cross_entropy = tensorflow.nn.softmax_cross_entropy_with_logits(logits=tensorflow.reduce_max(input_tensor=softmax_propabilities, reduction_indices=[1]), labels=label_tensor) #Summarizing the cross entropy into a single value (cost) to be minimized by the learning algorithm. cost = tensorflow.red...
3.使用TensorFlow构建CNN模型 使用creat_CNN函数创建CNN模型,该函数创建卷积层(conv)、ReLU激活函数、最大池化(max pooling)、dropout以及全连接层(full connection,FC),最后一层全连接层输出结果。每一层的输出都是下一层的输入,这就要求相邻两层之间的特征图尺寸大小要一致。此外,对于每个conv、ReLU以及最大池化层...
pooling layers, and fully connected layers, and it uses a backpropagation algorithm to learn spatial hierarchies of data automatically and adaptively. You will learn more about these terms in the following section.
Without convolutions, a machine learning algorithm would have to learn a separate weight for every cell in a largetensor. For example, a machine learning algorithm training on 2K x 2K images would be forced to find 4M separate weights. Thanks to convolutions, a machine learning algorithm only ...
Everything started with “Rich feature hierarchies for accurate object detection and semantic segmentation” (R-CNN) in 2014, which used an algorithm called Selective Search to propose possible regions of interest and a standard Convolutional Neural Network (CNN) to classify and adjust them. It quic...
9120 Algorithm 1 Macroblock Scaling Input: Fn(), I0∼N−1 /*Pre-trained model, training images Output: [c widthcm0 , ··· , c widthcmM−1 ]/*Compact model Procedure: • NZ() /*Computes the number of non-zero elements • RF() /*Computes receptive field size • flop...
Algorithm 1 Solving the optimization problem (14) via the CG method algorithm1 Require:The parameters of the problem: QQ and bb; the number of iterations: NN. Output: The solution of problem (14), y⋆y⋆. Select the initial point y0y0; g0=∇ h(y0)=Qy0−bg0=∇ h(y0)...
Full size image Overall, backpropagation is very simple and local. However, the reason why we can train a highly non-convex machine with many local minima, like neural networks, with a strong local learning algorithm is not really known even today. In practice, backpropagation can be computed...
In this study, we first extract three categories of features from videos and then build a CNN-based detection algorithm. Video feature extraction Content features Content features are derived from the video content. All audio information were converted into texts before feature extraction. In this st...
Each dimen- sion is first randomly sampled from uniform distribution U[ 0.25, 0.25], then pre-trained by the word2vec tool, an implementation of the unsupervised word embeddings learning algorithm proposed by Mikolov [19] on a unan- notated dataset of PubMed biomedical abstracts with about ...