August 2021 Conda install may be working now, as according to @ComputerScientist in the comments below, conda install tensorflow-gpu==2.4.1 will give cudatoolkit-10.1.243 and cudnn-7.6.5 The following was written in Jan 2021 and is out of date Currently conda install tensorflow-gpu installs tensorflow v2.3.0 and does NOT install the conda cudnn or cudatoolkit packages. Installing them … Read more
You could call tensorboard as a python module like this: python3 -m tensorboard.main –logdir=~/my/training/dir or add this to your .profile alias tensorboard=’python3 -m tensorboard.main’
Step 1 Move to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\bin Step 2 Rename file cusolver64_11.dll To cusolver64_10.dll cusolver64_10.dll
You should never need to manually set the quantization stats. Have you tried the post-training-quantization tutorials? https://www.tensorflow.org/lite/performance/post_training_integer_quant Basically they set the quantization options: converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] converter.inference_input_type = tf.uint8 converter.inference_output_type = tf.uint8 Then they pass a “representative dataset” to the converter, so that the converter can run the model a few batches to gather the … Read more
I am by no ways an expert in Tensorflow, but if I had to take a guess as to why this is happening, I would say that: tf.train.Saver(), saves a complete meta-graph. Therefore, all the information needed to perform any operations contained in your graph is already there. All tensorflow needs to do to load … Read more
A best practices approach suggested by peter-cordes is to see what gcc is going to make of your ‘what capabilities your cpu has’ by issuing the following: gcc -O3 -fverbose-asm -march=native -xc /dev/null -S -o- | less This command will provide information (all) about your cpu capabilities from the view of gcc, whom is going … Read more
If you want to find how much time was spent on each operation at TF, you can do this in tensorboard using runtime statistics. You will need to do something like this (check the full example in the above-mentioned link): run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) run_metadata = tf.RunMetadata() sess.run(<values_you_want_to_execute>, options=run_options, run_metadata=run_metadata) your_writer.add_run_metadata(run_metadata, ‘step%d’ % i) Better than … Read more
On Ubuntu 20.04, you can simply install NVIDIAs cuda toolkit cuda: sudo apt-get update sudo apt install nvidia-cuda-toolkit There are also install advices for Windows. The packge is around 1GB and it took a while to install… Some minutes later you need to export PATH variables so that it can be found: Find Shared Object … Read more
SparseCategoricalCrossentropy and CategoricalCrossentropy both compute categorical cross-entropy. The only difference is in how the targets/labels should be encoded. When using SparseCategoricalCrossentropy the targets are represented by the index of the category (starting from 0). Your outputs have shape 4×2, which means you have two categories. Therefore, the targets should be a 4 dimensional vector with … Read more
You can import Layer and InputSpec from TensorFlow as follows: from tensorflow.python.keras.layers import Layer, InputSpec UPDATE: 30/10/2019 from tensorflow.keras.layers import Layer, InputSpec