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@MarkDaoust @tensorflower-gardener @caisq
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# Copyright 2015 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.
# ==============================================================================
"""Simple MNIST classifier example with JIT XLA and timelines.
Note: Please see further comments in the BUILD file to invoke XLA.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.python.client import timeline
FLAGS = None
def main(_):
# Import data
mnist = input_data.read_data_sets(FLAGS.data_dir)
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
w = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.matmul(x, w) + b
# Define loss and optimizer
y_ = tf.placeholder(tf.int64, [None])
# The raw formulation of cross-entropy,
#
# tf.reduce_mean(-tf.reduce_sum(y_ * tf.math.log(tf.nn.softmax(y)),
# reduction_indices=[1]))
#
# can be numerically unstable.
#
# So here we use tf.compat.v1.losses.sparse_softmax_cross_entropy on the raw
# logit outputs of 'y', and then average across the batch.
cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y)
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
config = tf.ConfigProto()
jit_level = 0
if FLAGS.xla:
# Turns on XLA JIT compilation.
jit_level = tf.OptimizerOptions.ON_1
config.graph_options.optimizer_options.global_jit_level = jit_level
run_metadata = tf.RunMetadata()
sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=sess)
# Train
train_loops = 1000
for i in range(train_loops):
batch_xs, batch_ys = mnist.train.next_batch(100)
# Create a timeline for the last loop and export to json to view with
# chrome://tracing/.
if i == train_loops - 1:
sess.run(train_step,
feed_dict={x: batch_xs,
y_: batch_ys},
options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE),
run_metadata=run_metadata)
trace = timeline.Timeline(step_stats=run_metadata.step_stats)
with open('/tmp/timeline.ctf.json', 'w') as trace_file:
trace_file.write(trace.generate_chrome_trace_format())
else:
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
# Test trained model
correct_prediction = tf.equal(tf.argmax(y, 1), y_)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy,
feed_dict={x: mnist.test.images,
y_: mnist.test.labels}))
sess.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--data_dir',
type=str,
default='/tmp/tensorflow/mnist/input_data',
help='Directory for storing input data')
parser.add_argument(
'--xla', type=bool, default=True, help='Turn xla via JIT on')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
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