假设,有一张大小为32×32×3的输入图片,这是一张RGB模式的图片,想做手写体数字识别。32×32×3的RGB图片中含有某个数字,比如7,想识别它是从0-9这10个数字中的哪一个,构建一个神经网络来实现这个功能。 用的这个网络模型和经典网络LeNet-5非常相似,灵感也来源于此。LeNet-5是多年前Yann LeCun创建的,所...
Recurrent Neural Network (RNN) RNN,或者说最常用的LSTM,一般用于记住之前的状态,以供后续神经网络的判断,它由input gate、forget gate、output gate和cell memory组成,每个LSTM本质上就是一个neuron,特殊之处在于有4个输入: z z z和三门控制信号 z i z_i zi、 z f z_f zf和 z o z_...猜...
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Neural network, a computer program that operates in a manner inspired by the natural neural network in the brain. The objective of such artificial neural networks is to perform such cognitive functions as problem solving and machine learning. The theoret
with each variable within the formula weighted differently. If the output of applying that mathematical formula to the input exceeds a certain threshold, the node passes data to the next layer in the neural network. If the output is below the threshold, no data is passed to the next layer....
The most commonly chosen approach is the feedforward network using a so-called back-propagation algorithm. The back-propagation algorithm can be thought of as a way of performing a supervised learning process by means of examples, using the following general approach: A problem, for example, a ...
Typically, an ANN is initially trained, or fed large amounts of data. Training consists of providing input and telling the network what the output should be. For example, to build a network that identifies the faces of actors, the initial training might be a series of pictures, including ac...
21.4Scoring with Neural Network Learn to score withNeural Network. Scoring withNeural Networkis the same as any otherClassificationorRegressionalgorithm. The following functions are supported:PREDICTION,PREDICTION_PROBABILITY,PREDICTION_COST,PREDICTION_SET, andPREDICTION_DETAILS. ...
提出了一种用于快速多尺度目标检测的统一深度神经网络,即多尺度CNN (MS-CNN)。MS-CNN由建议子网络和检测子网络组成。在建议子网中,在多个输出层进行检测,使感受野匹配不同尺度的对象。这些互补的尺度特异性探测器被结合起来产生一个强大的多尺度目标探测器。通过优化多任务损失,实现了统一网络的端到端学习。此外,还...
Comparing primates and recurrent neural network models RNNs have vast computational capacities and can, in principle, be trained to establish arbitrarily complex functions17. The RNN solutions one can find for a specific task are not unique, and can vary with various factors, including network archi...