title: TensorFlow image classification
body: <p>I am very new to TensorFlow. I am doing the <strong>image classification</strong> using my own training database. </p>
  
  <p>However, after I trained my own dataset, I have no idea on how to classify the input image. </p>
  
  <p>Here is my code for <strong>preparing my own dataset</strong></p>
  
  <pre><code>filenames = ['01.jpg', '02.jpg', '03.jpg', '04.jpg']
  label = [0,1,1,1]
  filename_queue = tf.train.string_input_producer(filenames)
  
  reader = tf.WholeFileReader()
  filename, content = reader.read(filename_queue)
  image = tf.image.decode_jpeg(content, channels=3)
  image = tf.cast(image, tf.float32)
  resized_image = tf.image.resize_images(image, 224, 224)
  
  image_batch , label_batch= tf.train.batch([resized_image,label], batch_size=8, num_threads = 3, capacity=5000)
  </code></pre>
  
  <p><strong>Is this a correct code for training the dataset?</strong></p>
  
  <p>Afterwards, I try to use it to classify the input images with the following code.</p>
  
  <pre><code>test = ['test.jpg', 'test2.jpg']
  test_queue=tf.train.string_input_producer(test)
  reader = tf.WholeFileReader()
  testname, test_content = reader.read(test_queue)
  test = tf.image.decode_jpeg(test_content, channels=3)
  test = tf.cast(test, tf.float32)
  resized_image = tf.image.resize_images(test, 224,224)
  
  with tf.Session() as sess:
      coord = tf.train.Coordinator()
      threads = tf.train.start_queue_runners(coord=coord)
      res = sess.run(resized_image)
      coord.request_stop()
      coord.join(threads)
  </code></pre>
  
  <p>However, it does not return the predicted label for the input images.
  I am looking for someone to teach me how to classify the images by using my own dataset.</p>
  
  <p>Thank you.</p>