Backprogagation
$begingroup$
I am new to Deep Learning. Suppose that we have a neural network with one input layer, one output layer, and one hidden layer. Let's refer to the weights from input to hidden as w and the weights from hidden to output as v. Suppose that we have initialized w and v, and ran them through the neural network via the Feedforward algorithm. Suppose that we have calculated v via backprogagation. When estimating the ideal weights for w, do we keep the weights v constant when updating w via gradient descent given we already calculated v, or do we allow v to update along with w?
I understand that both w and v should update simultaneously when updating v, that's not my question. My question is related to if we need to update v when updating w, given we already calculated v.
neural-network deep-learning backpropagation
New contributor
$endgroup$
add a comment |
$begingroup$
I am new to Deep Learning. Suppose that we have a neural network with one input layer, one output layer, and one hidden layer. Let's refer to the weights from input to hidden as w and the weights from hidden to output as v. Suppose that we have initialized w and v, and ran them through the neural network via the Feedforward algorithm. Suppose that we have calculated v via backprogagation. When estimating the ideal weights for w, do we keep the weights v constant when updating w via gradient descent given we already calculated v, or do we allow v to update along with w?
I understand that both w and v should update simultaneously when updating v, that's not my question. My question is related to if we need to update v when updating w, given we already calculated v.
neural-network deep-learning backpropagation
New contributor
$endgroup$
add a comment |
$begingroup$
I am new to Deep Learning. Suppose that we have a neural network with one input layer, one output layer, and one hidden layer. Let's refer to the weights from input to hidden as w and the weights from hidden to output as v. Suppose that we have initialized w and v, and ran them through the neural network via the Feedforward algorithm. Suppose that we have calculated v via backprogagation. When estimating the ideal weights for w, do we keep the weights v constant when updating w via gradient descent given we already calculated v, or do we allow v to update along with w?
I understand that both w and v should update simultaneously when updating v, that's not my question. My question is related to if we need to update v when updating w, given we already calculated v.
neural-network deep-learning backpropagation
New contributor
$endgroup$
I am new to Deep Learning. Suppose that we have a neural network with one input layer, one output layer, and one hidden layer. Let's refer to the weights from input to hidden as w and the weights from hidden to output as v. Suppose that we have initialized w and v, and ran them through the neural network via the Feedforward algorithm. Suppose that we have calculated v via backprogagation. When estimating the ideal weights for w, do we keep the weights v constant when updating w via gradient descent given we already calculated v, or do we allow v to update along with w?
I understand that both w and v should update simultaneously when updating v, that's not my question. My question is related to if we need to update v when updating w, given we already calculated v.
neural-network deep-learning backpropagation
neural-network deep-learning backpropagation
New contributor
New contributor
New contributor
asked 6 mins ago
Joshua JonesJoshua Jones
1
1
New contributor
New contributor
add a comment |
add a comment |
0
active
oldest
votes
Your Answer
StackExchange.ifUsing("editor", function () {
return StackExchange.using("mathjaxEditing", function () {
StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix) {
StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
});
});
}, "mathjax-editing");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "557"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
Joshua Jones is a new contributor. Be nice, and check out our Code of Conduct.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f48478%2fbackprogagation%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
0
active
oldest
votes
0
active
oldest
votes
active
oldest
votes
active
oldest
votes
Joshua Jones is a new contributor. Be nice, and check out our Code of Conduct.
Joshua Jones is a new contributor. Be nice, and check out our Code of Conduct.
Joshua Jones is a new contributor. Be nice, and check out our Code of Conduct.
Joshua Jones is a new contributor. Be nice, and check out our Code of Conduct.
Thanks for contributing an answer to Data Science Stack Exchange!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f48478%2fbackprogagation%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown