title: Tensorflow batch size in input placholder
body: <p>I am new to Tensorflow and I can't get why the input placeholder is often dimensioned with the size of the batches used for training. </p>
  
  <p>In this example I found <a href="https://martin-thoma.com/tensor-flow-quick/" rel="nofollow noreferrer">here</a> and in the Official Mnist tutorial it is not</p>
  
  <pre><code>from get_mnist_data_tf import read_data_sets
  mnist = read_data_sets("MNIST_data/", one_hot=True)
  import tensorflow as tf
  sess = tf.InteractiveSession()
  x = tf.placeholder("float", shape=[None, 784])
  y_ = tf.placeholder("float", shape=[None, 10])
  W = tf.Variable(tf.zeros([784,10]))
  b = tf.Variable(tf.zeros([10]))
  sess.run(tf.initialize_all_variables())
  y = tf.nn.softmax(tf.matmul(x,W) + b)
  cross_entropy = -tf.reduce_sum(y_*tf.log(y))
  train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
  for i in range(1000):
    batch = mnist.train.next_batch(50)
    train_step.run(feed_dict={x: batch[0], y_: batch[1]})
  correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
  
  print(accuracy.eval(feed_dict={x: mnist.test.images,
                                 y_: mnist.test.labels}))
  </code></pre>
  
  <p>So what is the best and right way to dimension and create the model input and train it?</p>