A guide to train the inception-resnet-v2 model in TensorFlow. Visit here for more information.
Update: Note that if you're using the old TF 0.12 code that uses loss = slim.losses.softmax_cross_entropy(predictions, one_hot_labels), and decide to update to using the tf.losses.softmax_cross_entropy function, you should also change the positions of the arguments. For instance, you should do this: tf.losses.softmax_cross_entropy(onehot_labels=one_hot_labels, logits=predictions).
If you keep predictions in the first argument and one_hot_labels in the second argument for the tf.losses.softmax_cross_entropy function, you will encounter a problematic loss function that doesn't really help to train your model (as some of you have emailed me). I have updated the code to correct this issue that may be hard to detect (because the model will still train, except it trains poorly).