Google tensorflow tutorial 1 in a file with comments and stuff

tensorflowTutorialOne.py

import input_data
import tensorflow as tf

# http://www.tensorflow.org/tutorials/mnist/beginners/index.html
print '\033[93m' + 'Tutorial1 from:\nhttp://www.tensorflow.org/tutorials/mnist/beginners/index.html' + '\033[0m'
print 'MNIST INPUT DATA'
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

'PLACE HOLDERS'
# Make a placeholder / value to input on run
x = tf.placeholder("float", [None, 784])
# x is a 2D float tensor, None means the first dimension can be any length.
# The MNIST images have been converted into a 784-dimensional vector

'VARIABLES'
# Make some variables (weights and biases)
W = tf.Variable(tf.zeros([784, 10]))
# The shape of W determines the shape of the output.
# Want 10 catergories cause there are 10 digits
b = tf.Variable(tf.zeros([10]))
# bias adds to output

'MODEL'
# softmax regression
# evidence = the sum of [(Weight * input_data) + bias]
# softmax converts evidence set into probability distribution
# or in other words:
# softmax(x) = normalize(exp(x))
y = tf.nn.softmax(tf.matmul(x, W) + b)
# matmul multiples the x and W tensors together....
# adding bias as described above
# these two values contribute to the evidence tally
# Softmax normalises evidence and converts set to probablility distribution

'TRAINING PREP'
y_ = tf.placeholder("float", [None, 10])
# y_ is the predicted probability distribution
# y is the true distribution
# Cross Entropy measures the inefficiency of the predictions made by the model (cost)
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
# tf.log computes the log of each element in y
# multiplied by y_ and reduce_sum

# backpropagation algorithim
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
# minimise cost (ineffiency) in cross_entropy

print 'RUNNING'
init = tf.initialize_all_variables()  # get the placeholders ready
sess = tf.Session()  # make a session
sess.run(init)

for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)  # get batch of 100 random data points
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
    # run the previously defined train_step and feed it data that replaces the placeholder data

'EVALUATE THE MODEL'
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
# argmax gives the index of the highest entry in a tensor along some axis
# does the output match the actual result?
# output of tf equal is bool array
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
# bool array converted to float array, mean across all outputs gives accuracy
print '\033[93mACCURACY ON TEST DATA:'
print sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})
# again to run the session, we feed in the
print '\033[0m'