import tensorflow as tf
    from tensorflow.examples.tutorials.mnist import input_data
    max_steps=1000
    learning_rate=0.001
    dropout=0.9
    data_dir=‘/tmp/tensorflow/mnist/input_data‘
    log_dir=‘/tmp/tensorflow/mnist/logs/mnist_with_summaries‘
      # Import data
    mnist = input_data.read_data_sets(data_dir,one_hot=True)
    sess = tf.InteractiveSession()
      # Create a multilayer model.
      # Input placeholders
    with tf.name_scope(‘input‘):
      x = tf.placeholder(tf.float32, [None, 784], name=‘x-input‘)
      y_ = tf.placeholder(tf.float32, [None, 10], name=‘y-input‘)
    with tf.name_scope(‘input_reshape‘):
      image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
      tf.summary.image(‘input‘, image_shaped_input, 10)
      # We can‘t initialize these variables to 0 - the network will get stuck.
    def weight_variable(shape):
      """Create a weight variable with appropriate initialization."""
      initial = tf.truncated_normal(shape, stddev=0.1)
      return tf.Variable(initial)
    def bias_variable(shape):
      """Create a bias variable with appropriate initialization."""
      initial = tf.constant(0.1, shape=shape)
      return tf.Variable(initial)
    def variable_summaries(var):
      """Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
      with tf.name_scope(‘summaries‘):
        mean = tf.reduce_mean(var)
        tf.summary.scalar(‘mean‘, mean)
        with tf.name_scope(‘stddev‘):
          stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
        tf.summary.scalar(‘stddev‘, stddev)
        tf.summary.scalar(‘max‘, tf.reduce_max(var))
        tf.summary.scalar(‘min‘, tf.reduce_min(var))
        tf.summary.histogram(‘histogram‘, var)
    def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
      """Reusable code for making a simple neural net layer.
      It does a matrix multiply, bias add, and then uses relu to nonlinearize.
      It also sets up name scoping so that the resultant graph is easy to read,
      and adds a number of summary ops.
      """
      # Adding a name scope ensures logical grouping of the layers in the graph.
      with tf.name_scope(layer_name):
        # This Variable will hold the state of the weights for the layer
        with tf.name_scope(‘weights‘):
          weights = weight_variable([input_dim, output_dim])
          variable_summaries(weights)
        with tf.name_scope(‘biases‘):
          biases = bias_variable([output_dim])
          variable_summaries(biases)
        with tf.name_scope(‘Wx_plus_b‘):
          preactivate = tf.matmul(input_tensor, weights) + biases
          tf.summary.histogram(‘pre_activations‘, preactivate)
        activations = act(preactivate, name=‘activation‘)
        tf.summary.histogram(‘activations‘, activations)
        return activations
    hidden1 = nn_layer(x, 784, 500, ‘layer1‘)
    with tf.name_scope(‘dropout‘):
      keep_prob = tf.placeholder(tf.float32)
      tf.summary.scalar(‘dropout_keep_probability‘, keep_prob)
      dropped = tf.nn.dropout(hidden1, keep_prob)
      # Do not apply softmax activation yet, see below.
    y = nn_layer(dropped, 500, 10, ‘layer2‘, act=tf.identity)
    with tf.name_scope(‘cross_entropy‘):
        # The raw formulation of cross-entropy,
        #
        # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.softmax(y)),
        #                               reduction_indices=[1]))
        #
        # can be numerically unstable.
        #
        # So here we use tf.nn.softmax_cross_entropy_with_logits on the
        # raw outputs of the nn_layer above, and then average across
        # the batch.
      diff = tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_)
      with tf.name_scope(‘total‘):
        cross_entropy = tf.reduce_mean(diff)
    tf.summary.scalar(‘cross_entropy‘, cross_entropy)
    with tf.name_scope(‘train‘):
      train_step = tf.train.AdamOptimizer(learning_rate).minimize(
      cross_entropy)
    with tf.name_scope(‘accuracy‘):
      with tf.name_scope(‘correct_prediction‘):
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
      with tf.name_scope(‘accuracy‘):
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    tf.summary.scalar(‘accuracy‘, accuracy)
      # Merge all the summaries and write them out to /tmp/mnist_logs (by default)
    merged = tf.summary.merge_all()
    train_writer = tf.summary.FileWriter(log_dir + ‘/train‘, sess.graph)
    test_writer = tf.summary.FileWriter(log_dir + ‘/test‘)
    tf.global_variables_initializer().run()
      # Train the model, and also write summaries.
      # Every 10th step, measure test-set accuracy, and write test summaries
      # All other steps, run train_step on training data, & add training summaries
    def feed_dict(train):
      """Make a TensorFlow feed_dict: maps data onto Tensor placeholders."""
      if train:
        xs, ys = mnist.train.next_batch(100)
        k = dropout
      else:
        xs, ys = mnist.test.images, mnist.test.labels
        k = 1.0
      return {x: xs, y_: ys, keep_prob: k}
  
    saver = tf.train.Saver()  
    for i in range(max_steps):
      if i % 10 == 0:  # Record summaries and test-set accuracy
        summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))
        test_writer.add_summary(summary, i)
        print(‘Accuracy at step %s: %s‘ % (i, acc))
      else:  # Record train set summaries, and train
        if i % 100 == 99:  # Record execution stats
          run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
          run_metadata = tf.RunMetadata()
          summary, _ = sess.run([merged, train_step],
                                feed_dict=feed_dict(True),
                                options=run_options,
                                run_metadata=run_metadata)
          train_writer.add_run_metadata(run_metadata, ‘step%03d‘ % i)
          train_writer.add_summary(summary, i)
          saver.save(sess, log_dir+"/model.ckpt", i)
          print(‘Adding run metadata for‘, i)
        else:  # Record a summary
          summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
          train_writer.add_summary(summary, i)
    train_writer.close()
    test_writer.close()