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# 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.

This example uses APIs in Tensorflow 1.4 or above.

"""

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()

tensorflow/tensorflow/examples/learn/iris.py at master · codeplaysoftware/tensorflow · GitHub

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