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# 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.
"""Example of DNNClassifier for Iris plant dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import urllib
import tensorflow as tf
# Data sets
IRIS_TRAINING = 'iris_training.csv'
IRIS_TRAINING_URL = 'http://download.tensorflow.org/data/iris_training.csv'
IRIS_TEST = 'iris_test.csv'
IRIS_TEST_URL = 'http://download.tensorflow.org/data/iris_test.csv'
FEATURE_KEYS = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
def maybe_download_iris_data(file_name, download_url):
"""Downloads the file and returns the number of data."""
if not os.path.exists(file_name):
raw = urllib.urlopen(download_url).read()
with open(file_name, 'w') as f:
f.write(raw)
# The first line is a comma-separated string. The first one is the number of
# total data in the file.
with open(file_name, 'r') as f:
first_line = f.readline()
num_elements = first_line.split(',')[0]
return int(num_elements)
def input_fn(file_name, num_data, batch_size, is_training):
"""Creates an input_fn required by Estimator train/evaluate."""
# If the data sets aren't stored locally, download them.
def _parse_csv(rows_string_tensor):
"""Takes the string input tensor and returns tuple of (features, labels)."""
# Last dim is the label.
num_features = len(FEATURE_KEYS)
num_columns = num_features + 1
columns = tf.decode_csv(rows_string_tensor,
record_defaults=[[]] * num_columns)
features = dict(zip(FEATURE_KEYS, columns[:num_features]))
labels = tf.cast(columns[num_features], tf.int32)
return features, labels
def _input_fn():
"""The input_fn."""
dataset = tf.data.TextLineDataset([file_name])
# Skip the first line (which does not have data).
dataset = dataset.skip(1)
dataset = dataset.map(_parse_csv)
if is_training:
# For this small dataset, which can fit into memory, to achieve true
# randomness, the shuffle buffer size is set as the total number of
# elements in the dataset.
dataset = dataset.shuffle(num_data)
dataset = dataset.repeat()
dataset = dataset.batch(batch_size)
iterator = dataset.make_one_shot_iterator()
features, labels = iterator.get_next()
return features, labels
return _input_fn
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
num_training_data = maybe_download_iris_data(
IRIS_TRAINING, IRIS_TRAINING_URL)
num_test_data = maybe_download_iris_data(IRIS_TEST, IRIS_TEST_URL)
# Build 3 layer DNN with 10, 20, 10 units respectively.
feature_columns = [
tf.feature_column.numeric_column(key, shape=1) for key in FEATURE_KEYS]
classifier = tf.estimator.DNNClassifier(
feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3)
# Train.
train_input_fn = input_fn(IRIS_TRAINING, num_training_data, batch_size=32,
is_training=True)
classifier.train(input_fn=train_input_fn, steps=400)
# Eval.
test_input_fn = input_fn(IRIS_TEST, num_test_data, batch_size=32,
is_training=False)
scores = classifier.evaluate(input_fn=test_input_fn)
print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
if __name__ == '__main__':
tf.app.run()
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