In Top-1 test accuracy, GoogleNet can reach up to 78%. We are committed to sharing findings related to COVID-19 as quickly as possible. Train Deep Learning Network to Classify New Images This example shows how to use transfer learning to retrain a convolutional neural network to classify a new set of images. [1] Deng, Jia, et al. "Very deep convolutional networks for large-scale image recognition." As an important research component of computer vision analysis and machine learning, image classification is an important theoretical basis and technical support to promote the development of artificial intelligence. There doesn't seem to have a repository to have a list of image classification papers like deep_learning_object_detecti… The final classification accuracy corresponding to different kinds of kernel functions is different. The image classification is a classical problem of image processing, computer vision and machine learning fields. An example of an image data set is shown in Figure 8. For the performance in the TCIA-CT database, only the algorithm proposed in this paper obtains the best classification results. [3] Simonyan, Karen, and Andrew Zisserman. The database contains a total of 416 individuals from the age of 18 to 96. It is mainly divided into five steps: first, image preprocessing; second, initialize the network parameters and train the SAE layer by layer; third, a deep learning model based on stacked sparse autoencoder is established; fourth, establish a sparse representation classification of the optimized kernel function; fifth, test the model. To further verify the universality of the proposed method. Its basic steps are as follows:(1)First preprocess the image data. Finally, this paper uses the data enhancement strategy to complete the database, and obtains a training data set of 988 images and a test data set of 218 images. Some scholars have proposed image classification methods based on sparse coding. During the training process, the output reconstruction signal of each layer is used to compare with the input signal to minimize the error. Image Input Layer An imageInputLayer is where you specify the image size, which, in this case, is 28-by-28-by-1. In the microwave oven image, the appearance of the same model product is the same. P. Sermanet, D. Eigen, and X. Zhang, “Overfeat: integrated recognition, localization and detection using convolutional networks,” 2013, P. Tang, H. Wang, and S. Kwong, “G-MS2F: GoogLeNet based multi-stage feature fusion of deep CNN for scene recognition,”, F.-P. An, “Medical image classification algorithm based on weight initialization-sliding window fusion convolutional neural network,”, C. Zhang, J. Liu, and Q. Tian, “Image classification by non-negative sparse coding, low-rank and sparse decomposition,” in. (3)The approximation of complex functions is accomplished by the sparse representation of multidimensional data linear decomposition and the deep structural advantages of multilayer nonlinear mapping. The SSAE depth model directly models the hidden layer response of the network by adding sparse constraints to the deep network. Image classification involves the extraction of features from the image to observe some patterns in the dataset. Section 4 constructs the basic steps of the image classification algorithm based on the stacked sparse coding depth learning model-optimized kernel function nonnegative sparse representation. So, if the rotation expansion factor is too large, the algorithm proposed in this paper is not a true sparse representation, and its recognition is not accurate. Then, the kernel function is sparse to indicate that the objective equation is. In DNN, the choice of the number of hidden layer nodes has not been well solved. From left to right, the images of the differences in pathological information of the patient's brain image. Therefore, the SSAE-based deep learning model is suitable for image classification problems. At the same time, combined with the basic problem of image classification, this paper proposes a deep learning model based on the stacked sparse autoencoder. We will then proceed to use typical data augmentation techniques, and retrain our models. (4) Image classification method based on deep learning: in view of the shortcomings of shallow learning, in 2006, Hinton proposed deep learning technology [33]. However, these systems require an excessive amount … The size of each image is 512  512 pixels. With deep learning this has changed: given the right conditions, many computer vision tasks no longer require such careful feature crafting. Basic schematic diagram of the stacked sparse autoencoder. In order to further verify the classification effect of the proposed algorithm on general images, this section will conduct a classification test on the ImageNet database [54, 55] and compare it with the mainstream image classification algorithm. According to [44], the update method of RCD iswhere i is a random integer between [0, n]. Therefore, the activation values of all the samples corresponding to the node j are averaged, and then the constraints arewhere ρ is the sparse parameter of the hidden layer unit. Inspired by [44], the kernel function technique can also be applied to the sparse representation problem, reducing the classification difficulty and reducing the reconstruction error. Image Classification – Deep Learning Project in Python with Keras. The overall cost function can be expressed as follows: Among them, the coefficient β is a sparse penalty term, the value of related to W, b, and H (W, b) is a loss function, which can be expressed as follows: The abovementioned formula gives the overall cost function, and the residual or loss of each hidden layer node is the most critical to construct a deep learning model based on stacked sparse coding. On related examples learning model is widely used large-scale image recognition. the SSAEs are by. Functions such as dimensionality disaster and low computational efficiency supported by the method proposed in this paper to! Train a simple convolutional neural network for deep learning Toolbox model for ResNet-50 network, How to an... And train a simple convolutional neural network in Keras with python on CIFAR-10! Can automatically adjust the number of new ideas to improve the training,... 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