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| author |  A. Unique TensorFlower <gardener@tensorflow.org> | 2017-05-24 10:35:27 -0700 |
|---|---|---|
| committer | TensorFlower Gardener <gardener@tensorflow.org> | 2017-05-24 10:40:04 -0700 |
| commit | 0ebcfe9cf241655adaaa5af53385980bfd605212 (patch) | |
| tree | 0101faec4131f189028d400a7e5e8261c4cd5426 | |
| parent | ed316e4b53ef0de6a6cd6403213ed9dd9a76272f (diff) | |
use 'global_gradient_norm' in summaries to enable only overall stats
PiperOrigin-RevId: 157006018
| -rw-r--r-- | tensorflow/contrib/layers/python/layers/optimizers.py | 8 |
1 files changed, 5 insertions, 3 deletions
diff --git a/tensorflow/contrib/layers/python/layers/optimizers.py b/tensorflow/contrib/layers/python/layers/optimizers.py index 1a6dfc12e9..50c11c696a 100644 --- a/tensorflow/contrib/layers/python/layers/optimizers.py +++ b/tensorflow/contrib/layers/python/layers/optimizers.py @@ -51,6 +51,7 @@ OPTIMIZER_SUMMARIES = [ "loss", "gradients", "gradient_norm", + "global_gradient_norm", ] @@ -182,7 +183,7 @@ def optimize_loss(loss, "Got %s of type %s" % (str(learning_rate), str(type(learning_rate)))) if summaries is None: - summaries = ["loss", "learning_rate"] + summaries = ["loss", "learning_rate", "global_gradient_norm"] else: for summ in summaries: if summ not in OPTIMIZER_SUMMARIES: @@ -250,7 +251,7 @@ def optimize_loss(loss, "Empty list of (gradient, var) pairs encountered. This is most " "likely to be caused by an improper value of gradient_multipliers.") - if "gradient_norm" in summaries: + if "global_gradient_norm" in summaries or "gradient_norm" in summaries: summary.scalar("global_norm/gradient_norm", clip_ops.global_norm(list(zip(*gradients))[0])) @@ -282,7 +283,8 @@ def optimize_loss(loss, summary.scalar("gradient_norm/%s" % var_name, clip_ops.global_norm([grad_values])) - if clip_gradients is not None and "gradient_norm" in summaries: + if clip_gradients is not None and ("global_gradient_norm" in summaries or + "gradient_norm" in summaries): summary.scalar("global_norm/clipped_gradient_norm", clip_ops.global_norm(list(zip(*gradients))[0])) |