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PyTorch is a machine learning library based on the Torch library, [4] [5] [6] ... The following code-block defines a neural network with linear layers using the nn ...
Attention module – this can be a dot product of recurrent states, or the query-key-value fully-connected layers. The output is a 100-long vector w. H 500×100. 100 hidden vectors h concatenated into a matrix c 500-long context vector = H * w. c is a linear combination of h vectors weighted by w.
The un-embedding layer is a linear-softmax layer: = (+) The matrix has shape (,). The embedding matrix M {\displaystyle M} and the un-embedding matrix W {\displaystyle W} are sometimes required to be transposes of each other, a practice called weight tying.
Simpler modules like Linear, Tanh and Max make up the basic component modules. This modular interface provides first-order automatic gradient differentiation. What follows is an example use-case for building a multilayer perceptron using Modules: >
The Recurrent layer is used for text processing with a memory function. Similar to the Convolutional layer, the output of recurrent layers are usually fed into a fully-connected layer for further processing. See also: RNN model. [6] [7] [8] The Normalization layer adjusts the output data from previous layers to achieve a regular distribution ...
Plot of the ReLU (blue) and GELU (green) functions near x = 0. In the context of artificial neural networks, the rectifier or ReLU (rectified linear unit) activation function [1] [2] is an activation function defined as the non-negative part of its argument, i.e., the ramp function:
A bottleneck block [1] consists of three sequential convolutional layers and a residual connection. The first layer in this block is a 1x1 convolution for dimension reduction (e.g., to 1/2 of the input dimension); the second layer performs a 3x3 convolution; the last layer is another 1x1 convolution for dimension restoration.
This can be seen as the composition of K linear functions , …, and the softmax function (where denotes the inner product of and ). The operation is equivalent to applying a linear operator defined by w {\displaystyle \mathbf {w} } to vectors x {\displaystyle \mathbf {x} } , thus transforming the original, probably highly-dimensional, input to ...