from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import sys

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

FLAGS = None


def main(_):
  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)

  # Train & test simple softmax regression model.
  x = tf.placeholder(tf.float32, shape=[None, 784])
  y_ = tf.placeholder(tf.float32, shape=[None, 10])
  W = tf.Variable(tf.zeros([784,10]))
  b = tf.Variable(tf.zeros([10]))
  y = tf.matmul(x,W) + b
  cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, y_))
  train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
  sess = tf.InteractiveSession()
  tf.global_variables_initializer().run()
  for i in range(1000):
    batch = mnist.train.next_batch(100)
    train_step.run(feed_dict={x: batch[0], y_: batch[1]})
  correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  print(
    accuracy.eval(feed_dict={x: mnist.test.images, y_:  mnist.test.labels}))

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')
  FLAGS, unparsed = parser.parse_known_args()
  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import sys

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

FLAGS = None


def main(_):
  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)

  # Train & test simple softmax regression model.
  x = tf.placeholder(tf.float32, shape=[None, 784])
  y_ = tf.placeholder(tf.float32, shape=[None, 10])
  W = tf.Variable(tf.zeros([784,10]))
  b = tf.Variable(tf.zeros([10]))
  y = tf.matmul(x,W) + b
  cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, y_))
  train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
  sess = tf.InteractiveSession()
  tf.global_variables_initializer().run()
  for i in range(1000):
    batch = mnist.train.next_batch(100)
    train_step.run(feed_dict={x: batch[0], y_: batch[1]})
  correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  print(
    accuracy.eval(feed_dict={x: mnist.test.images, y_:  mnist.test.labels}))

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')
  FLAGS, unparsed = parser.parse_known_args()
  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

# Train & test simple softmax regression model.