tensorboard | TensorFlow 's Visualization Toolkit | Machine Learning library
kandi X-RAY | tensorboard Summary
kandi X-RAY | tensorboard Summary
The first step in using TensorBoard is acquiring data from your TensorFlow run. For this, you need summary ops. Summary ops are ops, just like tf.matmul and tf.nn.relu, which means they take in tensors, produce tensors, and are evaluated from within a TensorFlow graph. However, summary ops have a twist: the Tensors they produce contain serialized protobufs, which are written to disk and sent to TensorBoard. To visualize the summary data in TensorBoard, you should evaluate the summary op, retrieve the result, and then write that result to disk using a summary.FileWriter. A full explanation, with examples, is in the tutorial.
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tensorboard Examples and Code Snippets
Lines of Code : 118License : Non-SPDX (Apache License 2.0)
Copydef audio(name, tensor, sample_rate, max_outputs=3, collections=None,
family=None):
# pylint: disable=line-too-long
"""Outputs a `Summary` protocol buffer with audio.
The summary has up to `max_outputs` summary values containing audi def ensure_initialized(self):
"""Initialize handle and devices if not already done so."""
if self._initialized:
return
with self._initialize_lock:
if self._initialized:
return
assert self._context_devices is None def _convert_tensor_array_read_v3(pfor_input):
handle = pfor_input.unstacked_input(0)
index, index_stacked, _ = pfor_input.input(1)
dtype = pfor_input.get_attr("dtype")
flow, flow_stacked, _ = pfor_input.input(2)
if flow_stacked:
flow = x = Input(shape=(1,))
x = Dense(200,activation="relu")(x)
x = Dropout(0.1)(x, training=True)
x = Dense(2)(x)
out = tfp.layers.DistributionLambda(normal_exp, name='normal_exp')(x)
import numpy as np
import matplotliLooking for: ['python=3', 'tensorflow-gpu=1.4.1']
conda-forge/linux-64 Using cache
conda-forge/noarch Using cache
pkgs/main/linux-64 python is /opt/anaconda3/bin/python
python is /usr/local/bin/python
python is /usr/bin/python
def Exec_ShowImgGrid(ObjTensor, ch=1, size=(28,28), num=16):
#tensor: 128(pictures at the time ) * 784 (28*28)
Objdata= ObjTensor.detach().cpu().view(-1,ch,*size) #128 *1 *28*28
Objgrid= make_grid(Objdata[:num],nrow=4).permutepip install termcolor‑1.1.0‑py2.py3‑none‑any.whl
from tensorflow.keras import layers, Model
from tensorflow.keras.datasets import mnist
from tensorflow.keras.callbacks import TensorBoard
import numpy as np
class Encoder(Model):
def __init__(self, name: str = "encoder"):
supeWriter = tf.summary.create_file_writer(LOG_DIR)
Writer = tf.compat.v1.summary.FileWriter(LOG_DIR, tfs.graph)
Community Discussions
Trending Discussions on tensorboard
QUESTION
I'm trying to analyze my tensorflow application. The training runs well, but I get Failed to load libcupti (is it installed and accessible?) if I open the Profile-Tab in Tensorboard.
My configuration is:
- Windows 10
- Python 3.9.7
- Tensorflow 2.6.0
- CUDA Toolkit 11.2
- cuDNN 8.1.1 (installed as here by copying files as described)
- Visual Studio Professional 2019
CUDA_PATH is C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2
My Path-Variable contains:
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\binC:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\libnvvpC:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\extras\CUPTI\lib64C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\includeC:\Program Files\NVIDIA Corporation\Nsight Systems 2020.4.3\target-windows-x64
conda list (only relevant packages):
ANSWER
Answered 2022-Mar-21 at 18:36Hidden in the log output of jupyter I found an this error message: Could not load dynamic library 'cupti64_113.dll': dlerror: cupti64_113.dll not found
With this error message and that hint I was able to solve the problem:
I copied cupti64_2020.3.0.dll in C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\extras\CUPTI\lib64 and renamed it to cupti64_113.dll and now the profiler works.
QUESTION
So I'm trying to set up a GPU profiler on tensorboard but I am getting this error:
...ANSWER
Answered 2022-Mar-16 at 10:39TensorFlow 2.8 doesn't support CUDA 11.6. but requires 11.2 see docs
Seems you need to get in touch with the VM's owner to update the dependencies
QUESTION
I am training a Unet segmentation model for binary class. The dataset is loaded in tensorflow data pipeline. The images are in (512, 512, 3) shape, masks are in (512, 512, 1) shape. The model expects the input in (512, 512, 3) shape. But I am getting the following error. Input 0 of layer "model" is incompatible with the layer: expected shape=(None, 512, 512, 3), found shape=(512, 512, 3)
Here are the images in metadata dataframe.
Randomly sampling the indices to select the training and validation set
...ANSWER
Answered 2022-Mar-08 at 13:38Use train_batches in model.fit and not train_images. Also, you do not need to use repeat(), which causes an infinite dataset if you do not specify how many times you want to repeat your dataset. Regarding your labels error, try rewriting your model like this:
QUESTION
I have Tensorboard data and want it to download all of the csv files behind the data, but I could not find anything from the official documentation. From StackOverflow, I found only this question which is 7 years old and also it's about TensorFlow while I am using PyTorch.
We can do this manually, as we can see in the screenshot, manually there is an option. I wonder if we can do that via code or it is not possible? As I have a lot of data to process.
...ANSWER
Answered 2022-Feb-23 at 16:44With the help of this script Below is the shortest working code it gets all of the data in dataframe then you can play further.
QUESTION
I have pretrained model for object detection (Google Colab + TensorFlow) inside Google Colab and I run it two-three times per week for new images I have and everything was fine for the last year till this week. Now when I try to run model I have this message:
...ANSWER
Answered 2022-Feb-07 at 09:19It happened the same to me last friday. I think it has something to do with Cuda instalation in Google Colab but I don't know exactly the reason
QUESTION
I have tried the similar problems' solutions on here but none seem to work. It seems that I get a memory error when installing tensorflow from requirements.txt. Does anyone know of a workaround? I believe that installing with --no-cache-dir would fix it but I can't figure out how to get EB to do that. Thank you.
Logs:
...ANSWER
Answered 2022-Feb-05 at 22:37The error says MemoryError. You must upgrade your ec2 instance to something with more memory. tensorflow is very memory hungry application.
QUESTION
For me what I do is detect what is unpickable and make it into a string (I guess I could have deleted it too but then it will falsely tell me that field didn't exist but I'd rather have it exist but be a string). But I wanted to know if there was a less hacky more official way to do this.
Current code I use:
...ANSWER
Answered 2022-Jan-19 at 22:30Yes, a try/except is the best way to go about this.
Per the docs, pickle is capable of recursively pickling objects, that is to say, if you have a list of objects that are pickleable, it will pickle all objects inside of that list if you attempt to pickle that list. This means that you cannot feasibly test to see if an object is pickleable without pickling it. Because of that, your structure of:
QUESTION
I have this custom callback to log the reward in my custom vectorized environment, but the reward appears in console as always [0] and is not logged in tensorboard at all
...ANSWER
Answered 2021-Dec-25 at 01:10You need to add [0] as indexing,
so where you wrote self.logger.record('reward', self.training_env.get_attr('total_reward')) you just need to index with self.logger.record('reward', self.training_env.get_attr ('total_reward')[0])
QUESTION
I am trying to run the training of stylegan2-pytorch on a remote system. The remote system has gcc (9.3.0) installed on it. I'm using conda env that has the following installed (cudatoolkit=10.2, torch=1.5.0+, and ninja=1.8.2, gcc_linux-64=7.5.0). I encounter the following error:
...ANSWER
Answered 2021-Dec-12 at 16:12Just to share, not sure it will help you. However it shows that in standard conditions it is possible to use the conda gcc as described in the documentation instead of the system gcc.
QUESTION
I want to extract all data to make the plot, not with tensorboard. My understanding is all log with loss and accuracy is stored in a defined directory since tensorboard draw the line graph.
...ANSWER
Answered 2021-Sep-22 at 23:47Lightning do not store all logs by itself. All it does is streams them into the logger instance and the logger decides what to do.
The best way to retrieve all logged metrics is by having a custom callback:
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