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

INPUT_NODE = 784
OUTPUT_NODE = 10

LAYER1_NODE = 500

BATCH_SIZE = 100

LEARNING_RATE_BASE = 0.8

LEARNING_RATE_DECAY = 0.99

REGULARIZATION_RATE = 0.0001

TRAINING_STEPS = 30000
MOVING_AVERAGE_DECAY = 0.99

def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):
    if avg_class == None:
        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)
        return tf.matmul(layer1, weights2) + biases2
    else:
        layer1 = tf.nn.relu(
            tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))
        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)

def train(mnist):
    x = tf.placeholder(tf.float32, [None, INPUT_NODE], name = 'x-input')
    y_ = tf.placeholder(tf.float32, [None, OUTPUT_NODE], name = 'y-input')
    weights1 = tf.Variable(tf.truncated_normal([INPUT_NODE,LAYER1_NODE], stddev=0.1))
    biases1 =tf.Variable(tf.constant(0.1, shape = [LAYER1_NODE]))
    weights2 = tf.Variable(tf.truncated_normal([LAYER1_NODE, OUTPUT_NODE], stddev= 0.1))
    biases2 = tf.Variable(tf.constant(0.1, shape = [OUTPUT_NODE]))

    y = inference(x, None, weights1, biases1, weights2, biases2)
    global_step = tf.Variable(0, trainable = False)

    variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY,global_step)

    variables_averages_op = variable_averages.apply(tf.trainable_variables())

    average_y = inference(x, variable_averages, weights1, biases1, weights2, biases2)

    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits = y, labels = tf.argmax(y_, 1))

    cross_entropy_mean = tf.reduce_mean(cross_entropy)

    regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)

    regularization = regularizer(weights1) + regularizer(weights2)

    loss = cross_entropy_mean + regularization

    learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE, global_step, mnist.train.num_examples / BATCH_SIZE, LEARNING_RATE_DECAY)

    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step = global_step)

    with tf.control_dependencies([train_step, variables_averages_op]):
        train_op = tf.no_op(name = 'train')

    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))

    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    with tf.Session() as  sess:
        tf.initialize_all_variables().run()
        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}

        test_feed = {x : mnist.test.images, y_:mnist.test.labels}

        for i in range(TRAINING_STEPS):
            if i % 1000 == 0:
                validate_acc = sess.run(accuracy, feed_dict=validate_feed)
                print("After %d training step(s), validation accuracy using average model is  %g" % (i, validate_acc))

            xs, ys = mnist.train.next_batch(BATCH_SIZE)
            sess.run(train_op, feed_dict={x: xs, y_:ys})

        test_acc = sess.run(accuracy, feed_dict = test_feed)
        print("After %d training step(s), validation accuracy using average model is  %g" % (TRAINING_STEPS, test_acc))




if  __name__ == '__main__':
    mnist = input_data.read_data_sets("C:/Users/liuken/data", one_hot=True)
    train(mnist)