# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Simple MNIST classifier example with JIT XLA and timelines.
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Note: Please see further comments in the BUILD file to invoke XLA.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import argparse
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import sys
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import tensorflow as tf
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from tensorflow.examples.tutorials.mnist import input_data
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from tensorflow.python.client import timeline
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FLAGS = None
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def main(_):
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# Import data
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mnist = input_data.read_data_sets(FLAGS.data_dir)
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# Create the model
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x = tf.placeholder(tf.float32, [None, 784])
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w = tf.Variable(tf.zeros([784, 10]))
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b = tf.Variable(tf.zeros([10]))
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y = tf.matmul(x, w) + b
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# Define loss and optimizer
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y_ = tf.placeholder(tf.int64, [None])
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# The raw formulation of cross-entropy,
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#
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# tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),
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# reduction_indices=[1]))
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#
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# can be numerically unstable.
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#
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# So here we use tf.losses.sparse_softmax_cross_entropy on the raw
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# logit outputs of 'y', and then average across the batch.
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cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y)
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train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
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config = tf.ConfigProto()
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jit_level = 0
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if FLAGS.xla:
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# Turns on XLA JIT compilation.
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jit_level = tf.OptimizerOptions.ON_1
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config.graph_options.optimizer_options.global_jit_level = jit_level
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run_metadata = tf.RunMetadata()
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sess = tf.Session(config=config)
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tf.global_variables_initializer().run(session=sess)
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# Train
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train_loops = 1000
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for i in range(train_loops):
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batch_xs, batch_ys = mnist.train.next_batch(100)
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# Create a timeline for the last loop and export to json to view with
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# chrome://tracing/.
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if i == train_loops - 1:
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sess.run(train_step,
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feed_dict={x: batch_xs,
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y_: batch_ys},
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options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE),
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run_metadata=run_metadata)
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trace = timeline.Timeline(step_stats=run_metadata.step_stats)
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with open('/tmp/timeline.ctf.json', 'w') as trace_file:
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trace_file.write(trace.generate_chrome_trace_format())
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else:
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sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
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# Test trained model
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correct_prediction = tf.equal(tf.argmax(y, 1), y_)
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accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
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print(sess.run(accuracy,
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feed_dict={x: mnist.test.images,
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y_: mnist.test.labels}))
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sess.close()
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--data_dir',
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type=str,
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default='/tmp/tensorflow/mnist/input_data',
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help='Directory for storing input data')
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parser.add_argument(
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'--xla', type=bool, default=True, help='Turn xla via JIT on')
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FLAGS, unparsed = parser.parse_known_args()
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tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
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