CNN-LSTM Image Classification
Is it possible to reshape 512x512 rgb image to (timestep, dim)? Otherwards, I am trying to convert this reshape layer: Reshape((23, 3887)) to 512 vice 299. Also, is there any documentation explaining how to determine input_dim and timestep for Keras?
keras lstm rnn
asked Nov 26 '18 at 20:14
Hello WorldHello World
74111
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Is it possible to reshape 512x512 rgb image to (timestep, dim)? Otherwards, I am trying to convert this reshape layer: Reshape((23, 3887)) to 512 vice 299. Also, is there any documentation explaining how to determine input_dim and timestep for Keras?
keras lstm rnn
asked Nov 26 '18 at 20:14
Hello WorldHello World
74111
add a comment |
Is it possible to reshape 512x512 rgb image to (timestep, dim)? Otherwards, I am trying to convert this reshape layer: Reshape((23, 3887)) to 512 vice 299. Also, is there any documentation explaining how to determine input_dim and timestep for Keras?
keras lstm rnn
asked Nov 26 '18 at 20:14
Hello WorldHello World
74111
Is it possible to reshape 512x512 rgb image to (timestep, dim)? Otherwards, I am trying to convert this reshape layer: Reshape((23, 3887)) to 512 vice 299. Also, is there any documentation explaining how to determine input_dim and timestep for Keras?
keras lstm rnn
keras lstm rnn
asked Nov 26 '18 at 20:14
Hello WorldHello World
74111
asked Nov 26 '18 at 20:14
Hello WorldHello World
74111
asked Nov 26 '18 at 20:14
Hello WorldHello World
74111
asked Nov 26 '18 at 20:14
Hello WorldHello World
74111
asked Nov 26 '18 at 20:14
Hello WorldHello World
74111
74111
add a comment |
add a comment |
1 Answer
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It seems like your problem is similar to one that i had earlier today. Look at it here: Keras functional API: Combine CNN model with a RNN to to look at sequences of images
Now to add to the answer from the question i linked too. Let number_of_images be n. In your case the original data format would be (n, 512, 512, 3). All you then need to do decide how many images you want per sequence. Say you want a sequence of 5 images and have gotten 5000 images in total. Then reshaping to (1000, 5, 512, 512, 3) should do. This way the model sees 1000 sequences of 5 images.
answered Nov 26 '18 at 23:04
deKeijzerdeKeijzer
8212
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
It seems like your problem is similar to one that i had earlier today. Look at it here: Keras functional API: Combine CNN model with a RNN to to look at sequences of images
Now to add to the answer from the question i linked too. Let number_of_images be n. In your case the original data format would be (n, 512, 512, 3). All you then need to do decide how many images you want per sequence. Say you want a sequence of 5 images and have gotten 5000 images in total. Then reshaping to (1000, 5, 512, 512, 3) should do. This way the model sees 1000 sequences of 5 images.
answered Nov 26 '18 at 23:04
deKeijzerdeKeijzer
8212
add a comment |
It seems like your problem is similar to one that i had earlier today. Look at it here: Keras functional API: Combine CNN model with a RNN to to look at sequences of images
Now to add to the answer from the question i linked too. Let number_of_images be n. In your case the original data format would be (n, 512, 512, 3). All you then need to do decide how many images you want per sequence. Say you want a sequence of 5 images and have gotten 5000 images in total. Then reshaping to (1000, 5, 512, 512, 3) should do. This way the model sees 1000 sequences of 5 images.
answered Nov 26 '18 at 23:04
deKeijzerdeKeijzer
8212
add a comment |
It seems like your problem is similar to one that i had earlier today. Look at it here: Keras functional API: Combine CNN model with a RNN to to look at sequences of images
Now to add to the answer from the question i linked too. Let number_of_images be n. In your case the original data format would be (n, 512, 512, 3). All you then need to do decide how many images you want per sequence. Say you want a sequence of 5 images and have gotten 5000 images in total. Then reshaping to (1000, 5, 512, 512, 3) should do. This way the model sees 1000 sequences of 5 images.
answered Nov 26 '18 at 23:04
deKeijzerdeKeijzer
8212
It seems like your problem is similar to one that i had earlier today. Look at it here: Keras functional API: Combine CNN model with a RNN to to look at sequences of images
Now to add to the answer from the question i linked too. Let number_of_images be n. In your case the original data format would be (n, 512, 512, 3). All you then need to do decide how many images you want per sequence. Say you want a sequence of 5 images and have gotten 5000 images in total. Then reshaping to (1000, 5, 512, 512, 3) should do. This way the model sees 1000 sequences of 5 images.
answered Nov 26 '18 at 23:04
deKeijzerdeKeijzer
8212
answered Nov 26 '18 at 23:04
deKeijzerdeKeijzer
8212
answered Nov 26 '18 at 23:04
deKeijzerdeKeijzer
8212
answered Nov 26 '18 at 23:04
deKeijzerdeKeijzer
8212
8212
add a comment |
add a comment |
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