title: TensorFlow : create own dataset for training with different size of image
body: <p>Hello I continue to learn about the deep learning, so i try to creat my own data set for training. 
  I have data set of cat and dog(1000 images of cat &amp; 10000 images of dogs) with DIFFERENT size (because I dont find data set with same size) so I need to resize them.
  My code return no error but tensorflow doesnt fill my batch and I really dont understand why. It is my first script in deep learning so its complicated for me to find what is it wrong.</p>
  
  <p>I saw this thread : <a href="https://stackoverflow.com/questions/37340129/tensorflow-training-on-my-own-image">TensorFlow: training on my own image</a></p>
  
  <p>And the doc of the official web site</p>
  
  <p>code:</p>
  
  <pre><code>import tensorflow as tf #tensor flow
  import numpy as np
  import glob, os
  
  
  from pprint import pprint
  
  
  filenames = list()
  
  os.chdir(".")
  for file in glob.glob("*.jpg"):
      filenames.append( file)
  
  
  
  
  
  # filenames = ['im_01.jpg', 'im_02.jpg', 'im_03.jpg', 'im_04.jpg']
  
  
  filename_queue = tf.train.string_input_producer(filenames)
  
  
  reader = tf.WholeFileReader()
  filename, content = reader.read(filename_queue)
  image = tf.image.decode_jpeg(filename, channels=3)
  image = tf.cast(image, tf.float32)
  resized_image = tf.image.resize_images(image, [224, 224])
  
  
  image_batch = tf.train.batch([resized_image], batch_size=10000)
  
  print (image_batch)
  </code></pre>
  
  <p>if I change the batch size, it change nothing to the output.
  I really dont know why it doesnt works </p>
  
  <p>print return</p>
  
  <pre><code> Tensor("batch:0", shape=(10000, 224, 224, 3), dtype=float32)
  </code></pre>
  
  <p>How can I do ?
  thanks
  thomas</p>