Tensorflow minibatch training
<p>How can I train a network in TensorFlow using minibatches of data?
In the Deep-MNIST tutorial, they use:</p>

<pre><code>for i in range(1000):
   batch = mnist.train.next_batch(50)
   train_step.run(feed_dict={x: batch[0], y_: batch[1]})
</code></pre>

<p>My question is - are <code>x</code> and <code>y_</code> variables with dimensions suitable to a single example, and <code>batch[0]</code>,<code>batch[1]</code> are lists of such inputs and outputs? in this case, will TensorFlow automatically add the gradients for each training example in these lists? or should I create my model so that <code>x</code> and <code>y_</code> get an entire minibatch?</p>

<p>My problem is that when I try to feed it a list for each placeholder, it tries to input the entire list for the placeholder, and I therefore get a size mismatch: <code>Cannot feed value of shape (n, m) for Tensor u'ts:0', which has shape '(m,)'</code>, where <code>n</code> is the minibatch size and <code>m</code> is the individual input size.</p>

<p>Thanks.</p>