title: Dimensions must be equal, but are 64 and 32 for 'Conv2D_2' (op: 'Conv2D') with input shapes: [?,7,7,64], [5,5,32,64]
body: <p>This is a 5 convolutional-layered code:</p>
  
  <pre><code> # Template program with one convolution layer and one fully connected hidden layer, 
      # dropout and Adam Optimizer
      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()
  
      # xi is an image of size n. yi is the N labels of the image
      # X is mxn. Row xi of X is an image 
      # Y is mxN. Row yi of Y is the labels of xi
      X = tf.placeholder(tf.float32, shape=[None, 784])
      Y = tf.placeholder(tf.float32, shape=[None, 10])
  
  
      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')
  
      # First Max Pool layer -- to resize the image to half of the image size
      orig_image = tf.reshape(X, [-1,28,28,1])
      h_pool0 = max_pool_2x2(orig_image)
      ### End of first max pool layer ###
  
      # First Convolutional Layer
  
      W_conv1 = weight_variable([5, 5, 1, 32])
      b_conv1 = bias_variable([32])
  
      h_conv1 = tf.nn.relu(conv2d(h_pool0, W_conv1) + b_conv1)
      h_pool1 = max_pool_2x2(h_conv1)
  
      # Second Convolutional Layer
  
      W_conv2 = weight_variable([5, 5, 32, 64])
      b_conv2 = bias_variable([64])
  
      h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
  
      # Third Convolutional Layer
  
      W_conv3 = weight_variable([5, 5, 64, 128])
      b_conv3 = bias_variable([128])
  
      h_conv3 = tf.nn.relu(conv2d(h_conv2, W_conv2) + b_conv2)
  
      # Fourth Convolutional Layer
  
      W_conv3 = weight_variable([5, 5, 128, 256])
      b_conv3 = bias_variable([256])
  
      h_conv3 = tf.nn.relu(conv2d(h_conv2, W_conv2) + b_conv2)
  
      # Fifth Convolutional Layer
  
      W_conv3 = weight_variable([5, 5, 256, 512])
      b_conv3 = bias_variable([512])
  
      h_conv3 = tf.nn.relu(conv2d(h_conv2, W_conv2) + b_conv2)
  
      # Densely Connected Layer
  
      W_fc1 = weight_variable([7 * 7 * 512, 4096])
      b_fc1 = bias_variable([4096])
  
      h_pool2_flat = tf.reshape(h_conv3, [-1, 7*7*512])
      h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
  
      # Dropout
  
      keep_rate = 0.8
      keep_prob = tf.placeholder(tf.float32)
      h_fc1_drop = tf.nn.dropout(h_fc1, keep_rate)
  
      # Readout Layer
  
      W_fc2 = weight_variable([4096, 10])
      b_fc2 = bias_variable([10])
  
      y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
  
      cross_entropy = tf.reduce_mean(
          tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=y_conv))
      #train_step = tf.train.GradientDescentOptimizer(0.005).minimize(cross_entropy)
      train_step = tf.train.AdamOptimizer().minimize(cross_entropy)
  
      correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(Y,1))
      accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
      sess.run(tf.global_variables_initializer())
      for i in range(1000):
        batch = mnist.train.next_batch(100)
        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>
  
  <p>And I am not sure why, but I am receiving this error message whenever I run it:</p>
  
  <pre><code>Traceback (most recent call last):
    File "C:\Users\Inshal\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 671, in _call_cpp_shape_fn_impl
      input_tensors_as_shapes, status)
    File "C:\Users\Inshal\AppData\Local\Programs\Python\Python35\lib\contextlib.py", line 66, in __exit__
      next(self.gen)
    File "C:\Users\Inshal\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 466, in raise_exception_on_not_ok_status
      pywrap_tensorflow.TF_GetCode(status))
  tensorflow.python.framework.errors_impl.InvalidArgumentError: Dimensions must be equal, but are 64 and 32 for 'Conv2D_2' (op: 'Conv2D') with input shapes: [?,7,7,64], [5,5,32,64].
  
  During handling of the above exception, another exception occurred:
  
  Traceback (most recent call last):
    File "C:/Users/Inshal/Desktop/Inshal Haq/UTD/Spring 2017/Computer Vision/Project2/mnist3.py", line 57, in &lt;module&gt;
      h_conv3 = tf.nn.relu(conv2d(h_conv2, W_conv2) + b_conv2)
    File "C:/Users/Inshal/Desktop/Inshal Haq/UTD/Spring 2017/Computer Vision/Project2/mnist3.py", line 25, in conv2d
      return tf.nn.conv2d(X, W, strides=[1, 1, 1, 1], padding='SAME')
    File "C:\Users\Inshal\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 403, in conv2d
      data_format=data_format, name=name)
    File "C:\Users\Inshal\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 768, in apply_op
      op_def=op_def)
    File "C:\Users\Inshal\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 2338, in create_op
      set_shapes_for_outputs(ret)
    File "C:\Users\Inshal\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 1719, in set_shapes_for_outputs
      shapes = shape_func(op)
    File "C:\Users\Inshal\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 1669, in call_with_requiring
      return call_cpp_shape_fn(op, require_shape_fn=True)
    File "C:\Users\Inshal\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 610, in call_cpp_shape_fn
      debug_python_shape_fn, require_shape_fn)
    File "C:\Users\Inshal\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 676, in _call_cpp_shape_fn_impl
      raise ValueError(err.message)
  ValueError: Dimensions must be equal, but are 64 and 32 for 'Conv2D_2' (op: 'Conv2D') with input shapes: [?,7,7,64], [5,5,32,64].
  </code></pre>
  
  <p>Does anyone know what is the issue with my code, and how can I fix it?</p>
  
  <p>Thank you for anyone's input!</p>