https://www.katacoda.com/basiafusinska/courses/tensorflow-getting-started/tensorflow-mnist-beginner

app.py

import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)

from tensorflow.examples.tutorials.mnist import input_data 

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
                
                
image_size = 28
labels_size = 10
learning_rate = 0.05
steps_number = 10000
batch_size = 200                

training_data = tf.placeholder(tf.float32, [None, image_size*image_size])
labels = tf.placeholder(tf.float32, [None, labels_size])

# Variables to be tuned
W = tf.Variable(tf.truncated_normal([image_size*image_size, labels_size], stddev=0.1))
b = tf.Variable(tf.constant(0.1, shape=[labels_size]))

# Build the network (only output layer)
output = tf.matmul(training_data, W) + b

# Define the loss function
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=output))

# Training step
train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
# Accuracy calculation
correct_prediction = tf.equal(tf.argmax(output, 1), tf.argmax(labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# Run the training
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())


for i in range(steps_number):
  # Get the next batch
  input_batch, labels_batch = mnist.train.next_batch(batch_size)
  feed_dict = {training_data: input_batch, labels: labels_batch}

  # Run the training step
  train_step.run(feed_dict=feed_dict)

  # Print the accuracy progress on the batch every 100 steps
  if i%100 == 0:
    train_accuracy = accuracy.eval(feed_dict=feed_dict)
    print("Step %d, training batch accuracy %g %%"%(i, train_accuracy*100))

# Evaluate on the test set
test_accuracy = accuracy.eval(feed_dict={training_data: mnist.test.images, labels: mnist.test.labels})
print("Test accuracy: %g %%"%(test_accuracy*100))