DataBloom - Part 317

Misc

Custom Loss based on Input Features

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Post date May 2, 2022
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I built a neural network to solve a regression problem for me. I would like to wrap a physical equation around it which depends on the input features. Instead of predicting Y directly, I want to predict a parameter alpha which is part of a physical equation.

Let the model predict alpha
Instead of calculating the loss as MSE(y_true, y_pred) directly, calculate y_pred as follows before (simple example): y_pred = alpha * X[0] + X[1] where X is the vector containing the model input, alpha the parameter the NN should learn.

This is how I implemented it, you can see the custom_loss function and part where I call the fit() method. This is a workflow for testing I found on stackoverflow, if you know a better method to implement something like this, great!

def custom_loss(X, alpha): # Get target observations from data y_true = X[:, 0] num = X[:2, 0] y_pred = (alpha * X[0] + X[1] ) mse = tf.keras.losses.MeanSquaredError() return mse(y_true, y_pred) model.fit(train_data["Xtrain"], np.append(train_data["Ytrain"], train_data["Xtrain"], axis=1), epochs=10000, batch_size=200, validation_data=(train_data["Xval"], np.append(train_data["Yval"], train_data["Xval"], axis=1)))

However, my model is super unstable and doesn’t converge. Sometimes I get only NaNs as loss, sometimes the loss circles around a value but doesn’t finish the thing. Using only MSE, the model works quite well. Let’s assume the physical equation embedded in the loss function “makes sense”, this is just an example.

Did anybody do something like this before? Another problem I am facing – the input X is normalized (StandardScaler). For the physical equation to make sense, I would need the “de-normalized” values, right? How would I attempt this? scikit-learn works with numpy not with tensors I guess.

Thanks a lot!

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Misc

error: self._traceback = tf_stack.extract_stack() or self._traceback = tf_stack.extract_stack_for_node(self._c_op)

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Post date May 2, 2022
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Hi,

tried to run https://github.com/JiahuiYu/generative_inpainting

is the lib version causing this error?

tried this solution https://stackoverflow.com/questions/62466877/self-traceback-tf-stack-extract-stack but not working..

so code

import neuralgym as ng trainer = ng.train.MultiGPUTrainer( num_gpus=FLAGS.num_gpus_per_job, optimizer=g_optimizer, var_list=g_vars, max_iters=FLAGS.max_iters, graph_def=multigpu_graph_def, grads_summary=False, gradient_processor=None, graph_def_kwargs={ "model": model, "FLAGS": FLAGS, "image_ref_mask_data": image_ref_mask_data, "identity_model": identity_model, "loss_type": "g", }, spe=FLAGS.train_spe, #log_dir=FLAGS.log_dir, )

when running in tf 1.1.5, with package:

tensorboard 1.15.0 pyhb230dea_0 tensorboard-data-server 0.6.0 py37h03978a9_2 conda-forge tensorboard-plugin-wit 1.8.1 pyhd8ed1ab_0 conda-forge tensorflow 1.15.0 gpu_py37hc3743a6_0 tensorflow-base 1.15.0 gpu_py37h1afeea4_0 tensorflow-estimator 1.15.1 pyh2649769_0 tensorflow-gpu 1.15.0 h0d30ee6_0

got error:

 spe=FLAGS.train_spe, File " neuralgymneuralgymtrainmultigpu_trainer.py", line 25, in __init__ super().__init__(**self.context) File " neuralgymneuralgymtraintrainer.py", line 38, in __init__ self._train_op, self._loss = self.train_ops_and_losses() File " neuralgymneuralgymtrainmultigpu_trainer.py", line 75, in train_ops_and_losses gpu_id=gpu, **graph_def_kwargs) ... File " tensorflow115libsite-packageskerasenginebase_layer.py", line 489, in __call__ output = self.call(inputs, **kwargs) File " tensorflow115libsite-packageskerasenginenetwork.py", line 583, in call output_tensors, _, _ = self.run_internal_graph(inputs, masks) File " tensorflow115libsite-packageskerasenginenetwork.py", line 740, in run_internal_graph layer.call(computed_tensor, **kwargs)) File " tensorflow115libsite-packageskeraslayersnormalization.py", line 185, in call epsilon=self.epsilon) File " tensorflow115libsite-packageskerasbackendtensorflow_backend.py", line 2315, in normalize_batch_in_training epsilon=epsilon) File " tensorflow115libsite-packageskerasbackendtensorflow_backend.py", line 2288, in _fused_normalize_batch_in_training data_format=tf_data_format) File " tensorflow115libsite-packagestensorflow_corepythonopsnn_impl.py", line 1501, in fused_batch_norm name=name) File " tensorflow115libsite-packagestensorflow_corepythonopsgen_nn_ops.py", line 4620, in fused_batch_norm_v3 name=name) File " tensorflow115libsite-packagestensorflow_corepythonframeworkop_def_library.py", line 794, in _apply_op_helper op_def=op_def) File " tensorflow115libsite-packagestensorflow_corepythonutildeprecation.py", line 507, in new_func return func(*args, **kwargs) File " tensorflow115libsite-packagestensorflow_corepythonframeworkops.py", line 3357, in create_op attrs, op_def, compute_device) File " tensorflow115libsite-packagestensorflow_corepythonframeworkops.py", line 3426, in _create_op_internal op_def=op_def) File " tensorflow115libsite-packagestensorflow_corepythonframeworkops.py", line 1748, in __init__ self._traceback = tf_stack.extract_stack()

when running in tf 2.x:

 log_dir=FLAGS.log_dir, File " ngneuralgymtrainmultigpu_trainer.py", line 25, i __init__ super().__init__(**self.context) File " ngneuralgymtraintrainer.py", line 38, in __init_ self._train_op, self._loss = self.train_ops_and_losses() File " ngneuralgymtrainmultigpu_trainer.py", line 75, i train_ops_and_losses gpu_id=gpu, **graph_def_kwargs) File " inpaint_model.py", line 203, in build_graph_with_loses surface_attention=FLAGS.surface_attention, File " inpaint_model.py", line 127, in build_inpaint_net x = gen_deconv(x, cnum, name="allconv15_upsample") File " inpaint_ops.py", line 85, in gen_deconv x = resize(x, func=tf.compat.v1.image.resize_nearest_neighbor) File " ngneuralgymopslayers.py", line 152, in resize x = func(x, new_xs, align_corners=align_corners) File "C:ProgramDataAnaconda3libsite-packagestensorflowpythonopsimage_ps_impl.py", line 4659, in resize_nearest_neighbor name=name) File "C:ProgramDataAnaconda3libsite-packagestensorflowpythonopsgen_imge_ops.py", line 3873, in resize_nearest_neighbor name=name) File "C:ProgramDataAnaconda3libsite-packagestensorflowpythonframeworkp_def_library.py", line 742, in _apply_op_helper attrs=attr_protos, op_def=op_def) File "C:ProgramDataAnaconda3libsite-packagestensorflowpythonframeworkps.py", line 3784, in _create_op_internal op_def=op_def) File "C:ProgramDataAnaconda3libsite-packagestensorflowpythonframeworkps.py", line 2175, in __init__ self._traceback = tf_stack.extract_stack_for_node(self._c_op)

with setting:

tensorboard 2.8.0 pypi_0 pypi tensorboard-data-server 0.6.1 pypi_0 pypi tensorboard-plugin-wit 1.8.1 pypi_0 pypi tensorflow-gpu 2.8.0 pypi_0 pypi tensorflow-io-gcs-filesystem 0.25.0 pypi_0 pypi

thanks a lot

submitted by /u/boydbuilding
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Misc

BCE vs Sparse Categorical CE loss, metrics for binary segmentation

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Post date May 2, 2022
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Hello,I am training a segmentation model following this link. My problem at hand is a binary segmentation 0 for background class and 1 for object of interest. I noticed the predictions are good when Sparse Categorical CE loss is used compared to BCE. Can anyone give a valid reason to why this is the case? I am under an impression that BCE suits better for binary segmentation tasks. Can you please also recommend the best metric instead of accuracy? I am currently using mean IoU as a metric for evaluation.

submitted by /u/stashpot420
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Misc

Creating a Bidirectional LSTM

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Post date May 1, 2022
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So I’m trying to create a bidirectional LSTM and I have this code

model.add(Bidirectional(LSTM(5, return_sequences=True), input_shape=X.shape[1]))

I keep getting the following error,

TypeError: ‘int’ object is not iterable

it is over the input_shape parameter, but I’m confused on how to fix it.

The shape of the input is (494021, 118)

The shape of the output is (494021, 5)

submitted by /u/echaney456
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Misc

Tensorflow not using GPU

Hi, I’ve been having problems with running tensorflow on gpu for about a week now, so here I am, looking for ideas on how to fix this.I’m running tensorflow 2.8.0 with cuda-11.6 on a Ubuntu 20.04 virtual machine using passthrough for the 2 GPUs I have.

Now when I run this object detection project: https://github.com/nicknochnack/TFODCourse everything works but the training part doesn’t seem to use any of the GPU power, looking with nvidia-smi:

+-----------------------------------------------------------------------------+ | NVIDIA-SMI 510.47.03 Driver Version: 510.47.03 CUDA Version: 11.6 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 Quadro K2200 On | 00000000:07:00.0 Off | N/A | | 42% 26C P8 1W / 39W | 3543MiB / 4096MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ | 1 Quadro K2200 On | 00000000:08:00.0 Off | N/A | | 42% 21C P8 1W / 39W | 3543MiB / 4096MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=============================================================================| | 0 N/A N/A 1313 G /usr/lib/xorg/Xorg 2MiB | | 0 N/A N/A 2149 C python 3535MiB | | 1 N/A N/A 1313 G /usr/lib/xorg/Xorg 2MiB | | 1 N/A N/A 2149 C python 3535MiB | +-----------------------------------------------------------------------------+

Also, when I run tf.test.is_gpu_available(), I get True.

Finally, here’s the result from device_lib.list_local_devices():

2022-05-01 07:45:35.910286: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-05-01 07:45:35.910656: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /device:GPU:0 with 124 MB memory: -> device: 0, name: Quadro K2200, pci bus id: 0000:07:00.0, compute capability: 5.0 2022-05-01 07:45:35.910778: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-05-01 07:45:35.911123: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /device:GPU:1 with 124 MB memory: -> device: 1, name: Quadro K2200, pci bus id: 0000:08:00.0, compute capability: 5.0 [name: "/device:CPU:0" device_type: "CPU" memory_limit: 268435456 locality { } incarnation: 11170602163960232220 xla_global_id: -1 , name: "/device:GPU:0" device_type: "GPU" memory_limit: 130678784 locality { bus_id: 1 links { } } incarnation: 3083526702988944120 physical_device_desc: "device: 0, name: Quadro K2200, pci bus id: 0000:07:00.0, compute capability: 5.0" xla_global_id: 416903419 , name: "/device:GPU:1" device_type: "GPU" memory_limit: 130678784 locality { bus_id: 1 links { } } incarnation: 4460464810151506589 physical_device_desc: "device: 1, name: Quadro K2200, pci bus id: 0000:08:00.0, compute capability: 5.0" xla_global_id: 2144165316 ]

It seems like tensorflow can see my GPUs, when I was missing libraries I couldn’t get to this point, but now I’m stuck.

Oh, and here’s what I get when trying to train:

2022-04-30 21:53:01.961464: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-04-30 21:53:01.962055: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-04-30 21:53:02.071860: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-04-30 21:53:02.072626: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-04-30 21:53:02.073121: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-04-30 21:53:02.073627: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-04-30 21:53:02.407004: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-04-30 21:53:02.407485: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-04-30 21:53:02.407840: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-04-30 21:53:02.408204: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-04-30 21:53:02.408536: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-04-30 21:53:02.408865: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-04-30 21:53:04.298247: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-04-30 21:53:04.298711: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-04-30 21:53:04.299062: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-04-30 21:53:04.299421: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-04-30 21:53:04.299801: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-04-30 21:53:04.300924: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3385 MB memory: -> device: 0, name: Quadro K2200, pci bus id: 0000:07:00.0, compute capability: 5.0 2022-04-30 21:53:04.303496: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-04-30 21:53:04.303793: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 3385 MB memory: -> device: 1, name: Quadro K2200, pci bus id: 0000:08:00.0, compute capability: 5.0 INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1') I0430 21:53:04.485582 140329512097600 mirrored_strategy.py:374] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1') INFO:tensorflow:Maybe overwriting train_steps: 2000 I0430 21:53:04.494590 140329512097600 config_util.py:552] Maybe overwriting train_steps: 2000 INFO:tensorflow:Maybe overwriting use_bfloat16: False I0430 21:53:04.494734 140329512097600 config_util.py:552] Maybe overwriting use_bfloat16: False WARNING:tensorflow:From /home/oscar/Tensorflow/tfod/lib/python3.8/site-packages/object_detection/model_lib_v2.py:563: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version. Instructions for updating: rename to distribute_datasets_from_function W0430 21:53:04.534977 140329512097600 deprecation.py:337] From /home/oscar/Tensorflow/tfod/lib/python3.8/site-packages/object_detection/model_lib_v2.py:563: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version. Instructions for updating: rename to distribute_datasets_from_function INFO:tensorflow:Reading unweighted datasets: ['Tensorflow/workspace/annotations/train.record'] I0430 21:53:04.547105 140329512097600 dataset_builder.py:162] Reading unweighted datasets: ['Tensorflow/workspace/annotations/train.record'] INFO:tensorflow:Reading record datasets for input file: ['Tensorflow/workspace/annotations/train.record'] I0430 21:53:04.547340 140329512097600 dataset_builder.py:79] Reading record datasets for input file: ['Tensorflow/workspace/annotations/train.record'] INFO:tensorflow:Number of filenames to read: 1 I0430 21:53:04.547466 140329512097600 dataset_builder.py:80] Number of filenames to read: 1 WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards. W0430 21:53:04.547594 140329512097600 dataset_builder.py:86] num_readers has been reduced to 1 to match input file shards. WARNING:tensorflow:From /home/oscar/Tensorflow/tfod/lib/python3.8/site-packages/object_detection/builders/dataset_builder.py:100: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`. W0430 21:53:04.553132 140329512097600 deprecation.py:337] From /home/oscar/Tensorflow/tfod/lib/python3.8/site-packages/object_detection/builders/dataset_builder.py:100: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`. WARNING:tensorflow:From /home/oscar/Tensorflow/tfod/lib/python3.8/site-packages/object_detection/builders/dataset_builder.py:235: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.data.Dataset.map() W0430 21:53:04.594555 140329512097600 deprecation.py:337] From /home/oscar/Tensorflow/tfod/lib/python3.8/site-packages/object_detection/builders/dataset_builder.py:235: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.data.Dataset.map() WARNING:tensorflow:From /home/oscar/Tensorflow/tfod/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:1082: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version. Instructions for updating: Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead. W0430 21:53:11.867036 140329512097600 deprecation.py:337] From /home/oscar/Tensorflow/tfod/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:1082: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version. Instructions for updating: Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead. WARNING:tensorflow:From /home/oscar/Tensorflow/tfod/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:1082: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. Instructions for updating: `seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead. W0430 21:53:15.011741 140329512097600 deprecation.py:337] From /home/oscar/Tensorflow/tfod/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:1082: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. Instructions for updating: `seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead. WARNING:tensorflow:From /home/oscar/Tensorflow/tfod/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:1082: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. W0430 21:53:16.702110 140329512097600 deprecation.py:337] From /home/oscar/Tensorflow/tfod/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:1082: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead.

submitted by /u/OscarleBG
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Misc

Object Detection: How do I display results as text?

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Post date April 30, 2022
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Hi, I just followed a python tensorflow object detection tutorial and was able to successfully get back an image with a label box. I was wondering how I could get the output in text. For example: The output image had successfully labeled 2 cars a toyota and 1 car as a honda. I want another output of a string that says “2 toyotas and 1 honda found”. I am not sure how to do this.

Code: https://pastebin.com/cUq4Ezm4

submitted by /u/irissnare
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Misc

Seemingly Impossible Install Conditions For Tensorflow and Stable-Baselines OpenAI

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Post date April 30, 2022
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I’m trying to use OpenAI stable-baselines. This requires python 3.5 or higher. I set up my virtual environment etc., installed all dependencies. No problem. Then I try running a test script and it turns out that stable-baselines does not support Tensorflow 2. I need Tensorflow 1.15. No problem. I try to install tensorflow 1.15, but can’t because it is not available in pip anymore, for python3. Ok, so I need python 2.7 to use tensorflow 1.15… but then I can’t use stable-baselines, because it needs python 3… Help!

submitted by /u/HaikuHaiku
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Misc

No module named tensorflow.keras.optimizer error

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Post date April 30, 2022
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Hi,

I was recently working on a project which required the use of the tensorflow.keras.optimizer module. I am getting no module name tensorflow.keras.optimizer error though tensorflow and keras are both installed and up to date and I am using tensorflow in the same file and it gave me no error. Also tensorflow.keras.preprocessing.image shows no module named that found. Thanks

submitted by /u/StarLan7
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Misc

Pytorchs model.cuda() equivalent

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Post date April 30, 2022
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Dear Community I have worked with pytorch before and now I started a project with Tensorflow.

In pytorch I can define a model and then send the parameters to the GPU with the command in the header. I googled how to achieve the same with Tensorflow (and Keras) but I have not found a satisfying answer.

Thank for the help!

submitted by /u/whiteabc11
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Misc

I’m a beginner having really bad issues installing tensorflow on M1 MacBook, would anyone be able to hop on call and help out?

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Post date April 29, 2022
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submitted by /u/Alpay_
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