keras实现基于孪生网络的图片相似度计算方式
更新时间：2020年06月11日 15:55:55 作者：黑条纹的白斑马
这篇文章主要介绍了keras实现基于孪生网络的图片相似度计算方式，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
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：
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脚本之家。
相关文章
更改Python的pip install 默认安装依赖路径方法详解
今天小编就为大家分享一篇更改Python的pip install 默认安装依赖路径方法详解，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧2018-10-10
这篇文章主要介绍了jupyter notebook插入本地图片的实现，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧2020-04-04
今天小编就为大家分享一篇关于Python批量删除只保留最近几天table的代码实例，小编觉得内容挺不错的，现在分享给大家，具有很好的参考价值，需要的朋友一起跟随小编来看看吧2019-04-04
在Pytorch中，torch.utils.data中的Dataset与DataLoader是处理数据集的两个函数，用来处理加载数据集，这篇文章主要介绍了Pytorch中DataLoader的使用方法,需要的朋友可以参考下2022-09-09
这篇文章主要介绍了基于fastapi框架的异步解读，具有很好的参考价值，希望对大家有所帮助。如有错误或未考虑完全的地方，望不吝赐教2023-03-03
Math模块中，有很多基础的数学知识，我们必须要掌握的，例如：指数、对数、三角或幂函数等。因此，特意借着这篇文章，为大家讲解一些该库2021-10-10
这篇文章主要介绍了python之关于数组和列表的区别及说明，具有很好的参考价值，希望对大家有所帮助。如有错误或未考虑完全的地方，望不吝赐教2023-05-05
这篇文章主要为大家详细介绍了Python的队列，文中示例代码介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们可以参考一下，希望能够给你带来帮助2022-03-03
FastAPI中最核心的之一就是路径参数,所以这篇文章小编主要来和大家介绍一下FastAPI查询参数的作用以及基本使用,有需要的小伙伴可以参考下2024-03-03
VGG16是Visual Geometry Group的缩写,它的名字来源于提出该网络的实验室,本文我们将使用PyTorch来实现VGG16网络,用于猫狗预测的二分类任务,我们将对VGG16的网络结构进行适当的修改,以适应我们的任务,需要的朋友可以参考下2023-08-08
最新评论