1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
|
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Model training for Iris data set using Validation Monitor."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
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)
# Data sets
IRIS_TRAINING = os.path.join(os.path.dirname(__file__), "iris_training.csv")
IRIS_TEST = os.path.join(os.path.dirname(__file__), "iris_test.csv")
def main(unused_argv):
# Load datasets.
training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
filename=IRIS_TRAINING, target_dtype=np.int, features_dtype=np.float)
test_set = tf.contrib.learn.datasets.base.load_csv_with_header(
filename=IRIS_TEST, target_dtype=np.int, features_dtype=np.float)
validation_metrics = {
"accuracy":
tf.contrib.learn.MetricSpec(
metric_fn=tf.contrib.metrics.streaming_accuracy,
prediction_key=
tf.contrib.learn.prediction_key.PredictionKey.CLASSES),
"precision":
tf.contrib.learn.MetricSpec(
metric_fn=tf.contrib.metrics.streaming_precision,
prediction_key=
tf.contrib.learn.prediction_key.PredictionKey.CLASSES),
"recall":
tf.contrib.learn.MetricSpec(
metric_fn=tf.contrib.metrics.streaming_recall,
prediction_key=
tf.contrib.learn.prediction_key.PredictionKey.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)
# 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,
hidden_units=[10, 20, 10],
n_classes=3,
model_dir="/tmp/iris_model",
config=tf.contrib.learn.RunConfig(save_checkpoints_secs=1))
# Fit model.
classifier.fit(x=training_set.data,
y=training_set.target,
steps=2000,
monitors=[validation_monitor])
# Evaluate accuracy.
accuracy_score = classifier.evaluate(
x=test_set.data, y=test_set.target)["accuracy"]
print("Accuracy: {0:f}".format(accuracy_score))
# Classify two new flower samples.
new_samples = np.array(
[[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=float)
y = list(classifier.predict(new_samples))
print("Predictions: {}".format(str(y)))
if __name__ == "__main__":
tf.app.run()
|
