白云岛资源网

keras实现基于孪生网络的图片相似度计算方式
站长资源 2024/12/26 佚名
51 1538
白云岛资源网 Design By www.pvray.com

我就废话不多说了，大家还是直接看代码吧！

import keras
from keras.layers import Input,Dense,Conv2D
from keras.layers import MaxPooling2D,Flatten,Convolution2D
from keras.models import Model
import os
import numpy as np
from PIL import Image
from keras.optimizers import SGD
from scipy import misc
root_path = os.getcwd()
train_names = ['bear','blackswan','bus','camel','car','cows','dance','dog','hike','hoc','kite','lucia','mallerd','pigs','soapbox','stro','surf','swing','train','walking']
test_names = ['boat','dance-jump','drift-turn','elephant','libby']
 
def load_data(seq_names,data_number,seq_len): 
#生成图片对
  print('loading data.....')
  frame_num = 51
  train_data1 = []
  train_data2 = []
  train_lab = []
  count = 0
  while count < data_number:
    count = count + 1
    pos_neg = np.random.randint(0,2)
    if pos_neg==0:
      seed1 = np.random.randint(0,seq_len)
      seed2 = np.random.randint(0,seq_len)
      while seed1 == seed2:
       seed1 = np.random.randint(0,seq_len)
       seed2 = np.random.randint(0,seq_len)
      frame1 = np.random.randint(1,frame_num)
      frame2 = np.random.randint(1,frame_num)
      path1 = os.path.join(root_path,'data','simility_data',seq_names[seed1],str(frame1)+'.jpg')
      path2 = os.path.join(root_path, 'data', 'simility_data', seq_names[seed2], str(frame2) + '.jpg')
      image1 = np.array(misc.imresize(Image.open(path1),[224,224]))
      image2 = np.array(misc.imresize(Image.open(path2),[224,224]))
      train_data1.append(image1)
      train_data2.append(image2)
      train_lab.append(np.array(0))
    else:
     seed = np.random.randint(0,seq_len)
     frame1 = np.random.randint(1, frame_num)
     frame2 = np.random.randint(1, frame_num)
     path1 = os.path.join(root_path, 'data', 'simility_data', seq_names[seed], str(frame1) + '.jpg')
     path2 = os.path.join(root_path, 'data', 'simility_data', seq_names[seed], str(frame2) + '.jpg')
     image1 = np.array(misc.imresize(Image.open(path1),[224,224]))
     image2 = np.array(misc.imresize(Image.open(path2),[224,224]))
     train_data1.append(image1)
     train_data2.append(image2)
     train_lab.append(np.array(1))
  return np.array(train_data1),np.array(train_data2),np.array(train_lab)
 
def vgg_16_base(input_tensor):
  net = Conv2D(64(3,3),activation='relu',padding='same',input_shape=(224,224,3))(input_tensor)
  net = Convolution2D(64,(3,3),activation='relu',padding='same')(net)
  net = MaxPooling2D((2,2),strides=(2,2))(net)
 
  net = Convolution2D(128,(3,3),activation='relu',padding='same')(net)
  net = Convolution2D(128,(3,3),activation='relu',padding='same')(net)
  net= MaxPooling2D((2,2),strides=(2,2))(net)
 
  net = Convolution2D(256,(3,3),activation='relu',padding='same')(net)
  net = Convolution2D(256,(3,3),activation='relu',padding='same')(net)
  net = Convolution2D(256,(3,3),activation='relu',padding='same')(net)
  net = MaxPooling2D((2,2),strides=(2,2))(net)
 
  net = Convolution2D(512,(3,3),activation='relu',padding='same')(net)
  net = Convolution2D(512,(3,3),activation='relu',padding='same')(net)
  net = Convolution2D(512,(3,3),activation='relu',padding='same')(net)
  net = MaxPooling2D((2,2),strides=(2,2))(net)
 
  net = Convolution2D(512,(3,3),activation='relu',padding='same')(net)
  net = Convolution2D(512,(3,3),activation='relu',padding='same')(net)
  net = Convolution2D(512,(3,3),activation='relu',padding='same')(net)
  net = MaxPooling2D((2,2),strides=(2,2))(net)
  net = Flatten()(net)
  return net
 
def siamese(vgg_path=None,siamese_path=None):
  input_tensor = Input(shape=(224,224,3))
  vgg_model = Model(input_tensor,vgg_16_base(input_tensor))
  if vgg_path:
    vgg_model.load_weights(vgg_path)
  input_im1 = Input(shape=(224,224,3))
  input_im2 = Input(shape=(224,224,3))
  out_im1 = vgg_model(input_im1)
  out_im2 = vgg_model(input_im2)
  diff = keras.layers.substract([out_im1,out_im2])
  out = Dense(500,activation='relu')(diff)
  out = Dense(1,activation='sigmoid')(out)
  model = Model([input_im1,input_im2],out)
  if siamese_path:
    model.load_weights(siamese_path)
  return model
 
train = True
if train:
  model = siamese(siamese_path='model/simility/vgg.h5')
  sgd = SGD(lr=1e-6,momentum=0.9,decay=1e-6,nesterov=True)
  model.compile(optimizer=sgd,loss='mse',metrics=['accuracy'])
  tensorboard = keras.callbacks.TensorBoard(histogram_freq=5,log_dir='log/simility',write_grads=True,write_images=True)
  ckpt = keras.callbacks.ModelCheckpoint(os.path.join(root_path,'model','simility','vgg.h5'),
                    verbose=1,period=5)
  train_data1,train_data2,train_lab = load_data(train_names,4000,20)
  model.fit([train_data1,train_data2],train_lab,callbacks=[tensorboard,ckpt],batch_size=64,epochs=50)
else:
  model = siamese(siamese_path='model/simility/vgg.h5')
  test_im1,test_im2,test_labe = load_data(test_names,1000,5)
  TP = 0
  for i in range(1000):
   im1 = np.expand_dims(test_im1[i],axis=0)
   im2 = np.expand_dims(test_im2[i],axis=0)
   lab = test_labe[i]
   pre = model.predict([im1,im2])
   if pre>0.9 and lab==1:
    TP = TP + 1
   if pre<0.9 and lab==0:
    TP = TP + 1
  print(float(TP)/1000)

输入两张图片，标记1为相似，0为不相似。

损失函数用的是简单的均方误差，有待改成Siamese的对比损失。

总结：

1.随机生成了几组1000对的图片，测试精度0.7左右，效果一般。

2.问题 1）数据加载没有用生成器，还得继续认真看看文档 2）训练时划分验证集的时候，训练就会报错，什么输入维度的问题，暂时没找到原因 3)输入的shape好像必须给出数字，本想用shape= input_tensor.get_shape(),能训练，不能保存模型，会报（NOT JSON Serializable,Dimension(None)）类型错误

补充知识： keras 问答匹配孪生网络文本匹配 RNN 带有数据

用途：

这篇博客解释了如何搭建一个简单的匹配网络。并且使用了keras的lambda层。在建立网络之前需要对数据进行预处理。处理过后，文本转变为id字符序列。将一对question,answer分别编码可以得到两个向量，在匹配层中比较两个向量，计算相似度。

网络图示：

keras实现基于孪生网络的图片相似度计算方式

数据准备：

数据基于网上的淘宝客服对话数据，我也会放在我的下载页面中。原数据是对话，我筛选了其中label为1的对话。然后将对话拆解成QA对，q是用户，a是客服。然后对于每个q，有一个a是匹配的，label为1.再选择一个a，构成新的样本，label为0.

超参数：

比较简单，具体看代码就可以了。

# dialogue max pair q,a
max_pair = 30000
# top k frequent word ,k
MAX_FEATURES = 450
# fixed q,a length
MAX_SENTENCE_LENGTH = 30
embedding_size = 100
batch_size = 600
# learning rate
lr = 0.01
HIDDEN_LAYER_SIZE = n_hidden_units = 256 # neurons in hidden layer

细节：

导入一些库

# -*- coding: utf-8 -*-
from keras.layers.core import Activation, Dense, Dropout, SpatialDropout1D
from keras.layers.embeddings import Embedding
from keras.layers.recurrent import LSTM
from keras.preprocessing import sequence
from sklearn.model_selection import train_test_split
import collections
import matplotlib.pyplot as plt
import nltk
import numpy as np
import os
import pandas as pd
from alime_data import convert_dialogue_to_pair
from parameter import MAX_SENTENCE_LENGTH,MAX_FEATURES,embedding_size,max_pair,batch_size,HIDDEN_LAYER_SIZE
DATA_DIR = "../data"
NUM_EPOCHS = 2
# Read training data and generate vocabulary
maxlen = 0
num_recs = 0

数据准备，先统计词频，然后取出top N个常用词，然后将句子转换成 单词id的序列。把句子中的有效id靠右边放，将句子左边补齐padding。然后分成训练集和测试集

word_freqs = collections.Counter()
training_data = convert_dialogue_to_pair(max_pair)
num_recs = len([1 for r in training_data.iterrows()])
 
#for line in ftrain:
for line in training_data.iterrows():
  label ,sentence_q = line[1]['label'],line[1]['sentence_q']
  label ,sentence_a = line[1]['label'],line[1]['sentence_a']
  words = nltk.word_tokenize(sentence_q.lower())#.decode("ascii", "ignore")
  if len(words) > maxlen:
    maxlen = len(words)
  for word in words:
    word_freqs[word] += 1
  words = nltk.word_tokenize(sentence_a.lower())#.decode("ascii", "ignore")
  if len(words) > maxlen:
    maxlen = len(words)
  for word in words:
    word_freqs[word] += 1
  #num_recs += 1
## Get some information about our corpus
 
# 1 is UNK, 0 is PAD
# We take MAX_FEATURES-1 featurs to accound for PAD
vocab_size = min(MAX_FEATURES, len(word_freqs)) + 2
word2index = {x[0]: i+2 for i, x in enumerate(word_freqs.most_common(MAX_FEATURES))}
word2index["PAD"] = 0
word2index["UNK"] = 1
index2word = {v:k for k, v in word2index.items()}
# convert sentences to sequences
X_q = np.empty((num_recs, ), dtype=list)
X_a = np.empty((num_recs, ), dtype=list)
y = np.zeros((num_recs, ))
i = 0
def chinese_split(x):
  return x.split(' ')
 
for line in training_data.iterrows():
  label ,sentence_q,sentence_a = line[1]['label'],line[1]['sentence_q'],line[1]['sentence_a']
  #label, sentence = line.strip().split("\t")
  #print(label,sentence)
  #words = nltk.word_tokenize(sentence_q.lower())
  words = chinese_split(sentence_q)
  seqs = []
  for word in words:
    if word in word2index.keys():
      seqs.append(word2index[word])
    else:
      seqs.append(word2index["UNK"])
  X_q[i] = seqs
  #print('add_q')
  #words = nltk.word_tokenize(sentence_a.lower())
  words = chinese_split(sentence_a)
  seqs = []
  for word in words:
    if word in word2index.keys():
      seqs.append(word2index[word])
    else:
      seqs.append(word2index["UNK"])
  X_a[i] = seqs
  y[i] = int(label)
  i += 1
# Pad the sequences (left padded with zeros)
X_a = sequence.pad_sequences(X_a, maxlen=MAX_SENTENCE_LENGTH)
X_q = sequence.pad_sequences(X_q, maxlen=MAX_SENTENCE_LENGTH)
X = []
for i in range(len(X_a)):
  concat = [X_q[i],X_a[i]]
  X.append(concat)
 
# Split input into training and test
Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.2,
                        random_state=42)
#print(Xtrain.shape, Xtest.shape, ytrain.shape, ytest.shape)
Xtrain_Q = [e[0] for e in Xtrain]
Xtrain_A = [e[1] for e in Xtrain]
Xtest_Q = [e[0] for e in Xtest]
Xtest_A = [e[1] for e in Xtest]

最后建立网络。先定义两个函数，一个是句子编码器，另一个是lambda层，计算两个向量的绝对差。将QA分别用encoder处理得到两个向量，把两个向量放入lambda层。最后有了2*hidden size的一层，将这一层接一个dense层，接activation，得到分类概率。

from keras.layers.wrappers import Bidirectional
from keras.layers import Input,Lambda
from keras.models import Model
 
def encoder(inputs_seqs,rnn_hidden_size,dropout_rate):
  x_embed = Embedding(vocab_size, embedding_size, input_length=MAX_SENTENCE_LENGTH)(inputs_seqs)
  inputs_drop = SpatialDropout1D(0.2)(x_embed)
  encoded_Q = Bidirectional(
    LSTM(rnn_hidden_size, dropout=dropout_rate, recurrent_dropout=dropout_rate, name='RNN'))(inputs_drop)
  return encoded_Q
 
def absolute_difference(vecs):
  a,b =vecs
  #d = a-b
  return abs(a - b)
 
inputs_Q = Input(shape=(MAX_SENTENCE_LENGTH,), name="input")
# x_embed = Embedding(vocab_size, embedding_size, input_length=MAX_SENTENCE_LENGTH)(inputs_Q)
# inputs_drop = SpatialDropout1D(0.2)(x_embed)
# encoded_Q = Bidirectional(LSTM(HIDDEN_LAYER_SIZE, dropout=0.2, recurrent_dropout=0.2,name= 'RNN'))(inputs_drop)
inputs_A = Input(shape=(MAX_SENTENCE_LENGTH,), name="input_a")
# x_embed = Embedding(vocab_size, embedding_size, input_length=MAX_SENTENCE_LENGTH)(inputs_A)
# inputs_drop = SpatialDropout1D(0.2)(x_embed)
# encoded_A = Bidirectional(LSTM(HIDDEN_LAYER_SIZE, dropout=0.2, recurrent_dropout=0.2,name= 'RNN'))(inputs_drop)
encoded_Q = encoder(inputs_Q,HIDDEN_LAYER_SIZE,0.1)
encoded_A = encoder(inputs_A,HIDDEN_LAYER_SIZE,0.1)
 
# import tensorflow as tf
# difference = tf.subtract(encoded_Q, encoded_A)
# difference = tf.abs(difference)
similarity = Lambda(absolute_difference)([encoded_Q, encoded_A])
# x = concatenate([encoded_Q, encoded_A])
#
# matching_x = Dense(128)(x)
# matching_x = Activation("sigmoid")(matching_x)
polar = Dense(1)(similarity)
prop = Activation("sigmoid")(polar)
model = Model(inputs=[inputs_Q,inputs_A], outputs=prop)
model.compile(loss="binary_crossentropy", optimizer="adam",
       metrics=["accuracy"])
training_history = model.fit([Xtrain_Q, Xtrain_A], ytrain, batch_size=batch_size,
               epochs=NUM_EPOCHS,
               validation_data=([Xtest_Q,Xtest_A], ytest))
# plot loss and accuracy
def plot(training_history):
  plt.subplot(211)
  plt.title("Accuracy")
  plt.plot(training_history.history["acc"], color="g", label="Train")
  plt.plot(training_history.history["val_acc"], color="b", label="Validation")
  plt.legend(loc="best")
 
  plt.subplot(212)
  plt.title("Loss")
  plt.plot(training_history.history["loss"], color="g", label="Train")
  plt.plot(training_history.history["val_loss"], color="b", label="Validation")
  plt.legend(loc="best")
  plt.tight_layout()
  plt.show()
 
# evaluate
score, acc = model.evaluate([Xtest_Q,Xtest_A], ytest, batch_size = batch_size)
print("Test score: %.3f, accuracy: %.3f" % (score, acc))
 
for i in range(25):
  idx = np.random.randint(len(Xtest_Q))
  #idx2 = np.random.randint(len(Xtest_A))
  xtest_Q = Xtest_Q[idx].reshape(1,MAX_SENTENCE_LENGTH)
  xtest_A = Xtest_A[idx].reshape(1,MAX_SENTENCE_LENGTH)
  ylabel = ytest[idx]
  ypred = model.predict([xtest_Q,xtest_A])[0][0]
  sent_Q = " ".join([index2word[x] for x in xtest_Q[0].tolist() if x != 0])
  sent_A = " ".join([index2word[x] for x in xtest_A[0].tolist() if x != 0])
  print("%.0f\t%d\t%s\t%s" % (ypred, ylabel, sent_Q,sent_A))

最后是处理数据的函数，写在另一个文件里。

import nltk
from parameter import MAX_FEATURES,MAX_SENTENCE_LENGTH
import pandas as pd
from collections import Counter
def get_pair(number, dialogue):
  pairs = []
  for conversation in dialogue:
    utterances = conversation[2:].strip('\n').split('\t')
    # print(utterances)
    # break
 
    for i, utterance in enumerate(utterances):
      if i % 2 != 0: continue
      pairs.append([utterances[i], utterances[i + 1]])
      if len(pairs) >= number:
        return pairs
  return pairs
 
 
def convert_dialogue_to_pair(k):
  dialogue = open('dialogue_alibaba2.txt', encoding='utf-8', mode='r')
  dialogue = dialogue.readlines()
  dialogue = [p for p in dialogue if p.startswith('1')]
  print(len(dialogue))
  pairs = get_pair(k, dialogue)
  # break
  # print(pairs)
  data = []
  for p in pairs:
    data.append([p[0], p[1], 1])
  for i, p in enumerate(pairs):
    data.append([p[0], pairs[(i + 8) % len(pairs)][1], 0])
  df = pd.DataFrame(data, columns=['sentence_q', 'sentence_a', 'label'])
 
  print(len(data))
  return df

以上这篇keras实现基于孪生网络的图片相似度计算方式就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。

白云岛资源网 Design By www.pvray.com
广告合作：本站广告合作请联系QQ：858582 申请时备注：广告合作（否则不回）
免责声明：本站资源来自互联网收集,仅供用于学习和交流,请遵循相关法律法规,本站一切资源不代表本站立场,如有侵权、后门、不妥请联系本站删除！
白云岛资源网 Design By www.pvray.com
www.pvray.com 白云岛资源网
39,976影音资源
44,792技术资源
21,817软件资源
651,128站长资源

P70系列延期，华为新旗舰将在下月发布

3月20日消息，近期博主@数码闲聊站 透露，原定三月份发布的华为新旗舰P70系列延期发布，预计4月份上市。

而博主@定焦数码 爆料，华为的P70系列在定位上已经超过了Mate60，成为了重要的旗舰系列之一。它肩负着重返影像领域顶尖的使命。那么这次P70会带来哪些令人惊艳的创新呢？

根据目前爆料的消息来看，华为P70系列将推出三个版本，其中P70和P70 Pro采用了三角形的摄像头模组设计，而P70 Art则采用了与上一代P60 Art相似的不规则形状设计。这样的外观是否好看见仁见智，但辨识度绝对拉满。

友情链接

杰晶网络 DDR爱好者之家 桃源资源网 杰网资源 富贵资源网 南强小屋 铁雪资源网 幽灵资源网 万梅资源网 狼山资源网 白云岛资源网 昆仑资源网 相思资源网 明霞山资源网 内蒙古资源网 黑松山资源网 茶园资源网 饿虎岗资源网 大旗谷资源网 常春岛资源网 岱庙资源网 兴国资源网 快活林资源网 蝙蝠岛资源网 帝王谷资源网 白云城资源网 伏龙阁资源网 清风细雨楼 天枫庄资源网 圆月山庄资源网 无争山庄资源网 神水资源网 移花宫资源网 神剑山庄资源网 无为清净楼资源网 金钱帮资源网 丐帮资源网 华山资源网 极乐门资源网 小李飞刀资源网 凤求凰客栈 风云阁资源网 金狮镖局 鸳鸯亭资源网 千金楼资源网 更多链接
白云岛资源网 Design By www.pvray.com