Squeeze | Single-header library for parallel programming in JUCE | iOS library
kandi X-RAY | Squeeze Summary
kandi X-RAY | Squeeze Summary
This little JUCE add-on provides functionality for convenient parallelization of common code constructs using a provided thread pool. Why the name "squeeze": Because it can help to squeeze more Ju(i)ce out of the CPU… (I know, I know, but it needed some name…).
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Squeeze Examples and Code Snippets
Lines of Code : 92License : Non-SPDX (Apache License 2.0)
Copydef squeeze(input: ragged_tensor.Ragged, axis=None, name=None): # pylint: disable=redefined-builtin
"""Ragged compatible squeeze.
If `input` is a `tf.Tensor`, then this calls `tf.squeeze`.
If `input` is a `tf.RaggedTensor`, then this operati def squeeze(input, axis=None, name=None, squeeze_dims=None):
# pylint: disable=redefined-builtin
"""Removes dimensions of size 1 from the shape of a tensor.
Given a tensor `input`, this operation returns a tensor of the same type with
all di def squeeze_v2(input, axis=None, name=None):
"""Removes dimensions of size 1 from the shape of a tensor.
Given a tensor `input`, this operation returns a tensor of the same type with
all dimensions of size 1 removed. If you don't want to remov Community Discussions
Trending Discussions on Squeeze
QUESTION
Good day. My script is on progress and I need help or ideas to make it work properly. I am able to grab some data but its not really that readable and useful and your help and ideas are needed.
...ANSWER
Answered 2022-Apr-09 at 07:30Actually,selecting all data according to requirement a little bit complex.I apply css selector,however, you also can apply find_all/find method.
QUESTION
I am working on a spatial search case for spheres in which I want to find connected spheres. For this aim, I searched around each sphere for spheres that centers are in a (maximum sphere diameter) distance from the searching sphere’s center. At first, I tried to use scipy related methods to do so, but scipy method takes longer times comparing to equivalent numpy method. For scipy, I have determined the number of K-nearest spheres firstly and then find them by cKDTree.query, which lead to more time consumption. However, it is slower than numpy method even by omitting the first step with a constant value (it is not good to omit the first step in this case). It is contrary to my expectations about scipy spatial searching speed. So, I tried to use some list-loops instead some numpy lines for speeding up using numba prange. Numba run the code a little faster, but I believe that this code can be optimized for better performances, perhaps by vectorization, using other alternative numpy modules or using numba in another way. I have used iteration on all spheres due to prevent probable memory leaks and …, where number of spheres are high.
ANSWER
Answered 2022-Feb-14 at 10:23Have you tried FLANN?
This code doesn't solve your problem completely. It simply finds the nearest 50 neighbors to each point in your 500000 point dataset:
QUESTION
I'd like to squeeze a dataframe like this:
...ANSWER
Answered 2022-Mar-10 at 10:11For each row remove missing values in Series.dropna, rename columns by dictionary and last add missing columns in DataFrame.reindex:
QUESTION
I am having trouble when switching a model from some local dummy data to using a TF dataset.
Sorry for the long model code, I have tried to shorten it as much as possible.
The following works fine:
...ANSWER
Answered 2022-Mar-10 at 08:57You will have to explicitly set the shapes of the tensors coming from tf.py_functions. Using None will allow variable input lengths. The Bert output dimension (384,) is, however, necessary:
QUESTION
I would like to sample values from a categorical distribution without having duplicates in the return
I tried using tf.random.categorial but it seems that what I want to do is impossible...
...ANSWER
Answered 2022-Feb-18 at 22:16I couldn't find a way to do it using only tensorflow, maybe it's considering that, by been prohibited of repeating classes, you are messing with the probabilities since after a class is picked, it's probability of been picked again is 0
But you can easily do it with numpy using numpy.random.choice()
QUESTION
ANSWER
Answered 2022-Feb-17 at 16:00To do what you require you can use the :has() selector to find the tr elements which contain a mark, and then find() the checkbox within them.
Also note that you don't need to 'click' the checkbox to set its state, you can update the checked property directly, like this:
QUESTION
I am migrating a code from pytorch to tensorflow, and in the function that calculates the loss, I have the below line that I need to migrate to tensorflow.
...ANSWER
Answered 2022-Feb-16 at 13:57gather_nd takes inputs that have the same dimension as the input tensor, and will output a tensor of values being at those indices (which is what you want).
gather will output slices (but you can give as indice shape whatever you want, the output tensor will just be a bunch of slices that are structured accordingly to the shape of indices) which is not what you want.
So you should first make the indices match the dimensions of the initial matrix:
QUESTION
I have a model that uses a custom LambdaLayer as follows:
ANSWER
Answered 2022-Jan-06 at 15:05So the problem isn't the lambda function per se, it's that pickle doesn't work with functions that aren't just module-level functions (the way pickle treats functions is just as references to some module-level name). So, unfortunately, if you need to capture the start and end arguments, you won't be able to use a closure, you'd normally just want something like:
QUESTION
I'm trying to implement a neural network to generate sentences (image captions), and I'm using Pytorch's LSTM (nn.LSTM) for that.
The input I want to feed in the training is from size batch_size * seq_size * embedding_size, such that seq_size is the maximal size of a sentence. For example - 64*30*512.
After the LSTM there is one FC layer (nn.Linear).
As far as I understand, this type of networks work with hidden state (h,c in this case), and predict the next word each time.
My question is- in the training - do we have to manually feed the sentence word by word to the LSTM in the forward function, or the LSTM knows how to do it itself?
My forward function looks like this:
...ANSWER
Answered 2022-Jan-02 at 19:24The answer is, LSTM knows how to do it on its own. You do not have to manually feed each word one by one.
An intuitive way to understand is that the shape of the batch that you send, contains seq_length (batch.shape[1]), using which it decides the number of words in the sentence. The words are passed through LSTM Cell generating the hidden states and C.
QUESTION
Problem Description:
I am trying to load image data using Pytorch custom dataset. I did a little dive deep and found that my images set consist of 2 types of shape (512,512,3) and (1024,1024) . My assumption is, because of the above reason, it is throwing the below error.
Note: The code is able to read some of the images but, it is throwing the below error message for few of them. This was the reason to do a little EDA on the image data and found that there were 2 different shapes of images in the dataset.
Q1. How to preprocess such image data for training?
Q2. Is there any other reasons why I might be seeing the below error message?
Error message:
...ANSWER
Answered 2021-Oct-02 at 05:31found the issue with the code.
Pytorch Custom Dataloader function "getitem" uses idx to retrieve data and my guess is, it know the range of idx from len function, ex: 0, till len(rows in dataset).
In my case, I already had a panda dataset (train_data) with idx as one of the column. When I randomly split it into X_train and X_test, few of the data rows were moved to X_test along with the idx.
Now, when I send X_train to the custom dataloader, it is trying to get row's image_id with an idx and that idx just happens to be in X_test dataset. This lead to error as keyerror: 16481 i.e row with idx=16481 is not present in the X_train dataset. It was moved to X_test during split.
phew...
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