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tutorials/monitors/iris_monitor.py fixes (#8927)

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* Iris_Monitors.py validation metric prediction key

update to "classes"

* Iris_monitors.py import removed

code to import MetricSpec

* iris_monitors.py repetitive code removed

code should not have been duplicated
This commit is contained in:
MikeTam1021 2017-04-03 15:03:41 -05:00 committed by Rohan Jain
parent a336b06d29
commit 7ab36077ef
1 changed files with 3 additions and 27 deletions
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30
tensorflow/examples/tutorials/monitors/iris_monitors.py
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@ -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.PredictionKey.CLASSES),
prediction_key="classes"),
"precision":
tf.contrib.learn.MetricSpec(
metric_fn=tf.contrib.metrics.streaming_precision,
prediction_key=
tf.contrib.learn.PredictionKey.CLASSES),
prediction_key="classes"),
"recall":
tf.contrib.learn.MetricSpec(
metric_fn=tf.contrib.metrics.streaming_recall,
prediction_key=
tf.contrib.learn.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,