import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# number 1 to 10 data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

def add_layer(inputs, in_size, out_size, activation_function=None,):
  # add one more layer and return the output of this layer
  Weights = tf.Variable(tf.random_normal([in_size, out_size]))
  biases = tf.Variable(tf.zeros([1, out_size]) + 0.1,)
  Wx_plus_b = tf.matmul(inputs, Weights) + biases
  if activation_function is None:
    outputs = Wx_plus_b
  else:
    outputs = activation_function(Wx_plus_b,)
  return outputs

def compute_accuracy(v_xs, v_ys):
  global prediction
  y_pre = sess.run(prediction, feed_dict={xs: v_xs})
  correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys})
  return result

# define placeholder for inputs to network
xs = tf.placeholder(tf.float32, [None, 784]) # 28x28
ys = tf.placeholder(tf.float32, [None, 10])

# add output layer
prediction = add_layer(xs, 784, 10,  activation_function=tf.nn.softmax)

# the error between prediction and real data
cross_entropy = tf.reduce_mean(
  -tf.reduce_sum(ys * tf.log(prediction),
  reduction_indices=[1])) # loss
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

sess = tf.Session()
# important step
# tf.initialize_all_variables() no long valid from
# 2017-03-02 if using tensorflow >= 0.12
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
  init = tf.initialize_all_variables()
else:
  init = tf.global_variables_initializer()
sess.run(init)

for i in range(1000):
  batch_xs, batch_ys = mnist.train.next_batch(100)
  sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys})
  if i % 50 == 0:
    print(compute_accuracy(
      mnist.test.images, mnist.test.labels))