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")
