title: Tensorflow error "shape Tensorshape() must have rank 1"
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, 3)
     image.set_shape([32,32,3])
     flipped_images = tf.image.flip_up_down(image)
     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')
  
  x = tf.placeholder(tf.float32, shape=[None,32,32,3])
  y_ = tf.placeholder(tf.float32, shape=[None, 1])
  image,label=inputs()
  image=tf.reshape(image,[-1,32,32,3])
  label=tf.reshape(label,[-1,1])
  image_batch=tf.train.batch([image],batch_size=2)
  label_batch=tf.train.batch([label],batch_size=2)
  
  W_conv1 = weight_variable([5, 5, 3, 32])
  b_conv1 = bias_variable([32])
  
  image_4d=x_image = tf.reshape(image, [-1,32,32,3])
  
  h_conv1 = tf.nn.relu(conv2d(image_4d, 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_pool1, W_conv2) + b_conv2)
  h_pool2 = max_pool_2x2(h_conv2)
  
  W_fc1 = weight_variable([8 * 8 * 64, 1024])
  b_fc1 = bias_variable([1024])
  h_pool2_flat = tf.reshape(h_pool2, [-1, 8*8*64])
  h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
  keep_prob = tf.placeholder(tf.float32)
  h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
  
  W_fc2 = weight_variable([1024, 2])
  b_fc2 = bias_variable([2])
  
  y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
  cross_entropy= -tf.reduce_sum(tf.cast(image_batch[1],tf.float32)*tf.log(y_conv))
  train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
  
  init = tf.initialize_all_variables()
  sess = tf.Session()
  sess.run(init)
  
  for i in range(20000):
   sess.run(train_step,feed_dict={x:image_batch[0:1],y_:label_batch[0:1]})
  </code></pre>
  
  <p>I am trying to run the tensorflow convolutional model over my own images of dimension of[32x32x3] images. The images are being read properly and are being assigned to the placeholder, during training. The problem arises during running the train_step op. When I execute the graph I get the following error.</p>
  
  <pre><code>TensorShape([Dimension(2), Dimension(1), Dimension(32), Dimension(32), Dimension(3)]) must have rank 1
  </code></pre>
  
  <p>But when I see the example <a href="https://github.com/nlintz/TensorFlow-Tutorials/blob/master/5_convolutional_net.py">here</a>, the images are in the form of [batch_size,height,width,depth] tensors only. And this example works fine. 
  Am I missing anything?</p>