![]() ![]() #Navicat for mysql 11.2.6 serial key serial key#NAVICAT 12 FOR MYSQL SERIAL KEY ACTIVATION KEY.Batch Normalization also acts like a regularizer, reducing the need for other regularization techniques (such as dropout).improve upon the best published result on ImageNet classification: reaching 4.9% top-5 validation error (and 4.8% test error), exceeding the accuracy of human raters.They were able to use much larger learning rates, significantly speeding up the learning process.The networks were also much less sensitive to the weight initialization.The vanishing gradients problem was strongly reduced, to the point that they could use saturating activation functions such as the tanh and even the logistic activation function.considerably improved all the deep neural networks they experimented with.So, in total, four parameters are learned for each batch-normalized layer: These are typically efficiently computed during training using a moving average. Z ( i ) is the output of the BN operation: it is a scaled and shifted version of the inputs.Īt test time, there is no mini-batch to compute the empirical mean and standard deviation, so instead you simply use the whole training set’s mean and standard deviation. Ε is a tiny number to avoid division by zero (typicallyġ 0 – 3). Β is the shifting parameter (offset) for the layer. Γ is the scaling parameter for the layer. X ( i ) is the zero-centered and normalized input. M B is the number of instances in the mini-batch. Σ B is the empirical standard deviation, also evaluated over the whole mini-batch. Μ B is the empirical mean, evaluated over the whole mini-batch Normal distribution with mean 0 and standard deviation Xavier initialization (when using the logistic activation function) This initialization strategy is often called Xavier initialization (after the author’s first name), or sometimes Glorot initialization.Įquation 11-1. N o u t p u t s are the number of input and output connections for the layer whose weights are being initialized (also called fan-in and fan-out). The connection weights must be initialized randomly as described in Equation 11-1, where We need the variance of the outputs of each layer to be equal to the variance of its inputs, and we also need the gradients to have equal variance before and after flowing through a layer in the reverse direction. We don’t want the signal to die out (approachesĠ), nor do we want it to explode (approaches infinity) and saturate (stays at a constant). We need the signal to flow properly in both directions: in the forward direction when making predictions, and in the reverse direction when backpropagating gradients. This is actually made worse by the fact that the logistic function has a mean of 0.5, not 0. Going forward in the network, the variance keeps increasing after each layer until the activation function saturates at the top layers. With this activation function and this initialization scheme, the variance of the outputs of each layer is much greater than the variance of its inputs. Some suspects for vanishing gradients: the logistic sigmoid activation function and random initialization using a normal distribution with a mean of 0 and a standard deviation of 1. As a result, weights connecting lower layers stay unchanged during training process, and the algorithm never converges to an optimal solution.Įxploding gradients problem: the gradients grow larger and larger, among which some gradients grow insanely high, and the training diverges. Vanishing gradients problem: gradients often get smaller and smaller as the algorithm progresses down to the lower layers. ![]() This note thus may miss some import contents.ġ1.1 Vanishing/Exploding Gradients Problems Hands-On Machine Learning with Scikit-Learn and TensorFlow读书笔记Ī chapter that you will never miss a single word. Chapter 11 Training Deep Neural Nets OReilly. ![]()
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