CN107958257A - A kind of Chinese traditional medicinal materials recognition method based on deep neural network - Google Patents

A kind of Chinese traditional medicinal materials recognition method based on deep neural network Download PDF

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CN107958257A
CN107958257A CN201710939672.2A CN201710939672A CN107958257A CN 107958257 A CN107958257 A CN 107958257A CN 201710939672 A CN201710939672 A CN 201710939672A CN 107958257 A CN107958257 A CN 107958257A
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mrow
msub
training
chinese medicine
medicinal materials
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文贵华
庄奕珊
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South China University of Technology SCUT
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent

Abstract

The invention discloses a kind of Chinese traditional medicinal materials recognition method based on deep neural network, include the following steps：Take pictures web crawlers and manually and gather Chinese medicine picture as the input of data set and pre-processed；Training uses the Bagging methods of integrated study with prediction process, i.e., produces more sub- training sets using stochastical sampling method；Training is finely adjusted to each sub- training set using classical convolutional neural networks model, generates multiple Weak Classifiers, the convolutional neural networks model of use includes AlexNet, SqueezeNet and GoogleNet, finally coordinates Softmax sorting algorithms；And strong classifier is obtained using integrated study combined strategy, classification results are obtained, the integrated study combined strategy uses ballot method.The method of the present invention is used to assist in identifying Chinese medicine, reduces the mistake that amateur occurs in identification, and energy accuracy is high, recognition speed is fast, performance stably analyzes Chinese medicine.

Description

A kind of Chinese traditional medicinal materials recognition method based on deep neural network
Technical field
The present invention relates to image recognition and integrated study to apply the technical field in Chinese traditional medicinal materials recognition, and in particular to Yi Zhongji In the Chinese traditional medicinal materials recognition method of deep neural network.
Background technology
Traditional Chinese medicine regards gas, shape, the entity of god as using yin-yang and five elements as theoretical foundation, by human body, by " hoping news ask Cut " method that ginseng is closed in the four methods of diagnosis, seek the cause of disease, characteristic of disease, sick position, the analysis interpretation of the cause, onset and process of an illness and human five internal organs' six internal organs, channels and collaterals joint, qi-blood-body fluid Change, judge pathogenic and vital contention, and then draw name of disease, summarize illness type, with dialectical treatmert principle, formulate " sweat, spit, under, With, temperature, it is clear, mend, disappear " etc. therapy, using a variety for the treatment of means such as Chinese medicine, acupuncture, massage, massage, cup, the qigong, dietotherapy, make Human body reaches negative and positive and reconciles and rehabilitation.Chinese medicine is one of common and effective treatment means, is used under instruction of Chinese Medicine theory pre- Anti-, diagnosis, treatment disease or the medicine for adjusting function of human body.Mostly autonomic drug, also have animal drugs, mineral drug and part chemistry, Biological drug.
Chinese traditional medicinal materials recognition is the required skill of each traditional Chinese medicine practitioner, is also based on China of traditional Chinese medicine traditional culture The technical ability of the necessary to master of modern healthy service practitioner.From ancient times to the present, the traditional Chinese medical science or traditional Chinese medical science apprentice are from understanding Chinese medicine Understand its character and effect starts it and is engaged in traditional Chinese medicine career, shennong went into the mountains collecting, tasting and testing different kinds of herbs to be used as medicine, is exactly to be for the first time in human history Recognize to the scale of system property the process of Chinese herbal medicine and understanding and the study and research of definite effect.
The identification of Chinese medicine at present is expert according to knowledge and experience, or the judgement that picture is compared and made.Although people To the warm surging pursuit of traditional Chinese medical science health care's idea, but because most of and layman, to knowing in terms of Chinese medicine Know very deficient；Chinese medicine species is various at the same time, and the good and bad jumbled together in market, or even many procurement staff recognize unclear.In addition, because Know Chinese medicine by mistake and cause serious consequence also very common.
Image recognition is all an important and popular research direction in computer realm all the time.Deep learning is near One of most important breakthrough that artificial intelligence field obtains for over ten years, regards in speech recognition, natural language processing, computer Feel, the numerous areas such as image and video analysis, multimedia all achieves immense success.Now, learnt using deep neural network, Training pattern, intelligent image identification, so that recognition accuracy is improved, using very extensive.
Chen Yaodan etc. [face identification method [J] Northeast China Normal University's journals (natural science) based on convolutional neural networks, 2016,48(2):70-76.] study and realize a kind of face identification method based on convolutional neural networks；Wang Qian etc. [is based on Automotive Style Recognition [J] modern computer publications appearing once every ten days of deep neural network, 2015 (12):61-64.] research be based on deep learning Vehicle automatic identification technology；It is handsome wait [plant leaf blade recognizer research [D] the Beijing Forestry University based on deep learning, 2016.] the deep learning algorithm based on convolutional neural networks is studied；A kind of [Expression Recognition based on deep learning such as Wang Jianyun Method [J] computers and modernization, 2015 (1):84-87.] propose a kind of expression recognition method based on deep learning.
But due to the particularity of application, recognition of face, Emotion identification, vehicle cab recognition and plant based on deep learning are known It Deng not apply, it is difficult to be generalized to Chinese traditional medicinal materials recognition.
The content of the invention
The purpose of the present invention is to solve drawbacks described above of the prior art, there is provided a kind of based on deep neural network Chinese traditional medicinal materials recognition method, deep learning is combined with Chinese traditional medicinal materials recognition, is carried out using classical convolutional neural networks Models Sets micro- Training is adjusted, then recognition accuracy is improved using Ensemble Learning Algorithms, for assisting in identifying Chinese medicine, amateur is reduced and is identifying The mistake of middle appearance, and can accuracy is high, recognition speed is fast, performance stably analyzes Chinese medicine.
The purpose of the present invention can be reached by adopting the following technical scheme that：
A kind of Chinese traditional medicinal materials recognition method based on deep neural network, the Chinese traditional medicinal materials recognition method include following step Suddenly：
The input of S1, collection Chinese medicine picture as data set, the Chinese medicine picture of collection is pre-processed；
S2, using stochastical sampling method produce multiple sub- training sets of Chinese medicine；
S3, using convolutional neural networks model be finely adjusted training to the sub- training set of each Chinese medicine, generates multiple Chinese medicines Material identifies Weak Classifier；
S4, using integrated study combined strategy obtain Chinese traditional medicinal materials recognition strong classifier, obtains Classification of Chinese Drug result.
Further, Chinese medicine picture is gathered by web crawlers and artificial photographic method in the step S1 and is used as number According to the input of collection, then the unbalanced Chinese medicine classification of quantity is equalized.
Further, the entirety training in the step S2, step S3, step S4 uses integrated study with prediction process Bagging methods.
Further, stochastical sampling method uses self-service sampling method in the step S2, is obtained by self-service sampling method Multiple variant sub- training sets.
Further, by introducing deep learning technology in the step S3, using convolutional neural networks model to each A sub- training set of Chinese medicine is finely adjusted training, generates multiple Chinese traditional medicinal materials recognition Weak Classifiers, wherein, the convolutional Neural net Network model includes AlexNet, SqueezeNet and GoogleNet.
Further, the step S3 Chinese traditional medicinal materials recognitions Weak Classifier using convolutional neural networks make feature extraction and Make to classify using Softmax graders, it is specific as follows：
For T sub- training sets, training is finely adjusted to each sub- training set using classical convolutional neural networks model, Generate T Weak Classifier；
Chinese medicine picture is as inputting, by multiple convolutional layers and down-sampling layer output characteristic figure, wherein, the public affairs of convolutional layer Formula is as follows：
Conv_output=f (WTx+b)
Wherein f (*) represents activation primitive, and x represents input data, W and b representation parameters, the present invention is using ReLU activation letters Number, formula are as follows：
F (x)=max (0, x)
Its derivation form is：
The formula of down-sampling layer is as follows, using Max-pooling methods：
Pooling_output=max (X)
Wherein X represents n*n matrixes；
The input of full articulamentum is a vector, and obtaining vector by rasterisation is connected to full articulamentum, finally by Softmax graders obtain the classification results of the grader, which receives the input data of 4096 dimensions, output 98 dimensions as a result, the result represents the confidence level that the input sample corresponds to 98 Chinese medicine classifications, where then taking its maximum Classification is classification results, and the formula of Softmax graders is as follows：
Wherein j=1,2 ..., K, K represent the number of class, wherein value 98, z=WT x+b, W, b be Softmax parameter, x For the input feature vector of 4096 dimensions.
Further, training algorithm uses stochastic gradient descent method in the step S3, and training connects this method every time By a certain number of training datas, by after, the difference of output and data physical tags is weighed using loss function before network Away from, carry out reverse train network parameter followed by this measurement, wherein, loss function uses cross entropy loss function and cohesion The combination of loss function, formula are as follows：
Wherein, λ is used for controlling ratio of the cohesion loss function in total loss function, xiRepresent original signal, ziGeneration Table reconstruction signal, represents that length is d in the form of vectors, and can be transform as the form of inner product of vectors easily, and K represents every The sample number of secondary iteration, CyiRepresent yiThe class heart of category feature, yiFor original signal xiClass label.
Further, integrated study combined strategy uses ballot method in the step S4.
The present invention is had the following advantages relative to the prior art and effect：
1st, the method for the present invention utilizes the self-service sampling method in stochastical sampling, puts back to more sub-samplings, produces multiple variant The sub- training set of Chinese medicine of property；
2nd, the method for the present invention uses convolutional neural networks, automatically learns with strongly expressed power by convolutional neural networks Convolution kernel and these convolution kernels combination, obtain best Chinese medicine feature representation；
3rd, the method for the present invention uses Softmax sorting techniques, is a kind of supervised learning method, suitable for more classification problems, Classifying quality is notable；
4th, the method for the present invention uses the Bagging methods of integrated study, and classification accuracy is apparently higher than single grader；
5th, the method for the present invention uses and is combined deep learning with Chinese traditional medicinal materials recognition, compared with conventional method, accuracy height, Recognition speed is fast, performance is stablized, and this method has certain market value and promotional value.
Brief description of the drawings
Fig. 1 is a kind of step flow chart of Chinese traditional medicinal materials recognition method based on deep neural network disclosed in the present invention；
Fig. 2 be in a kind of Chinese traditional medicinal materials recognition method based on deep neural network disclosed in the present invention integrally training with it is pre- The schematic diagram of the Bagging methods of integrated study during survey；
Fig. 3 is convolutional neural networks in a kind of Chinese traditional medicinal materials recognition method based on deep neural network disclosed in the present invention General structure schematic diagram.
Embodiment
To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention In attached drawing, the technical solution in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is Part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art All other embodiments obtained without making creative work, belong to the scope of protection of the invention.
Embodiment
A kind of Chinese traditional medicinal materials recognition method based on deep neural network, includes the following steps：
The input of S1, collection Chinese medicine picture as data set, the Chinese medicine picture of collection is pre-processed；
In above-mentioned steps S1, input of the collection Chinese medicine picture as data set, wherein Chinese medicine image data collection is logical Web crawlers and artificial photographic method are crossed, and the image data set marked by artificial screening, size are normalized to 256*256, And needed data set being divided into training set and test set, ratio 7 according to neural network algorithm:2.Due to partial category Chinese medicine Material picture is less, causes whole data set quantitatively unbalanced, occurs over-fitting situation in the training process in order to prevent, right Training set uses clone method, allows each classification quantitatively to reach consistent；Also, considered based on neural network properties, to defeated Enter data and carry out equalization operation, formula is as follows：
Wherein, mean value calculation formula is：
R, G, B value is respectively the rgb value in pixel, Rmij, Gmij, Bmij be m-th picture sample the (i, J) rgb value of a pixel.
S2, using stochastical sampling method produce multiple sub- training sets of Chinese medicine；
S3, be finely adjusted each Chinese medicine training set using classical convolutional neural networks model training, and generation is more A Chinese traditional medicinal materials recognition Weak Classifier；
S4, using integrated study combined strategy obtain Chinese traditional medicinal materials recognition strong classifier, obtains Classification of Chinese Drug result.
In above-mentioned steps S2, S3, S4, overall training uses the Bagging methods of integrated study, the process with prediction process As shown in Figure 2.
Integrated study is to be combined into a strong classifier with multiple or multiple Weak Classifiers, so as to reach lifting sorting technique Effect, it is instrument very powerful in machine learning algorithm, its thought is simple, by gathering the ability of multiple models, effect Fruit exceedes the ability of single model, and main method has two methods of Bagging and Boosting.Bagging is used in the present invention Method, the main distinction in Bagging methods Weak Classifier be that parallelization produces, and Boosting methods are to be based on weights, The generation of Weak Classifier is to pass through serial mode.
Bagging methods, flow are summarized as follows：
First, by statistics stochastical sampling method, used here as self-service sampling method, multiple variant son instructions are obtained Practice collection.Self-service sampling method is using there is the mode for putting back to repeated sampling to carry out data acquisition, i.e., from training set size is every time m's In data set, take a sample then the sample to be put back to as an element in sub- training set, repeat behavior m times, this Sample has just obtained the sub- training set that size is m, some samples repeat in sub- training set, some samples do not occur then Cross.The above process, which is repeated several times, can obtain multiple sub- training sets with otherness.The reason for carrying out this method sampling：
The probability that each sample is not adopted is：
M sampling is carried out, the probability which is not adopted is：
So the limit is taken to have
It is approximately equal to 0.368, therefore about 36.8% data do not occur in sub- training set on original training set Cross.
Next, introduce deep learning technology, using classical convolutional neural networks model include AlexNet, SqueezeNet and GoogleNet, is finely adjusted training generation Chinese traditional medicinal materials recognition Weak Classifier；
The advantage of convolutional neural networks is to set up hierarchical structure, is obtained automatically with reference to the training method of mass data Must have the feature of strongly expressed power, while its internal using weights shared mechanism, reduce training parameter, make the network of complexity Structure becomes simpler and is easy to trained.In the convolutional neural networks used in the present invention, AlexNet is volume the most classical Product neutral net, including 5 convolution and down-sampling layer, 3 full articulamentum compositions；SqueezeNet is primarily to reduce CNN Model parameter quantity and design, parameter reduces 50 times on Imagenet data sets, and model size is substantially reduced； The GoogleNet network numbers of plies have 22 layers, and expression effect is optimal in 3 kinds of selected networks.
It is finely adjusted and is trained for the weak of Chinese medicine data set using the model and Chinese medicine data set of these three networks Grader.Sorting algorithm uses Softmax classifier algorithms, and this method is suitable for more classification problems, at the same can directly with volume Product neutral net is connected, and the classifier algorithm can be led everywhere, therefore can realize training method end to end.Specifically such as Under：
For T sub- training sets, training is finely adjusted to each sub- training set using classical convolutional neural networks model, Generate T Weak Classifier.
Be illustrated in figure 3 the general structure of convolutional neural networks, Chinese medicine picture as input, by multiple convolutional layers and Down-sampling layer output characteristic figure, wherein, the formula of convolutional layer is as follows：
Conv_output=f (WTx+b)
Wherein f (*) represents activation primitive, and x represents input data, W and b representation parameters, the present invention is using ReLU activation letters Number, formula are as follows：
F (x)=max (0, x)
Its derivation form is：
The formula of down-sampling layer is as follows, and the present invention uses Max-pooling methods：
Pooling_output=max (X)
Wherein X represents n*n matrixes.
But the input of full articulamentum is a vector, therefore vector is obtained by rasterisation and is connected to full articulamentum, most The classification results of the grader are obtained by Softmax graders afterwards.
The Softmax graders receive 4096 dimension input datas, output 98 dimension as a result, the result represents the input sample The confidence level of 98 Chinese medicine classifications of this correspondence, it is classification results then to take classification where its maximum, and the formula of Softmax is such as Under：
Wherein j=1,2 ..., K, K represent the number of class, K=98 in the present invention.
Wherein z=WT x+b, W, b are the parameter of Softmax, and x is the input feature vector of 4096 dimensions.
The training algorithm of this method uses stochastic gradient descent method, and training receives a certain number of training to this method every time Data, by after, the gap of output and data physical tags is weighed using loss function before network, followed by this weighing apparatus Amount carrys out reverse train network parameter, wherein, loss function employs the combination of cross entropy loss function and cohesion loss function Body, formula are as follows：
A. cross entropy loss function：
Wherein x represents original signal, and z represents reconstruction signal, in the form of vectors represent length be d, and can easily by It transform the form of inner product of vectors as, and K represents the sample number of each iteration.
B. cohesion loss function：
Wherein, K represents the sample number of each iteration, CyiRepresent yiThe class heart of category feature, yiFor original signal xiClassification Label.
We use the combination of both loss functions, as follows：
Wherein, λ is used for controlling ratio of the cohesion loss function in total loss function.
Finally, strong classifier is obtained using integrated study combined strategy, so as to obtain classification results, wherein combined strategy is adopted With simple vote method：Assuming that prediction classification is { c1,c2,...cK, for any one forecast sample x, T Weak Classifier it is pre- It is (h respectively to survey result1(x),h2(x)...hT(x)).Simple vote method is that the minority is subordinate to the majority, that is, T Weak Classifier To in the prediction result of sample x, the most classification c of quantityiFor final class categories, if more than one classification obtains highest Ticket, then randomly choose one and do final classification.
Embodiment two
A kind of base is specifically introduced in terms of the present embodiment is built from frame, data set preparation, model training, actual test are several In the method for the Chinese traditional medicinal materials recognition of deep neural network, detailed process is introduced as follows.
First, frame build process is as follows：
The 1st, GPU drivings and computing environment are installed；
2nd, fitting deep learning framework Caffe environment.
2nd, data set set-up procedure is as follows：
1st, web crawlers instrument for Chinese medicine image data is write using Python, using the instrument multi-thread Network Chinese medicine picture is gathered in the case of journey and records Chinese medicine label, and the Chinese medicine picture to collecting automatically, is carried out just Walk artificial screening；
2nd, video capture is carried out to Chinese medicine using high-definition camera to medicinal material shop, and using keyframe techniques extraction wherein Some pictures as Chinese medicine supplementary data；
3rd, the data that above two mode obtains are integrated；
4th, for categorical measure imbalance problem, using random reproduction method by its equilibrating；
5th, equalization processing is carried out to Chinese medicine image data；
6th, the Chinese medicine picture being disposed is converted into the receivable lmdb data lattice of Caffe frames together with its label Formula.
3rd, training process is as follows：
1st, AlexNet, SqueezeNet and GoogleNet convolutional Neural are downloaded by Caffe public networks distribution platform Network model；
2nd, the output node number of complete last layer of articulamentum of above-mentioned network is changed to 98, because the Chinese medicine data set of collection Classification number is 98 classes；
3rd, the lmdb formatted datas for obtaining data set set-up procedure input convolutional neural networks；
4th, by multiple convolutional layers in AlexNet or SqueezeNet or GoogleNet convolutional neural networks models and Down-sampling layer output characteristic figure；
5th, rasterization process obtains a feature vector and is connected to full articulamentum；
6th, by Softmax graders obtain the grader as a result, sending the result to loss function layer；
7th, according to result and cross entropy-cohesion loss function, its loss and passback gradient are calculated；
8th, by back-propagation algorithm, convolutional neural networks parameter is adjusted；Wherein back-propagation algorithm is using following super ginseng Number, which obtained by multiple cross validation：
Parameter name Value
batch_size 64
base_lr 0.001
max_iter 30000
9th, the process of above 3-7 is repeated, until loss function value is less than threshold value or maximum iteration, training terminates.
4th, test process is as follows：
Multithreading test script is write using Python, including following operation：
1st, Chinese medicine picture size to be tested is normalized to 256*256, and completes equalization operation；
2nd, be loaded into the obtained each Weak Classifier of training, i.e., after fine setting, AlexNet suitable for Chinese medicine, SqueezeNet and GoogleNet convolutional neural networks models；
3rd, by the picture to be tested after processing in a manner of blockette, the picture of 10 224*224 sizes is obtained；
4th, 10 pictures are all inputted into all Weak Classifiers by more than, obtain the recognition result of each weak typing；
5th, the ballot mode of each one ticket of Weak Classifier, the classification for obtaining most polls are the classification knots of final strong classifier Fruit, that is, the Chinese medicine classification predicted.
In conclusion the method for the present invention utilizes the self-service sampling method in stochastical sampling, more sub-samplings are put back to, are produced multiple The sub- training set of variant Chinese medicine；Using convolutional neural networks, automatically learn to have by convolutional neural networks strong The combination of the convolution kernel of expressiveness and these convolution kernels, obtains best Chinese medicine feature representation；Using Softmax points Class method, is a kind of supervised learning method, notable suitable for more classification problems, classifying quality；Using the Bagging of integrated study Method, classification accuracy is apparently higher than single grader；It is combined using by deep learning with Chinese traditional medicinal materials recognition, for aiding in knowing Other Chinese medicine, reduces the mistake that amateur occurs in identification, and compared with conventional method, can accuracy height, recognition speed Hurry up, performance stably analyzes Chinese medicine, this method has certain market value and promotional value.
Above-described embodiment is the preferable embodiment of the present invention, but embodiments of the present invention and from above-described embodiment Limitation, other any Spirit Essences without departing from the present invention with made under principle change, modification, replacement, combine, simplification, Equivalent substitute mode is should be, is included within protection scope of the present invention.

Claims (8)

  1. A kind of 1. Chinese traditional medicinal materials recognition method based on deep neural network, it is characterised in that the Chinese traditional medicinal materials recognition method bag Include following steps：
    The input of S1, collection Chinese medicine picture as data set, the Chinese medicine picture of collection is pre-processed；
    S2, using stochastical sampling method produce multiple sub- training sets of Chinese medicine；
    S3, using convolutional neural networks model be finely adjusted training to the sub- training set of each Chinese medicine, generates multiple Chinese medicines and knows Other Weak Classifier；
    S4, using integrated study combined strategy obtain Chinese traditional medicinal materials recognition strong classifier, obtains Classification of Chinese Drug result.
  2. 2. a kind of Chinese traditional medicinal materials recognition method based on deep neural network according to claim 1, it is characterised in that described Step S1 in the input that Chinese medicine picture is used as data set gathered by web crawlers and artificial photographic method, then to quantity Unbalanced Chinese medicine classification equalization.
  3. 3. a kind of Chinese traditional medicinal materials recognition method based on deep neural network according to claim 1, it is characterised in that described Step S2, step S3, the entirety training in step S4 the Bagging methods of integrated study are used with prediction process.
  4. 4. a kind of Chinese traditional medicinal materials recognition method based on deep neural network according to claim 1, it is characterised in that described Step S2 in stochastical sampling method use self-service sampling method, pass through self-service sampling method and obtain multiple variant son training Collection.
  5. 5. a kind of Chinese traditional medicinal materials recognition method based on deep neural network according to claim 1, it is characterised in that described Step S3 in by introducing deep learning technology, using convolutional neural networks model to the sub- training set of each Chinese medicine carry out it is micro- Adjust training, generate multiple Chinese traditional medicinal materials recognition Weak Classifiers, wherein, the convolutional neural networks model include AlexNet, SqueezeNet and GoogleNet.
  6. 6. a kind of Chinese traditional medicinal materials recognition method based on deep neural network according to claim 5, it is characterised in that described Step S3 Chinese traditional medicinal materials recognitions Weak Classifier make feature extraction using convolutional neural networks and divided using Softmax graders Class, it is specific as follows：
    For T sub- training sets, training is finely adjusted to each sub- training set using classical convolutional neural networks model, is generated T Weak Classifier；
    Chinese medicine picture is as inputting, by multiple convolutional layers and down-sampling layer output characteristic figure, wherein, the formula of convolutional layer is such as Under：
    Conv_output=f (WTx+b)
    Wherein f (*) represents activation primitive, and x represents input data, W and b representation parameters, and the present invention uses ReLU activation primitives, public Formula is as follows：
    F (x)=max (0, x)
    Its derivation form is：
    <mrow> <msup> <mi>f</mi> <mo>&amp;prime;</mo> </msup> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>1</mn> <mo>,</mo> <mi>x</mi> <mo>&gt;</mo> <mn>0</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>0</mn> <mo>,</mo> <mi>x</mi> <mo>&amp;le;</mo> <mn>0</mn> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
    The formula of down-sampling layer is as follows, using Max-pooling methods：
    Pooling_output=max (X)
    Wherein X represents n*n matrixes；
    The input of full articulamentum is a vector, and obtaining vector by rasterisation is connected to full articulamentum, finally by Softmax Grader obtains the classification results of the grader, which receives the input data of 4096 dimensions, the knot of the dimension of output 98 Fruit, the result represent the confidence level that the input sample corresponds to 98 Chinese medicine classifications, and classification where then taking its maximum is point Class is as a result, the formula of Softmax graders is as follows：
    <mrow> <mi>f</mi> <msub> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> <mi>j</mi> </msub> <mo>=</mo> <mfrac> <msup> <mi>e</mi> <msub> <mi>z</mi> <mi>j</mi> </msub> </msup> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>K</mi> </munderover> <msup> <mi>e</mi> <msub> <mi>z</mi> <mi>k</mi> </msub> </msup> </mrow> </mfrac> </mrow>
    Wherein j=1,2 ..., K, K represent the number of class, wherein value 98, z=WTX+b, W, b are the parameter of Softmax, and x is The input feature vector of 4096 dimensions.
  7. 7. a kind of Chinese traditional medicinal materials recognition method based on deep neural network according to claim 5, it is characterised in that described Step S3 in training algorithm use stochastic gradient descent method, training receives a certain number of training datas to this method every time, By after, the gap of output and data physical tags is weighed using loss function before network, come followed by this measurement Reverse train network parameter, wherein, loss function is using the combination of cross entropy loss function and cohesion loss function, formula It is as follows：
    <mrow> <mi>L</mi> <mo>=</mo> <msub> <mi>L</mi> <mi>H</mi> </msub> <mo>+</mo> <msub> <mi>&amp;lambda;L</mi> <mi>C</mi> </msub> <mo>=</mo> <mo>-</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>K</mi> </munderover> <msub> <mi>x</mi> <mi>i</mi> </msub> <mi>l</mi> <mi>o</mi> <mi>g</mi> <mrow> <mo>(</mo> <msub> <mi>z</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mi>l</mi> <mi>o</mi> <mi>g</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <msub> <mi>z</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <mfrac> <mi>&amp;lambda;</mi> <mn>2</mn> </mfrac> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>K</mi> </munderover> <mo>|</mo> <mo>|</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>c</mi> <msub> <mi>y</mi> <mi>i</mi> </msub> </msub> <mo>|</mo> <msubsup> <mo>|</mo> <mn>2</mn> <mn>2</mn> </msubsup> </mrow>
    Wherein, λ is used for controlling ratio of the cohesion loss function in total loss function, xiRepresent original signal, ziRepresent weight Structure signal, represents that length is d in the form of vectors, and can be transform as the form of inner product of vectors easily, and K is represented every time repeatedly The sample number in generation, CyiRepresent yiThe class heart of category feature, yiFor original signal xiClass label.
  8. 8. a kind of Chinese traditional medicinal materials recognition method based on deep neural network according to claim 1, it is characterised in that described Step S4 in integrated study combined strategy use ballot method.
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Cited By (29)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108776805A (en) * 2018-05-03 2018-11-09 北斗导航位置服务（北京）有限公司 It is a kind of establish image classification model, characteristics of image classification method and device
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CN110610136A (en) * 2019-08-21 2019-12-24 广西电网有限责任公司电力科学研究院 Transformer substation equipment identification module and identification method based on deep learning
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160180195A1 (en) * 2013-09-06 2016-06-23 Toyota Jidosha Kabushiki Kaisha Augmenting Layer-Based Object Detection With Deep Convolutional Neural Networks
CN106951825A (en) * 2017-02-13 2017-07-14 北京飞搜科技有限公司 A kind of quality of human face image assessment system and implementation method
CN107092906A (en) * 2017-05-01 2017-08-25 刘至键 A kind of Chinese traditional medicinal materials recognition device based on deep learning
CN107103338A (en) * 2017-05-19 2017-08-29 杭州电子科技大学 Merge the SAR target identification methods of convolution feature and the integrated learning machine that transfinites

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160180195A1 (en) * 2013-09-06 2016-06-23 Toyota Jidosha Kabushiki Kaisha Augmenting Layer-Based Object Detection With Deep Convolutional Neural Networks
CN106951825A (en) * 2017-02-13 2017-07-14 北京飞搜科技有限公司 A kind of quality of human face image assessment system and implementation method
CN107092906A (en) * 2017-05-01 2017-08-25 刘至键 A kind of Chinese traditional medicinal materials recognition device based on deep learning
CN107103338A (en) * 2017-05-19 2017-08-29 杭州电子科技大学 Merge the SAR target identification methods of convolution feature and the integrated learning machine that transfinites

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
SHIMENG YANG等: "A Light and Discriminative Deep Networks for Off-line Handwritten Chinese Character Recognition", 《2017 32ND YOUTH ACADEMIC ANNUAL CONFERENCE OF CHINESE ASSOCIATION OF AUTOMATION》 *
刘余霞等: "基于Bagging集成学习的字符识别方法", 《计算机工程与应用》 *
孙鑫等: "基于深度卷积网络的中药饮片图像识别", 《世界科学技术-中医药现代化》 *
王化祥: "《现代传技术及应用》", 30 June 2016, 天津：天津大学出版社 *

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