code: def train_and_test(
      dataset_url, training_iterations, batch_size, evaluation_interval
  ):
      """
      Train a model for training iterations with a batch size batch_size, printing accuracy every log_interval.
      :param dataset_url: The MNIST dataset url.
      :param training_iterations: The training iterations to train for.
      :param batch_size: The batch size for training.
      :param evaluation_interval: The interval used to print the accuracy.
      :return:
      """
      with make_reader(
          os.path.join(dataset_url, "train"), num_epochs=None
      ) as train_reader:
          with make_reader(
              os.path.join(dataset_url, "test"), num_epochs=None
          ) as test_reader:
              train_readout = tf_tensors(train_reader)
              train_image = tf.cast(
                  tf.reshape(train_readout.image, [784]), tf.float32
              )
              train_label = train_readout.digit
              batch_image, batch_label = tf.train.batch(
                  [train_image, train_label], batch_size=batch_size
              )
  
              W = tf.Variable(tf.zeros([784, 10]))
              b = tf.Variable(tf.zeros([10]))
              y = tf.matmul(batch_image, W) + b
  
              # The raw formulation of cross-entropy,
              #
              #   tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),
              #                                 reduction_indices=[1]))
              #
              # can be numerically unstable.
              #
              # So here we use tf.losses.sparse_softmax_cross_entropy on the raw
              # outputs of 'y', and then average across the batch.
              cross_entropy = tf.losses.sparse_softmax_cross_entropy(
                  labels=batch_label, logits=y
              )
              train_step = tf.train.GradientDescentOptimizer(
                  0.5
              ).minimize(cross_entropy)
  
              correct_prediction = tf.equal(
                  tf.argmax(y, 1), batch_label
              )
              accuracy = tf.reduce_mean(
                  tf.cast(correct_prediction, tf.float32)
              )
  
              test_readout = tf_tensors(test_reader)
              test_image = tf.cast(
                  tf.reshape(test_readout.image, [784]), tf.float32
              )
              test_label = test_readout.digit
              test_batch_image, test_batch_label = tf.train.batch(
                  [test_image, test_label], batch_size=batch_size
              )
  
              # Train
              print(
                  "Training model for {0} training iterations with batch size {1} and evaluation interval {2}".format(
                      training_iterations,
                      batch_size,
                      evaluation_interval,
                  )
              )
              with tf.Session() as sess:
                  sess.run(
                      [
                          tf.local_variables_initializer(),
                          tf.global_variables_initializer(),
                      ]
                  )
                  coord = tf.train.Coordinator()
                  threads = tf.train.start_queue_runners(
                      sess=sess, coord=coord
                  )
                  try:
                      for i in range(training_iterations):
                          if coord.should_stop():
                              break
  
                          sess.run(train_step)
  
                          if (i % evaluation_interval) == 0 or i == (
                              training_iterations - 1
                          ):
                              (
                                  feed_batch_image,
                                  feed_batch_label,
                              ) = sess.run(
                                  [test_batch_image, test_batch_label]
                              )
                              print(
                                  "After {0} training iterations, the accuracy of the model is: {1:.2f}".format(
                                      i,
                                      sess.run(
                                          accuracy,
                                          feed_dict={
                                              batch_image: feed_batch_image,
                                              batch_label: feed_batch_label,
                                          },
                                      ),
                                  )
                              )
                  finally:
                      coord.request_stop()
                      coord.join(threads)
  
file_path: petastorm/examples/mnist/tf_example.py
imports: from __future__ import division, print_function
  import argparse
  import os
  import tensorflow.compat.v1
  from examples.mnist import DEFAULT_MNIST_DATA_PATH
  from petastorm import make_reader
  from petastorm.tf_utils import tf_tensors
label_desc: Use image node_id 1 for calling a global function with example usage: train_and_test(dataset_url, training_iterations, batch_size, evaluation_interval) without return types