diff options
author | 2016-11-30 12:50:52 -0800 | |
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committer | 2016-11-30 13:04:47 -0800 | |
commit | c532a5b558a451d599190f1dbbdf68f08dfcaa88 (patch) | |
tree | 03ad4a5a6fc3055c52467e184c5741423cc7ba80 /tensorflow/models/image/cifar10/cifar10.py | |
parent | 36ee2ec0e6480910720530c67ab18da0312f24dc (diff) |
Migrate tf summary ops to use tf.contrib.deprecated endpoints.
Change: 140639557
Diffstat (limited to 'tensorflow/models/image/cifar10/cifar10.py')
-rw-r--r-- | tensorflow/models/image/cifar10/cifar10.py | 17 |
1 files changed, 9 insertions, 8 deletions
diff --git a/tensorflow/models/image/cifar10/cifar10.py b/tensorflow/models/image/cifar10/cifar10.py index 1c51b76f09..55c34ba84b 100644 --- a/tensorflow/models/image/cifar10/cifar10.py +++ b/tensorflow/models/image/cifar10/cifar10.py @@ -91,8 +91,9 @@ def _activation_summary(x): # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training # session. This helps the clarity of presentation on tensorboard. tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name) - tf.histogram_summary(tensor_name + '/activations', x) - tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(x)) + tf.contrib.deprecated.histogram_summary(tensor_name + '/activations', x) + tf.contrib.deprecated.scalar_summary(tensor_name + '/sparsity', + tf.nn.zero_fraction(x)) def _variable_on_cpu(name, shape, initializer): @@ -316,8 +317,8 @@ def _add_loss_summaries(total_loss): for l in losses + [total_loss]: # Name each loss as '(raw)' and name the moving average version of the loss # as the original loss name. - tf.scalar_summary(l.op.name +' (raw)', l) - tf.scalar_summary(l.op.name, loss_averages.average(l)) + tf.contrib.deprecated.scalar_summary(l.op.name + ' (raw)', l) + tf.contrib.deprecated.scalar_summary(l.op.name, loss_averages.average(l)) return loss_averages_op @@ -345,7 +346,7 @@ def train(total_loss, global_step): decay_steps, LEARNING_RATE_DECAY_FACTOR, staircase=True) - tf.scalar_summary('learning_rate', lr) + tf.contrib.deprecated.scalar_summary('learning_rate', lr) # Generate moving averages of all losses and associated summaries. loss_averages_op = _add_loss_summaries(total_loss) @@ -360,12 +361,12 @@ def train(total_loss, global_step): # Add histograms for trainable variables. for var in tf.trainable_variables(): - tf.histogram_summary(var.op.name, var) + tf.contrib.deprecated.histogram_summary(var.op.name, var) # Add histograms for gradients. for grad, var in grads: if grad is not None: - tf.histogram_summary(var.op.name + '/gradients', grad) + tf.contrib.deprecated.histogram_summary(var.op.name + '/gradients', grad) # Track the moving averages of all trainable variables. variable_averages = tf.train.ExponentialMovingAverage( @@ -394,5 +395,5 @@ def maybe_download_and_extract(): print() statinfo = os.stat(filepath) print('Successfully downloaded', filename, statinfo.st_size, 'bytes.') - + tarfile.open(filepath, 'r:gz').extractall(dest_directory) |