from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

sess = tf.InteractiveSession()
# 784 is the one dimension vector of the image. The images are 28x28. They have been flattened
# 10 is the number of digits one digit can be. 0-9


def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)


def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)


def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')



mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])     # Correct Answers

# FIRST CONVOLUTION LAYER
W_conv1 = weight_variable([5, 5, 1, 32])    # Compute 32 features each of 5x5 patch
b_conv1 = bias_variable([32])
'''Reshaping x to a 4d sensor, with the second and third dimensions corresponding to 
image width and height, and the final dimension corresponding to the no. of color channels'''
x_image = tf.reshape(x, [-1, 28, 28, 1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

# SECOND CONVOLUTION LAYER
W_conv2 = weight_variable([5, 5, 32, 64])   # 64 features
b_conv2 = weight_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

# DENSELY CONNECTED LAYER
# Image size has reduced to 7x7
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

# TO REDUCE OVER-FITTING, DROPOUT IS USED.
keep_prob = tf.placeholder(tf.float32)  # Probability that a neuron's output is kept
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

# READOUT LAYER
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
# Cross-entropy equation
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))
# Using gradient algorithm with a learning rate of 0.5.
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
# Evaluating the model
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))  # Gives a list of booleans
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
init = tf.initialize_all_variables()
sess.run(init)
for i in range(20000):
    batch = mnist.train.next_batch(50)
    if i % 100 == 0:
        train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_:batch[1], keep_prob: 1.0})
        print("Step %d, Training accuracy %g" % (i, train_accuracy))
    train_step.run(feed_dict={x: batch[0], y_:batch[1], keep_prob: 1.0})

print("Test accuracy %g" % accuracy.eval(feed_dict={x: mnist.test.images, y_:mnist.test.labels, keep_prob: 1.0}))
