - trainable params: 0 ________________________________________________________________________Copy the code

Next, we will use training data to fit the model.

Print (scores) loss of 24.20518Copy the code

Predict and visualize results

Now we can use the model of training to predict the test data.

predict(xtest)
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We will check the accuracy of the forecast with RMSE metrics.

Cat ("RMSE:", RMSE(ytest, ypred)) RMSE: 4.935908Copy the code

Finally, we’ll visualize the results in the chart to check for errors.

x_axes = seq(1:length(ypred))

lines(x_axes, ypred, col = "red", type = "l", lwd = 2)
legend("topl
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In this tutorial, we briefly learned how to use the Keras CNN model in R to fit and predict regression data.

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