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, one_hot=True)

  # 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.float32, [None, 10])

  # The raw formulation of cross-entropy,

  #

  #   tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),

  #                                 reduction_indices=[1]))

  #

  # can be numerically unstable.

  #

  # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw

  # outputs of 'y', and then average across the batch.

  cross_entropy = tf.reduce_mean(

      tf.nn.softmax_cross_entropy_with_logits(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.

    # 开启XLA JIT编译

    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

  # 训练

  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/.

    # 在最后一次循环创建时间线文件，用chrome://tracing/打开分析

    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('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), tf.argmax(y_, 1))

  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)