Month: March 2022

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?

Hi everyone,

I’m trying to implement my own seq2seq model and employ the widely recommended attention layer on it. Oddly getting an error during the concatenation like below:

2 frames/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)53 ctx.ensure_initialized()54 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,—> 55 inputs, attrs, num_outputs)56 except core._NotOkStatusException as e:57 if name is not None:InvalidArgumentError: Graph execution error:Detected at node ‘model_3/concat_layer/concat’ defined at (most recent call last):File “/usr/lib/python3.7/runpy.py”, line 193, in _run_module_as_main”__main__”, mod_spec)File “/usr/lib/python3.7/runpy.py”, line 85, in _run_codeexec(code, run_globals)File “/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py”, line 16, in <module>app.launch_new_instance()File “/usr/local/lib/python3.7/dist-packages/traitlets/config/application.py”, line 846, in launch_instanceapp.start()File “/usr/local/lib/python3.7/dist-packages/ipykernel/kernelapp.py”, line 499, in startself.io_loop.start()File “/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py”, line 132, in startself.asyncio_loop.run_forever()File “/usr/lib/python3.7/asyncio/base_events.py”, line 541, in run_foreverself._run_once()File “/usr/lib/python3.7/asyncio/base_events.py”, line 1786, in _run_oncehandle._run()File “/usr/lib/python3.7/asyncio/events.py”, line 88, in _runself._context.run(self._callback, *self._args)File “/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py”, line 122, in _handle_eventshandler_func(fileobj, events)File “/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py”, line 300, in null_wrapperreturn fn(*args, **kwargs)File “/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py”, line 452, in _handle_eventsself._handle_recv()File “/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py”, line 481, in _handle_recvself._run_callback(callback, msg)File “/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py”, line 431, in _run_callbackcallback(*args, **kwargs)File “/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py”, line 300, in null_wrapperreturn fn(*args, **kwargs)File “/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py”, line 283, in dispatcherreturn self.dispatch_shell(stream, msg)File “/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py”, line 233, in dispatch_shellhandler(stream, idents, msg)File “/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py”, line 399, in execute_requestuser_expressions, allow_stdin)File “/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py”, line 208, in do_executeres = shell.run_cell(code, store_history=store_history, silent=silent)File “/usr/local/lib/python3.7/dist-packages/ipykernel/zmqshell.py”, line 537, in run_cellreturn super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)File “/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py”, line 2718, in run_cellinteractivity=interactivity, compiler=compiler, result=result)File “/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py”, line 2822, in run_ast_nodesif self.run_code(code, result):File “/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py”, line 2882, in run_codeexec(code_obj, self.user_global_ns, self.user_ns)File “<ipython-input-15-f9cbfc42a957>”, line 491, in <module>train()File “<ipython-input-15-f9cbfc42a957>”, line 321, in train1)[:, 1:]))File “/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py”, line 64, in error_handlerreturn fn(*args, **kwargs)File “/usr/local/lib/python3.7/dist-packages/keras/engine/training.py”, line 1384, in fittmp_logs = self.train_function(iterator)File “/usr/local/lib/python3.7/dist-packages/keras/engine/training.py”, line 1021, in train_functionreturn step_function(self, iterator)File “/usr/local/lib/python3.7/dist-packages/keras/engine/training.py”, line 1010, in step_functionoutputs = model.distribute_strategy.run(run_step, args=(data,))File “/usr/local/lib/python3.7/dist-packages/keras/engine/training.py”, line 1000, in run_stepoutputs = model.train_step(data)File “/usr/local/lib/python3.7/dist-packages/keras/engine/training.py”, line 859, in train_stepy_pred = self(x, training=True)File “/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py”, line 64, in error_handlerreturn fn(*args, **kwargs)File “/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py”, line 1096, in __call__outputs = call_fn(inputs, *args, **kwargs)File “/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py”, line 92, in error_handlerreturn fn(*args, **kwargs)File “/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py”, line 452, in callinputs, training=training, mask=mask)File “/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py”, line 589, in _run_internal_graphoutputs = node.layer(*args, **kwargs)File “/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py”, line 64, in error_handlerreturn fn(*args, **kwargs)File “/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py”, line 1096, in __call__outputs = call_fn(inputs, *args, **kwargs)File “/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py”, line 92, in error_handlerreturn fn(*args, **kwargs)File “/usr/local/lib/python3.7/dist-packages/keras/layers/merge.py”, line 183, in callreturn self._merge_function(inputs)File “/usr/local/lib/python3.7/dist-packages/keras/layers/merge.py”, line 531, in _merge_functionreturn backend.concatenate(inputs, axis=self.axis)File “/usr/local/lib/python3.7/dist-packages/keras/backend.py”, line 3313, in concatenatereturn tf.concat([to_dense(x) for x in tensors], axis)Node: ‘model_3/concat_layer/concat’ConcatOp : Dimension 1 in both shapes must be equal: shape[0] = [128,7,300] vs. shape[1] = [128,128,300][[{{node model_3/concat_layer/concat}}]] [Op:__inference_train_function_43001]

You can reach the whole Python code from here: https://pastebin.pl/view/16b32d92 (I could not share it here as it was too long to be included here).

Could you please help me to find out what I’m missing?

Many many thanks in advance!

p.s. I’m using Keras for the implementation.

I am trying to implement a normalizing flow according to the RealNVP model for density estimation. First, I am trying to make it work on the “moons” toy dataset.

The model produces the expected result when not using the BatchNormalization bijector. However, when adding the BatchNormalization bijector to the model, the methods prob and log_prob return unexpected results.

Following is a code snippet setting up the model:

“`python layers = 6 dimensions = 2 hidden_units = [512, 512] bijectors = []

base_dist = tfd.Normal(loc=0.0, scale=1.0) # specify base distribution

for i in range(layers): # Adding the BatchNormalization bijector corrupts the results bijectors.append(tfb.BatchNormalization()) bijectors.append(RealNVP(input_shape=dimensions, n_hidden=hidden_units)) bijectors.append(tfp.bijectors.Permute([1, 0]))

bijector = tfb.Chain(bijectors=list(reversed(bijectors))[:-1], name=’chain_of_real_nvp’)

flow = tfd.TransformedDistribution( distribution=tfd.Sample(base_dist, sample_shape=[dimensions]), bijector=bijector ) “`

When to BatchNormalization bijector is omitted both sampling and evaluating the probability return expected results:

Heatmap of probabilities and samples without BN

However, when the BatchNormalization bijector is added, sampling is as expected but evaluating the probability seems wrong:

Heatmap of probabilities and samples with BN

Because I am interested in density estimation the prob method is crucial. The full code can be found in the following jupyter notebook: https://github.com/mmsbrggr/normalizing-flows/blob/master/moons_training_rnvp.ipynb

I know that the BatchNormalization bijector behaves differently during training and inference. Could the problem be that the BN bijector is still in training mode? If so how can I move the flow to inference mode?