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 mlpack/examples#92
Still, during the execution, I had this issue: mlpack/examples#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]

@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]

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! 👍

[shrit]

keep open

[mlpack-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! 👍

[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.

[rcurtin]

No problem @kartikdutt18, I just wanted to check if the issue was still relevant. I suppose we should leave it open until mlpack/examples#84 is fixed, then we can propagate that fix to this repository?

[kartikdutt18]

Sure, Sounds good to me.

[mlpack-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! 👍

[shrit]

Keep open

[mlpack-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! 👍

[shrit]

Closing this issue as it has been resolved by #156