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How does GlobalMaxPooling work on the output of Conv1D?
Output a word instead of a vector after word embedding?In a convolutional neural network (CNN), when convolving the image, is the operation used the dot product or the sum of element-wise multiplication?how to deal with varying output layerHow filters are made in a CNN?Visualizing word embeddingsWhat is the purpose of a 1x1 convolutional layer?degeneracy of a CNN having only 1 convolution kernel down to a fully connected NNWhat is the motivation for row-wise convolution and folding in Kalchbrenner et al. (2014)?Weight update to fully convolutional network when supervision is only for a patchHow to set kernel size (height and width) for 1D convolution layer in CNN Keras R API for doc2vec input?
$begingroup$
In the field of text classification, it is common to use Conv1D filters running over word embeddings and then getting a single value on the output for each filter using GlobalMaxPooling1D.
As I understand the process, the convolutional filter is a matrix of the same size as the $$textsize of filter matrix = textembedding dimcdottextwidth of the filter$$ The filter matrix is then applied to the input embeddings (multiplied element by element) which produces a matrix of the same size for each filter position. Not a single number.
So how does the global max pooling get a single number on the output? Does it simply take a maximum over all the values in all the output matrices, or is there any other processing?
Please correct me if I'm wrong.
keras nlp cnn
asked Apr 7 at 18:57
MSKLMSKL
713
$endgroup$
add a comment |
$begingroup$
In the field of text classification, it is common to use Conv1D filters running over word embeddings and then getting a single value on the output for each filter using GlobalMaxPooling1D.
As I understand the process, the convolutional filter is a matrix of the same size as the $$textsize of filter matrix = textembedding dimcdottextwidth of the filter$$ The filter matrix is then applied to the input embeddings (multiplied element by element) which produces a matrix of the same size for each filter position. Not a single number.
So how does the global max pooling get a single number on the output? Does it simply take a maximum over all the values in all the output matrices, or is there any other processing?
Please correct me if I'm wrong.
keras nlp cnn
asked Apr 7 at 18:57
MSKLMSKL
713
$endgroup$
add a comment |
$begingroup$
In the field of text classification, it is common to use Conv1D filters running over word embeddings and then getting a single value on the output for each filter using GlobalMaxPooling1D.
As I understand the process, the convolutional filter is a matrix of the same size as the $$textsize of filter matrix = textembedding dimcdottextwidth of the filter$$ The filter matrix is then applied to the input embeddings (multiplied element by element) which produces a matrix of the same size for each filter position. Not a single number.
So how does the global max pooling get a single number on the output? Does it simply take a maximum over all the values in all the output matrices, or is there any other processing?
Please correct me if I'm wrong.
keras nlp cnn
asked Apr 7 at 18:57
MSKLMSKL
713
$endgroup$
In the field of text classification, it is common to use Conv1D filters running over word embeddings and then getting a single value on the output for each filter using GlobalMaxPooling1D.
As I understand the process, the convolutional filter is a matrix of the same size as the $$textsize of filter matrix = textembedding dimcdottextwidth of the filter$$ The filter matrix is then applied to the input embeddings (multiplied element by element) which produces a matrix of the same size for each filter position. Not a single number.
So how does the global max pooling get a single number on the output? Does it simply take a maximum over all the values in all the output matrices, or is there any other processing?
Please correct me if I'm wrong.
keras nlp cnn
keras nlp cnn
asked Apr 7 at 18:57
MSKLMSKL
713
asked Apr 7 at 18:57
MSKLMSKL
713
edited Apr 7 at 19:14
MSKL
asked Apr 7 at 18:57
MSKLMSKL
713
asked Apr 7 at 18:57
MSKLMSKL
713
asked Apr 7 at 18:57
MSKLMSKL
713
713
add a comment |
add a comment |
1 Answer
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$begingroup$
Apparently I forgot how the convolution works. The input is multiplied element wise with the filter weights and the products are then summed. That's how a single value is obtained on the output.
answered Apr 7 at 19:19
MSKLMSKL
713
$endgroup$
add a comment |
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$begingroup$
Apparently I forgot how the convolution works. The input is multiplied element wise with the filter weights and the products are then summed. That's how a single value is obtained on the output.
answered Apr 7 at 19:19
MSKLMSKL
713
$endgroup$
add a comment |
$begingroup$
Apparently I forgot how the convolution works. The input is multiplied element wise with the filter weights and the products are then summed. That's how a single value is obtained on the output.
answered Apr 7 at 19:19
MSKLMSKL
713
$endgroup$
add a comment |
$begingroup$
Apparently I forgot how the convolution works. The input is multiplied element wise with the filter weights and the products are then summed. That's how a single value is obtained on the output.
answered Apr 7 at 19:19
MSKLMSKL
713
$endgroup$
Apparently I forgot how the convolution works. The input is multiplied element wise with the filter weights and the products are then summed. That's how a single value is obtained on the output.
answered Apr 7 at 19:19
MSKLMSKL
713
answered Apr 7 at 19:19
MSKLMSKL
713
answered Apr 7 at 19:19
MSKLMSKL
713
answered Apr 7 at 19:19
MSKLMSKL
713
713
add a comment |
add a comment |
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