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Using remote sensing and an ensemble of convolutional neural networks, the study could guide sustainable forest management and climate mitigation efforts.
A new study from researchers at ETH Zurich’s EcoVision Lab is the first to produce an interactive Global Canopy Height map. Using a newly developed deep learning algorithm that processes publicly available satellite images, the study could help scientists identify areas of ecosystem degradation and deforestation. The work could also guide sustainable forest management by identifying areas for prime carbon storage—a cornerstone in mitigating climate change.
“Global high-resolution data on vegetation characteristics are needed to sustainably manage terrestrial ecosystems, mitigate climate change, and prevent biodiversity loss. With this project, we aim to fill the missing data gaps by merging data from two space missions with the help of deep learning,” said Konrad Schindler, a Professor in the Department of Civil, Environmental, and Geomatic Engineering at ETH Zurich.
From rainforests to boreal woodland, forests play a key role in climate mitigation, absorbing up to 2 billion tons of carbon dioxide every year. Aboveground biomass, which includes all parts of the tree such as the trunk, bark, or branches, correlates with the amount of carbon stored in a forest.
Tree height is often an indicator of biomass, meaning accurate measurements could help with more precise carbon sequestration data and climate science models. This information could also guide forest management by identifying areas in need of conservation, restoration, and reforestation.
There have been many studies using AI-powered remote sensing models for forest monitoring. However, these typically work regionally and pose a compute challenge due to vast amounts of data. Models have also been unsuccessful in measuring heights over 30 meters, leading to an underestimation of tall canopies.
A handful of current studies are deploying satellites for capturing and measuring vegetation from space. One such mission, NASA’s Global Ecosystem Dynamics Investigation (GEDI), aims to monitor the structure of forests worldwide using a space-borne laser scanner. However, it captures only sparse samples that cover less than 4% of the global landmass.
Other global remote sensing missions offer complete coverage. The Copernicus Sentinel-2 satellites capture images at a resolution of 10×10 meters per pixel and the entire globe is captured every 5 days. However, it only sees a bird’s-eye view of the vegetation and does not measure height.
The researchers developed, trained, and deployed deep learning algorithms using data from these separate remote sensing missions to create the first global vegetation height map. The team trained an ensemble of fully convolutional neural networks (CNNs) on canopy top height from the GEDI data. Using a dataset of 600 million GEDI footprints, along with the corresponding Sentinel-2 image patches, the algorithm learns to extract canopy height from spectral and textural image patterns.
When the models are up and running, they automatically process over 250,000 images and estimate canopy height for the map at a 10-meter ground-sampling distance. It takes 10 days to cover the globe using a high-performance cluster equipped with NVIDIA RTX 2080 GPUs. According to Schindler, ETH Zurich’s high-performance computing system Euler, contains a variety of GPUs running over 1,500 graphics cards.
By modeling the uncertainty in the data and using an ensemble of five separately trained CNNs the models have a level of transparency not often seen in deep learning algorithms. Uncertainty is quantified for every single pixel estimate, which could give researchers and forest managers confidence when making decisions based on the information.
The researchers found that protected areas, such as the Oregon Coast Range and the Ulu Temburong National Park in Borneo often contained higher vegetation. About 34% of canopies taller than 30 meters grow in these areas.
According to the study, the model can be deployed annually to track canopy height change over time. The researchers also point out that the maps could be used to evaluate regions where wildfires have occurred, giving a more accurate map of damage.
“We hope that this work will advance future research in climate, carbon, and biodiversity modeling. We also hope that our freely available map can support the work of conservationists in practice. In the future, we would like to expand our approach to mapping biomass as well as temporal changes on a global scale,” said lead author Nico Lang, a PhD student in the EcoVision Lab, part of the Photogrammetry and Remote Sensing group at ETH Zürich.
EcoVision also plans to make the code available soon. The lab, founded by ETH Zurich Professor Konrad Schindler and University of Zurich Professor Jan Dirk Wegner in 2017, is dedicated to developing machine learning algorithms for large-scale environmental data analysis. For more information refer to their project page, A high-resolution canopy height model of the Earth.
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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.
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!
submitted by /u/tensornoob
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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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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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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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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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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.
submitted by /u/irissnare
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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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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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