Deep Learning with R Cookbook

How to do it...

After the initial installation and setup, we can start building deep learning models by simply loading the TensorFlow library into the R environment:

1. Let's start by simulating some dummy data:

x_data = matrix(runif(1000*2),nrow = 1000,ncol = 1)y_data = matrix(runif(1000),nrow = 1000,ncol = 1)

2. Now, we need to initialize some TensorFlow variables; that is, the weights and biases:

W <- tf$Variable(tf$random_uniform(shape(1L), -1.0, 1.0))b <- tf$Variable(tf$zeros(shape(1L)))

3. Now, let's define the model:

y_hat <- W * x_data + b

4. Then, we need to define the loss function and optimizer:

loss <- tf$reduce_mean((y_hat - y_data) ^ 2)optimizer <- tf$train$GradientDescentOptimizer(0.5)train <- optimizer$minimize(loss)

5. Next, we ...