import tensorflow as tf
import random
import matplotlib.pyplot as plt

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

# hyper parameters
learning_rate = 0.001
training_epochs = 20
batch_size = 100

# dropout (keep_prob) rate  0.7~0.5 on training, but should be 1 for testing
keep_prob = tf.placeholder(tf.float32)

# input place holders
X = tf.placeholder(tf.float32, [None, 784])
X_img = tf.reshape(X, [-1, 28, 28, 1])   # img 28x28x1 (black/white)
Y = tf.placeholder(tf.float32, [None, 10])

# L1
W1 = tf.Variable(tf.random_normal([3, 3, 1, 32], stddev=0.01))
L1 = tf.nn.conv2d(X_img, W1, strides=[1, 1, 1, 1], padding='SAME')
L1 = tf.nn.relu(L1)
L1 = tf.nn.max_pool(L1, ksize=[1, 2, 2, 1],
                    strides=[1, 2, 2, 1], padding='SAME')
L1 = tf.nn.dropout(L1, keep_prob=keep_prob)

# L2
W2 = tf.Variable(tf.random_normal([3, 3, 32, 64], stddev=0.01))
L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME')
L2 = tf.nn.relu(L2)
L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],
                    strides=[1, 2, 2, 1], padding='SAME')
L2 = tf.nn.dropout(L2, keep_prob=keep_prob)

# L3
W3 = tf.Variable(tf.random_normal([3, 3, 64, 128], stddev=0.01))
L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME')
L3 = tf.nn.relu(L3)
L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],
                    strides=[1, 2, 2, 1], padding='SAME')
L3 = tf.nn.dropout(L3, keep_prob=keep_prob)

# L4
W4 = tf.Variable(tf.random_normal([3, 3, 128, 256], stddev=0.01))
L4 = tf.nn.conv2d(L3, W4, strides=[1, 1, 1, 1], padding='SAME')
L4 = tf.nn.relu(L4)
L4 = tf.nn.max_pool(L4, ksize=[1, 2, 2, 1], strides=[
                    1, 2, 2, 1], padding='SAME')
L4 = tf.nn.dropout(L4, keep_prob=keep_prob)
L4_flat = tf.reshape(L4, [-1, 256 * 2 * 2])

# L5 FC
W5 = tf.get_variable("W5", shape=[256 * 2 * 2, 256],
                     initializer=tf.contrib.layers.xavier_initializer())
b5 = tf.Variable(tf.random_normal([256]))
L5 = tf.nn.relu(tf.matmul(L4_flat, W5) + b5)
L5 = tf.nn.dropout(L5, keep_prob=keep_prob)

# L6 FC
W6 = tf.get_variable("W6", shape=[256, 512],
                     initializer=tf.contrib.layers.xavier_initializer())
b6 = tf.Variable(tf.random_normal([512]))
L6 = tf.nn.relu(tf.matmul(L5, W6) + b6)
L6 = tf.nn.dropout(L6, keep_prob=keep_prob)

# L7 Final FC
W7 = tf.get_variable("W7", shape=[512, 10],
                     initializer=tf.contrib.layers.xavier_initializer())
b7 = tf.Variable(tf.random_normal([10]))
logits = tf.matmul(L6, W7) + b7

# define cost/loss & optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
    logits=logits, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# initialize
sess = tf.Session()
sess.run(tf.global_variables_initializer())

# train my model
print('Learning started. It takes sometime.')
for epoch in range(training_epochs):
    avg_cost = 0
    total_batch = int(mnist.train.num_examples / batch_size)

    for i in range(total_batch):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        feed_dict = {X: batch_xs, Y: batch_ys, keep_prob: 0.7}
        c, _ = sess.run([cost, optimizer], feed_dict=feed_dict)
        avg_cost += c / total_batch

    print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))

print('Learning Finished!')

# Test model and check accuracy
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print('Accuracy:', sess.run(accuracy, feed_dict={
      X: mnist.test.images, Y: mnist.test.labels, keep_prob: 1}))

# Get one and predict
r = random.randint(0, mnist.test.num_examples - 1)
print("Label: ", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1)))
print("Prediction: ", sess.run(
    tf.argmax(logits, 1), feed_dict={X: mnist.test.images[r:r + 1], keep_prob: 1}))

plt.imshow(mnist.test.images[r:r + 1].
           reshape(28, 28), cmap='Greys', interpolation='nearest')
plt.show()
