Jupyter TensorFlow Examples

Examples using Jupyter and TensorFlow in Kubeflow Notebooks

Mnist Example

(adapted from tensorflow/tensorflow - mnist_softmax.py)

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

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

  3. 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()
    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}))
  4. 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