Logistic regression model.py

# Create an interactive Tensorflow session
sess = tf.InteractiveSession()

# These will be inputs for the model
# Input pixels of images, flattened
# 1296 = 36*36 which is the size of images
x = tf.placeholder("float", [None, 1296])
# Known labels
y_ = tf.placeholder("float", [None,2])

# Variables
# W is for weights
# b is for bias
# these 2 variables will be updated during the training
W = tf.Variable(tf.zeros([1296,2]))
b = tf.Variable(tf.zeros([2]))

# Initialize variables
sess.run(tf.global_variables_initializer())

# Define our logistic regression model
y = tf.nn.softmax(tf.matmul(x,W) + b)

# Finish model specification, let us start training the model