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| author |  A. Unique TensorFlower <gardener@tensorflow.org> | 2017-04-04 16:10:08 -0800 |
|---|---|---|
| committer | TensorFlower Gardener <gardener@tensorflow.org> | 2017-04-04 17:24:57 -0700 |
| commit | ccbc8991db3943ef984405881a1c917c530f902f (patch) | |
| tree | a7b5c760155bfa4ff95ffc0ebd3823c649668997 /tensorflow/examples/tutorials | |
| parent | 9477900946f923cb43ed76ed215490d01474bfe7 (diff) | |
Merge changes from github.
Change: 152200430
Diffstat (limited to 'tensorflow/examples/tutorials')
| -rw-r--r-- | tensorflow/examples/tutorials/deepdream/deepdream.ipynb | 2 | ||||
| -rw-r--r-- | tensorflow/examples/tutorials/monitors/iris_monitors.py | 30 |
2 files changed, 4 insertions, 28 deletions
diff --git a/tensorflow/examples/tutorials/deepdream/deepdream.ipynb b/tensorflow/examples/tutorials/deepdream/deepdream.ipynb index 016b21cd12..4ff8e368c4 100644 --- a/tensorflow/examples/tutorials/deepdream/deepdream.ipynb +++ b/tensorflow/examples/tutorials/deepdream/deepdream.ipynb @@ -278,7 +278,7 @@ " tensor = n.attr['value'].tensor\n", " size = len(tensor.tensor_content)\n", " if size > max_const_size:\n", - " tensor.tensor_content = bytes(\"<stripped %d bytes>\"%size)\n", + " tensor.tensor_content = tf.compat.as_bytes(\"<stripped %d bytes>\"%size)\n", " return strip_def\n", " \n", "def rename_nodes(graph_def, rename_func):\n", diff --git a/tensorflow/examples/tutorials/monitors/iris_monitors.py b/tensorflow/examples/tutorials/monitors/iris_monitors.py index a4bf353856..850d105f7b 100644 --- a/tensorflow/examples/tutorials/monitors/iris_monitors.py +++ b/tensorflow/examples/tutorials/monitors/iris_monitors.py @@ -21,7 +21,6 @@ import os import numpy as np import tensorflow as tf -from tensorflow.contrib.learn.python.learn.metric_spec import MetricSpec tf.logging.set_verbosity(tf.logging.INFO) @@ -41,18 +40,15 @@ def main(unused_argv): "accuracy": tf.contrib.learn.MetricSpec( metric_fn=tf.contrib.metrics.streaming_accuracy, - prediction_key= - tf.contrib.learn.prediction_key.PredictionKey.CLASSES), + prediction_key="classes"), "precision": tf.contrib.learn.MetricSpec( metric_fn=tf.contrib.metrics.streaming_precision, - prediction_key= - tf.contrib.learn.prediction_key.PredictionKey.CLASSES), + prediction_key="classes"), "recall": tf.contrib.learn.MetricSpec( metric_fn=tf.contrib.metrics.streaming_recall, - prediction_key= - tf.contrib.learn.prediction_key.PredictionKey.CLASSES) + prediction_key="classes") } validation_monitor = tf.contrib.learn.monitors.ValidationMonitor( test_set.data, @@ -66,26 +62,6 @@ def main(unused_argv): # Specify that all features have real-value data feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)] - validation_metrics = { - "accuracy": MetricSpec( - metric_fn=tf.contrib.metrics.streaming_accuracy, - prediction_key="classes"), - "recall": MetricSpec( - metric_fn=tf.contrib.metrics.streaming_recall, - prediction_key="classes"), - "precision": MetricSpec( - metric_fn=tf.contrib.metrics.streaming_precision, - prediction_key="classes") - } - validation_monitor = tf.contrib.learn.monitors.ValidationMonitor( - test_set.data, - test_set.target, - every_n_steps=50, - metrics=validation_metrics, - early_stopping_metric="loss", - early_stopping_metric_minimize=True, - early_stopping_rounds=200) - # Build 3 layer DNN with 10, 20, 10 units respectively. classifier = tf.contrib.learn.DNNClassifier( feature_columns=feature_columns, |