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The "batch" is same as in mini-batch gradient descent. The mean in batch-norm here would be the average of each feature map in your batch (in your case either 32 or 64 depending on which you use) generally batch is used quite consistently in ML right now, where it refers to the inputs you send in together for forward/backward pass.


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One issue is that a normalized set of initial weights may not stay normalized as learning progresses; so given that we adjust weights proportionately according to their relative values and also when working on a subset of the learning data the model may become convinced that one subset of features is important and others not, this can result in the weights ...


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Why do we update all layers simultaneously while training a neural network? We typically train a neural network with gradient descent and back-propagation. Gradient descent is the iterative algorithm used to update the parameters and back-propagation is the algorithm used to compute the gradient of the loss function with respect to each of these parameters. ...


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"Naturally, this will affect the gradient through the network." this statement is only partially true, let's see why by starting explaining the real aim of batch normalisation. As the title of the paper suggest, the aim of batch normalisation is to decrease training time by reducing covariance shift. What is covariance shift? We can conceive it as the ...


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I think the answer to your question is much more a rule of thumb than an appropriate analytical answer. First of all, I would like to remark that Batch Normalization [1] are applied most commonly to convolutional layer, constituting what is called a "convolutional block" (Convolution + Batch Normalization + Activation). Thus, for giving you an idea ...


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This is how I understand it. Batch normalization is used to remove internal covariate shift by normalizing the input for each hidden layer using the statistics across the entire mini-batch, which averages each individual sample, so the input for each layer is always in the same range. This can be seen from the BN equation: $$ \textrm{BN}(x)= \gamma\left(\...


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Well, some time ago I also faced the same issue in the semantic segmentation task. Batch normalization is expected to improve convergence, because the normalization of activations prevents the explosion of the gradients magnitude and leads to more steady convergence. Adam is an adaptive optimizer with momentum and division by the weighted sum of gradients on ...


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Your intuition is correct. We will be normalizing the inputs of the layer under consideration (just right before applying the activation function). So, if this layer receives an input $\mathrm{x}=\left(x^{(1)} \ldots x^{(d)}\right)$, the formula for normalizing the $k^{th}$ dimension of $\mathrm{x}$ would look as follows: $$\widehat{x}^{(k)}=\frac{x^{(k)}-\...


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Definition and Explaination For how Batch Normalization works exactly, I'll suggest you to read the following papers: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift How Does Batch Normalization Help Optimization? The recent interpretation on How BN works is that it can reduce the high-order effect as mentioned ...


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In the literature, it differs. You will see models do it after or before pooling only, and sometimes you see it after every single convolution. Batch normalization's assistance to neural networks wasn't really understood for the longest time, initially it was thought to assist with internal covariate shift (hypothesized by the initial paper: Batch ...


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