embedding | Embedding data into immersive environments | Augmented Reality library
kandi X-RAY | embedding Summary
kandi X-RAY | embedding Summary
Embedding is a javascript library that makes it easy to create data-driven environments. It is built on three.js, and takes inspiration from D3. A major hypothesis driving the development of Embedding is that direct navigation and manipulation of environments provide fundamentally new opportunities in the creation of compelling data visualization, exploration, and discovery experiences - in particular, the ability to directly change the viewpoint via position- and direction-tracked camera in VR.
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embedding Key Features
embedding Examples and Code Snippets
Lines of Code : 59License : Strong Copyleft (CC BY-SA 4.0)
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Example of Embedding YouTube Video inside Bootstrap Modal
Launch Demo Modal
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YouTube Video
def convert_single_example(ex_index, example, label_list, max_seq_length,
tokenizer):
"""Converts a single `InputExample` into a single `InputFeatures`."""
label_map = {}
for (i, label) in enumerate(label_list):
from keras.models import Model
from keras.layers import Input, LSTM, Dense, Embedding
import numpy as np
emb_dim = 20
latent_dim = 100 # Latent dimensionality of the encoding space.
vocab_size = 12 # digits 0-9, 10 is for start token, 11Embedding(len(word_index)+1, 300, weights=[embedding_matrix], trainable=False)(input_layer)
import numpy as np
from keras.preprocessing.text import one_hot
from keras.preprocessing.sequence import pad_sequences
fro
amp-list
{{title}}
function renderFullPage(html, preloadedState) {
return `
Redux Universal Example
${html}
`
}
from keras import backend as K
import numpy as np
from keras.utils.np_utils import accuracy
from keras.models import Sequential, Model
from keras.layers import Input, Lambda, Dense, merge
from keras.layers.embeddings import Embedding
k = Community Discussions
Trending Discussions on embedding
QUESTION
Good day, everyone.
I want to have two separate TensorFlow models (f and g) and train both of them on the loss of f(g(x)). However, I want to use them separately, like g(x) or f(e), where e is an embedded vector but received not from g.
For example, the classical way to create the model with embedding looks like this:
...ANSWER
Answered 2021-Jun-15 at 10:53This can be achieved by weight sharing or shared layers. To share layers in different models in keras, you just need to pass the same instance of layer to both of the models.
Example Codes:
QUESTION
I was creating a todo's list with JS. I have attached the code snippet for the same. I Have three hard coded todos in html and with each todo there are two buttons('x' to remove the todo 'y' to mark it as done) associated. Now for this hard coded todos everything is working fine. Now, to add a new todo I have this tag where todo text is entered and after clicking enter I am embedding them to html using innerHTML, while embedding the dynamically added todos are visible in DOM. But The problem is that the buttons('x' and 'y') associated with the todo is not working as expected. Further I tried to debug my problem and I found out that the buttons('x' and 'y') that are not getting selected. I could not understand why is this happening. Any help will be highly appreciated.
...ANSWER
Answered 2021-Jun-14 at 08:20While delegation is a perfectly fine solution, I tend to take an (in my opionion) simpler approach, and simply bind the handlers to the new elements:
QUESTION
I want to force the Huggingface transformer (BERT) to make use of CUDA.
nvidia-smi showed that all my CPU cores were maxed out during the code execution, but my GPU was at 0% utilization. Unfortunately, I'm new to the Hugginface library as well as PyTorch and don't know where to place the CUDA attributes device = cuda:0 or .to(cuda:0).
The code below is basically a customized part from german sentiment BERT working example
...ANSWER
Answered 2021-Jun-12 at 16:19You can make the entire class inherit torch.nn.Module like so:
QUESTION
I am trying to produce ELMo embeddings for batches of tokenised strings. However I keep receiving the following error:
...ANSWER
Answered 2021-Jun-09 at 15:14It works for me when the trailing spaces in tokens are removed such that at least one entry does not end in b'' i.e.
QUESTION
Below is a simplified snippet of my data.
...ANSWER
Answered 2021-Jun-09 at 11:39try sanitizing the json-string before deserealizing it:
QUESTION
Tensoflow Embedding Layer (https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding) is easy to use, and there are massive articles talking about "how to use" Embedding (https://machinelearningmastery.com/what-are-word-embeddings/, https://www.sciencedirect.com/topics/computer-science/embedding-method) . However, I want to know the Implemention of the very "Embedding Layer" in Tensorflow or Pytorch. Is it a word2vec? Is it a Cbow? Is it a special Dense Layer?
...ANSWER
Answered 2021-Jun-09 at 09:22Structure wise, both Dense layer and Embedding layer are hidden layers with neurons in it. The difference is in the way they operate on the given inputs and weight matrix.
A Dense layer performs operations on the weight matrix given to it by multiplying inputs to it ,adding biases to it and applying activation function to it. Whereas Embedding layer uses the weight matrix as a look-up dictionary.
The Embedding layer is best understood as a dictionary that maps integer indices (which stand for specific words) to dense vectors. It takes integers as input, it looks up these integers in an internal dictionary, and it returns the associated vectors. It’s effectively a dictionary lookup.
QUESTION
I'm having a problem with developing a NN model with tensorflow 2.3 that appears as soon as I include BiLSTM layers into the model. I've tried a custom model, but this is one from the Keras documentation page and it is also failing.
- It cannot be a problem with input shapes, as this happens in compile time and the input data has yet not been provided to the model.
- Tried it in another machine and it is working fine with same version of tensorflow.
The code I'm using is:
...ANSWER
Answered 2021-Jun-09 at 08:35I found the problem and so I'm answering my own question.
There is a setting in Keras that specifies the way of working with (and supossedly affecting only) image data.
Channels Last. Image data is represented in a three-dimensional array where the last channel represents the color channels, e.g. [rows][cols][channels].
Channels First. Image data is represented in a three-dimensional array where the first channel represents the color channels, e.g. [channels][rows][cols].
Keras keeps this setting differently for different backends, and this is supossedly set as Channels Last for Tensorflow, BUT it looks like in our planets it is set as Channels First.
Thankfully, this can be set manually and I managed to fix it with:
QUESTION
I have 2 arrays:
...ANSWER
Answered 2021-Jun-08 at 21:11Here's a quick, possibly quite dirty attempt.
The overlap looks like this in the first iteration:
Let's look at everything relative to list2 first. In the first iteration
QUESTION
- I'm working on the NLP dataset.
- I Have a data-set which I want to train on classification problem (with 5 classes) and after this phase, I want to use the trained model on the test data in order to build embedding vectors which will be used for the clustering algorithm.
I built the following model:
...ANSWER
Answered 2021-Jun-07 at 16:52You are looking for model output and not model weights. To get model output from your Flatten layer you simply have to initialize a new model to extract and produce the output you want...
QUESTION
I am calling the python script with the flair package with a www-data user (no sudo rights). The models are in path for which that user has access rights, which I have set flair.cache_root = Path("tools/flair")
However, when I run the script with that user I get a Permission Error:
...ANSWER
Answered 2021-Jun-07 at 11:52The error is caused by the transformer model that flair loads. The cache directory for transformers has to be specified in additional by setting the environment variable TRANSFORMERS_CACHE=/path/to/transformers
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