# disable warnings which are just informing you if you build TensorFlow from source it can be faster on your machine.
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'

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):
	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]) # 28*28, the size of pic
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)

init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init) # very important!!!

for i in range(1000):
	# trainng
	batch_xs, batch_ys = mnist.train.next_batch(100)  # extract 100 data from minist. Not load all data for quick training
	sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys})
	if i%50 == 0 :
		# to see the accuracy
		print(compute_accuracy(mnist.test.images, mnist.test.labels))
# disable warnings which are just informing you if you build TensorFlow from source it can be faster on your machine.
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'

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):
	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]) # 28*28, the size of pic
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)

init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init) # very important!!!

for i in range(1000):
	# trainng
	batch_xs, batch_ys = mnist.train.next_batch(100)  # extract 100 data from minist. Not load all data for quick training
	sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys})
	if i%50 == 0 :
		# to see the accuracy
		print(compute_accuracy(mnist.test.images, mnist.test.labels))
# disable warnings which are just informing you if you build TensorFlow from source it can be faster on your machine. # number 1 to 10 data # define placeholder for inputs to network # 28*28, the size of pic # add output layer # the error between prediction and real data # loss # very important!!! # trainng # extract 100 data from minist. Not load all data for quick training # to see the accuracy