不懂keras孪生网络的图片相似度怎么计算？？其实想解决这个问题也不难，下面让小编带着大家一起学习怎么去解决，希望大家阅读完这篇文章后大所收获。

我就废话不多说了，大家还是直接看代码吧！

import kerasfrom keras.layers import Input,Dense,Conv2Dfrom keras.layers import MaxPooling2D,Flatten,Convolution2Dfrom keras.models import Modelimport osimport numpy as npfrom PIL import Imagefrom keras.optimizers import SGDfrom scipy import miscroot_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) path2 = os.path.join(root_path,'data','simility_data',seq_names[seed1],str(frame1)+'.jpg') path3 = os.path.join(root_path, 'data', 'simility_data', seq_names[seed2], str(frame2) + '.jpg') image1 = np.array(misc.imresize(Image.open(path2),[224,224])) image2 = np.array(misc.imresize(Image.open(path3),[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) path2 = os.path.join(root_path, 'data', 'simility_data', seq_names[seed], str(frame1) + '.jpg') path3 = os.path.join(root_path, 'data', 'simility_data', seq_names[seed], str(frame2) + '.jpg') image1 = np.array(misc.imresize(Image.open(path2),[224,224])) image2 = np.array(misc.imresize(Image.open(path3),[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 = Trueif train: model = siamese(siamese_path='model/simility/vgg.h6') 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.h6'), 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.h6') 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,amax_pair = 30000# top k frequent word ,kMAX_FEATURES = 450# fixed q,a lengthMAX_SENTENCE_LENGTH = 30embedding_size = 100batch_size = 600# learning ratelr = 0.01HIDDEN_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, SpatialDropout1Dfrom keras.layers.embeddings import Embeddingfrom keras.layers.recurrent import LSTMfrom keras.preprocessing import sequencefrom sklearn.model_selection import train_test_splitimport collectionsimport matplotlib.pyplot as pltimport nltkimport numpy as npimport osimport pandas as pdfrom alime_data import convert_dialogue_to_pairfrom parameter import MAX_SENTENCE_LENGTH,MAX_FEATURES,embedding_size,max_pair,batch_size,HIDDEN_LAYER_SIZEDATA_DIR = "../data"NUM_EPOCHS = 2# Read training data and generate vocabularymaxlen = 0num_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 PADvocab_size = min(MAX_FEATURES, len(word_freqs)) + 2word2index = {x[0]: i+2 for i, x in enumerate(word_freqs.most_common(MAX_FEATURES))}word2index["PAD"] = 0word2index["UNK"] = 1index2word = {v:k for k, v in word2index.items()}# convert sentences to sequencesX_q = np.empty((num_recs, ), dtype=list)X_a = np.empty((num_recs, ), dtype=list)y = np.zeros((num_recs, ))i = 0def 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 testXtrain, 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 Bidirectionalfrom keras.layers import Input,Lambdafrom 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 accuracydef 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() # evaluatescore, 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 nltkfrom parameter import MAX_FEATURES,MAX_SENTENCE_LENGTHimport pandas as pdfrom collections import Counterdef 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

感谢你能够认真阅读完这篇文章，希望小编分享keras孪生网络的图片相似度怎么计算？内容对大家有帮助，同时也希望大家多多支持亿速云，关注亿速云行业资讯频道，遇到问题就找亿速云，详细的解决方法等着你来学习!