mnist

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

# Norbert Batfai, 27 Nov 2016
# Some modifications and additions to the original code:
# https://github.com/tensorflow/tensorflow/blob/r0.11/tensorflow/examples/tutorials/mnist/mnist_softmax.py
# See also http://progpater.blog.hu/2016/11/13/hello_samu_a_tensorflow-bol
# ==============================================================================

"""A very simple MNIST classifier.

See extensive documentation at
http://tensorflow.org/tutorials/mnist/beginners/index.md
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse

# Import data
from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

import matplotlib.pyplot


FLAGS = None


def readimg():
    file = tf.read_file("sajat8a.png")
    img = tf.image.decode_png(file)
    return img

def main(_):
  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(y, y_))
  train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

  sess = tf.InteractiveSession()
  # Train
  tf.initialize_all_variables().run()
  print("-- A halozat tanitasa")
  for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
    if i % 100 == 0:
      print(i/10, "%")
  print("----------------------------------------------------------")

  # Test trained model
  print("-- A halozat tesztelese")
  correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  print("-- Pontossag: ", sess.run(accuracy, feed_dict={x: mnist.test.images,
                                      y_: mnist.test.labels}))
  print("----------------------------------------------------------")

  writer = tf.train.SummaryWriter("/tmp/mnist_softmax_UDPROG61", sess.graph)

  img = W[:,0]
  image = img.eval()
  print("-- W_0[14,14] =", image[14*28+14])
  print("-- Egy vedesi reszfeladat az elso heten: a W_0 sulyok abrazolasa, mutatom, a tovabblepeshez csukd be az ablakat")
  #matplotlib.pyplot.imshow(image.reshape(28, 28))#, cmap=matplotlib.pyplot.cm.binary)
  matplotlib.pyplot.imshow(image.reshape(28, 28), cmap="hot")
  matplotlib.pyplot.savefig("w0.png")
  matplotlib.pyplot.show()
  print("----------------------------------------------------------")

  print("-- A MNIST 42. tesztkepenek felismerese, mutatom a szamot, a tovabblepeshez csukd be az ablakat")

  img = mnist.test.images[42]
  image = img

  matplotlib.pyplot.imshow(image.reshape(28, 28), cmap=matplotlib.pyplot.cm.binary)
  matplotlib.pyplot.savefig("4.png")
  matplotlib.pyplot.show()

  classification = sess.run(tf.argmax(y, 1), feed_dict={x: [image]})

  print("-- Ezt a halozat ennek ismeri fel: ", classification[0])
  print("----------------------------------------------------------")

  print("-- A sajat kezi 8-asom felismerese, mutatom a szamot, a tovabblepeshez csukd be az ablakat")

  img = readimg()
  image = img.eval()
  image = image.reshape(28*28)

  matplotlib.pyplot.imshow(image.reshape(28, 28), cmap=matplotlib.pyplot.cm.binary)
  matplotlib.pyplot.savefig("8.png")
  matplotlib.pyplot.show()

  classification = sess.run(tf.argmax(y, 1), feed_dict={x: [image]})

  print("-- Ezt a halozat ennek ismeri fel: ", classification[0])
  print("----------------------------------------------------------")

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 = parser.parse_args()
  tf.app.run()