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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.
# ==============================================================================
"""Debug the tf-learn iris example, based on the tf-learn tutorial."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import sys
import tempfile
from six.moves import urllib
import tensorflow as tf
from tensorflow.contrib.learn.python.learn.datasets import base
from tensorflow.python import debug as tf_debug
# URLs to download data sets from, if necessary.
IRIS_TRAINING_DATA_URL = "https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/examples/tutorials/monitors/iris_training.csv"
IRIS_TEST_DATA_URL = "https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/examples/tutorials/monitors/iris_test.csv"
def maybe_download_data(data_dir):
"""Download data sets if necessary.
Args:
data_dir: Path to where data should be downloaded.
Returns:
Paths to the training and test data files.
"""
if not os.path.isdir(data_dir):
os.makedirs(data_dir)
training_data_path = os.path.join(data_dir,
os.path.basename(IRIS_TRAINING_DATA_URL))
if not os.path.isfile(training_data_path):
train_file = open(training_data_path, "wt")
urllib.request.urlretrieve(IRIS_TRAINING_DATA_URL, train_file.name)
train_file.close()
print("Training data are downloaded to %s" % train_file.name)
test_data_path = os.path.join(data_dir, os.path.basename(IRIS_TEST_DATA_URL))
if not os.path.isfile(test_data_path):
test_file = open(test_data_path, "wt")
urllib.request.urlretrieve(IRIS_TEST_DATA_URL, test_file.name)
test_file.close()
print("Test data are downloaded to %s" % test_file.name)
return training_data_path, test_data_path
_IRIS_INPUT_DIM = 4
def iris_input_fn():
iris = base.load_iris()
features = tf.reshape(tf.constant(iris.data), [-1, _IRIS_INPUT_DIM])
labels = tf.reshape(tf.constant(iris.target), [-1])
return features, labels
def main(_):
# Load datasets.
if FLAGS.fake_data:
def training_input_fn():
return ({"features": tf.random_normal([128, 4])},
tf.random_uniform([128], minval=0, maxval=3, dtype=tf.int32))
def test_input_fn():
return ({"features": tf.random_normal([32, 4])},
tf.random_uniform([32], minval=0, maxval=3, dtype=tf.int32))
feature_columns = [
tf.feature_column.numeric_column("features", shape=(4,))]
else:
training_data_path, test_data_path = maybe_download_data(FLAGS.data_dir)
column_names = [
"sepal_length", "sepal_width", "petal_length", "petal_width", "label"]
batch_size = 32
def training_input_fn():
return tf.data.experimental.make_csv_dataset([training_data_path],
batch_size,
column_names=column_names,
label_name="label")
def test_input_fn():
return tf.data.experimental.make_csv_dataset([test_data_path],
batch_size,
column_names=column_names,
label_name="label")
feature_columns = [tf.feature_column.numeric_column(feature)
for feature in column_names[:-1]]
# Build 3 layer DNN with 10, 20, 10 units respectively.
model_dir = FLAGS.model_dir or tempfile.mkdtemp(prefix="debug_tflearn_iris_")
classifier = tf.estimator.DNNClassifier(
feature_columns=feature_columns,
hidden_units=[10, 20, 10],
n_classes=3,
model_dir=model_dir)
if FLAGS.debug and FLAGS.tensorboard_debug_address:
raise ValueError(
"The --debug and --tensorboard_debug_address flags are mutually "
"exclusive.")
hooks = []
if FLAGS.debug:
hooks.append(tf_debug.LocalCLIDebugHook(ui_type=FLAGS.ui_type,
dump_root=FLAGS.dump_root))
elif FLAGS.tensorboard_debug_address:
hooks.append(tf_debug.TensorBoardDebugHook(FLAGS.tensorboard_debug_address))
# Train model, using tfdbg hook.
classifier.train(training_input_fn,
steps=FLAGS.train_steps,
hooks=hooks)
# Evaluate accuracy, using tfdbg hook.
accuracy_score = classifier.evaluate(test_input_fn,
steps=FLAGS.eval_steps,
hooks=hooks)["accuracy"]
print("After training %d steps, Accuracy = %f" %
(FLAGS.train_steps, accuracy_score))
# Make predictions, using tfdbg hook.
predict_results = classifier.predict(test_input_fn, hooks=hooks)
print("A prediction result: %s" % next(predict_results))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.register("type", "bool", lambda v: v.lower() == "true")
parser.add_argument(
"--data_dir",
type=str,
default="/tmp/iris_data",
help="Directory to save the training and test data in.")
parser.add_argument(
"--model_dir",
type=str,
default="",
help="Directory to save the trained model in.")
parser.add_argument(
"--train_steps",
type=int,
default=10,
help="Number of steps to run training for.")
parser.add_argument(
"--eval_steps",
type=int,
default=1,
help="Number of steps to run evaluation foir.")
parser.add_argument(
"--ui_type",
type=str,
default="curses",
help="Command-line user interface type (curses | readline)")
parser.add_argument(
"--fake_data",
type="bool",
nargs="?",
const=True,
default=False,
help="Use fake MNIST data for unit testing")
parser.add_argument(
"--debug",
type="bool",
nargs="?",
const=True,
default=False,
help="Use debugger to track down bad values during training. "
"Mutually exclusive with the --tensorboard_debug_address flag.")
parser.add_argument(
"--dump_root",
type=str,
default="",
help="Optional custom root directory for temporary debug dump data")
parser.add_argument(
"--tensorboard_debug_address",
type=str,
default=None,
help="Connect to the TensorBoard Debugger Plugin backend specified by "
"the gRPC address (e.g., localhost:1234). Mutually exclusive with the "
"--debug flag.")
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)