run models

[sahooora]

How can we run the models? I want to run vae model but It's not specified how!

[kartikdutt18]

Hi @sara-hoseininasab, This repo is undergoing restructuring right now, so that portion was edited in the readme. Sorry about that. We do have a completely working repository here. It has a make file and scripts to download datasets and run models as well. Here is the VAE example.

[sahooora]

Hi @sara-hoseininasab, This repo is undergoing restructuring right now, so that portion was edited in the readme. Sorry about that. We do have a completely working repository here. It has a make file and scripts to download datasets and run models as well. Here is the VAE example.

Thanks! After running mnist_vae_cnn I got this error: [FATAL] Cannot open file './../data/mnist_full.csv'. I should mention that before running it I executed download_dataset.py in the tools directory.

[kartikdutt18]

Can you check that outside vae directory there is a data folder with mnist_full.csv? Regards.

[sahooora]

Can you check that outside vae directory there is a data folder with mnist_full.csv? Regards.

There doesn't exist mnist_full.csv in the data directory. Just mnist_test.csv and mnist_train.csv are included in this directory.

[kartikdutt18]

Hmm, Let me try replicating the issue.

[sahooora]

Hmm, Let me try replicating the issue.

Thanks!

[shrit]

@kartikdutt18 I do not remember what there was in mnist_full.csv. I can understand from the name that it merges both of the train and test files. @sara-hoseininasab You can replace mnist_full.csv by mnist_train.csv or merge manually both file and create a mnist_full.csv

[kartikdutt18]

Right, I am facing electricity outage here, Most probably its mnist_train or mnist_all. Once I'm back, I'll try to find out what was there in the file.

[shrit]

@kartikdutt18 I will handle it, no worries.

[kartikdutt18]

@shrit, Awesome! Thanks a lot

[shrit]

@kartikdutt18 I open a pull request here https://github.com/mlpack/examples/pull/92 Still, during the execution, I had this issue: https://github.com/mlpack/examples/issues/84

[kartikdutt18]

Hmm, Let me look into that now.

[kartikdutt18]

Hey @shrit, I just downloaded all the datasets. I also got mnist_full.csv in my datafolder.

[shrit]

Hmm, are you sure this is not an old one?

[kartikdutt18]

Yeah looks that way. Sorry about that.

[kartikdutt18]

Still, during the execution, I had this issue: mlpack/examples#84

I am able to fix this issue. Another thing is mnist_train has one extra labels column. We also need to drop that.

[shrit]

@kartikdutt18 You meant the first column as parsed as in this line https://github.com/mlpack/examples/blob/fbce4998c09abe433c42f2e1519df1b9c94aba5f/mnist_simple/mnist_simple.cpp#L76 Or there is another one?

[kartikdutt18]

Right this one, Labels are not needed vae so maybe mnist_full was the csv with train without labels.

[sahooora]

@kartikdutt18 I do not remember what there was in mnist_full.csv. I can understand from the name that it merges both of the train and test files. @sara-hoseininasab You can replace mnist_full.csv by mnist_train.csv or merge manually both file and create a mnist_full.csv

@shrit Thanks. I replaced mnist_train.csv by mnist_ful.csv and I got this error: [FATAL] The output width / output height is not possible given the other parameters of the layer.

terminate called after throwing an instance of 'std::runtime_error'

[shrit]

@kartikdutt18 Exactly, in this case, I will remove it using Armadillo from inside mnist_vae. @sara-hoseininasab we are working on this issue, you need to remove the first columns of this dataset using armadillo.

[kartikdutt18]

@sara-hoseininasab, The padding needs to be valid in transposed layer. Earlier I think boost visitors took care of that but now we need to specify it manually. I'll open a PR shortly to fix that.

[sahooora]

armadillo

@kartikdutt18 Exactly, in this case, I will remove it using Armadillo from inside mnist_vae. @sara-hoseininasab we are working on this issue, you need to remove the first columns of this dataset using armadillo.

@shrit I am completely new in this scope. Could you please tell me how can I remove the first columns of the dataset using armadillo? regards

[kartikdutt18]

I think you can add the following line :

fullData = fullData.rows(1, fullData.n_rows - 1);

at line 65.

[sahooora]

I think you can add the following line :

fullData = fullData.rows(1, fullData.n_rows - 1);

at line 65.

It should be added in mnist_vae_cnn.cpp file, am I right? I did it but I still get the same error.

[kartikdutt18]

Right, To fix that error you need to make the following change :

// Add the first transposed convolution(deconvolution) layer.
    decoder->Add<TransposedConvolution<>>(
        24,  // Number of input activation maps.
        16,  // Number of output activation maps.
        5,   // Filter width.
        5,   // Filter height.
        1,   // Stride along width.
        1,   // Stride along height.
        0,   // Padding width.
        0,   // Padding height.
        10,  // Input width.
        10,  // Input height.
        14,  // Output width.
        14,
        "valid"); // Output height.

    decoder->Add<LeakyReLU<>>();
    decoder->Add<TransposedConvolution<>>(16, 1, 15, 15, 1, 1, 1, 1,
        14, 14, 28, 28,"valid");

Replace the similar layers with this one.

[sahooora]

Right, To fix that error you need to make the following change :

// Add the first transposed convolution(deconvolution) layer.
    decoder->Add<TransposedConvolution<>>(
        24,  // Number of input activation maps.
        16,  // Number of output activation maps.
        5,   // Filter width.
        5,   // Filter height.
        1,   // Stride along width.
        1,   // Stride along height.
        0,   // Padding width.
        0,   // Padding height.
        10,  // Input width.
        10,  // Input height.
        14,  // Output width.
        14,
        "valid"); // Output height.

    decoder->Add<LeakyReLU<>>();
    decoder->Add<TransposedConvolution<>>(16, 1, 15, 15, 1, 1, 1, 1,
        14, 14, 28, 28,"valid");

Replace the similar layers with this one.

@kartikdutt18 Thank, the previous error has been solved. But I get the new one: error: subtraction: incompatible matrix dimensions: 784x28 and 59999x28 terminate called after throwing an instance of 'std::logic_error' what(): subtraction: incompatible matrix dimensions: 784x28 and 59999x28 Aborted (core dumped)

[shrit]

784x28 is the size of the older mnist_full matrix. @kartikdutt18 I do not know how VAE works in detail, but the neural models inside seem to be tailored to the old dataset.

[kartikdutt18]

Right using mnist_full.csv doesn't give this error.

[sahooora]

Right using mnist_full.csv doesn't give this error.

As you said I renamed mnist_train.csv to mnist_full.csv and then run the model.

[kartikdutt18]

The valid part of the error is fixed in mlpack/mlpack#2436.

[kartikdutt18]

Could you download the dataset from here. Thanks.

[kartikdutt18]

784x28 is the size of the older mnist_full matrix. @kartikdutt18 I do not know how VAE works in detail, but the neural models inside seem to be tailored to the old dataset.

I'll try to figure that out too.

[shrit]

@kartikdutt18 Actually it is the input size 28 * 28 = 784 It seems that the entire dataset treated as an image

[kartikdutt18]

Right. VAE model takes in an image and outputs an image.

[sahooora]

Could you download the dataset from here. Thanks.

@kartikdutt18 I downloaded it. with the new mnist_full.csv I got the below error: error: subtraction: incompatible matrix dimensions: 784x50 and 783x50 terminate called after throwing an instance of 'std::logic_error' what(): subtraction: incompatible matrix dimensions: 784x50 and 783x50

[kartikdutt18]

Ahh, You don't have to remove columns with this one. Just make the change in model only.

[sahooora]

Ahh, You don't have to remove columns with this one. Just make the change in model only.

@kartikdutt18 Thanks. I fixed it and run again. I encountered with core dumped: Training ... Initial loss -> 726.155 terminate called after throwing an instance of 'std::bad_array_new_length' what(): std::bad_array_new_length Aborted (core dumped)

Thanks

[kartikdutt18]

I'll look into that tomorrow. Sorry about the mess though, we will update the ReadMe and get all examples running. Thanks a lot for being so patient.

[sahooora]

I'll look into that tomorrow. Sorry about the mess though, we will update the ReadMe and get all examples running. Thanks a lot for being so patient.

Ok, thank you so much.

[kartikdutt18]

Hey @sara-hoseininasab, There are couple of more things that would need to be fixed. I'll create a PR today with simple VAE (non cnn type). You can use that till the issue is resolved.

[sahooora]

Hey @sara-hoseininasab, There are couple of more things that would need to be fixed. I'll create a PR today with simple VAE (non cnn type). You can use that till the issue is resolved.

Hi, @kartikdutt18 Thank you so much. I'm waiting. Please let me know when it's ready. Regards

[kartikdutt18]

I created a branch here that has normal vae but it still segfaults. I don't know why. I tried rest of the examples again and they work fine. The problem lies with vae and that I think requires a bit of debugging.

[sahooora]

I created a branch here that has normal vae but it still segfaults. I don't know why. I tried rest of the examples again and they work fine. The problem lies with vae and that I think requires a bit of debugging.

@kartikdutt18 Please let me know when the bug is fixed. Regards

[sahooora]

@kartikdutt18 Any news about the vae?

[kartikdutt18]

In the source of mlpack, the parameter error is fixed. The only error left is segmentation fault. I'll try to find why that happens in the next couple of days.

[mlpack-bot[bot]]

This issue has been automatically marked as stale because it has not had any recent activity. It will be closed in 7 days if no further activity occurs. Thank you for your contributions! :+1:

[shrit]

keep open

[mlpack-bot[bot]]

This issue has been automatically marked as stale because it has not had any recent activity. It will be closed in 7 days if no further activity occurs. Thank you for your contributions! :+1:

[rcurtin]

Just wanted to check in @shrit and @kartikdutt18, do you think that we have resolved this issue? Or is there work we still need to do to be able to run these models?

[kartikdutt18]

Hey @rcurtin, There is partial fix made in mlpack/mlpack#2436. However, The VAE Model still segfaults. Since this has more to do with the examples repo, we also have an issue here.