I try to use tensorboard in jupyter lab but the use of the %tensorboard --logdir logs/fit line magic do not work.
Jupyterlab: '0.33.12'
ipython: '7.2.0'
python: '3.6.7'
tensorflow: '2.0.0-dev20190426'
I have this message when I try %load_ext tensorboard.
The tensorboard module is not an IPython extension.
For older versions of Tensorflow 1 and Tensorflow 2, you want to load tensorboard notebook in ipython first:
%load_ext tensorboard.notebook
Then run it like this:
%tensorboard --logdir logs/scalars
Related
I trained a custom CNN model using keras and tensorflow 2.2.0 as background. After that, I saved the model as .ckpt file having assets, variables, .pb file as subfolders init. After that to convert it into IR in openvino documentation it is given that we have use the following command:
**To convert such TensorFlow model:
Go to the <INSTALL_DIR>/deployment_tools/model_optimizer directory
Run the mo_tf.py script with a path to the SavedModel directory to convert a model:
python3 mo_tf.py --saved_model_dir <SAVED_MODEL_DIRECTORY>**
so, I went to the following directory as mentioned and tired the following command:
python3 mo_tf.py --saved_model_dir C:\Users\vyas\Desktop\saved_model\cp.ckpt
There is no output or anything. There is no error also.
Also, I tried the following command:
python3 mo_tf.py --saved_model_dir C:\Users\vyas\Desktop\saved_model\cp.ckpt --output_dir C:\Users\vyas\Desktop\out
Still there is no output.
Can someone please help.
I am using tensorflow 2.2.0
Can someone please help me with this
--saved_model_dir must provide a path to the SavedModel directory.
Modify your command as follows:
python3 mo_tf.py --saved_model_dir C:\Users\vyas\Desktop\saved_model
Anyone know if Tensorflow Lite has GPU support for Python? I've seen guides for Android and iOS, but I haven't come across anything about Python. If tensorflow-gpu is installed and tensorflow.lite.python.interpreter is imported, will GPU be used automatically?
According to this thread, it is not.
one solution is to convert tflite to onnx and use onnxruntime-gpu
convert to onnx with https://github.com/onnx/tensorflow-onnx:
pip install tf2onnx
python3 -m tf2onnx.convert --opset 11 --tflite path/to/model.tflite --output path/to/model.onnx
then pip install onnxruntime-gpu
and run like:
session = onnxruntime.InferenceSession(('/path/to/model.onnx'))
raw_output = self.detection_session.run(['output_name'], {'input_name': img})
you can get the input and output names by:
for i in range(len(session.get_inputs)):
print(session.get_inputs()[i].name)
and the same but replace 'get_inputs' with 'get_outputs'
You can force the computation to take place on a GPU:
import tensorflow as tf
with tf.device('/gpu:0'):
for i in range(10):
t = np.random.randint(len(x_test) )
...
Hope this helps.
I was installing tensorboard using pip install tensorboard. All worked fine
I run my network and the writer also worked fine
with tf.Session() as sess:
writer = tf.summary.FileWriter("pathtofolder", sess.graph)
print(sess.run(h))
writer.close()
now I wanted to see in tensorboard how it learned
I inserted
import tensorflow, tensorboard
tensorboard --logdir /pathtofolder
and received the error message.
NameError: name 'logdir' is not defined
You should execute the command on terminal/cmd.
Open your terminal/cmd and run:
tensorboard --logdir ./PATH_TO_THE_EVENT_FILES
If you want to run Tensorboard on python file see here:
I can't seem to find exact question about what I am about to ask here. I just started following a Tensorflow tutorial on YouTube and got stuck at the very beginning. I wrote in my spyder IDE the below code:
import tensorflow as tf
a = tf.constant(2)
b = tf.constant(3)
x = tf.add(a,b)
#writer = tf.summary.FileWriter('./graphs', tf.get_default_graph())
with tf.Session() as sess:
writer = tf.summary.FileWriter('./graphs', sess.graph)
print(sess.run(x))
writer.close()
And via anaconda terminal I activated my env (which I newly created, installed all packages required, spyder as well) I typed python tftuts.py and got 2018-10-05 11:50:49.431174: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
5
Then I typed tensorboard --logdir="./graphs" --port 6006 as suggested in tutorial I am watching.
Now, when I go to http://localhost:6006/ the page shows
I am on Win10, using python 3.6.6 in Anaconda env, tensorflow 1.10.0.
How can solve this issue?
Following the tutorial on TensorFlow for Poets (Android) (https://codelabs.developers.google.com/codelabs/tensorflow-for-poets-2/#0)
Attempting to use Inception model instead of Mobilenet
Trying to strip DecodeJpeg Op from the retrained model using strip_unused.py, but encountered the following error.
Error:
/home/user/tensorflow/bin/python: Error while finding spec for 'tensorflow.python.tools.strip_unused.py' (AttributeError: module 'tensorflow.python.tools.strip_unused' has no attribute '__path__')
Command line:
python -m tensorflow.python.tools.strip_unused.py --input_graph=tf_files/retrained_graph.pb --output_graph=tf_files/stripped_graph.pb --input_node_names="Mul" --output_node_names="final_result" --input_binary=true
Machine:
Ubuntu 16.04 LTS
Python 3.5.2
TensorFlow 1.4.1
Any assistance is greatly appreciated. Thanks!
Might be due to the typo mistake as file extension .py was specified. This seemed to work:
python -m tensorflow.python.tools.strip_unused --input_graph=tf_files/retrained_graph.pb --output_graph=tf_files/stripped_graph.pb --input_node_names="Mul" --output_node_names="final_result" --input_binary=true
Result: 997 ops in the final graph.