A neural network = running several logistic regressions at the same time 如果我们输入一个向量通过一系列逻辑回归函数,那么我们得到一个输出向量,但是我们不需要提前决定这些逻辑回归试图预测的变量是什么。 我们可以输入另一个logistic回归函数。损失函数将指导中间隐藏变量应该是什么,以便更好地预测下一层的目标。我...
an image is pushed to the network; this is called the input image. Then, the input image goes through an infinite number of steps; this is the convolutional part of the network. Finally, the neural network can predict the digit on the image. ...
3)Further Observations 其他相关实验比较。 Kalchbrenner et al. (2014) report much worse results with a CNN that has essentially the same architecture as our single channel model. Forexample, theirMax-TDNN(Time Delay Neural Network) with randomly initialized words obtains 37.4% on the SST-1 data...
Our neural network architecture has 60 million parameters. 我们的神经网络结构有6000万个参数。 Although the 1000 classes of ILSVRC make each training example impose 10 bits of constraint on the mapping from image to label, this turns out to be insufficient to learn so many parameters without consi...
For example, you can specify Mdl.TrainingHistory to get more information about the training history of the neural network model. Evaluate the performance of the classifier on the test set by computing the test set classification error. Visualize the results by using a confusion matrix. test...
This example shows how to train a neural network that is robust to adversarial examples using fast gradient sign method (FGSM) adversarial training. Neural networks can be susceptible to a phenomenon known asadversarial examples[1], where very small changes to an input can cause it to be miscl...
We trained a large, deep convolutional neural network to classify the1.2 million high-resolution images in the ImageNet LSVRC-2010 contestinto the 1000 different classes. On the test data, we achieved top-1 and top5 error rates of 37.5% and 17.0% which is considerably better than theprevious...
Our neural network architecture has 60 million parameters. Although the 1000 classes of ILSVRC make each training example impose 10 bits of constraint on the mapping from image to label, this turns out to be insufficient to learn so many parameters without considerable overfitting. Below, we...
Our neural network architecture has 60 million parameters. Although the 1000 classes of ILSVRC make each training example impose 10 bits of constraint on the mapping from image to label, this turns out to be insufficient to learn so many parameters without considerable overfitting. Below, we descri...
(2)Bayesian neural network(BNN):对权重设定某个先验假设,并使用基于权重参数的分布代替确定的网络权重参数。代替直接优化权重,BNN平均所有可能的权重,这个过程也被称作marginalization。 网络的输出为fW(x)∈Rh,对应的模型似然为p(y|fW(x)),通常的做法是P(y=c|x,W)=softmax(fW(x))。