# -*- coding: utf-8 -*-
"""
Created on Mon May 30 16:30:28 2016

@author: pablo.natal
"""

import tensorflow as tf


from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", 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]))

layer = tf.matmul(x, W) + b

y = tf.nn.softmax(layer)


# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])

cross_entropy = -tf.reduce_sum(y_ * tf.log(y))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)


# Initialize
init = tf.initialize_all_variables()

sess = tf.Session()
sess.run(init)

# Create summary file for TensorBoard
writer = tf.train.SummaryWriter("/tmp/mnist_logs", sess.graph_def)

# Train
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})


# Test
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

