I had the very same question after reading the Transformer paper. I found no complete and detailed answer to the question in the Internet so I’ll try to explain my understanding of Masked Multi-Head Attention. The short answer is – we need masking to make the training parallel. And the parallelization is good as it … Read more
A more elegant solution would be: args, unknown = parser.parse_known_args() instead of args = parser.parse_args()
The following worked for me. With error message: error: Can’t find libdevice directory ${CUDA_DIR}/nvvm/libdevice Firstly I searched for nvvm directory and then verified that libdevice directory existed: $ find / -type d -name nvvm 2>/dev/null /usr/lib/cuda/nvvm $ cd /usr/lib/cuda/nvvm /usr/lib/cuda/nvvm$ ls libdevice /usr/lib/cuda/nvvm$ cd libdevice /usr/lib/cuda/nvvm/libdevice$ ls libdevice.10.bc Then I exported the environment variable: export … Read more
If you have two versions of tensorboard installed in your system,you need to uninstall one of them. I was stuck this for hours but I finally fixed it using: Worked like a charm: https://github.com/pytorch/pytorch/issues/22676 pip uninstall tb-nightly tensorboardX tensorboard pip install tensorboard
While the answer here is as requested within tensorboard it only allows to download a csv for a single run of a single tag. If you have for example 10 tags and 20 runs (what is not at all much) you would need to do the above step 200 times (that alone will probably take … Read more
You can actually do that now with tf.gather_nd. Let’s say you have a matrix m like the following: | 1 2 3 4 | | 5 6 7 8 | And you want to build a matrix r of size, let’s say, 3×2, built from elements of m, like this: | 3 6 | | … Read more
It will shuffle your entire dataset (x, y and sample_weight together) first and then make batches according to the batch_size argument you passed to fit. Edit As @yuk pointed out in the comment, the code has been changed significantly since 2018. The documentation for the shuffle parameter now seems more clear on its own. You … Read more
You may use TensorFlow Lite Python interpreter to test your tflite model. It allows you to feed input data in python shell and read the output directly like you are just using a normal tensorflow model. I have answered this question here. And you can read this TensorFlow lite official guide for detailed information. You … Read more
I’ve added built-in support for keras in tqdm so you could use it instead (pip install “tqdm>=4.41.0”): from tqdm.keras import TqdmCallback … model.fit(…, verbose=0, callbacks=[TqdmCallback(verbose=2)]) This turns off keras‘ progress (verbose=0), and uses tqdm instead. For the callback, verbose=2 means separate progressbars for epochs and batches. 1 means clear batch bars when done. 0 means … Read more
TensorFlow stores all operations on an operational graph. This graph defines what functions output to where, and it links it all together so that it can follow the steps you have set up in the graph to produce your final output. If you try to input a Tensor or operation on one graph into a … Read more