import tensorflow as tf

import numpy as np

from tensorflow.examples.tutorials.mnist import input_data

def initWeights(shape):

    return tf.Variable(tf.random_normal(shape, stddev = 0.1))

def initBiases(shape):

    return tf.Variable(tf.random_normal(shape, stddev = 0.1))

def model(X, weights, baises):

    return tf.matmul(X, weights) + baises

mnist = input_data.read_data_sets('MNIST_data/', one_hot = True)

trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels

X = tf.placeholder('float', [None, 784])

Y = tf.placeholder('float', [None, 10])

learning_rate = 0.05

epcoh = 100

weights = initWeights([784,10])

biases = initBiases([10])

y_ = model(X, weights, biases)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_, Y))

train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

predict_op = tf.argmax(y_, 1)

with tf.Session() as sess:

    tf.initialize_all_variables().run()

    for i in range(epcoh):

        for start, end in zip(range(0, len(trX), 128), range(128, len(trX)+1, 128)):

            sess.run(train_op, feed_dict = {X: trX[start:end], Y: trY[start:end]})

        print (i, np.mean(np.argmax(teY, axis=1) == sess.run(predict_op, feed_dict={X: teX})))