import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

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

def compute_accuracy(v_xs,v_ys):

    global prediction

    y_pre=sess.run(prediction,feed_dict={xs:v_xs,keep_prob:})

    correct_prediction=tf.equal(tf.argmax(y_pre,),tf.argmax(v_ys,))

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

    result=sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys})

    return result

def weight_varirable(shape):

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

    return tf.Variable(inital)

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=[,,,],padding='SAME')

def max_poo_(x):

     return tf.nn.max_pool(x, ksize=[,,,], strides=[,,,], padding='SAME')

xs=tf.placeholder(tf.float32,[None,])

ys=tf.placeholder(tf.float32,[None,])

keep_prob=tf.placeholder(tf.float32)

x_image=tf.reshape(xs,[-,,,])

W_conv1=weight_varirable([,,,])

b_conv1=bias_variable([])

h_conv1=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)

h_pool1=max_poo_(h_conv1)

W_conv2=weight_varirable([,,,])

b_conv2=bias_variable([])

h_conv2=tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2)

h_pool2=max_poo_(h_conv2)

W_fc1=weight_varirable([**,])

b_fc1=bias_variable([])

h_pool2_flat=tf.reshape(h_pool2,[-,**])

h_fcl=tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1)

h_fc1_drop=tf.nn.dropout(h_fcl,keep_prob)

W_fc2=weight_varirable([,])

b_fc2=bias_variable([])

prediction=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)

cross_entropy=tf.reduce_mean(

    -tf.reduce_sum(ys*tf.log(prediction),

    reduction_indices=[]))

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

sess=tf.Session()

sess.run(tf.global_variables_initializer())

for i in range():

    batch_xs, batch_ys = mnist.train.next_batch()

    sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys,keep_prob:0.5})

    if i %  == :

        print(compute_accuracy(

            mnist.test.images, mnist.test.labels))