Jupyter TensorFlow Examples
Examples using Jupyter and TensorFlow in Kubeflow Notebooks
Mnist Example
(adapted from tensorflow/tensorflow  mnist_softmax.py)

When creating your notebook server choose a container image which has Jupyter and TensorFlow installed.

Use Jupyter’s interface to create a new Python 3 notebook.

Copy the following code and paste it into your notebook:
from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) import tensorflow as tf x = tf.placeholder(tf.float32, [None, 784]) W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) y = tf.nn.softmax(tf.matmul(x, W) + b) y_ = tf.placeholder(tf.float32, [None, 10]) cross_entropy = tf.reduce_mean(tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1])) train_step = tf.train.GradientDescentOptimizer(0.05).minimize(cross_entropy) sess = tf.InteractiveSession() tf.global_variables_initializer().run() for _ in range(1000): batch_xs, batch_ys = mnist.train.next_batch(100) sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print("Accuracy: ", sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

Run the code. You should see a number of
WARNING
messages from TensorFlow, followed by a line showing a training accuracy something like this:Accuracy: 0.9012
Next steps
 See a simple example of creating Kubeflow pipelines in a Jupyter notebook.
 Build machinelearning pipelines with the Kubeflow Pipelines SDK.
 Learn the advanced features available from a Kubeflow notebook, such as submitting Kubernetes resources or building Docker images.
Feedback
Was this page helpful?
Glad to hear it! Please tell us how we can improve.
Sorry to hear that. Please tell us how we can improve.
Last modified November 19, 2021: update `Kubeflow Notebooks` docs (#3003) (5ad6019a)