# -*- coding:utf-8 -*-

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

import os

import argparse

import sys

DATA_DIR = os.path.join('.', 'mnist_link')

# =======================================

#            COMMON OPERATIONS

# =======================================

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')

def init_weight(shape):

    init = tf.truncated_normal(shape, stddev=0.1)

    return tf.Variable(init)

def init_bias(shape):

    init = tf.constant(0.1, shape=shape)

    return tf.Variable(init)

# =======================================

#              BUILD CNN

# =======================================

def build_cnn(x):

    '''

    build the cnn model

    '''

    x_image = tf.reshape(x, [-1,28,28,1])

    w1 = init_weight([5,5,1,32])

    b1=init_bias([32])

    conv1 = tf.nn.relu(conv2d(x_image, w1) + b1)

    pool1 = max_pool_2x2(conv1)

    w2 = init_weight([5,5,32,64])

    b2 = init_bias([64])

    conv2 = tf.nn.relu(conv2d(pool1, w2) + b2)

    pool2 = max_pool_2x2(conv2)

    # fc

    w_fc1 = init_weight([7*7*64, 1024])

    b_fc1 = init_bias([1024])

    pool2_flat = tf.reshape(pool2, [-1, 7*7*64])

    fc1 = tf.nn.relu(tf.matmul(pool2_flat, w_fc1) + b_fc1)

    # dropout

    keep_prob = tf.placeholder(tf.float32)

    fc1_dropout = tf.nn.dropout(fc1, keep_prob)

    # fc2

    w_fc2 = init_weight([1024, 10])

    b_fc2 = init_bias([10])

    y_conv = tf.matmul(fc1_dropout, w_fc2) + b_fc2

    return y_conv, keep_prob

# =======================================

#            train and test

# =======================================

def main():

    '''

    feed data into cnn model, and train and test the model

    '''

    # import data

    print('import data...')

    mnist = input_data.read_data_sets(DATA_DIR, one_hot=True)

    # create graph for cnn

    x = tf.placeholder(tf.float32, [None, 784])

    y_ = tf.placeholder(tf.float32, [None, 10])

    y_conv, keep_prob = build_cnn(x)

    cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y_, logits = y_conv))

    optimizer = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

    correct_predictions = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))

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

    init = tf.global_variables_initializer()

    print('start training...')

    with tf.Session() as sess:

        sess.run(init)

        for i in range(2000):

            batch = mnist.train.next_batch(128)

            optimizer.run(feed_dict={x:batch[0], y_:batch[1], keep_prob:0.5})

            if i%100 == 0:

                train_acc = accuracy.eval(

                    feed_dict = {x:batch[0], y_:batch[1], keep_prob:1.0})

                print('step {}, accuracy is {}'.format(i, train_acc))

        test_acc = accuracy.eval(feed_dict={x:mnist.test.images, y_:mnist.test.labels, keep_prob:1.0})

        print('test accuracy is {}'.format(test_acc))

if __name__ == '__main__':

    print('run main')

    main()