text: import tensorflow as tf
  import numpy as np
  from six.moves import cPickle as pickle
  from six.moves import range
  
  pickle_file = "notMNIST.pickle"
  with open(pickle_file, 'rb') as f:
      save = pickle.load(f)
      train_dataset = save['train_dataset']
      train_labels = save['train_labels']
      valid_dataset = save['valid_dataset']
      valid_labels = save['valid_labels']
      test_dataset = save['test_dataset']
      test_labels = save['test_labels']
      del save
      print('Training set', train_dataset.shape, train_labels.shape)
      print('Validation set', valid_dataset.shape, valid_labels.shape)
      print('Test set', test_dataset.shape, test_labels.shape)
  
  image_size = 28
  num_labels = 10
  
  
  def reformat(dataset, labels):
  
      dataset = dataset.reshape((-1, image_size*image_size)).astype(np.float32)
      labels = (np.arange(num_labels) == labels[:, None]).astype(np.float32)
      return dataset, labels
  
  train_dataset, train_labels = reformat(train_dataset, train_labels)
  valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)
  test_dataset, test_labels = reformat(test_dataset, test_labels)
  print('Training set', train_dataset.shape, train_labels.shape)
  print('Validation set', valid_dataset.shape, valid_labels.shape)
  print('Test set', test_dataset.shape, test_labels.shape)
  
  # With gradient descent training, even this much data is prohibitive.
  # Subset the training data for faster turnaround.
  train_subset = 10000
  
  graph = tf.Graph()
  with graph.as_default():
      # Input data.
      # Load the training, validation and test data into constants that are
      # attached to the graph.
      tf_train_dataset = tf.constant(train_dataset[:train_subset, :])
      tf_train_labels = tf.constant(train_labels[:train_subset])
      tf_valid_dataset = tf.constant(valid_dataset)
      tf_test_dataset = tf.constant(test_dataset)
  
      # Variables.
      # These are the parameters that we are going to be training. The weight
      # matrix will be initialized using random values following a (truncated)
      # normal distribution. The biases_1 get initialized to zero.
      weights = tf.Variable(
          tf.truncated_normal([image_size * image_size, num_labels]))
      biases = tf.Variable(tf.zeros([num_labels]))
  
      # Training computation.
      # We multiply the inputs with the weight matrix, and add biases_1. We compute
      # the softmax and cross-entropy (it's one operation in TensorFlow, because
      # it's very common, and it can be optimized). We take the average of this
      # cross-entropy across all training examples: that's our loss.
      logits = tf.matmul(tf_train_dataset, weights) + biases
  
      loss = tf.reduce_mean(
          tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits))
  
      # Optimizer.
      # We are going to find the minimum of this loss using gradient descent.
      optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
  
      # Predictions for the training, validation, and test data.
      # These are not part of training, but merely here so that we can report
      # accuracy figures as we train.
      train_prediction = tf.nn.softmax(logits)
      valid_prediction = tf.nn.softmax(tf.matmul(tf_valid_dataset, weights) + biases)
      test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)
  
  num_steps = 801
  
  
  def accuracy(predictions, labels):
      return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))
              / predictions.shape[0])
  
  
  with tf.Session(graph=graph) as session:
      # This is a one-time operation which ensures the parameters get initialized as
      # we described in the graph: random weights_1 for the matrix, zeros for the
      # biases_1.
      tf.global_variables_initializer().run()
      print('Initialized')
      for step in range(num_steps):
          # Run the computations. We tell .run() that we want to run the optimizer,
          # and get the loss value and the training predictions returned as numpy
          # arrays.
          _, l, predictions = session.run([optimizer, loss, train_prediction])
          if (step % 100 == 0):
              print('Loss at step %d: %f' % (step, l))
              print('Training accuracy: %.1f%%' % accuracy(
                  predictions, train_labels[:train_subset, :]))
              # Calling .eval() on valid_prediction is basically like calling run(), but
              # just to get that one numpy array. Note that it recomputes all its graph
              # dependencies.
              print('Validation accuracy: %.1f%%' % accuracy(
                  valid_prediction.eval(), valid_labels))
      print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels))