#!/usr/bin/env python
# coding: utf-8

# Install
# =====
# Installing tensorflow environment from command line:
# 1. conda create -n tensorflow python=3.5
# 2. activate tensorflow
# 3. install jupyter
# 
# Or use Anaconda Navigator to manage environments from there.
# 
# Tutorial
# =====
# When one learns how to program, there's a tradition that the first thing you do is print "Hello World." Just like programming has Hello World, machine learning has MNIST.
# 
# MNIST is a simple computer vision dataset. It consists of images of handwritten digits. It also includes labels for each image, telling us which digit it is.
# 
# In this tutorial, we're going to train a model to look at images and predict what digits they are. Our goal isn't to train a really elaborate model that achieves state-of-the-art performance but rather to dip a toe into using TensorFlow. As such, we're going to start with a very simple model, called a Softmax Regression.
# 
# The actual code for this tutorial is very short, and all the interesting stuff happens in just three lines. However, it is very important to understand the ideas behind it: both how TensorFlow works and the core machine learning concepts. 
# 
# ## Read input and test data

# In[1]:


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


# ## Implementing the model
# 
# $$ y = softmax(x \cdot W + B) $$

# In[5]:


import tensorflow as tf

x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

y = tf.nn.softmax(tf.matmul(x, W) + b)


# In[6]:





# ## Training

# In[12]:


y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

init = tf.global_variables_initializer()

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

for i in range(1000):
  batch_xs, batch_ys = mnist.train.next_batch(200)
  sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})


# ## Evaluating the Model

# In[13]:


correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))


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