Tensorflow: how to create batch with different type of data from different source (folder)?
I solved the ratio problem!! I create two batch, one for validation, one for train. Then i concatenate them with image_batch = tf.concat([image_validation_batch, image_train_batch], 0). This is only for image batch, i will investigate on the label.
I can't find an appropriate question title, sorry.
I have a graph composed by two main data flow: image classification and label cleaning. I have two type of data:

(image_data, noisy_label, verified_label) from validation set
(image_data, noisy_label) from train set

The first is used to train the label cleaning part of the graph.
The second is used to train the image classification after its noisy label is cleaned.
Every batch need to have a ratio of 1:9.
How can i create this type of batch?? is it possible in tensorflow??