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𝗡𝗘𝗪 • 𝗚𝗔𝗥𝗬 𝗔𝗡𝗗𝗘𝗥𝗦𝗢𝗡 𝗟𝗘𝗚𝗘𝗡𝗗 𝗘𝗗𝗜𝗧𝗜𝗢𝗡 Only 100 pieces available 🎨 Each one hand signed by Gary Anderson ✍️ Available here ➡️
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Night Tiger is Here! 🐾🔥 Like + Comment + Follow + Retweet ❗️ Join in for a chance to win this fierce and mysterious Night Tiger! One lucky winner will take it home! 🎉 😍 Only $223.2 (Use code ‘Tiger10’)🐯 🗓️Available from Nov 9 - Nov 14 (PT) 👉 Limited-time offer, only 20 pieces available! ✨ Available in many colors! 🌈 Get yours now at: 😱 The price is incredibly favorable this time. Don’t let it slip away! —————————————————— 同步闲鱼：FURRJOI工作室 #Furrjoi #SiliconeMask #FetishFashion #BDSM #cosplay
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![Batch Normalization by hand ✍️ ~ 7 steps walkthrough below Batch normalization is common practice for improving training and achieving faster convergence. It sounds simple. But it is often misunderstood. 🤔 Does batch normalization involve trainable parameters, tunable hyper-parameters, or both? 🤔 Is batch normalization applied to inputs, features, weights, biases, or outputs? 🤔 How is batch normalization different from layer normalization? So I drew and calculated one entirely by hand. Goal: normalize a mini-batch of 4 examples to mean 0 and variance 1, then let the network scale it back. = 1. Given = A mini-batch of 4 training examples, each with 3 features. = 2. Linear layer = Let us multiply by the weights and add the biases. Batch norm sits after this, which answers the second question: what gets normalized is features, not inputs, weights or biases. = 3. ReLU = We apply the activation, and -2 becomes 0. Negative values are suppressed before any statistic is taken. = 4. Batch statistics = Let us compute the sum, mean, variance and standard deviation, one row at a time. A row is a feature and the four columns are the four examples, so every number here measures one feature against the rest of the batch. That is the "batch" in batch normalization, and it is exactly what layer normalization does not do. The statistics are rounded to whole numbers, which is what keeps the rest of the page doable in pen. = 5. Shift to mean 0 = We subtract the mean, in green. The four values in each feature now average to zero. = 6. Scale to variance 1 = Let us divide by the standard deviation, in orange. Each feature now has variance one, whatever scale it arrived at. = 7. Scale and shift = We multiply by a linear transformation and pass the result on. The diagonal and the last column are trainable, so having just forced every feature to mean 0 and variance 1, we hand the network the means to undo it. The outputs: Mean of each feature = [2, 1, 2] Std dev of each feature = [1, 1, 2] To the next layer = [2, -2, 2, 0], [-3, 3, 6, -3], [2, 0, 1, 2] The answers: 🤔 Both. The scale and shift are trainable, the statistics are not. Epsilon and the momentum on the running statistics are the hyper-parameters, and one mini-batch by hand needs neither. 🤔 Features, after the linear layer, not inputs, weights or biases. 🤔 Batch norm measures across the batch, one feature at a time. Layer norm measures across the features, one example at a time. 💾 Save this post!](https://image.24vids.com/tw-2079570896423911664/ext_tw_video_thumb/2079570876530298880/pu/img/w_TJ6G4bD69KBKuz.jpg)
