# 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 machine-learning pipelines with the Kubeflow Pipelines SDK.
- Learn the advanced features available from a Kubeflow notebook, such as submitting Kubernetes resources or building Docker images.

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Last modified November 19, 2021: update `Kubeflow Notebooks` docs (#3003) (5ad6019)