title: Tensorflow Incompatible Shapes Error in Tutorial
body: <p>I've been trying to create the convolutional network from the <a href="https://www.tensorflow.org/versions/master/tutorials/mnist/pros/index.html" rel="nofollow noreferrer">Tensorflow tutorial</a>, but I've been having trouble.  For some reason, I'm getting errors where the size of y_conv is 4x larger than the size of y_, and I have no idea why.  I found <a href="https://stackoverflow.com/questions/34223315/incompatible-shapes-on-tensorflow-equal-op-for-correct-predictions-evaluation">this question</a>, but it appears to be a different problem than mine, though it looks similar.</p>
  
  <p>To be clear, the batch size in the below code is 50, but the error it's coming up with is</p>
  
  <blockquote>
    <p>tensorflow.python.framework.errors.InvalidArgumentError: Incompatible shapes: [200] vs. [50]</p>
  </blockquote>
  
  <p>and when I change the batch size to 10, I get</p>
  
  <blockquote>
    <p>tensorflow.python.framework.errors.InvalidArgumentError: Incompatible shapes: [40] vs. [10]</p>
  </blockquote>
  
  <p>so it's related to the batch size somehow, but I can't figure that out.  Can anybody tell me what's wrong with this code?  It's pretty much straight from the tutorial linked above.</p>
  
  <pre class="lang-py prettyprint-override"><code>from tensorflow.examples.tutorials.mnist import input_data
  mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
  
  import tensorflow as tf
  sess = tf.InteractiveSession()
  
  def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)
  
  def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)
  
  def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
  
  def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides = [1, 2, 2, 1], padding='SAME')
  
  x = tf.placeholder("float", shape=[None, 784])
  y_ = tf.placeholder("float", shape=[None, 10])
  
  w_conv1 = weight_variable([5, 5, 1, 32])
  b_conv1 = bias_variable([32])
  x_image = tf.reshape(x, [-1, 28, 28, 1])
  h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1)
  h_pool1 = max_pool_2x2(h_conv1)
  
  w_conv2 = weight_variable([5, 5, 32, 64])
  b_conv2 = bias_variable([64])
  h_conv2 = tf.nn.relu(conv2d(h_conv1, w_conv2) + b_conv2)
  h_pool2 = max_pool_2x2(h_conv2)
  
  w_fc1 = weight_variable([7*7*64, 1024])
  b_fc1 = bias_variable([1024])
  h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
  h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)
  
  keep_prob = tf.placeholder("float")
  h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
  
  W_fc2 = weight_variable([1024, 10])
  b_fc2 = bias_variable([10])
  
  y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
  
  cross_entropy = -tf.reduce_sum(y_ * tf.log(tf.clip_by_value(y_conv, 1e-10, 1.0)))
  train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
  correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
  sess.run(tf.initialize_all_variables())
  for i in range(20000):
    batch = mnist.train.next_batch(50)
    if i%100 == 0:
      train_accuracy = accuracy.eval(feed_dict={x:batch[0], y_: batch[1], keep_prob: 1.0})
      print("step %d, training accuracy %g"%(i, train_accuracy))
    train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
  
  print("test accuracy %g"%accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
  </code></pre>