We use a 1dimensional convolutional function to apply the CNN model. We need Keras R interface to use the Keras neural network API in R. You need to install it if it is not available on your development environment. The tutorial covers:
 Preparing the data
 Defining and fitting the model
 Predicting and visualizing the results
 Source code listing
library(keras)
library(caret)
Preparing the data
We use the Boston housing dataset as a target regression data in this tutorial. First, we'll load the dataset and split it into the train and test parts.
set.seed(123)
boston = MASS::Boston
indexes = createDataPartition(boston$medv, p = .85, list = F)
train = boston[indexes,]
test = boston[indexes,]
Next, we'll separate x input and y output parts of both train and test data and convert them into the matrix type. As you may know, the 'medv' is the output, y data in the Boston housing dataset and it is the final column (14) in it. The remaining columns are the x input data.
xtrain = as.matrix(train[,14])
ytrain = as.matrix(train[,14])
xtest = as.matrix(test[,14])
ytest = as.matrix(test[, 14])
We'll check the dimensions.
dim(xtrain)
[1] 432 13
dim(ytrain)
[1] 432 1
Next, we'll reshape the x input data by adding another onedimension.
xtrain = array(xtrain, dim = c(nrow(xtrain), 13, 1))
xtest = array(xtest, dim = c(nrow(xtest), 13, 1))
dim(xtrain)
[1] 432 13 1
dim(xtest)
[1] 74 13 1
Here, we can extract the input dimension for the keras model.
in_dim = c(dim(xtrain)[2:3])
print(in_dim)
[1] 13 1
Defining and fitting the model
We'll define the Keras sequential model and add a onedimensional convolutional layer. Input shape becomes as it is defined above (13,1). We'll add Flatten and Dense layers and compile it with 'Adam' optimizer.
model = keras_model_sequential() %>%
layer_conv_1d(filters = 64, kernel_size = 2,
input_shape = in_dim, activation = "relu") %>%
layer_flatten() %>%
layer_dense(units = 32, activation = "relu") %>%
layer_dense(units = 1, activation = "linear")
model %>% compile(
loss = "mse",
optimizer = "adam")
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: 24,833
Trainable params: 24,833
Nontrainable params: 0
________________________________________________________________________

Next, we 'll fit the model with train data.
model %>% fit(xtrain, ytrain, epochs = 100, batch_size=16, verbose = 0)
scores = model %>% evaluate(xtrain, ytrain, verbose = 0)
print(scores)
loss
24.20518
Predicting and visualizing the results
Now we can predict the test data with the trained model.
ypred = model %>% predict(xtest)
We'll check the accuracy of prediction through the RMSE metrics.
cat("RMSE:", RMSE(ytest, ypred))
RMSE: 4.935908
Finally, we'll visualize the result in a plot to check the difference.
x_axes = seq(1:length(ypred))
plot(x_axes, ytest, ylim = c(min(ypred), max(ytest)),
col = "burlywood", type = "l", lwd = 2, ylab = "medv")
lines(x_axes, ypred, col = "red", type = "l", lwd = 2)
legend("topleft", legend = c("ytest", "ypred"),
col = c("burlywood", "red"), lty=1, cex=0.7, lwd=2, bty='n')
In this tutorial, we've briefly learned how to fit and predict regression data with the keras CNN model in R. The full source code is listed below.
Source code listing
library(keras)
library(caret)
set.seed(123)
boston = MASS::Boston
indexes = createDataPartition(boston$medv, p = .85, list = F)
train = boston[indexes,]
test = boston[indexes,]
xtrain = as.matrix(train[,14])
ytrain = as.matrix(train[,14])
xtest = as.matrix(test[,14])
ytest = as.matrix(test[, 14])
dim(xtrain)
dim(ytrain)
xtrain = array(xtrain, dim = c(nrow(xtrain), 13, 1))
xtest = array(xtest, dim = c(nrow(xtest), 13, 1))
dim(xtrain)
dim(xtest)
in_dim = c(dim(xtrain)[2:3])
print(in_dim)
model = keras_model_sequential() %>%
layer_conv_1d(filters = 64, kernel_size = 2,
input_shape = in_dim, activation = "relu") %>%
layer_flatten() %>%
layer_dense(units = 32, activation = "relu") %>%
layer_dense(units = 1, activation = "linear")
model %>% compile(
loss = "mse",
optimizer = "adam")
model %>% summary()
model %>% fit(xtrain, ytrain, epochs = 100, batch_size=16, verbose = 0)
scores = model %>% evaluate(xtrain, ytrain, verbose = 0)
print(scores)
ypred = model %>% predict(xtest)
cat("RMSE:", RMSE(ytest, ypred))
x_axes = seq(1:length(ypred))
plot(x_axes, ytest, ylim = c(min(ypred), max(ytest)),
col = "burlywood", type = "l", lwd = 2, ylab = "medv")
lines(x_axes, ypred, col = "red", type = "l", lwd = 2)
legend("topleft", legend = c("ytest", "ypred"),
col = c("burlywood", "red"), lty = 1, cex=0.7, lwd=2, bty='n')
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