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AlexNet contains eight layers: the first five are convolutional layers, some of them followed by max-pooling layers, and the last three are fully connected layers. The network, except the last layer, is split into two copies, each run on one GPU. [1] The entire structure can be written as
java.io A streamlined version of the java.io package found in the standard edition for doing Input/Output operations. java.lang Contains classes that are essential to the Java language. This package contains standard java types like Integers and Strings as well as basic exceptions, math functions, system functions, threading and security functions.
A fully connected RNN with 4 neurons. Fully recurrent neural networks (FRNN) connect the outputs of all neurons to the inputs of all neurons. In other words, it is a fully connected network. This is the most general neural network topology, because all other topologies can be represented by setting some connection weights to zero to simulate ...
The MLP consists of three or more layers (an input and an output layer with one or more hidden layers) of nonlinearly-activating nodes. Since MLPs are fully connected, each node in one layer connects with a certain weight w i j {\displaystyle w_{ij}} to every node in the following layer.
MATLAB (an abbreviation of "MATrix LABoratory" [18]) is a proprietary multi-paradigm programming language and numeric computing environment developed by MathWorks.MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages.
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 ...
LeNet-4 was a larger version of LeNet-1 designed to fit the larger MNIST database. It had more feature maps in its convolutional layers, and had an additional layer of hidden units, fully connected to both the last convolutional layer and to the output units. It has 2 convolutions, 2 average poolings, and 2 fully connected layers.
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.