title: Accessing the accuracy value in Tensorflow
body: <p>I've wrote a code based on <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard" rel="nofollow noreferrer">Tensorflow example</a> : </p>
  
  <pre><code>    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(targets=y_, logits=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(FLAGS.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(FLAGS.summaries_dir + '/train',
                                        sess.graph)
  test_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/test')
  tf.global_variables_initializer().run()
  </code></pre>
  
  <p>I want to get the accuracy variable as a float during the training and the testing, for that here what I've done:</p>
  
  <pre><code>..........................................................
  
  for i in range(FLAGS.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
      summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
      train_writer.add_summary(summary, i)
      print("accuracy  ", accuracy.eval) ## Here's the problem  
  </code></pre>
  
  <p>here is the output of the last print</p>
  
  <pre><code>&lt;bound method Tensor.eval of &lt;tf.Tensor 'cross_entropy/accuracy/accuracy/Mean:0' shape=() dtype=float32&gt;&gt;
  </code></pre>
  
  <p>My question is how can I get the accuracy values during the training the same way as in the test phase ? </p>