DataBloom

Hi guys

ive worked on machine vision with tensorflow 1.14 and recently upgraded rtx2070s to rtx3090.

and seems like rtx3090 doesnt support tf<2.5 version as it needs cuda 11.0. ( cuz i met many strange errors that i havent seen on rtx2070s)

so i wanna know if it is still possible to use 1.14 version with rtx3090 or should i just give up and use recent tensorflow version ?

thank you

0

I am doing transfer learning with google audioset embeddings. According to the documentation,

the embedding layer does not include a final non-linear activation, so the embedding value is pre-activation

I want to train and test a new model on top of these embedding layer with the embedding data. I have planned to do the following

1. Create new dense layers.
2. Convert the embeddings from byte string to tensor. Split these embeddings to train, test and split dataset.
3. Input these tensors to the new model.
4. Validate and test the model using validate dataset and test dataset.

I have two confusions with this implementation

• Is using the embeddings as input of the new layers enough for the transfer learning? I have seen in some Transfer Learning implementation that they load pre-trained weights to the new model and freeze the layers involving those weights. But in those implementation, they use new data for training, not the embeddings from the pre-trained model. I am confused how that works.
• Is it okay to split the embeddings to train, test and validate dataset? I am not sure if all the embeddings were used for training the pre-trained model. If they all were used, then does it make sense to use part of them as validation and test dataset?

Hello.

I’m new in to tensorflow. Been trying recreate VAE from tutorial ( author does not respond for questions) but i keeps getting error while training network :

ERROR:

Traceback (most recent call last): File "...train.py", line 45, in <module> autoencoder = train(x_train, LEARNING_RATE, BATCH_SIZE, EPOCHS) #here is problem File "...train.py", line 36, in train autoencoder.compile(learning_rate) File "...autoencoder.py", line 61, in compile self.model.compile(optimizer=optimizer, File "...libsite-packagestensorflowpythontrainingtrackingbase.py", line 530, in _method_wrapper result = method(self, *args, **kwargs) File "...libsite-packagestensorflowpythonkerasenginetraining_v1.py", line 444, in compile self._cache_output_metric_attributes(metrics, weighted_metrics) File "...libsite-packagestensorflowpythonkerasenginetraining_v1.py", line 1800, in _cache_output_metric_attributes self._per_output_metrics = training_utils_v1.collect_per_output_metric_info( File "...libsite-packagestensorflowpythonkerasenginetraining_utils_v1.py", line 910, in collect_per_output_metric_info metric_fn._from_serialized = from_serialized # pylint: disable=protected-access AttributeError: 'method' object has no attribute '_from_serialized' 

LINE 41-48 in train.py ( first two errors):

if __name__ == "__main__": x_train = load_fsdd(SPECTROGRAMS_PATH) autoencoder = train(x_train,LEARNING_RATE,BATCH_SIZE, EPOCHS) autoencoder.save("modelv1.0") 

LINE 59-64 in autoencoder.py:

def compile(self, learning_rate=0.0001): optimizer = Adam(learning_rate=learning_rate) self.model.compile(optimizer=optimizer, loss=self._calculate_combined_loss, metrics=[self._calculate_reconstruction_loss, self._calculate_kl_loss]) 

I’m using PyCharm CE 2021.1.1 and tensorflow 2.3.1

I am doing transfer learning with google audioset embeddings. The audioset corpus consists of pre-trained embeddings that are pre-activation (the final layers are removed).

While studying transfer learning, I have noticed that the checkpoint weights are used and the layers that produced these weights are frozen while the a model is built on top of the previous model.

Should I use the pre-trained weights of Audioset while using the embeddings of audioset itself for training the new model? It does not sound right as these embeddings are the bi product of these weights already. Please correct me if I am wrong.

I am using labelImg to annotate custom datasets for Faster RCNN. What tools do you use to annotate images for Mask RCNN – create the masks?

Hello,

I was wondering

Lets say if I get the output of an intermediary layer, would it be possible to feed the data back into the intermediary layer and resume the processing only from that layer?

Just wondering…..

I have been training a model using MS Coco 2017 dataset.

The Coco dataset is divided between train, test and validation. The key difference is the test dataset does not have bounding box annotations.

I didn’t realise this and I set the eval_input _reader to point at the Coco test dataset .tfrecord files.

Is this incorrect? Should I instead point it towards the validation dataset which has the bounding-box annotations? It’s strange because my model is still working. Though not very well.

Very confused by it all. Why doesn’t the cocodataset label the test images?