hidden layer

collocation in English

meanings of hiddenand layer

These words are often used together. Click on the links below to explore the meanings. Or, see other collocations with layer.
hidden
adjective[usually before noun]
uk /ˈhɪd.ən/ us /ˈhɪd.ən/
not easy ...
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layer
noun[C]
uk /ˈleɪ.ər/ us /ˈleɪ.ɚ/
a level of material, such as a type of rock or gas, that is different from the material above or below it, or a thin sheet of ...
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Examples of hidden layer

These examples are from corpora and from sources on the web. Any opinions in the examples do not represent the opinion of the Cambridge Dictionary editors or of Cambridge University Press or its licensors.
This sensitivity to the temporal context of stimuli is somehow captured by representations formed at the hiddenlayer of the network.
Subsequent analysis of the activation profiles of the hiddenlayer confirmed the crucial role of distributed representations.
The input layer has neurons with a linear function that simply feeds the input signals to the hiddenlayer.
The connection between the output layer and the hiddenlayer are weighted.
For the dashboard and mushroom constraints, we have three neurons in the hiddenlayer whereas we have five for breast-cancer-wisconsin and house-votes-84.
To back our claim, we calculate the similarity of a hiddenlayer output vector for all tags, and cluster tags into several groups.
A similar representation can be obtained for networks with more than one hiddenlayer.
The number of neurons in the hiddenlayer can be varied and it is only possible to find the correct number through trial and error.
The unusual nonlinear parametric dependence of the regressor vector is due to the nonlinearities used in the hiddenlayer.
The neural network has two hidden layers, and each hiddenlayer has 15 units.
The neural network architecture is different for each detection only at hiddenlayer level.
The hiddenlayer contained eight units, based on pilot runs that varied the number of hidden units.
For networks with more than one hiddenlayer, derivation of the update equations is similar and can be realized by backpropagation.
There are eight inputs, five neurons in the hiddenlayer and three in the output layer.
Units in the hiddenlayer had localized receptive fields identical to those in the mixture-of-experts model; weights were shared across the different receptive-field locations.
The hiddenlayer neurons have sigmoidal transfer functions and the output neurons use linear activation functions.
We use a neural network with 45 neurons at the hiddenlayer.
Chapter 6 is dedicated to backpropagation, the generalisation (and overtraining) of multilayered networks, and the role of hiddenlayer neurons as feature extractors.
These examples are from corpora and from sources on the web. Any opinions in the examples do not represent the opinion of the Cambridge Dictionary editors or of Cambridge University Press or its licensors.
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