Nasnet.py changes

Part 1 (2018)
35

Hello,

You are correct and apologies for the incorrect comment about the default in dataset.py. Thank you for looking into this.

Looking at ‘from_csv’ below from dataset.py, it does pull through 20% if it has not been specified. So the default will be 20% of the images, not 1 image.

    def from_csv(cls, path, folder, csv_fname, bs=64, tfms=(None,None),
               val_idxs=None, suffix='', test_name=None, continuous=False, skip_header=True, num_workers=8):
        """ Read in images and their labels given as a CSV file.

        This method should be used when training image labels are given in an CSV file as opposed to
        sub-directories with label names.

        Arguments:
            path: a root path of the data (used for storing trained models, precomputed values, etc)
            folder: a name of the folder in which training images are contained.
            csv_fname: a name of the CSV file which contains target labels.
            bs: batch size
            tfms: transformations (for data augmentations). e.g. output of `tfms_from_model`
            val_idxs: index of images to be used for validation. e.g. output of `get_cv_idxs`.
                If None, default arguments to get_cv_idxs are used.
            suffix: suffix to add to image names in CSV file (sometimes CSV only contains the file name without file
                    extension e.g. '.jpg' - in which case, you can set suffix as '.jpg')
            test_name: a name of the folder which contains test images.
            continuous: TODO
            skip_header: skip the first row of the CSV file.
            num_workers: number of workers

        Returns:
            ImageClassifierData
        """
        fnames,y,classes = csv_source(folder, csv_fname, skip_header, suffix, continuous=continuous)

        **val_idxs = get_cv_idxs(len(fnames)) if val_idxs is None else val_idxs**
        ((val_fnames,trn_fnames),(val_y,trn_y)) = split_by_idx(val_idxs, np.array(fnames), y)

        test_fnames = read_dir(path, test_name) if test_name else None
        if continuous:
            f = FilesIndexArrayRegressionDataset
        else:
            f = FilesIndexArrayDataset if len(trn_y.shape)==1 else FilesNhotArrayDataset
        datasets = cls.get_ds(f, (trn_fnames,trn_y), (val_fnames,val_y), tfms,
                               path=path, test=test_fnames)
        return cls(path, datasets, bs, num_workers, classes=classes)

In my case, I found a log loss figure improved in Kaggle when I trained on a whole data set -1 (val_idxs = [0]).

About unfreezing for the Dog Breed classification data, apparently it is best to use the pre-trained model for this particular case:

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