2024年6月7日 · It is called a "loss" when it is used in a loss function to measure a distance between two vectors, $\left \| y_1 - y_2 \right \|^2_2$, or to measure the size of a vector, $\left \| \theta \right \|^2_2$. This goes with a loss minimization that tries to bring these quantities to the "least" possible value.

2020年7月28日 · An "l2 loss" would be any loss that uses the "l2 norm" as a regularisation term (and, in that case, you will get MAP). This loss can be the MSE or it can e.g. the cross-entropy, i.e. l2 norm can be used for regression or classification. $\endgroup$ –

Effects of L2 loss and smooth L1 loss. Ask Question Asked 5 years, 10 months ago.

2019年9月16日 · Stack Exchange Network. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

2019年6月4日 · $\begingroup$ I think that depends on the definition of square loss, the form of. $(1-yf(x))^2$ suggest classificaiton. Also, the wiki page is indeed just discussing loss function for classification. If we define square loss for regressionto be $(y_i-f(x_i))^2$, then the relation still hold. $\endgroup$ –

L2 loss is based on the square of the weights of your Network. As a given weight increases in size, the loss will increase exponentially (quadratically, to be precise). Neural Networks are therefore "pushed" to spread the weight values more evenly across each layer, since for such a quadratic loss factor it's better to have a lot of smaller ...

2022年6月21日 · The l2 regularization term is being added to the loss itself. But then you need to find the gradient of this new loss; since gradients are additive, this is the same as the gradient of the unpenalized loss plus the gradient of the l2 term, the latter of which is the quantity specified in the last line of code.

2019年8月8日 · – L2 regularization term on weights. I have seen data scientists using both of these parameters at the same time, ideally either you use L1 or L2 not both together. While reading about tuning LGBM parameters I cam across one such case: Kaggle official GBDT Specification and Optimization Workshop in Paris where Instructors are ML experts.

It almost does the opposite of L2 and Dropout by simplifying the network and muting nome neurons. If you notice that adding a small regularization decreases your accuracy / increases your loss, it's probably because your network was overfitting.

2020年6月12日 · Let's say you have a layer with kernel_regularize=tf.keras.regularizers.l2(1.5). The regularization loss will then be 1.5*tf.reduce_sum(W**2), where W is are the weights without the bias. For an L1 loss, the absolute value is taken instead of the squared value. The regularization loss at each layer will linearly add to the overall loss function.