Mohammed Innat

# The Current Workaround Currently, we need to `flatten` the base model (above `pretrained_model`). It means, disclosing the target base model in the following manner. ```python inputs = keras.Input(shape=IMG_SHAPE) pretrained_model...

@fchollet yes, that makes sense. Thanks for your details answer. I also understood that it's not a bug, I should mention that earlier. The thing is, I saw many practitioners...

Are you training in the image-net dataset? I've just tested with a small-size dataset from tensorflow-dataset and it works fine.

[HERE.](https://colab.research.google.com/drive/1lUMr2aZWiqSLAmC3T5F_8IJJFs8XS3tB?usp=sharing)

I don't have an ImageNet locally to test. But I did a test with `include_top=true` on the natural image. It did run but the results are kinda non-sense even on...

Oh, I see. Next, the either we can look at the source code to find anomaly or search other possible solution. Can you check other similar architecture on image-net, for...

ref. https://github.com/gaudelbijay/SNNL-Loss/blob/main/soft_nearest_neighbor_loss.py

@owenvallis Hello, regarding this , > **WARNING**: This sampler requires that each TF Record file contain contiguous blocks of classes where the size of each block is a multiple of...

Also, unlike `TFDatasetMultiShotMemorySampler`, the `TFRecordDatasetSampler` doesn't have any positional paramo for augmentation. Shouldn't it be included?

I need to adopt the following approach, but the problem is that I don't have control over the occurrence of number of each class per batch. ```python def dataset_for_class(i): i...