title: TensorFlow for binary classification
body: <p>I am trying to adapt <a href="https://www.tensorflow.org/versions/0.6.0/how_tos/summaries_and_tensorboard/index.html" rel="noreferrer">this MNIST example</a> to binary classification.</p>
  
  <p>But when changing my <code>NLABELS</code> from <code>NLABELS=2</code> to <code>NLABELS=1</code>, the loss function always returns 0 (and accuracy 1).</p>
  
  <pre><code>from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.examples.tutorials.mnist import input_data
  import tensorflow as tf
  
  # Import data
  mnist = input_data.read_data_sets('data', one_hot=True)
  NLABELS = 2
  
  sess = tf.InteractiveSession()
  
  # Create the model
  x = tf.placeholder(tf.float32, [None, 784], name='x-input')
  W = tf.Variable(tf.zeros([784, NLABELS]), name='weights')
  b = tf.Variable(tf.zeros([NLABELS], name='bias'))
  
  y = tf.nn.softmax(tf.matmul(x, W) + b)
  
  # Add summary ops to collect data
  _ = tf.histogram_summary('weights', W)
  _ = tf.histogram_summary('biases', b)
  _ = tf.histogram_summary('y', y)
  
  # Define loss and optimizer
  y_ = tf.placeholder(tf.float32, [None, NLABELS], name='y-input')
  
  # More name scopes will clean up the graph representation
  with tf.name_scope('cross_entropy'):
      cross_entropy = -tf.reduce_mean(y_ * tf.log(y))
      _ = tf.scalar_summary('cross entropy', cross_entropy)
  with tf.name_scope('train'):
      train_step = tf.train.GradientDescentOptimizer(10.).minimize(cross_entropy)
  
  with tf.name_scope('test'):
      correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
      accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
      _ = tf.scalar_summary('accuracy', accuracy)
  
  # Merge all the summaries and write them out to /tmp/mnist_logs
  merged = tf.merge_all_summaries()
  writer = tf.train.SummaryWriter('logs', sess.graph_def)
  tf.initialize_all_variables().run()
  
  # Train the model, and feed in test data and record summaries every 10 steps
  
  for i in range(1000):
      if i % 10 == 0:  # Record summary data and the accuracy
          labels = mnist.test.labels[:, 0:NLABELS]
          feed = {x: mnist.test.images, y_: labels}
  
          result = sess.run([merged, accuracy, cross_entropy], feed_dict=feed)
          summary_str = result[0]
          acc = result[1]
          loss = result[2]
          writer.add_summary(summary_str, i)
          print('Accuracy at step %s: %s - loss: %f' % (i, acc, loss)) 
     else:
          batch_xs, batch_ys = mnist.train.next_batch(100)
          batch_ys = batch_ys[:, 0:NLABELS]
          feed = {x: batch_xs, y_: batch_ys}
      sess.run(train_step, feed_dict=feed)
  </code></pre>
  
  <p>I have checked the dimensions of both <code>batch_ys</code> (fed into <code>y</code>) and <code>_y</code> and they are both 1xN matrices when <code>NLABELS=1</code> so the problem seems to be prior to that. Maybe something to do with the matrix multiplication?</p>
  
  <p>I actually have got this same problem in a real project, so any help would be appreciated... Thanks!</p>