title: Tensorflow Dataset with different shapes
body: <p>I use convolutional network for clasifying images with different shapes. I can not find a way to load images in Tensorflow. Based on <a href="https://github.com/tensorflow/tensorflow/issues/2354" rel="nofollow noreferrer">this issue</a> it should work with tf.data.Dataset(). I create Dataset in this manner:</p>
  
  <pre class="lang-py prettyprint-override"><code>dataset = tf.data.Dataset.from_tensor_slices(filenames)
  dataset = dataset.map(read_file)
  dataset = dataset.shuffle(samples_in_buffer)
  dataset = dataset.batch(batch_size)
  dataset = dataset.repeat()
  </code></pre>
  
  <p>When executed I am getting this error</p>
  
  <pre><code>HandleElementToSlice Cannot copy slice: number of elements does not match.  Shapes are: [element]: [295,256,3], [parent slice]: [276,128,3]
  </code></pre>
  
  <p>Is it possible to load images with different sizes, shuffle and batch them using Tensorflow?</p>
  
  <p>Remark: I want to use Spatial pyramid pooling to handle different image sizes.</p>