# encoding=utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

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)

myGraph = tf.Graph()
with myGraph.as_default():
    with tf.name_scope('inputsAndLabels'):
        x_raw = tf.placeholder(tf.float32, shape=[None, 784])
        y = tf.placeholder(tf.float32, shape=[None, 10])

    with tf.name_scope('hidden1'):
        x = tf.reshape(x_raw, shape=[-1,28,28,1])
        W_conv1 = weight_variable([5,5,1,32])
        b_conv1 = bias_variable([32])
        l_conv1 = tf.nn.relu(tf.nn.conv2d(x,W_conv1, strides=[1,1,1,1],padding='SAME') + b_conv1)
        l_pool1 = tf.nn.max_pool(l_conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')

        tf.summary.image('x_input',x,max_outputs=10)
        tf.summary.histogram('W_con1',W_conv1)
        tf.summary.histogram('b_con1',b_conv1)

    with tf.name_scope('hidden2'):
        W_conv2 = weight_variable([5,5,32,64])
        b_conv2 = bias_variable([64])
        l_conv2 = tf.nn.relu(tf.nn.conv2d(l_pool1, W_conv2, strides=[1,1,1,1], padding='SAME')+b_conv2)
        l_pool2 = tf.nn.max_pool(l_conv2, ksize=[1,2,2,1],strides=[1,2,2,1], padding='SAME')

        tf.summary.histogram('W_con2', W_conv2)
        tf.summary.histogram('b_con2', b_conv2)

    with tf.name_scope('fc1'):
        W_fc1 = weight_variable([64*7*7, 1024])
        b_fc1 = bias_variable([1024])
        l_pool2_flat = tf.reshape(l_pool2, [-1, 64*7*7])
        l_fc1 = tf.nn.relu(tf.matmul(l_pool2_flat, W_fc1) + b_fc1)
        keep_prob = tf.placeholder(tf.float32)
        l_fc1_drop = tf.nn.dropout(l_fc1, keep_prob)

        tf.summary.histogram('W_fc1', W_fc1)
        tf.summary.histogram('b_fc1', b_fc1)

    with tf.name_scope('fc2'):
        W_fc2 = weight_variable([1024, 10])
        b_fc2 = bias_variable([10])
        y_conv = tf.matmul(l_fc1_drop, W_fc2) + b_fc2

        tf.summary.histogram('W_fc1', W_fc1)
        tf.summary.histogram('b_fc1', b_fc1)

    with tf.name_scope('train'):
        cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y_conv, labels=y))
        train_step = tf.train.AdamOptimizer(learning_rate=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))

        tf.summary.scalar('loss', cross_entropy)
        tf.summary.scalar('accuracy', accuracy)


with tf.Session(graph=myGraph) as sess:
    sess.run(tf.global_variables_initializer())
    saver = tf.train.Saver()

    merged = tf.summary.merge_all()
    summary_writer = tf.summary.FileWriter('./mnistEven/', graph=sess.graph)

    for i in range(10001):
        batch = mnist.train.next_batch(50)
        sess.run(train_step,feed_dict={x_raw:batch[0], y:batch[1], keep_prob:0.5})
        if i%100 == 0:
            train_accuracy = accuracy.eval(feed_dict={x_raw:batch[0], y:batch[1], keep_prob:1.0})
            print('step %d training accuracy:%g'%(i, train_accuracy))

            summary = sess.run(merged,feed_dict={x_raw:batch[0], y:batch[1], keep_prob:1.0})
            summary_writer.add_summary(summary,i)

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

    saver.save(sess,save_path='./model/mnistmodel',global_step=1)

WARNING:tensorflow:From <ipython-input-4-a36400cc0616>:4: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use alternatives such as official/mnist/dataset.py from tensorflow/models.
WARNING:tensorflow:From /home/binder/.pyenv/versions/3.6.5/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.
Instructions for updating:
Please write your own downloading logic.
WARNING:tensorflow:From /home/binder/.pyenv/versions/3.6.5/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.data to implement this functionality.
Extracting MNIST_data/train-images-idx3-ubyte.gz
WARNING:tensorflow:From /home/binder/.pyenv/versions/3.6.5/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.data to implement this functionality.
Extracting MNIST_data/train-labels-idx1-ubyte.gz
WARNING:tensorflow:From /home/binder/.pyenv/versions/3.6.5/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.one_hot on tensors.
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
WARNING:tensorflow:From /home/binder/.pyenv/versions/3.6.5/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use alternatives such as official/mnist/dataset.py from tensorflow/models.
WARNING:tensorflow:From <ipython-input-4-a36400cc0616>:60: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:

Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.

See @{tf.nn.softmax_cross_entropy_with_logits_v2}.

step 0 training accuracy:0.08
step 100 training accuracy:0.9
step 200 training accuracy:0.94
step 300 training accuracy:0.94
step 400 training accuracy:0.94
step 500 training accuracy:0.86
step 600 training accuracy:0.96
step 700 training accuracy:0.92
step 800 training accuracy:0.96
step 900 training accuracy:0.98
step 1000 training accuracy:0.96
step 1100 training accuracy:0.96
step 1200 training accuracy:1
step 1300 training accuracy:0.96
step 1400 training accuracy:0.98
step 1500 training accuracy:0.98
step 1600 training accuracy:0.98
step 1700 training accuracy:1
step 1800 training accuracy:0.96
step 1900 training accuracy:1
step 2000 training accuracy:1
step 2100 training accuracy:0.94
step 2200 training accuracy:1
step 2300 training accuracy:1
step 2400 training accuracy:1
step 2500 training accuracy:1
step 2600 training accuracy:0.98
step 2700 training accuracy:0.96
step 2800 training accuracy:1
step 2900 training accuracy:1
step 3000 training accuracy:0.98
step 3100 training accuracy:0.96
step 3200 training accuracy:0.96
step 3300 training accuracy:1
step 3400 training accuracy:0.98
step 3500 training accuracy:0.98
step 3600 training accuracy:0.96
step 3700 training accuracy:0.96
step 3800 training accuracy:0.96
step 3900 training accuracy:0.98
step 4000 training accuracy:0.98
step 4100 training accuracy:0.98
step 4200 training accuracy:1
step 4300 training accuracy:0.98
step 4400 training accuracy:0.98
step 4500 training accuracy:1
step 4600 training accuracy:0.98
step 4700 training accuracy:1
step 4800 training accuracy:1
step 4900 training accuracy:0.98
step 5000 training accuracy:0.98
step 5100 training accuracy:1
step 5200 training accuracy:1
step 5300 training accuracy:1
step 5400 training accuracy:1
step 5500 training accuracy:0.98
step 5600 training accuracy:1
step 5700 training accuracy:1
step 5800 training accuracy:0.98
step 5900 training accuracy:0.98
step 6000 training accuracy:1
step 6100 training accuracy:1
step 6200 training accuracy:0.96
step 6300 training accuracy:1
step 6400 training accuracy:1
step 6500 training accuracy:1
step 6600 training accuracy:0.96
step 6700 training accuracy:1
step 6800 training accuracy:1
step 6900 training accuracy:1
step 7000 training accuracy:1
step 7100 training accuracy:1
step 7200 training accuracy:1
step 7300 training accuracy:1
step 7400 training accuracy:1
step 7500 training accuracy:1
step 7600 training accuracy:1
step 7700 training accuracy:1
step 7800 training accuracy:0.98
step 7900 training accuracy:1
step 8000 training accuracy:1
step 8100 training accuracy:1
step 8200 training accuracy:1
step 8300 training accuracy:1
step 8400 training accuracy:1
step 8500 training accuracy:1
step 8600 training accuracy:0.98
step 8700 training accuracy:1
step 8800 training accuracy:0.98
step 8900 training accuracy:1
step 9000 training accuracy:1
step 9100 training accuracy:1
step 9200 training accuracy:0.98
step 9300 training accuracy:1
step 9400 training accuracy:1
step 9500 training accuracy:1
step 9600 training accuracy:1
step 9700 training accuracy:1
step 9800 training accuracy:1
step 9900 training accuracy:1
step 10000 training accuracy:1