title: Tensorflow Relu Misunderstanding
body: <p>I've recently been doing a Udacity Deep Learning course which is based around <code>TensorFlow</code>. I have a simple <code>MNIST</code> program which is about 92% accurate:</p>
  
  <pre>
  <code>
  from tensorflow.examples.tutorials.mnist import input_data
  import tensorflow as tf
  
  mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
  
  x = tf.placeholder(tf.float32, [None, 784])
  W = tf.Variable(tf.zeros([784, 10]))
  b = tf.Variable(tf.zeros([10]))
  
  y = tf.nn.softmax(tf.matmul(x, W) + b)
  
  y_ = tf.placeholder(tf.float32, [None, 10])
  cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
  
  train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
  
  init = tf.initialize_all_variables()
  
  sess = tf.Session()
  sess.run(init)
  
  for i in range(1000):
      batch_xs, batch_ys = mnist.train.next_batch(100)
      sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
  
  correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})) 
  </code>
  </pre>
  
  <p>My next assignment it to <code>Turn the logistic regression example with SGD into a 1-hidden layer neural network with rectified linear units nn.relu() and 1024 hidden nodes</code></p>
  
  <p>I am having a mental block about this. Currently I have a 784 x 10 Matrix of weights, and a 10 element long bias vector. I don't understand how I connect the resulting 10 element vector from <code>WX + Bias</code> to 1024 <code>Relu</code>s.</p>
  
  <p>If anyone could explain this to me I'd be very grateful.</p>