What is the job of “RepeatVector” and “TimeDistributed”?
$begingroup$
I read about them in Keras documentation and other websites, but I couldn't exactly understand what do exactly they do and how should we use them in designing many-to-many or encoder-decoder LSTM networks?
May someone explains what do they do in this code:
model = Sequential()
model.add(LSTM(input_dim=1, output_dim=hidden_neurons, return_sequences=False))
model.add(RepeatVector(10))
model.add(LSTM(output_dim=hidden_neurons, return_sequences=True))
model.add(TimeDistributed(Dense(1)))
model.add(Activation('linear'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['accuracy'])
It's solution of this problem here(https://github.com/keras-team/keras/issues/6063).
Thank you in advanced!
lstm
asked yesterday
user145959user145959
1027
$endgroup$
add a comment |
$begingroup$
I read about them in Keras documentation and other websites, but I couldn't exactly understand what do exactly they do and how should we use them in designing many-to-many or encoder-decoder LSTM networks?
May someone explains what do they do in this code:
model = Sequential()
model.add(LSTM(input_dim=1, output_dim=hidden_neurons, return_sequences=False))
model.add(RepeatVector(10))
model.add(LSTM(output_dim=hidden_neurons, return_sequences=True))
model.add(TimeDistributed(Dense(1)))
model.add(Activation('linear'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['accuracy'])
It's solution of this problem here(https://github.com/keras-team/keras/issues/6063).
Thank you in advanced!
lstm
asked yesterday
user145959user145959
1027
$endgroup$
add a comment |
$begingroup$
I read about them in Keras documentation and other websites, but I couldn't exactly understand what do exactly they do and how should we use them in designing many-to-many or encoder-decoder LSTM networks?
May someone explains what do they do in this code:
model = Sequential()
model.add(LSTM(input_dim=1, output_dim=hidden_neurons, return_sequences=False))
model.add(RepeatVector(10))
model.add(LSTM(output_dim=hidden_neurons, return_sequences=True))
model.add(TimeDistributed(Dense(1)))
model.add(Activation('linear'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['accuracy'])
It's solution of this problem here(https://github.com/keras-team/keras/issues/6063).
Thank you in advanced!
lstm
asked yesterday
user145959user145959
1027
$endgroup$
I read about them in Keras documentation and other websites, but I couldn't exactly understand what do exactly they do and how should we use them in designing many-to-many or encoder-decoder LSTM networks?
May someone explains what do they do in this code:
model = Sequential()
model.add(LSTM(input_dim=1, output_dim=hidden_neurons, return_sequences=False))
model.add(RepeatVector(10))
model.add(LSTM(output_dim=hidden_neurons, return_sequences=True))
model.add(TimeDistributed(Dense(1)))
model.add(Activation('linear'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['accuracy'])
It's solution of this problem here(https://github.com/keras-team/keras/issues/6063).
Thank you in advanced!
lstm
lstm
asked yesterday
user145959user145959
1027
asked yesterday
user145959user145959
1027
edited 15 hours ago
user145959
asked yesterday
user145959user145959
1027
asked yesterday
user145959user145959
1027
asked yesterday
user145959user145959
1027
1027
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
tf.keras.layers.RepeatVector
According to the docs :
Repeats the input n times.
They have also provided an example :
model = Sequential()
model.add(Dense(32, input_dim=32))
# now: model.output_shape == (None, 32)
# note: `None` is the batch dimension
model.add(RepeatVector(3))
# now: model.output_shape == (None, 3, 32)
In the above example, the RepeatVector layer repeats the incoming inputs a specific number of time. The shape of the input in the above example was ( 32 , ). But the output shape of the RepeatVector was ( 3 , 32 ), since the inputs were repeated 3 times.
tf.keras.layers.TimeDistributed()
According to the docs :
This wrapper allows to apply a layer to every temporal slice of an input.
The input should be at least 3D, and the dimension of index one will be considered to be the temporal dimension.
You can refer to the example at their website.
TimeDistributed layer applies a specific layer such as Dense to every sample it receives as an input. Suppose the input size is ( 13 , 10 , 6 ). Now, I need to apply a Dense layer to every slice of shape ( 10 , 6 ). Then I would wrap the Dense layer in a TimeDistributed layer.
model.add( TimeDistributed( Dense( 12 , input_shape=( 10 , 6 ) )) )
The output shape of such a layer would be ( 13 , 10 , 12 ). Hence, the operation of the Dense layer was applied to each temporal slice as mentioned.
answered 19 hours ago
Shubham PanchalShubham Panchal
2114
$endgroup$
$begingroup$
Thanks Shubham. I edited my question and added a new question. Please help me on that too.
$endgroup$
– user145959
15 hours ago
add a comment |
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
});
}
});
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%2f46491%2fwhat-is-the-job-of-repeatvector-and-timedistributed%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
tf.keras.layers.RepeatVector
According to the docs :
Repeats the input n times.
They have also provided an example :
model = Sequential()
model.add(Dense(32, input_dim=32))
# now: model.output_shape == (None, 32)
# note: `None` is the batch dimension
model.add(RepeatVector(3))
# now: model.output_shape == (None, 3, 32)
In the above example, the RepeatVector layer repeats the incoming inputs a specific number of time. The shape of the input in the above example was ( 32 , ). But the output shape of the RepeatVector was ( 3 , 32 ), since the inputs were repeated 3 times.
tf.keras.layers.TimeDistributed()
According to the docs :
This wrapper allows to apply a layer to every temporal slice of an input.
The input should be at least 3D, and the dimension of index one will be considered to be the temporal dimension.
You can refer to the example at their website.
TimeDistributed layer applies a specific layer such as Dense to every sample it receives as an input. Suppose the input size is ( 13 , 10 , 6 ). Now, I need to apply a Dense layer to every slice of shape ( 10 , 6 ). Then I would wrap the Dense layer in a TimeDistributed layer.
model.add( TimeDistributed( Dense( 12 , input_shape=( 10 , 6 ) )) )
The output shape of such a layer would be ( 13 , 10 , 12 ). Hence, the operation of the Dense layer was applied to each temporal slice as mentioned.
answered 19 hours ago
Shubham PanchalShubham Panchal
2114
$endgroup$
$begingroup$
Thanks Shubham. I edited my question and added a new question. Please help me on that too.
$endgroup$
– user145959
15 hours ago
add a comment |
$begingroup$
tf.keras.layers.RepeatVector
According to the docs :
Repeats the input n times.
They have also provided an example :
model = Sequential()
model.add(Dense(32, input_dim=32))
# now: model.output_shape == (None, 32)
# note: `None` is the batch dimension
model.add(RepeatVector(3))
# now: model.output_shape == (None, 3, 32)
In the above example, the RepeatVector layer repeats the incoming inputs a specific number of time. The shape of the input in the above example was ( 32 , ). But the output shape of the RepeatVector was ( 3 , 32 ), since the inputs were repeated 3 times.
tf.keras.layers.TimeDistributed()
According to the docs :
This wrapper allows to apply a layer to every temporal slice of an input.
The input should be at least 3D, and the dimension of index one will be considered to be the temporal dimension.
You can refer to the example at their website.
TimeDistributed layer applies a specific layer such as Dense to every sample it receives as an input. Suppose the input size is ( 13 , 10 , 6 ). Now, I need to apply a Dense layer to every slice of shape ( 10 , 6 ). Then I would wrap the Dense layer in a TimeDistributed layer.
model.add( TimeDistributed( Dense( 12 , input_shape=( 10 , 6 ) )) )
The output shape of such a layer would be ( 13 , 10 , 12 ). Hence, the operation of the Dense layer was applied to each temporal slice as mentioned.
answered 19 hours ago
Shubham PanchalShubham Panchal
2114
$endgroup$
$begingroup$
Thanks Shubham. I edited my question and added a new question. Please help me on that too.
$endgroup$
– user145959
15 hours ago
add a comment |
$begingroup$
tf.keras.layers.RepeatVector
According to the docs :
Repeats the input n times.
They have also provided an example :
model = Sequential()
model.add(Dense(32, input_dim=32))
# now: model.output_shape == (None, 32)
# note: `None` is the batch dimension
model.add(RepeatVector(3))
# now: model.output_shape == (None, 3, 32)
In the above example, the RepeatVector layer repeats the incoming inputs a specific number of time. The shape of the input in the above example was ( 32 , ). But the output shape of the RepeatVector was ( 3 , 32 ), since the inputs were repeated 3 times.
tf.keras.layers.TimeDistributed()
According to the docs :
This wrapper allows to apply a layer to every temporal slice of an input.
The input should be at least 3D, and the dimension of index one will be considered to be the temporal dimension.
You can refer to the example at their website.
TimeDistributed layer applies a specific layer such as Dense to every sample it receives as an input. Suppose the input size is ( 13 , 10 , 6 ). Now, I need to apply a Dense layer to every slice of shape ( 10 , 6 ). Then I would wrap the Dense layer in a TimeDistributed layer.
model.add( TimeDistributed( Dense( 12 , input_shape=( 10 , 6 ) )) )
The output shape of such a layer would be ( 13 , 10 , 12 ). Hence, the operation of the Dense layer was applied to each temporal slice as mentioned.
answered 19 hours ago
Shubham PanchalShubham Panchal
2114
$endgroup$
tf.keras.layers.RepeatVector
According to the docs :
Repeats the input n times.
They have also provided an example :
model = Sequential()
model.add(Dense(32, input_dim=32))
# now: model.output_shape == (None, 32)
# note: `None` is the batch dimension
model.add(RepeatVector(3))
# now: model.output_shape == (None, 3, 32)
In the above example, the RepeatVector layer repeats the incoming inputs a specific number of time. The shape of the input in the above example was ( 32 , ). But the output shape of the RepeatVector was ( 3 , 32 ), since the inputs were repeated 3 times.
tf.keras.layers.TimeDistributed()
According to the docs :
This wrapper allows to apply a layer to every temporal slice of an input.
The input should be at least 3D, and the dimension of index one will be considered to be the temporal dimension.
You can refer to the example at their website.
TimeDistributed layer applies a specific layer such as Dense to every sample it receives as an input. Suppose the input size is ( 13 , 10 , 6 ). Now, I need to apply a Dense layer to every slice of shape ( 10 , 6 ). Then I would wrap the Dense layer in a TimeDistributed layer.
model.add( TimeDistributed( Dense( 12 , input_shape=( 10 , 6 ) )) )
The output shape of such a layer would be ( 13 , 10 , 12 ). Hence, the operation of the Dense layer was applied to each temporal slice as mentioned.
answered 19 hours ago
Shubham PanchalShubham Panchal
2114
answered 19 hours ago
Shubham PanchalShubham Panchal
2114
answered 19 hours ago
Shubham PanchalShubham Panchal
2114
answered 19 hours ago
Shubham PanchalShubham Panchal
2114
2114
$begingroup$
Thanks Shubham. I edited my question and added a new question. Please help me on that too.
$endgroup$
– user145959
15 hours ago
add a comment |
$begingroup$
Thanks Shubham. I edited my question and added a new question. Please help me on that too.
$endgroup$
– user145959
15 hours ago
$begingroup$
Thanks Shubham. I edited my question and added a new question. Please help me on that too.
$endgroup$
– user145959
15 hours ago
$begingroup$
Thanks Shubham. I edited my question and added a new question. Please help me on that too.
$endgroup$
– user145959
15 hours ago
add a comment |
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%2f46491%2fwhat-is-the-job-of-repeatvector-and-timedistributed%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
