1# Copyright 2015 The TensorFlow Authors. All Rights Reserved. 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================== 15"""Simple MNIST classifier example with JIT XLA and timelines. 16 17""" 18from __future__ import absolute_import 19from __future__ import division 20from __future__ import print_function 21 22import argparse 23import sys 24 25import tensorflow as tf 26 27from tensorflow.examples.tutorials.mnist import input_data 28from tensorflow.python.client import timeline 29 30FLAGS = None 31 32 33def main(_): 34 # Import data 35 mnist = input_data.read_data_sets(FLAGS.data_dir) 36 37 # Create the model 38 x = tf.placeholder(tf.float32, [None, 784]) 39 w = tf.Variable(tf.zeros([784, 10])) 40 b = tf.Variable(tf.zeros([10])) 41 y = tf.matmul(x, w) + b 42 43 # Define loss and optimizer 44 y_ = tf.placeholder(tf.int64, [None]) 45 46 # The raw formulation of cross-entropy, 47 # 48 # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)), 49 # reduction_indices=[1])) 50 # 51 # can be numerically unstable. 52 # 53 # So here we use tf.losses.sparse_softmax_cross_entropy on the raw 54 # logit outputs of 'y', and then average across the batch. 55 cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y) 56 train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) 57 58 config = tf.ConfigProto() 59 jit_level = 0 60 if FLAGS.xla: 61 # Turns on XLA JIT compilation. 62 jit_level = tf.OptimizerOptions.ON_1 63 64 config.graph_options.optimizer_options.global_jit_level = jit_level 65 run_metadata = tf.RunMetadata() 66 sess = tf.Session(config=config) 67 tf.global_variables_initializer().run(session=sess) 68 # Train 69 train_loops = 1000 70 for i in range(train_loops): 71 batch_xs, batch_ys = mnist.train.next_batch(100) 72 73 # Create a timeline for the last loop and export to json to view with 74 # chrome://tracing/. 75 if i == train_loops - 1: 76 sess.run(train_step, 77 feed_dict={x: batch_xs, 78 y_: batch_ys}, 79 options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE), 80 run_metadata=run_metadata) 81 trace = timeline.Timeline(step_stats=run_metadata.step_stats) 82 with open('timeline.ctf.json', 'w') as trace_file: 83 trace_file.write(trace.generate_chrome_trace_format()) 84 else: 85 sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) 86 87 # Test trained model 88 correct_prediction = tf.equal(tf.argmax(y, 1), y_) 89 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 90 print(sess.run(accuracy, 91 feed_dict={x: mnist.test.images, 92 y_: mnist.test.labels})) 93 sess.close() 94 95 96if __name__ == '__main__': 97 parser = argparse.ArgumentParser() 98 parser.add_argument( 99 '--data_dir', 100 type=str, 101 default='/tmp/tensorflow/mnist/input_data', 102 help='Directory for storing input data') 103 parser.add_argument( 104 '--xla', type=bool, default=True, help='Turn xla via JIT on') 105 FLAGS, unparsed = parser.parse_known_args() 106 tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) 107