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...
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.
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...
Then, the Lassoregression algorithm is used to filter out the indicators with poor prediction ability. By integrating autoregressive integrated moving average (ARIMA) and support vector regression (SVR) models, a combined forecasting model is established to capture linear and nonlinear features, ...
Algorithm 1 The CA-EGNN. Full size image Result and discussion Problem definition Few-shot classification is the process of learning a classifier with only a small number of training samples for each data category. Where each Few-shot classification task T contains two parts, the support set S...
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...
Full size image Dataset Pre-processing An image’s preprocessing quality has a major impact on how well a classification model performs. We begin by scaling the image to 32 × 32 pixels to do this. Next, we use a set of well-known methods, such as CLAHE and Dilation, to improve ...
Full size image Optimizer selection This section discusses the CNN learning process. Two major issues are included in the learning process: the first issue is the learning algorithm selection (optimizer), while the second issue is the use of many enhancements (such as AdaDelta, Adagrad, and momen...
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...
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...