for each input node vs for each hidden node

Both phrases are correct, but they are used in different contexts. 'For each input node' is used when referring to nodes at the input layer of a neural network, while 'for each hidden node' is used when referring to nodes at the hidden layers of a neural network.

Last updated: March 17, 2024 • 556 views

for each input node

This phrase is correct and commonly used when referring to nodes at the input layer of a neural network.

This phrase is used when iterating over or performing operations on each node at the input layer of a neural network.

Examples:

For each input node, calculate the weighted sum of its inputs.

Show examples from the web [+]

May 28, 2015 ... If the flow contains more than one input node, one transaction is started for each input node when it receives input data. A transaction is started ...
If a filter is placed between the sequence and target node, the bool condition is checked for each input node i.e. each item in the sequence. More exactly, a ...
The connections are tallied for each input node and scaled relative to all other inputs. A single value is obtained for each explanatory variable that describes the ...
Apr 27, 2015 ... The connec- tions are tallied for each input node and scaled relative to all other inputs. A single value is obtained for each explanatory variable ...

Alternatives:

For every input node
For all input nodes
For each node in the input layer

for each hidden node

This phrase is correct and commonly used when referring to nodes at the hidden layers of a neural network.

This phrase is used when iterating over or performing operations on each node at the hidden layers of a neural network.

Examples:

For each hidden node, apply the activation function.

Show examples from the web [+]

... bias (which forces at least some activations to happen), and the result is passed through the activation algorithm producing one output a for each hidden node.
For each hidden node, ReLU outputs an activation, a, and the activations are summed going into the output node, which simply passes the activations' sum ...
Compute neti and yi for each hidden node, i=1,..., h: Compute netj and yj for each output node, j=1,...,m: Step 2: Backward Propagation. Compute ´2's for each ...
Apr 19, 2015 ... For each hidden node: Zero all input weights to the singly selected hidden node and record the performance. Rank the performances. 2.

Alternatives:

For every hidden node
For all hidden nodes
For each node in the hidden layer

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for each input node vs for each hidden node