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tensorflower-gardenerSanders Kleinfeld
tensorflower-gardener
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Sanders Kleinfeld
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Code and data for tf.contrib.learn monitors tutorial.
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‎tensorflow/examples/tutorials/monitors/iris_monitors.py

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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Model training for Iris data set using Validation Monitor."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import tensorflow as tf
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tf.logging.set_verbosity(tf.logging.INFO)
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# Data sets
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IRIS_TRAINING = "iris_training.csv"
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IRIS_TEST = "iris_test.csv"
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# Load datasets.
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training_set = tf.contrib.learn.datasets.base.load_csv(filename=IRIS_TRAINING,
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target_dtype=np.int)
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test_set = tf.contrib.learn.datasets.base.load_csv(filename=IRIS_TEST,
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target_dtype=np.int)
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validation_metrics = {"accuracy": tf.contrib.metrics.streaming_accuracy,
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"precision": tf.contrib.metrics.streaming_precision,
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"recall": tf.contrib.metrics.streaming_recall}
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validation_monitor = tf.contrib.learn.monitors.ValidationMonitor(
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test_set.data,
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test_set.target,
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every_n_steps=50,
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metrics=validation_metrics,
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early_stopping_metric="loss",
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early_stopping_metric_minimize=True,
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early_stopping_rounds=200)
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# Build 3 layer DNN with 10, 20, 10 units respectively.
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classifier = tf.contrib.learn.DNNClassifier(hidden_units=[10, 20, 10],
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n_classes=3,
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model_dir="/tmp/iris_model",
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config=tf.contrib.learn.RunConfig(
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save_checkpoints_secs=1))
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# Fit model.
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classifier.fit(x=training_set.data,
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y=training_set.target,
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steps=2000,
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monitors=[validation_monitor])
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# Evaluate accuracy.
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accuracy_score = classifier.evaluate(x=test_set.data,
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y=test_set.target)["accuracy"]
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print("Accuracy: {0:f}".format(accuracy_score))
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# Classify two new flower samples.
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new_samples = np.array(
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[[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=float)
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y = classifier.predict(new_samples)
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print("Predictions: {}".format(str(y)))

‎tensorflow/examples/tutorials/monitors/iris_test.csv

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30,4,setosa,versicolor,virginica
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5.9,3.0,4.2,1.5,1
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6.9,3.1,5.4,2.1,2
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5.1,3.3,1.7,0.5,0
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6.0,3.4,4.5,1.6,1
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5.5,2.5,4.0,1.3,1
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6.2,2.9,4.3,1.3,1
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5.5,4.2,1.4,0.2,0
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6.3,2.8,5.1,1.5,2
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5.6,3.0,4.1,1.3,1
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6.7,2.5,5.8,1.8,2
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7.1,3.0,5.9,2.1,2
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4.3,3.0,1.1,0.1,0
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5.6,2.8,4.9,2.0,2
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5.5,2.3,4.0,1.3,1
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6.0,2.2,4.0,1.0,1
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5.1,3.5,1.4,0.2,0
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5.7,2.6,3.5,1.0,1
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4.8,3.4,1.9,0.2,0
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5.1,3.4,1.5,0.2,0
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5.7,2.5,5.0,2.0,2
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5.4,3.4,1.7,0.2,0
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5.6,3.0,4.5,1.5,1
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6.3,2.9,5.6,1.8,2
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6.3,2.5,4.9,1.5,1
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5.8,2.7,3.9,1.2,1
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6.1,3.0,4.6,1.4,1
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5.2,4.1,1.5,0.1,0
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6.7,3.1,4.7,1.5,1
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6.7,3.3,5.7,2.5,2
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6.4,2.9,4.3,1.3,1

‎tensorflow/examples/tutorials/monitors/iris_training.csv

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120,4,setosa,versicolor,virginica
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6.4,2.8,5.6,2.2,2
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5.0,2.3,3.3,1.0,1
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4.9,2.5,4.5,1.7,2
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4.9,3.1,1.5,0.1,0
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5.7,3.8,1.7,0.3,0
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4.4,3.2,1.3,0.2,0
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5.4,3.4,1.5,0.4,0
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6.9,3.1,5.1,2.3,2
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6.7,3.1,4.4,1.4,1
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5.1,3.7,1.5,0.4,0
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5.2,2.7,3.9,1.4,1
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6.9,3.1,4.9,1.5,1
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5.8,4.0,1.2,0.2,0
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5.4,3.9,1.7,0.4,0
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7.7,3.8,6.7,2.2,2
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6.3,3.3,4.7,1.6,1
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6.8,3.2,5.9,2.3,2
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7.6,3.0,6.6,2.1,2
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6.4,3.2,5.3,2.3,2
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5.7,4.4,1.5,0.4,0
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6.7,3.3,5.7,2.1,2
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6.4,2.8,5.6,2.1,2
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5.4,3.9,1.3,0.4,0
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6.1,2.6,5.6,1.4,2
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7.2,3.0,5.8,1.6,2
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5.2,3.5,1.5,0.2,0
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5.8,2.6,4.0,1.2,1
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5.9,3.0,5.1,1.8,2
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5.4,3.0,4.5,1.5,1
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6.7,3.0,5.0,1.7,1
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6.3,2.3,4.4,1.3,1
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5.1,2.5,3.0,1.1,1
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6.4,3.2,4.5,1.5,1
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6.8,3.0,5.5,2.1,2
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6.2,2.8,4.8,1.8,2
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6.9,3.2,5.7,2.3,2
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6.5,3.2,5.1,2.0,2
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5.8,2.8,5.1,2.4,2
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5.1,3.8,1.5,0.3,0
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4.8,3.0,1.4,0.3,0
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7.9,3.8,6.4,2.0,2
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5.8,2.7,5.1,1.9,2
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6.7,3.0,5.2,2.3,2
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5.1,3.8,1.9,0.4,0
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4.7,3.2,1.6,0.2,0
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6.0,2.2,5.0,1.5,2
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4.8,3.4,1.6,0.2,0
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7.7,2.6,6.9,2.3,2
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4.6,3.6,1.0,0.2,0
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7.2,3.2,6.0,1.8,2
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5.0,3.3,1.4,0.2,0
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6.6,3.0,4.4,1.4,1
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6.1,2.8,4.0,1.3,1
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5.0,3.2,1.2,0.2,0
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7.0,3.2,4.7,1.4,1
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6.0,3.0,4.8,1.8,2
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7.4,2.8,6.1,1.9,2
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5.8,2.7,5.1,1.9,2
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6.2,3.4,5.4,2.3,2
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5.0,2.0,3.5,1.0,1
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5.6,2.5,3.9,1.1,1
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6.7,3.1,5.6,2.4,2
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6.3,2.5,5.0,1.9,2
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6.4,3.1,5.5,1.8,2
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6.2,2.2,4.5,1.5,1
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7.3,2.9,6.3,1.8,2
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4.4,3.0,1.3,0.2,0
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7.2,3.6,6.1,2.5,2
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6.5,3.0,5.5,1.8,2
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5.0,3.4,1.5,0.2,0
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4.7,3.2,1.3,0.2,0
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6.6,2.9,4.6,1.3,1
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5.5,3.5,1.3,0.2,0
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7.7,3.0,6.1,2.3,2
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6.1,3.0,4.9,1.8,2
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4.9,3.1,1.5,0.1,0
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5.5,2.4,3.8,1.1,1
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5.7,2.9,4.2,1.3,1
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6.0,2.9,4.5,1.5,1
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6.4,2.7,5.3,1.9,2
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5.4,3.7,1.5,0.2,0
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6.1,2.9,4.7,1.4,1
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6.5,2.8,4.6,1.5,1
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5.6,2.7,4.2,1.3,1
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6.3,3.4,5.6,2.4,2
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4.9,3.1,1.5,0.1,0
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6.8,2.8,4.8,1.4,1
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5.7,2.8,4.5,1.3,1
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6.0,2.7,5.1,1.6,1
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5.0,3.5,1.3,0.3,0
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6.5,3.0,5.2,2.0,2
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6.1,2.8,4.7,1.2,1
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5.1,3.5,1.4,0.3,0
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4.6,3.1,1.5,0.2,0
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6.5,3.0,5.8,2.2,2
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4.6,3.4,1.4,0.3,0
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4.6,3.2,1.4,0.2,0
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7.7,2.8,6.7,2.0,2
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5.9,3.2,4.8,1.8,1
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5.1,3.8,1.6,0.2,0
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4.9,3.0,1.4,0.2,0
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4.9,2.4,3.3,1.0,1
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4.5,2.3,1.3,0.3,0
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5.8,2.7,4.1,1.0,1
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5.0,3.4,1.6,0.4,0
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5.2,3.4,1.4,0.2,0
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5.3,3.7,1.5,0.2,0
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5.0,3.6,1.4,0.2,0
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5.6,2.9,3.6,1.3,1
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4.8,3.1,1.6,0.2,0
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6.3,2.7,4.9,1.8,2
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5.7,2.8,4.1,1.3,1
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5.0,3.0,1.6,0.2,0
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6.3,3.3,6.0,2.5,2
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5.0,3.5,1.6,0.6,0
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5.5,2.6,4.4,1.2,1
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5.7,3.0,4.2,1.2,1
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4.4,2.9,1.4,0.2,0
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4.8,3.0,1.4,0.1,0
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5.5,2.4,3.7,1.0,1

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