python-2.7 - 解释tensorflow中sess.run的输出

import sys import math import random import time from PIL import Image import Queue import numpy as np import threading from cStringIO import StringIO import tensorflow as tf dataset_name = 'AmazonFashion6ImgPartitioned.npy' #Hyper-prameters K = 100 # Latent dimensionality lambda1 = 0.001 # Weight decay lambda2 = 1.0 # Regularizer for theta_u learning_rate = 1e-4 training_epoch = 20 batch_size = 128 dropout = 0.5 # Dropout, probability to keep units numldprocess=4 # multi-threading for loading images dataset = np.load('../'+dataset_name) [user_train, user_validation, user_test, Item, usernum, itemnum] = dataset # Create some wrappers for simplicity def conv2d(x, W, b, strides=1): # Conv2D wrapper, with bias and relu activation x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME') x = tf.nn.bias_add(x, b) return tf.nn.relu(x) def maxpool2d(x, k=2): return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME') def avgpool2d(x, k=2): return tf.nn.avg_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME') weights = { 'wc1': [11, 11, 3, 64], 'wc2': [5, 5, 64, 256], 'wc3': [3, 3, 256, 256], 'wc4': [3, 3, 256, 256], 'wc5': [3, 3, 256, 256], 'wd1': [7*7*256, 4096], 'wd2': [4096, 4096], 'wd3': [4096, K], } biases = { 'bc1': [64], 'bc2': [256], 'bc3': [256], 'bc4': [256], 'bc5': [256], 'bd1': [4096], 'bd2': [4096], 'bd3': [K], } def Weights(name): return tf.get_variable(name,dtype=tf.float32,shape=weights[name],initializer=tf.contrib.layers.xavier_initializer()) def Biases(name): return tf.get_variable(name,dtype=tf.float32,initializer=tf.zeros(biases[name])) # Create CNN model def CNN(x,dropout): # Reshape input picture x = tf.reshape(x, shape=[-1, 224, 224, 3]) conv1 = conv2d(x, Weights('wc1'), Biases('bc1'), strides=4) conv1 = tf.nn.relu(conv1) conv1 = maxpool2d(conv1, k=2) conv2 = conv2d(conv1, Weights('wc2'), Biases('bc2')) conv2 = tf.nn.relu(conv2) conv2 = maxpool2d(conv2, k=2) conv3 = conv2d(conv2, Weights('wc3'), Biases('bc3')) conv3 = tf.nn.relu(conv3) conv4 = conv2d(conv3, Weights('wc4'), Biases('bc4')) conv4 = tf.nn.relu(conv4) conv5 = conv2d(conv4, Weights('wc5'), Biases('bc5')) conv5 = tf.nn.relu(conv5) conv5 = maxpool2d(conv5, k=2) fc1 = tf.reshape(conv5, [-1,weights['wd1'][0]]) fc1 = tf.add(tf.matmul(fc1, Weights('wd1')), Biases('bd1')) fc1 = tf.nn.relu(fc1) fc1 = tf.nn.dropout(fc1, dropout) fc2 = tf.add(tf.matmul(fc1, Weights('wd2')), Biases('bd2')) fc2 = tf.nn.relu(fc2) fc2 = tf.nn.dropout(fc2, dropout) fc3 = tf.add(tf.matmul(fc2, Weights('wd3')), Biases('bd3')) return fc3 image1=(tf.to_float(image1)-127.5)/127.5 image2=(tf.to_float(image2)-127.5)/127.5 _image_test=(tf.to_float(image_test)-127.5)/127.5 u=tf.reshape(u,shape=[batch_size]) i=tf.reshape(i,shape=[batch_size]) j=tf.reshape(j,shape=[batch_size]) keep_prob = tf.placeholder(tf.float32) #dropout (keep probability) #siamese networks with tf.variable_scope("DVBPR") as scope: result1 = CNN(image1,dropout) scope.reuse_variables() result2 = CNN(image2,dropout) result_test = CNN(_image_test,1.0) nn_regularizers = sum(map(tf.nn.l2_loss,[Weights('wd1'), Weights('wd2'), Weights('wd3'), Weights('wc1'), Weights('wc2'), Weights('wc3'), Weights('wc4'), Weights('wc5')])) thetau = tf.Variable(tf.random_uniform([usernum,K],minval=0,maxval=1)/100) cost_train = tf.reduce_sum(tf.log(tf.sigmoid(tf.reduce_sum(tf.multiply(tf.gather(thetau,u),tf.subtract(result1,result2)),1,keep_dims=True)))) regularizers = tf.nn.l2_loss(tf.gather(thetau,u)) cost_train -= lambda1 * nn_regularizers + lambda2 * regularizers optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(-cost_train) # Initializing the variables init = tf.initialize_all_variables() def AUC(train,test,U,I): ans=0 cc=0 for u in train: i=test[u][0]['productid'] T=np.dot(U[u,:],I.T) cc+=1 M=set() for item in train[u]: M.add(item['productid']) M.add(i) count=0 tmpans=0 #for j in xrange(itemnum): for j in random.sample(xrange(itemnum),100): #sample if j in M: continue if T[i]>T[j]: tmpans+=1 count+=1 tmpans/=float(count) ans+=tmpans ans/=float(cc) return ans def Evaluation(step): print '...' U=sess.run(thetau) I=np.zeros([itemnum,K],dtype=np.float32) idx=np.array_split(range(itemnum),(itemnum+batch_size-1)/batch_size) input_images=np.zeros([batch_size,224,224,3],dtype=np.int8) for i in range(len(idx)): cc=0 for j in idx[i]: input_images[cc]=np.uint8(np.asarray(Image.open(StringIO(Item[j]['imgs'])).convert('RGB').resize((224,224)))) cc+=1 I[idx[i][0]:(idx[i][-1]+1)]=sess.run(result_test,feed_dict={image_test:input_images})[:(idx[i][-1]-idx[i][0]+1)] print 'export finised!' np.save('UI_'+str(K)+'_'+str(step)+'.npy',[U,I]) return AUC(user_train,user_validation,U,I), AUC(user_train,user_test,U,I) def sample(user): u = random.randrange(usernum) numu = len(user[u]) i = user[u][random.randrange(numu)]['productid'] M=set() for item in user[u]: M.add(item['productid']) while True: j=random.randrange(itemnum) if (not j in M): break return (u,i,j) def load_image_async(): while True: (uuu,iii,jjj)=sample(user_train) jpg1=np.uint8(np.asarray(Image.open(StringIO(Item[iii]['imgs'])).convert('RGB').resize((224,224)))) jpg2=np.uint8(np.asarray(Image.open(StringIO(Item[jjj]['imgs'])).convert('RGB').resize((224,224)))) sess.run(batch_train_queue_op,feed_dict={queueu:np.asarray([uuu]), queuei:np.asarray([iii]), queuej:np.asarray([jjj]), queueimage1:jpg1,queueimage2:jpg2, }) f=open('DVBPR.log','w') config = tf.ConfigProto(log_device_placement=False,allow_soft_placement=True) sess=tf.Session(config=config) sess.run(init) t=[0]*numldprocess for i in range(numldprocess): t[i] = threading.Thread(target=load_image_async) t[i].daemon=True t[i].start() oneiteration = 0 for item in user_train: oneiteration+=len(user_train[item]) step = 1 saver = tf.train.Saver([k for k in tf.global_variables() if k.name.startswith('DVBPR')]) epoch=0 while step * batch_size <= training_epoch*oneiteration+1: sess.run(optimizer, feed_dict={keep_prob: dropout}) print 'Step#'+str(step)+' CNN update' if step*batch_size / oneiteration >epoch: epoch+=1 saver.save(sess,'./DVBPR_auc_'+str(K)+'_'+str(step)+'.ckpt') auc_valid,auc_test=Evaluation(step) print 'Epoch #'+str(epoch)+':'+str(auc_test)+' '+str(auc_valid)+'\n' f.write('Epoch #'+str(epoch)+':'+str(auc_test)+' '+str(auc_valid)+'\n') f.flush() step += 1 print "Optimization Finished!"

from cStringIO import StringIO def loadimg(item): return np.round(np.array(Image.open(StringIO(Item[item]['imgs'])).convert('RGB').resize((224,224)),dtype=np.float64)) with tf.device('/cpu:0'): # creating placeholder variable # _image_test has form <tf.Tensor 'div_4:0' shape=(64, 224, 224, 3) dtype=float32> image_test=tf.placeholder(dtype=tf.uint8,shape=[64,224,224,3]) _image_test=(tf.to_float(image_test)-127.5)/127.5 keep_prob = tf.placeholder(tf.float32) #dropout (keep probability) # sharing variable by capturing scope and setting reuse with tf.variable_scope("DVBPR") as scope: # creating model from input image and drop out, returns tensor shape (100,1) result_test = CNN(_image_test,1.0) # creating variable thetau which is tensor of shape [usernum,D] filled with random values in range 0 to 1 thetau = tf.Variable(tf.random_uniform([usernum,D],minval=0,maxval=1)/100) # Initializing the variables init = tf.initialize_all_variables() config = tf.ConfigProto(log_device_placement=False,allow_soft_placement=True) config.gpu_options.allow_growth = True # creating session sess=tf.Session(config=config) sess.run(init) # creates object to save all variables in saver = tf.train.Saver([k for k in tf.global_variables() if k.name.startswith('DVBPR')]) # restore model weights from DVBPR check point saver.restore(sess,DVBPR_ckpt_path) # creating tensor for image with same shape as gan_image tstImg3=np.round(np.array(Image.open(StringIO(Item[119]['imgs'])).convert('RGB').resize((224,224)),dtype=np.float32)) rep_gan=tf.reshape(tstImg3, shape=[-1, 224, 224, 3]) # Note: don't have gpu switch to cpu with tf.device('/cpu:0'): # need to figure out how to substitute image of product customer hasn't seen for get_gen gan_image=rep_gan # shaping image tensor from gan_image image=tf.image.resize_nearest_neighbor(images=gan_image, size=[224,224], align_corners=None, name=None) with tf.variable_scope("DVBPR") as scope: scope.reuse_variables() # running model defined previously to get tensor from "image" variable above result = CNN(image,1.0) _user=12 # idx and user defined in opt section user=tf.placeholder(dtype=tf.int32,shape=[1]) idx=tf.reduce_sum(tf.matmul(result,tf.transpose(tf.gather(thetau,user))),1) # Note: I'm not sure what the purpose of idx is # run piece from Preference Score with DVBPR instead of opt print(sess.run([gan_image,idx],feed_dict={user:[_user]})) # the results below appear to return an image tensor and a preference score Output: [array([[[[ 255., 255., 255.], [ 255., 255., 255.], [ 255., 255., 255.], ..., [ 255., 255., 255.], [ 255., 255., 255.], [ 255., 255., 255.]], [[ 255., 255., 255.], [ 255., 255., 255.], [ 255., 255., 255.], ..., [ 255., 255., 255.], [ 255., 255., 255.], [ 255., 255., 255.]], [[ 255., 255., 255.], [ 255., 255., 255.], [ 255., 255., 255.], ..., [ 255., 255., 255.], [ 255., 255., 255.], [ 255., 255., 255.]], ..., [[ 255., 255., 255.], [ 255., 255., 255.], [ 255., 255., 255.], ..., [ 255., 255., 255.], [ 255., 255., 255.], [ 255., 255., 255.]], [[ 255., 255., 255.], [ 255., 255., 255.], [ 255., 255., 255.], ..., [ 255., 255., 255.], [ 255., 255., 255.], [ 255., 255., 255.]], [[ 255., 255., 255.], [ 255., 255., 255.], [ 255., 255., 255.], ..., [ 255., 255., 255.], [ 255., 255., 255.], [ 255., 255., 255.]]]], dtype=float32), array([ 99.19335938], dtype=float32)]