title: Tensorflow weight matrix rank error
body: <pre><code>import tensorflow as tf
  import numpy as np
  import os
  from PIL import Image
  cur_dir = os.getcwd()
  
  def modify_image(image):
     resized = tf.image.resize_images(image, 180, 180, 1)
     resized.set_shape([180,180,3])
     flipped_images = tf.image.flip_up_down(resized)
     return flipped_images
  
  def read_image(filename_queue):
     reader = tf.WholeFileReader()
     key,value = reader.read(filename_queue)
     image = tf.image.decode_jpeg(value)
     return key,image
  
  def inputs():
     filenames = ['standard_1.jpg', 'standard_2.jpg' ]
     filename_queue = tf.train.string_input_producer(filenames)
     filename,read_input = read_image(filename_queue)
     reshaped_image = modify_image(read_input)
     reshaped_image = tf.cast(reshaped_image, tf.float32)
     label=tf.constant([1])
     return reshaped_image,label
  
  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')
  
  
  
  image,label = inputs()
  W_conv1=weight_variable([5,5,3,32])
  b_conv1 = bias_variable([32])
  h_conv1 = tf.nn.relu(conv2d(image, W_conv1) + b_conv1)
  h_pool1 = max_pool_2x2(h_conv1)
  
  
  W_conv2=weights_variable([5,5,32,64])
  b_conv2 = bias_variable([32])
  h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
  h_pool2 = max_pool_2x2(h_conv2)
  W_fc1 = weight_variable([8 * 8 * 32, 512])
  b_fc1 = bias_variable([512])
  
  h_pool2_flat = tf.reshape(h_pool2, [-1, 8*8*32])
  h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
  W_fc2 = weight_variable([512, 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(y_conv))   
  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, tf.float32)) 
  
  init = tf.initialize_all_variables()
  sess = tf.Session()
  sess.run(init)
  tf.train.start_queue_runners(sess=sess)
  for i in xrange(100):
      img,label = sess.run(image)
      print (label)
      train_step.run({img, label, 0.5})
  </code></pre>
  
  <p>When I run the code, I get the error,</p>
  
  <pre><code>"ValueError: ShapesTensorShape([Dimension(180),Dimension(180),Dimension(3)]) and TensorShape([Dimension(None), Dimension(None), Dimension(None), Dimension(None)]) must have the same rank"
  </code></pre>
  
  <p>But the Weights have been initialized and even then, it shows them as empty tensors.
  The files and labels are being read and transfered properly.
  The first convolutional layer has a 5x5 window with a depth of 3 and I want to have 32 such 5X5 filters. Hence a shape of [5,5,3,32] for W_conv1.</p>