logging.info(f"Starting epoche {e}") # ===================================================== # Training step logging.info("Start train") for it, (imgs, labels) in enumerate(pipeTrain.ds.take(subitsTrain)): # Calculate current global step step = e * subitsTrain + it # Prepartion for cafe models: imgsCaf = 255 * imgs - tf.constant([123.68, 116.779, 103.939]) # Do one training step loss, logits = classy.trainStep(imgsCaf, labels) # Calculate accuracy accTrain.update_state(tf.math.argmax(logits, axis=-1), tf.math.argmax(labels, axis=-1)) # Write to Tensorboard with trainSummaryWriter.as_default(): tf.summary.scalar('loss', loss, step=step) tf.summary.scalar('accuracy', accTrain.result().numpy(), step=step) # Logging images if step % logImagesIt == 0: logging.info("Logging images")