The 2 most popular data science languages – Python and R – are often pitted as rivals. This couldn’t be further from the truth. Data scientists that learn to use the strengths of both languages are valuable because they have NO LIMITS.

• Machine Learning and Deep Learning: They can switch to Python to leverage scikit learn and tensorflow.
• Data Wrangling, Visualization, Apps & Reporting: They can quickly change to R to use tidyverse, shiny and rmarkdown.

The bottom line is that knowing both R and Python makes you SUPER PRODUCTIVE.

Article Updates:

• This article has been updated. View the updated TensorFlow article at Business Science.

• Check out this article to set up Scikit Learn in a Python 3.8 Environment.

Have 5 minutes?
Then let’s set up TensorFlow 2 for Deep Learning

TensorFlow in R!

We’re going to go through the essential setup tips of the PRO’s – those that use Python from R via reticulate.

• Install TensorFlow in R

• Do a Image Recognition Analysis to verify TensorFlow is working.

Here’s the BONUS Image Reconition Tutorial. You’ll classify Fashion Images.

TensorFlow Ankle Boot Classification – Tutorial to Test if TF is Working

Using TensorFlow & R
How do you use them together in Business Projects?

Setting up TensorFlow in R is an insane productivity booster. You can leverage the best of Python + R. But you still need to learn how to use Python and R together for real business projects. And, it’s impossible to teach you all the in’s and out’s in 1 short article. But, I have great news!

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• Lab 37: TensorFlow for Energy Demand Forecasting
• Lab 38: Social Media Text Analytics!

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Installing TensorFlow in R

This process should take under 5-minutes. First we have some requirments to get TensorFlow 2 installed.

TensorFlow 2.0.0 Requirements

These may (will) change in the future, but currently the requirements are:

• Python 3.5-3.7
• Windows 7 or Later
• MacOS 10.12.6 or Later
• Ubunto 16.04 or Later

If you’ve followed the Scikit Learn in R tutorial, we used Python 3.8 (latest stable version). We can’t use Python 3.8 for TensorFlow, so we need to create a new environment. We’ll use Python 3.6 in this tutorial.

Conda Requirements

If you don’t have Conda installed, please install here: Anaconda Installation.

Installing TensorFlow in R with reticulate

Do this in R. Install and load tidyverse, reticulate, and tensorflow.

library(tidyverse)
library(reticulate)
library(tensorflow)

Next, run install_tensorflow() in your R environment. This will take about 3-5 minutes to install TensorFlow in a new Conda Environment named “py3.6”.

install_tensorflow(
    method               = "conda", 
    version              = "default", # Installs TF 2.0.0 (as of May 15, 2020)
    envname              = "py3.6", 
    conda_python_version = "3.6", 
    extra_packages       = c("matplotlib", "numpy", "pandas", "scikit-learn")
)

Side note: You can actually specify which TensorFlow version to install with the version arg. This can be helpful to switch from the CPU vesion to GPU version (greater power, greater responsibility) or to access older versions of TF.

We can check to see that py3.6 conda environment has been created.

conda_list()

##        name                                             python
## 1 anaconda3            /Users/mdancho/opt/anaconda3/bin/python
## 2     py3.6 /Users/mdancho/opt/anaconda3/envs/py3.6/bin/python
## 3     py3.7 /Users/mdancho/opt/anaconda3/envs/py3.7/bin/python
## 4     py3.8 /Users/mdancho/opt/anaconda3/envs/py3.8/bin/python

Next, we tell reticulate to use the py3.6 conda environment.

use_condaenv("py3.6", required = TRUE)

Congrats on the Installation is now complete! – Now Let’s Use TensorFlow to classify images.

Image Recognition Analysis
To Verify TensorFlow is Working

Let’s run through a short image recognition tutorial. This tutorial comes from Google’s Basic classification: Classify images of clothing

Step 1 – Make a Python Code Chunk

Use Pro-Tip #1 Below to make a “Python Code Chunk”.

Python Code Chunk

Step 2 – Import Libraries

Import the libraries needed:

• Deep Learning: tensorflow and keras
• Math: numpy
• Visualization: matplotlib

# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras

# Helper libraries
import numpy as np
import matplotlib.pyplot as plt

Check the version of tensorflow to make sure we’re using 2.0.0+.

print(tf.__version__)

## 2.0.0

Step 3 – Load the Fashion Images

Load the fashion_mnist dataset from keras.

fashion_mnist = keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

We have 60,000 training images that have been labeled.

train_images.shape

## (60000, 28, 28)

We can check the unique labels to see what classifications the images belong to. Note that these are numeric values ranging from 0 to 9.

np.unique(train_labels)

## array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8)

The corresponding labels are:

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

We can see what the first image looks like using matplotlib.

plt.figure()
plt.imshow(train_images[0])
plt.colorbar()

## <matplotlib.colorbar.Colorbar object at 0x7fee0098f278>

plt.grid(False)
plt.show()

And we can also check out the first 25 images.

plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(train_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[train_labels[i]])
plt.show()

Step 4 – Modeling with Keras

Make a keras model using the Sequential() with 3 steps: Flatten, Dense, and Dense.

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10)
])

Next, compile the model with the “adam” optimizer.

model.compile(
    optimizer = 'adam',
    loss      = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    metrics   = ['accuracy']
)

Inspect the model summary.

model.summary()

## Model: "sequential"
## _________________________________________________________________
## Layer (type)                 Output Shape              Param #   
## =================================================================
## flatten (Flatten)            (None, 784)               0         
## _________________________________________________________________
## dense (Dense)                (None, 128)               100480    
## _________________________________________________________________
## dense_1 (Dense)              (None, 10)                1290      
## =================================================================
## Total params: 101,770
## Trainable params: 101,770
## Non-trainable params: 0
## _________________________________________________________________

Step 5 – Fit the Keras Model

CRITICAL STEP – Fit the model. Make sure this step works!</p>

model.fit(train_images, train_labels, epochs=10, verbose=1)

TensorFlow Model Training

Step 6 – Training History

# Python
history = model.history.history
history

## {'loss': [3.1226694132328032, 0.6653605920394262, 0.5747007430752118, 0.5286751741568247, 0.508751327864329, 0.5023731174985567, 0.48900006746848423, 0.4814090680360794, 0.4810072046995163, 0.47561218699614205], 'accuracy': [0.68145, 0.74085, 0.79331666, 0.8143, 0.8228833, 0.8244333, 0.8283167, 0.83428335, 0.8348, 0.83521664]}

I’ll plot this using R. Note – This is an R Code Chunk (not a Python Code Chunk).

# R Code Chunk (not Python)
py$history %>% 
    as_tibble() %>%
    unnest(loss, accuracy) %>%
    rowid_to_column() %>%
    pivot_longer(-rowid) %>%
    ggplot(aes(rowid, value, color = name)) +
    geom_line() +
    geom_point() +
    labs(title = "Training Accuracy")

Step 7 – Test Accuracy

Evaluate accuracy on the out-of-sample images.

# Python
test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)

## 10000/1 - 0s - loss: 0.4470 - accuracy: 0.8098

Step 8 – Make Predictions

The model produces linear outputs cakked “logits”. The softmax layer to converts the logits to probabilities.

probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])

We can then classify all of the test images (held out)

predictions = probability_model.predict(test_images)

We can make a prediction for the first image.

predictions[0]

## array([7.7921860e-21, 3.3554016e-13, 0.0000000e+00, 1.8183892e-15,
##        0.0000000e+00, 4.0730215e-03, 8.1443369e-20, 4.2793788e-03,
##        2.6940727e-18, 9.9164760e-01], dtype=float32)

Use np.argmax() to determine which index has the highest probability.

np.argmax(predictions[0])

## 9

The index value can be retrieved with np.max().

np.max(predictions[0])

## 0.9916476

Get the class name.

class_names[np.argmax(predictions[0])]

## 'Ankle boot'

And visualize the image.

plt.figure()
plt.imshow(test_images[0])
plt.colorbar()

## <matplotlib.colorbar.Colorbar object at 0x7fee14906240>

plt.grid(False)
plt.show()

Nice work – If you made it through this tutorial unscathed, then you are doing well! And your ready for the TensorFlow Learning Labs.

Pro Tips (Python in R)

Now that you have python running in R, use these pro-tips to make your experience way more enjoyable.

Pro-Tip #1 – Python Chunk Keyboard Shortcut

I can’t stress this one enough – Set up a Keyboard shortcut for Python Code Chunks. This is a massive productivity booster for Rmarkdown documents.

• My preference: Ctrl + Alt + P

When you hit Ctrl + Alt + P, a {python} code chunk will appear in your R Markdown document.

Pro-Tip #2 – Use Python Interactively

For debugging Python Code Chunks in R Markdown, it can help to use the repl_python() to convert your Console to a Python Code Console. To do so:

• In R Console, you can run python interactively using repl_python(). You will see >>> indicating you are in Python Mode.
• Make sure the correct Python / Conda Environment is selected.
• To escape Python in the console, just hit escape.

Pro-Tip #3 – My Top 4 Conda Terminal Commands

At some point you will need to create, modify, add more packages to your Conda Environment(s). Here are 4 useful commands:

1. Run conda env list to list the available conda environments
2. Run conda activate <env_name> to activate a conda environment
3. Run conda update --all to update all python packages in a conda environment.
4. Run conda install <package_name> to install a new package

Use Python inside Shiny Apps

Up until now we haven’t talked about Shiny – the web application framework that is used to take your python and R machine learning models into Production.

Business Science Application Library
A Meta-Application that houses Shiny Apps

R Shiny needs to be in your toolbox if you want to productionize Data Science. You simply cannot put machine learning applications into production with other “BI” Tools like Tableau, PowerBI, and QlikView.

CRITICAL POINT: You can USE SHINY to productionize Scikit Learn and TensorFlow models.

If you need to learn R Shiny as fast as possible, I have the perfect program for you. It will accelerate your career. The 4-Course R-Track Bundle through Business Science.

Have questions on using Python + R?

Make a comment in the chat below. ????

And, if you plan on using Python + R at work, it’s a no-brainer – attend my Learning Labs (they are FREE to attend live).