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What does an Input layer of shape=(None,) or (None,12) actually mean?
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What does an Input layer of shape=(None,) or (None,12) actually mean?
Skip gram Word2Vec model, neural network implementationKeras — Transfer learning — changing Input tensor shapeLSTM unit: cell state dimensionHow to set the number of neurons and layers in neural networksDimension problem in keras neural networksNumber of parameters keras dense layer with a 2D inputDefining Input Shape for Time Series using LSTM in KerasConnect a dense layer to a LSTM architectureHow to set kernel size (height and width) for 1D convolution layer in CNN Keras R API for doc2vec input?How smaller does the input data get reduced in a LSTM autoencoder
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
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Is this telling the model that there are two dimensions (i.e. it’s a matrix) but we don’t yet know the size of that particular dimension? If so, how can the model be compiled? Doesn’t the size of each dimension affect the number of nodes in middle layers?
neural-network keras
asked 8 hours ago
Nic CottrellNic Cottrell
1566 bronze badges
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add a comment |
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Is this telling the model that there are two dimensions (i.e. it’s a matrix) but we don’t yet know the size of that particular dimension? If so, how can the model be compiled? Doesn’t the size of each dimension affect the number of nodes in middle layers?
neural-network keras
asked 8 hours ago
Nic CottrellNic Cottrell
1566 bronze badges
$endgroup$
add a comment |
$begingroup$
Is this telling the model that there are two dimensions (i.e. it’s a matrix) but we don’t yet know the size of that particular dimension? If so, how can the model be compiled? Doesn’t the size of each dimension affect the number of nodes in middle layers?
neural-network keras
asked 8 hours ago
Nic CottrellNic Cottrell
1566 bronze badges
$endgroup$
Is this telling the model that there are two dimensions (i.e. it’s a matrix) but we don’t yet know the size of that particular dimension? If so, how can the model be compiled? Doesn’t the size of each dimension affect the number of nodes in middle layers?
neural-network keras
neural-network keras
asked 8 hours ago
Nic CottrellNic Cottrell
1566 bronze badges
asked 8 hours ago
Nic CottrellNic Cottrell
1566 bronze badges
asked 8 hours ago
Nic CottrellNic Cottrell
1566 bronze badges
asked 8 hours ago
Nic CottrellNic Cottrell
1566 bronze badges
asked 8 hours ago
Nic CottrellNic Cottrell
1566 bronze badges
1566 bronze badges
add a comment |
add a comment |
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In keras, a None dimension means that it can be any scalar number, so that you use this model to infer on an arbitrarily long input. This dimension does not affect the size of the network, it just denotes that you are free to select the length (number of samples) of your input during testing.
answered 8 hours ago
pcko1pcko1
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$begingroup$
In keras, a None dimension means that it can be any scalar number, so that you use this model to infer on an arbitrarily long input. This dimension does not affect the size of the network, it just denotes that you are free to select the length (number of samples) of your input during testing.
answered 8 hours ago
pcko1pcko1
1,9315 silver badges20 bronze badges
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add a comment |
$begingroup$
In keras, a None dimension means that it can be any scalar number, so that you use this model to infer on an arbitrarily long input. This dimension does not affect the size of the network, it just denotes that you are free to select the length (number of samples) of your input during testing.
answered 8 hours ago
pcko1pcko1
1,9315 silver badges20 bronze badges
$endgroup$
add a comment |
$begingroup$
In keras, a None dimension means that it can be any scalar number, so that you use this model to infer on an arbitrarily long input. This dimension does not affect the size of the network, it just denotes that you are free to select the length (number of samples) of your input during testing.
answered 8 hours ago
pcko1pcko1
1,9315 silver badges20 bronze badges
$endgroup$
In keras, a None dimension means that it can be any scalar number, so that you use this model to infer on an arbitrarily long input. This dimension does not affect the size of the network, it just denotes that you are free to select the length (number of samples) of your input during testing.
answered 8 hours ago
pcko1pcko1
1,9315 silver badges20 bronze badges
answered 8 hours ago
pcko1pcko1
1,9315 silver badges20 bronze badges
answered 8 hours ago
pcko1pcko1
1,9315 silver badges20 bronze badges
answered 8 hours ago
pcko1pcko1
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1,9315 silver badges20 bronze badges
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