from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

from tensorflow.examples.tutorials.mnist import input_data

from datetime import datetime

import os

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

import tensorflow as tf

def _bias_variable(name,shape,initializer):

var = tf.get_variable(name, shape, initializer=initializer, dtype=tf.float32)

return var

def _weight_variable(name,shape,std):

return _bias_variable(name, shape, initializer=tf.truncated_normal_initializer(stddev=std,dtype=tf.float32),

)

def inference(input):

return softmax_linear

def loss(logits,labels):

return cross_entropy_mean

batch_size = 100

training_epoch = 20

with tf.Graph().as_default() as g:

x_place = tf.placeholder(tf.float32, shape=[None, 784], name='x')

y_place = tf.placeholder(tf.float32, shape=[None, 10], name='y')

with open('./graph.pb','rb') as f:

graph_def = tf.GraphDef()

graph_def.ParseFromString(f.read())

tf.import_graph_def(graph_def, name='')

graph_op = tf.import_graph_def(graph_def,name='',input_map={'input/x:0':x_place},

return_elements=['layer2/layer2:0','layer1/weights:0'])

# x_place = g.get_tensor_by_name('input/x:0')

#y_place = g.get_tensor_by_name('input/y:0')

#layer2 = g.get_tensor_by_name('layer2/layer2:0')

#weight_test = g.get_tensor_by_name('layer1/weights:0')

#layer2 = g.get_tensor_by_name('layer2/layer2:0')

#layer2 = tf.stop_gradient(layer2,name='stop_gradient')

layer2 = graph_op[0]

weight_test = graph_op[1]

soft_result = inference(layer2)

cost = loss(soft_result,y_place)

opt = tf.train.AdamOptimizer(0.001).minimize(cost)

correct_prediction = tf.equal(tf.argmax(y_place, 1), tf.argmax(soft_result, 1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))

with tf.Session(graph=g) as sess:

value=[]

#saver.restore(sess, tf.train.latest_checkpoint('./'))

sess.run(tf.global_variables_initializer())

#weight_test = sess.g.get_tensor_by_name('layer1/weights:0')

for epoch in range(training_epoch):

avg_cost = 0.0

total_batch = int(mnist.train.num_examples / batch_size)

for _ in range(total_batch):

batch_xs, batch_ys = mnist.train.next_batch(batch_size)

sess.run(opt, feed_dict={x_place: batch_xs, y_place: batch_ys})

cost_ = sess.run(cost, feed_dict={x_place: batch_xs, y_place: batch_ys})

weight_test_value = sess.run(weight_test,feed_dict={x_place: batch_xs, y_place: batch_ys})

print(("%s epoch: %d,cost: %.6f") % (datetime.now(), epoch + 1, cost_))

if (epoch+1) % 5 == 0:

value.append(weight_test_value)

for i in range(len(value)-1):

if value[i].all()==value[i+1].all():

print("weight is equal")

mean_accuary = sess.run(accuracy, {x_place: mnist.test.images, y_place: mnist.test.labels})

print("accuracy %3.f" % mean_accuary)