You’ll use TensorFlow to classify images from the notMNIST dataset – a dataset of images of English letters from A to J. You can see a few example images below.
Your goal is to automatically detect the letter based on the image in the dataset. You’ll be working on your own computer for this lab, so, first things first, install TensorFlow!
OS X, Linux, Windows
You’re going to use an Anaconda environment for this class. If you’re unfamiliar with Anaconda environments, check out the official documentation. More information, tips, and troubleshooting for installing tensorflow on Windows can be found here.
Note: If you’ve already created the environment for Term 1, you shouldn’t need to do so again here!
Run the following commands to setup your environment:
conda create --name=IntroToTensorFlow python=3 anaconda source activate IntroToTensorFlow conda install -c conda-forge tensorflow
That’s it! You have a working environment with TensorFlow. Test it out with the code in the Hello, world! section below.
Docker on Windows
Docker instructions were offered prior to the availability of a stable Windows installation via pip or Anaconda. Please try Anaconda first, Docker instructions have been retained as an alternative to an installation via Anaconda.
Download and install Docker from the official Docker website.
Run the Docker Container
Run the command below to start a jupyter notebook server with TensorFlow:
docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow
Users in China should use the
b.gcr.io/tensorflow/tensorflow instead of
You can access the jupyter notebook at localhost:8888. The server includes 3 examples of TensorFlow notebooks, but you can create a new notebook to test all your code.
Try running the following code in your Python console to make sure you have TensorFlow properly installed. The console will print “Hello, world!” if TensorFlow is installed. Don’t worry about understanding what it does. You’ll learn about it in the next section.
import tensorflow as tf # Create TensorFlow object called tensor hello_constant = tf.constant('Hello World!') with tf.Session() as sess: # Run the tf.constant operation in the session output = sess.run(hello_constant) print(output)
If you’re getting the error
tensorflow.python.framework.errors.InvalidArgumentError: Placeholder:0 is both fed and fetched, you’re running an older version of TensorFlow. Uninstall TensorFlow, and reinstall it using the instructions above. For more solutions, check out the Common Problems section.
Getting the input is great, but now you need to use it. You’re going to use basic math functions that everyone knows and loves – add, subtract, multiply, and divide – with tensors. (There’s many more math functions you can check out in the documentation.)
x = tf.add(5, 2) # 7
You’ll start with the add function. The
tf.add() function does exactly what you expect it to do. It takes in two numbers, two tensors, or one of each, and returns their sum as a tensor.
Subtraction and Multiplication
Here’s an example with subtraction and multiplication.
x = tf.subtract(10, 4) # 6 y = tf.multiply(2, 5) # 10
x tensor will evaluate to
10 - 4 = 6. The
y tensor will evaluate to
2 * 5 = 10. That was easy!
It may be necessary to convert between types to make certain operators work together. For example, if you tried the following, it would fail with an exception:
tf.subtract(tf.constant(2.0),tf.constant(1)) # Fails with ValueError: Tensor conversion requested dtype float32 for Tensor with dtype int32:
That’s because the constant
1 is an integer but the constant
2.0 is a floating point value and
subtract expects them to match.
In cases like these, you can either make sure your data is all of the same type, or you can cast a value to another type. In this case, converting the
2.0 to an integer before subtracting, like so, will give the correct result:
tf.subtract(tf.cast(tf.constant(2.0), tf.int32), tf.constant(1)) # 1
Let’s apply what you learned to convert an algorithm to TensorFlow. The code below is a simple algorithm using division and subtraction. Convert the following algorithm in regular Python to TensorFlow and print the results of the session. You can use
tf.constant() for the values