title: ValueError: Dimensions must be equal, but are 784 and 500 for 'MatMul_1' (op: 'MatMul') with input shapes: [?,784], [500,500]
body: <p>I'm new to tensorflow and I'm following a tutorial by sentdex. I keep on getting the error -</p>
  
  <pre><code>ValueError: Dimensions must be equal, but are 784 and 500 for 
  'MatMul_1' (op: 'MatMul') with input shapes: [?,784], [500,500].
  </code></pre>
  
  <p>The snippet where I believe is causing the issue is -</p>
  
  <pre><code>l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), 
  hidden_1_layer['biases'])
  l1 = tf.nn.relu(l1)
  
  l2 = tf.add(tf.matmul(data, hidden_2_layer['weights']), 
  hidden_2_layer['biases'])
  l2 = tf.nn.relu(l2)
  
  l3 = tf.add(tf.matmul(data, hidden_3_layer['weights']), 
  hidden_3_layer['biases'])
  l3 = tf.nn.relu(l3)
  
  output = tf.add(tf.matmul(l3, output_layer['weights']), 
  output_layer['biases'])
  
  return output
  </code></pre>
  
  <p>Although I'm a noob and may be wrong. My entire code is -</p>
  
  <pre><code>mnist = input_data.read_data_sets("/tmp/ data/", one_hot=True)
  
  n_nodes_hl1 = 500
  n_nodes_hl2 = 500
  n_nodes_hl3 = 500
  
  n_classes = 10
  batch_size = 100
  
  x = tf.placeholder('float', [None, 784])
  y = tf.placeholder('float')
  
  
  def neural_network_model(data):
  hidden_1_layer = {'weights': tf.Variable(tf.random_normal([784, 
  n_nodes_hl1])),
                    'biases': 
  tf.Variable(tf.random_normal([n_nodes_hl1]))}
  
  hidden_2_layer = {'weights': 
  tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
                    'biases': 
  tf.Variable(tf.random_normal([n_nodes_hl2]))}
  
  hidden_3_layer = {'weights': 
  tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
                    'biases': 
  tf.Variable(tf.random_normal([n_nodes_hl3]))}
  
  output_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl3, 
  n_classes])),
                  'biases': tf.Variable(tf.random_normal([n_classes]))}
  
  l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), 
  hidden_1_layer['biases'])
  l1 = tf.nn.relu(l1)
  
  l2 = tf.add(tf.matmul(data, hidden_2_layer['weights']), 
  hidden_2_layer['biases'])
  l2 = tf.nn.relu(l2)
  
  l3 = tf.add(tf.matmul(data, hidden_3_layer['weights']), 
  hidden_3_layer['biases'])
  l3 = tf.nn.relu(l3)
  
  output = tf.add(tf.matmul(l3, output_layer['weights']), 
  output_layer['biases'])
  
  return output
  
  
  def train_neural_network(x):
  prediction = neural_network_model(x)
  cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits
  (logits=prediction, labels=y))
  optimizer = tf.train.AdamOptimizer().minimize(cost)
  
  hm_epochs = 10
  
  with tf.Session() as sess:
      sess.run(tf.global_variables_initializer())
  
      for epoch in range(hm_epochs):
          epoch_loss = 0
          for _ in range(int(mnist.train.num_examples / batch_size)):
              epoch_x, epoch_y = mnist.train.next_batch(batch_size)
              _, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, 
  y: epoch_y})
              epoch_loss += c
          print('Epoch', epoch, 'completed out of', hm_epochs, 'loss:', 
  epoch_loss)
  
      correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
      accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
      print('Accuracy:', accuracy.eval({x: mnist.test.images, y: 
  mnist.test.labels}))
  
  
  train_neural_network(x)
  </code></pre>
  
  <p>Please help. By the way, I'm running on Mac in a virtual environment with Python 3.6.1 and Tensorflow 1.2. And I am using the IDE Pycharm CE. If any of that information is useful.</p>