TensorFlow

Using TensorFlow with R

The TensorFlow API is composed of a set of Python modules that enable constructing and executing TensorFlow graphs. The tensorflow package provides access to the complete TensorFlow API from within R. Here’s a simple example of making up some data in two dimensions and then fitting a line to it:

library(tensorflow)

# Create 100 phony x, y data points, y = x * 0.1 + 0.3
x_data <- runif(100, min=0, max=1)
y_data <- x_data * 0.1 + 0.3

# Try to find values for W and b that compute y_data = W * x_data + b
# (We know that W should be 0.1 and b 0.3, but TensorFlow will
# figure that out for us.)
W <- tf$Variable(tf$random_uniform(shape(1L), -1.0, 1.0))
b <- tf$Variable(tf$zeros(shape(1L)))
y <- W * x_data + b

# Minimize the mean squared errors.
loss <- tf$reduce_mean((y - y_data) ^ 2)
optimizer <- tf$train$GradientDescentOptimizer(0.5)
train <- optimizer$minimize(loss)

# Launch the graph and initialize the variables.
sess = tf$Session()
sess$run(tf$initialize_all_variables())

# Fit the line (Learns best fit is W: 0.1, b: 0.3)
for (step in 1:201) {
  sess$run(train)
  if (step %% 20 == 0)
    cat(step, "-", sess$run(W), sess$run(b), "\n")
}

The first part of this code builds the data flow graph. TensorFlow does not actually run any computation until the session is created and the runfunction is called.

MNIST Tutorials

To whet your appetite further, we suggest you check out what a classical machine learning problem looks like in TensorFlow. In the land of neural networks the most “classic” classical problem is the MNIST handwritten digit classification. We offer two introductions here, one for machine learning newbies, and one for pros. If you’ve already trained dozens of MNIST models in other software packages, please take the red pill. If you’ve never even heard of MNIST, definitely take the blue pill. If you’re somewhere in between, we suggest skimming blue, then red.

Images licensed CC BY-SA 4.0; original by W. Carter

If you’re already sure you want to learn and install TensorFlow you can skip these and charge ahead. Don’t worry, you’ll still get to see MNIST – we’ll also use MNIST as an example in our technical tutorial where we elaborate on TensorFlow features.

Download and Setup

devtools::install_github("rstudio/tensorflow")

Sys.setenv(TENSORFLOW_PYTHON="/usr/local/bin/python")
devtools::install_github("rstudio/tensorflow")

Sys.setenv(TENSORFLOW_PYTHON_VERSION = 3)
devtools::install_github("rstudio/tensorflow")

library(tensorflow)
sess = tf$Session()
hello <- tf$constant('Hello, TensorFlow!')
sess$run(hello)

Recommended Next Steps

Once you’ve installed the base TensorFlow system and the tensorflow R package, you will likely want work though the series of tutorials that cover TensorFlow basics:

• MNIST For ML Beginners
• Deep MNIST for Experts
• TensorFlow Mechanics 101

These articles cover the core concepts of TensorFlow in more depth as well describe the details of using the TensorFlow API from R:

• TensorFlow API Basic Usage
• Using the TensorFlow API from R

These articles provide more in depth treatments of various topics:

• Variables: Creation, Initialization, Saving, and Loading
• TensorFlow Mechanics 101
• TensorBoard: Visualizing Learning
• TensorBoard: Graph Visualization

Finally, to learn more about neural networks you might enjoy the TensorFlow playground, which lets you tinker with a neural network in your browser.