I am very eager to your reply

[FranNetty]

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

def weight_varible(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')

mnist=input_data.read_data_sets('MNIST_data',one_hot=True)
sess=tf.InteractiveSession()

################ input,for 2 models i use the same input ################3
x=tf.placeholder(tf.float32,[None,784])
x_image=tf.reshape(x,[-1,28,28,1])

######################### the first model ########################################
W_conv1=weight_varible([5,5,1,32])
b_conv1=weight_varible([32])

h_conv1=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)
h_pool1=max_pool_2x2(h_conv1)

W_conv2=weight_varible([5,5,32,64])
b_conv2=weight_varible([64])

h_conv2=tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2)
h_pool2=max_pool_2x2(h_conv2)

W_fc1=weight_varible([7764,1024])
b_fc1=weight_varible([1024])

h_pool2_flat=tf.reshape(h_pool2,[-1,7764])
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_varible([1024,10])
b_fc2=weight_varible([10])

y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)
y_=tf.placeholder(tf.float32,[None,10])

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))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
sess.run(tf.initialize_all_variables())

################## the second parallel model,i use the same input as the first ###############################

W2_conv1=weight_varible([5,5,1,32])
b2_conv1=weight_varible([32])

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

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

h2_conv1=tf.nn.relu(conv2d(x_image,W2_conv1)+b2_conv1)
h2_pool1=max_pool_2x2(h2_conv1)

W2_conv2=weight_varible([5,5,32,64])
b2_conv2=weight_varible([64])

h2_conv2=tf.nn.relu(conv2d(h2_pool1,W2_conv2)+b2_conv2)
h2_pool2=max_pool_2x2(h2_conv2)

W2_fc1=weight_varible([7764,1024])
b2_fc1=weight_varible([1024])

h2_pool2_flat=tf.reshape(h2_pool2,[-1,7764])
h2_fc1=tf.nn.relu(tf.matmul(h2_pool2_flat,W2_fc1)+b2_fc1)

keep_prob=tf.placeholder(tf.float32)
h2_fc1_drop=tf.nn.dropout(h2_fc1,keep_prob)

W2_fc2=weight_varible([1024,10])
b2_fc2=weight_varible([10])

y2_=tf.placeholder(tf.float32,[None,10])
y2_conv=tf.nn.softmax(tf.matmul(h2_fc1_drop,W2_fc2)+b2_fc2)

cross_entropy=tf.reduce_sum(y2_*tf.log(y2_conv))
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction=tf.equal(tf.argmax(y2_conv,1),tf.argmax(y2_,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
sess.run(tf.initialize_all_variables())

##########################################################

for i in range(20000):
batch=mnist.train.next_batch(50)
if i%100==0:
train_accuracy=accuracy.eval(feed_dict={x:batch[0],y_:batch[1],keep_prob:1})
print('step %d,train accuracy %g'%(i,train_accuracy))
train_step.run([y_conv,y2_conv],feed_dict={x:batch[0],y_:batch[1],keep_prob:0.5})
print('test accuracy %g'%accuracy.eval(feed_dict={x:mnist.test.images,y_:mnist.test.labels,keep_prob:1.0}))