import input_data
import tensorflow as tf

mnist = input_data.read_data_sets('MNIST_data/', one_hot = True)
sess = tf.InteractiveSession()

# Build model
x   = tf.placeholder(tf.float32, shape=[None, 784]) #shape is optional but helps in debugging
y_  = tf.placeholder(tf.float32, shape=[None, 10])

W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

# initialize all variables
sess.run(tf.global_variables_initializer())

y = tf.matmul(x, W) + b # our model

#loss / cost
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
# ^^ Note that tf.nn.softmax_cross_entropy_with_logits internally applies
# the softmax on the model's unnormalized model prediction and sums
# across all classes, and tf.reduce_mean takes the average over these sums.

# Train the model
train = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

for _ in range(1000):
    batch = mnist.train.next_batch(100)
    train.run(feed_dict={x: batch[0], y_: batch[1]})
    sess.run(train, {x: batch[0], y_: batch[1]}) # both are same

# evaluate
correct_prediction = tf.equal(tf.arg_max(y,1), tf.arg_max(y_,1))
# after casting true false becomes 1 and 0.
# getting mean will then be adding all 1 and 0 divide by all = % of 1's
# that means total % correct i.e accuracy
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
# print(sess.run(accuracy, {x: mnist.test.images, y_: mnist.test.labels})) # both are same

####
# Build a Multilayer Convolutional Network
####
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)

# convolution and pooling
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')

# The first two dimensions are the patch size, the next is the number
# of input channels, and the last is the number of output channels.
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

# we first reshape x to a 4d tensor, with the second and third dimensions
# corresponding to image width and height, and the final dimension
# corresponding to the number of color channels.
# I think -1 is used to say any size output of sth like that
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 convolutional layer
W_conv2 = weight_variable([5, 5, 32, 64]) # output from 1st layer is 32, so input on 2nd layer is 32 and output is 64
b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

# Densely Connected layer
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)

#Dropout
# This allows us to turn dropout on during training, and turn it off during testing.
# To reduce overfitting, we will apply dropout before the readout layer
keep_prob = tf.placeholder(tf.float32)
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.matmul(h_fc1_drop, W_fc2) + b_fc2

cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# Train and Evaluate
with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())
  for i in range(2000):
    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: 0.5})

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