TensorFlow image classification
<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>