experiment1.py

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
from PIL import Image, ImageOps
import numpy as np

FLAGS = None

def predict(x, W1, b1, W2, b2):
  a1 = tf.matmul(x, W1) + b1
  z1 = tf.sigmoid(a1)
  a2 = tf.matmul(z1, W2) + b2
  y = tf.nn.softmax(a2)

  return y

def get_result(sess, image_filenames, W1, b1, W2, b2):
  data = []
  
  for filename in image_filenames:
    img = np.array(Image.open(filename).convert("L"))
    data.append(tf.constant((255 - img.flatten())/255, dtype=tf.float32))

  result = sess.run(predict(data, W1, b1, W2, b2))
  result = np.argmax(result, axis=1)

  return result

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

  # Create the model
  std = 0.01
  x = tf.placeholder(tf.float32, [None, 784])
  W1 = tf.Variable(tf.random_normal([784, 100], stddev=std))
  b1 = tf.Variable(tf.zeros([100]))
  W2 = tf.Variable(tf.random_normal([100, 10], stddev=std))
  b2 = tf.Variable(tf.zeros([10]))

  y = predict(x, W1, b1, W2, b2)

  y_ = tf.placeholder(tf.float32, [None, 10])
  cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
  train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

  sess = tf.InteractiveSession()
  tf.global_variables_initializer().run()

  # Train
  for _ in range(10000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

  # Test
  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}))

  print(get_result(sess, ['1.png', '5.png', '7.png'], W1, b1, W2, b2))

  
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)