saver = tf.train.Saver()   
saver.save(sess, "model_data/model.ckpt") 

# -*- coding:utf-8 -*-  
import cv2 
import tensorflow as tf 
import numpy as np 
from sys import path 
path.append('../..') 
from common import extract_mnist 
 
#初始化單個卷積核上的參數(shù) 
def weight_variable(shape): 
  initial = tf.truncated_normal(shape, stddev=0.1) 
  return tf.Variable(initial) 
 
#初始化單個卷積核上的偏置值 
def bias_variable(shape): 
  initial = tf.constant(0.1, shape=shape) 
  return tf.Variable(initial) 
 
#輸入特征x，用卷積核W進行卷積運算，strides為卷積核移動步長， 
#padding表示是否需要補齊邊緣像素使輸出圖像大小不變 
def conv2d(x, W): 
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') 
 
#對x進行最大池化操作，ksize進行池化的范圍， 
def max_pool_2x2(x): 
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') 
 
 
def main(): 
   
  #定義會話 
  sess = tf.InteractiveSession() 
   
  #聲明輸入圖片數(shù)據(jù)，類別 
  x = tf.placeholder('float',[None,784]) 
  x_img = tf.reshape(x , [-1,28,28,1]) 
 
  W_conv1 = weight_variable([5, 5, 1, 32]) 
  b_conv1 = bias_variable([32]) 
  W_conv2 = weight_variable([5,5,32,64]) 
  b_conv2 = bias_variable([64]) 
  W_fc1 = weight_variable([7*7*64,1024]) 
  b_fc1 = bias_variable([1024]) 
  W_fc2 = weight_variable([1024,10]) 
  b_fc2 = bias_variable([10]) 
 
  saver = tf.train.Saver(write_version=tf.train.SaverDef.V1)  
  saver.restore(sess , 'model_data/model.ckpt') 
 
  #進行卷積操作，并添加relu激活函數(shù) 
  h_conv1 = tf.nn.relu(conv2d(x_img,W_conv1) + b_conv1) 
  #進行最大池化 
  h_pool1 = max_pool_2x2(h_conv1) 
 
  #同理第二層卷積層 
  h_conv2 = tf.nn.relu(conv2d(h_pool1,W_conv2) + b_conv2) 
  h_pool2 = max_pool_2x2(h_conv2) 
   
  #將卷積的產出展開 
  h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64]) 
  #神經(jīng)網(wǎng)絡計算，并添加relu激活函數(shù) 
  h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1) + b_fc1) 
 
  #輸出層，使用softmax進行多分類 
  y_conv=tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2) 
 
  # mnist_data_set = extract_mnist.MnistDataSet('../../data/') 
  # x_img , y = mnist_data_set.next_train_batch(1) 
  im = cv2.imread('images/888.jpg',cv2.IMREAD_GRAYSCALE).astype(np.float32) 
  im = cv2.resize(im,(28,28),interpolation=cv2.INTER_CUBIC) 
  #圖片預處理 
  #img_gray = cv2.cvtColor(im , cv2.COLOR_BGR2GRAY).astype(np.float32) 
  #數(shù)據(jù)從0~255轉為-0.5~0.5 
  img_gray = (im - (255 / 2.0)) / 255 
  #cv2.imshow('out',img_gray) 
  #cv2.waitKey(0) 
  x_img = np.reshape(img_gray , [-1 , 784]) 
 
  print x_img 
  output = sess.run(y_conv , feed_dict = {x:x_img}) 
  print 'the y_con :  ', '\n',output 
  print 'the predict is : ', np.argmax(output) 
 
  #關閉會話 
  sess.close() 
 
if __name__ == '__main__': 
  main() 

# -*- coding:utf-8 -*-   
from sys import path 
import numpy as np 
import tensorflow as tf 
import time 
import cv2 
from PIL import Image 
path.append('../..') 
from common import extract_cifar10 
from common import inspect_image 
 
 
#初始化單個卷積核上的參數(shù) 
def weight_variable(shape): 
  initial = tf.truncated_normal(shape, stddev=0.1) 
  return tf.Variable(initial) 
 
#初始化單個卷積核上的偏置值 
def bias_variable(shape): 
  initial = tf.constant(0.1, shape=shape) 
  return tf.Variable(initial) 
 
#卷積操作 
def conv2d(x, W): 
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') 
 
 
 
def main(): 
  #定義會話 
  sess = tf.InteractiveSession() 
   
  #聲明輸入圖片數(shù)據(jù)，類別 
  x = tf.placeholder('float',[None,32,32,3]) 
  y_ = tf.placeholder('float',[None,10]) 
 
  #第一層卷積層 
  W_conv1 = weight_variable([5, 5, 3, 64]) 
  b_conv1 = bias_variable([64]) 
  #進行卷積操作，并添加relu激活函數(shù) 
  conv1 = tf.nn.relu(conv2d(x,W_conv1) + b_conv1) 
  # pool1 
  pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],padding='SAME', name='pool1') 
  # norm1 
  norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75,name='norm1') 
 
 
  #第二層卷積層 
  W_conv2 = weight_variable([5,5,64,64]) 
  b_conv2 = bias_variable([64]) 
  conv2 = tf.nn.relu(conv2d(norm1,W_conv2) + b_conv2) 
  # norm2 
  norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75,name='norm2') 
  # pool2 
  pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1],strides=[1, 2, 2, 1], padding='SAME', name='pool2') 
 
  #全連接層 
  #權值參數(shù) 
  W_fc1 = weight_variable([8*8*64,384]) 
  #偏置值 
  b_fc1 = bias_variable([384]) 
  #將卷積的產出展開 
  pool2_flat = tf.reshape(pool2,[-1,8*8*64]) 
  #神經(jīng)網(wǎng)絡計算，并添加relu激活函數(shù) 
  fc1 = tf.nn.relu(tf.matmul(pool2_flat,W_fc1) + b_fc1) 
   
  #全連接第二層 
  #權值參數(shù) 
  W_fc2 = weight_variable([384,192]) 
  #偏置值 
  b_fc2 = bias_variable([192]) 
  #神經(jīng)網(wǎng)絡計算，并添加relu激活函數(shù) 
  fc2 = tf.nn.relu(tf.matmul(fc1,W_fc2) + b_fc2) 
 
 
  #輸出層，使用softmax進行多分類 
  W_fc2 = weight_variable([192,10]) 
  b_fc2 = bias_variable([10]) 
  y_conv=tf.maximum(tf.nn.softmax(tf.matmul(fc2, W_fc2) + b_fc2),1e-30) 
 
  # 
  saver = tf.train.Saver() 
  saver.restore(sess , 'model_data/model.ckpt') 
  #input 
  im = Image.open('images/dog8.jpg') 
  im.show() 
  im = im.resize((32,32)) 
  # r , g , b = im.split() 
  # im = Image.merge("RGB" , (r,g,b)) 
  print im.size , im.mode 
 
  im = np.array(im).astype(np.float32) 
  im = np.reshape(im , [-1,32*32*3]) 
  im = (im - (255 / 2.0)) / 255 
  batch_xs = np.reshape(im , [-1,32,32,3]) 
  #print batch_xs 
  #獲取cifar10數(shù)據(jù) 
  # cifar10_data_set = extract_cifar10.Cifar10DataSet('../../data/') 
  # batch_xs, batch_ys = cifar10_data_set.next_train_batch(1) 
  # print batch_ys 
  output = sess.run(y_conv , feed_dict={x:batch_xs}) 
  print output 
  print 'the out put is :' , np.argmax(output) 
  #關閉會話 
  sess.close() 
 
if __name__ == '__main__': 
  main()