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Nov 16, 2021
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Fix layers_normalizations.ipynb typos #2606

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4b8df7b
Update layers_normalizations.ipynb
inyong37 Nov 15, 2021
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Update layers_normalizations.ipynb
inyong37 Nov 15, 2021
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4 changes: 2 additions & 2 deletions docs/tutorials/layers_normalizations.ipynb
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Expand Up @@ -67,7 +67,7 @@
"* **Instance Normalization** (TensorFlow Addons)\n",
"* **Layer Normalization** (TensorFlow Core)\n",
"\n",
"The basic idea behind these layers is to normalize the output of an activation layer to improve the convergence during training. In contrast to [batch normalization](https://keras.io/layers/normalization/) these normalizations do not work on batches, instead they normalize the activations of a single sample, making them suitable for recurrent neual networks as well. \n",
"The basic idea behind these layers is to normalize the output of an activation layer to improve the convergence during training. In contrast to [batch normalization](https://keras.io/layers/normalization/) these normalizations do not work on batches, instead they normalize the activations of a single sample, making them suitable for recurrent neural networks as well. \n",
"\n",
"Typically the normalization is performed by calculating the mean and the standard deviation of a subgroup in your input tensor. It is also possible to apply a scale and an offset factor to this as well.\n",
"\n",
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"### Introduction\n",
"Layer Normalization is special case of group normalization where the group size is 1. The mean and standard deviation is calculated from all activations of a single sample.\n",
"\n",
"Experimental results show that Layer normalization is well suited for Recurrent Neural Networks, since it works batchsize independt.\n",
"Experimental results show that Layer normalization is well suited for Recurrent Neural Networks, since it works batchsize independently.\n",
"\n",
"### Example\n",
"\n",
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