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

# import datasets (images: 55,000, test: 10,000, validation: 5,000)
## images: 784 (28*28 pixels) values between 0 and 1
## labels: 10 one-hot vector
def main():
    mnist = input_data.read_data_sets("MNIST_data/", one_hot = True)
    sess = tf.InteractiveSession()

    x = tf.placeholder(tf.float32, shape=[None, 784])
    y_ = tf.placeholder(tf.float32, shape=[None, 10])
    W = tf.Variable(tf.zeros([784, 10]))
    b = tf.Variable(tf.zeros([10]))

    #sess.run(tf.initialize_all_variables())
    y = tf.nn.softmax(tf.matmul(x, W) + b)

    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])

    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)

    W_conv2 = weight_variable([5, 5, 32, 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)

    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)

    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    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 = -tf.reduce_sum(y_ * tf.log(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))
    with tf.name_scope('accuracy'):
      accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    tf.scalar_summary("accuracy", accuracy)
    merged = tf.merge_all_summaries()
    train_writer = tf.train.SummaryWriter("/tmp/mnist_logs/train", sess.graph)
    tf.initialize_all_variables().run()

    for i in range(7000):
      batch = mnist.train.next_batch(50)
      if i%100 == 0:
        summary, acc = sess.run([merged, accuracy], feed_dict={x:batch[0], y_: batch[1], keep_prob: 1.0})
        train_writer.add_summary(summary, i)
        print("step %d, training accuracy %f"%(i, acc))
      train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
    saver = tf.train.Saver()
    saver.save(sess, "model.ckpt")
    print("test accuracy %f"%accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
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')

if __name__ == "__main__":
    main()
