Noisy | jQuery plugin that adds random noise | Plugin library
kandi X-RAY | Noisy Summary
kandi X-RAY | Noisy Summary
A jQuery plugin that adds random noise to the background of an element.
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Lines of Code : 92License : Non-SPDX (Apache License 2.0)
Copydef noisy_linear_cosine_decay(learning_rate,
global_step,
decay_steps,
initial_variance=1.0,
variance_decay=0.55,
def __init__(
self,
initial_learning_rate,
decay_steps,
initial_variance=1.0,
variance_decay=0.55,
num_periods=0.5,
alpha=0.0,
beta=0.001,
name=None):
"""Applies noisy linear cosine decay to t Community Discussions
Trending Discussions on Noisy
QUESTION
I am learning how to control P10 Led matrix 64x32 with NodeModule MCU ESP8266, I google and found this library https://github.com/2dom/PxMatrix and this tutorial https://www.instructables.com/RGB-LED-Matrix-With-an-ESP8266/. I believed that I wire between P10 and ESP8266 in true way in the tutorial, but that P10 led does not display as the example:
The true result will be:
This is my wire diagram:
This is my code:
...ANSWER
Answered 2021-Jun-10 at 09:17I fixed this by adding
display.setPanelsWidth(2);
display.setMuxPattern(SHIFTREG_ABC_BIN_DE);
because my led is combined by 2 matrix 32x16.
QUESTION
I always had this problem. When training neural networks, the validation loss can be noisy (sometimes even the training loss if you are using stochastic layers such as dropout). This is especially true when the dataset is small.
This makes that when using callbacks such as EarlyStopping or ReduceLROnPlateau, these are triggered too early (even using large patience). Also, sometimes I don't want to use large patience in the ReduceLROnPLateau callback.
A solution to this is instead of directly monitoring a certain metric (e.g. val_loss), to monitor a filtered version (across epochs) of the metric (e.g. exponential moving average of val_loss). However, I do not see any easy way to solve this because the callbacks only accept metrics that not depend on the previous epochs. I have tried using a custom training loop to reproduce the functionality of these callbacks with my custom filtered metric, but I don't think it is the correct way. Is there another (simpler) way to do the monitor the filtered version of the loss in the callbacks, without reimplementing the whole functionality of the callbacks?
Edit:
This is what I mean by monitoring a filtered version of a metric. The current EarlyStopping works something like this:
ANSWER
Answered 2021-May-27 at 18:29I often encountered this problem so I wrote a custom callback that initially monitors training accuracy and adjust the learning rate based on that. This avoids the problem where validation loss can initially change radically, I have a parameter called 'threshold'. Once the training accuracy exceeds the threshold value the callback switches over to monitor validation loss and adjusts the learning rate based on that. At the conclusion of training the callback also always sets the weights of the model to the weights for the epoch with the lowest loss. This code is to lengthy to provide here. However to learn how to write a custom callback the documentation is here.
QUESTION
Context: I am trying to find the directional heading from a small image of a compass. Directional heading meaning if the red (north) point is 90 degrees counter-clockwise from the top, the viewer is facing East, 180 degrees is south, 270 is west, 0 is north. etc. I understand there are limitations with such a small blurry image but I'd like to be as accurate as possible. The compass is overlaid on street view imagery meaning the background is noisy and unpredictable.
The first strategy I thought of was to find the red pixel that is furthest away from the center and calculate the directional heading from that. The math is simple enough.
The tough part for me is differentiating the red pixels from everything else. Especially because almost any color could be in the background.
My first thought was to black out the completely transparent parts to eliminate the everything but the white transparent ring and the tips of the compass.
True Compass Values: 35.9901, 84.8366, 104.4101
These values are taken from the source code.
I then used this solution to find the closest RGB value to a user given list of colors. After calibrating the list of colors I was able to create a list that found some of the compass's inner most pixels. This yielded the correct result within +/- 3 degrees. However, when I tried altering the list to include every pixel of the red compass tip, there would be background pixels that would be registered as "red" and therefore mess up the calculation.
I have manually found the end of the tip using this tool and the result always ends up within +/- 1 degree ( .5 in most cases ) so I hope this should be possible
The original RGB value of the red in the compass is (184, 42, 42) and (204, 47, 48) but the images are from screenshots of a video which results in the tip/edge pixels being blurred and blackish/greyish.
Is there a better way of going about this than the closest_color() method? If so, what, if not, how can I calibrate a list of colors that will work?
...ANSWER
Answered 2021-Jun-04 at 08:45If you don't have hard time constraints (e.g. live detection from video), and willing to switch to NumPy, OpenCV, and scikit-image, you might use template matching. You can derive quite a good template (and mask) from the image of the needle you provided. In some loop, you'll iterate angles from 0° to 360° with a desired resolution – the finer the longer takes the whole procedure – and perform the template matching. For each angle, you save the value of the best match, and finally search for the best score over all angles.
That'd be my code:
QUESTION
I try to train two DNN jointly, The model is trained and goes to the validation phase after every 5 epochs, the problem is after the 5 epochs it is okay and no problem with memory, but after 10 epochs the model complains about Cuda memory. Any help to solve the memory issue.
...ANSWER
Answered 2021-Jun-02 at 15:11Don't use retain_graph = True on the second backwards pass. This flag is making your code store the computation graphs for each batch, conceivably in perpetuity.
You should only use retain_graph for all but the last call to backward() that back-propagate through the same variables/parameters.
QUESTION
I just got a dependabot saying:
...ANSWER
Answered 2021-May-26 at 22:31Dependabot has some concept of compatibility score to ensure your confidence with the version bumps. But for many of the dependency updates, they lack of compatibility score as well.
Also, dependabot PRs are like regular PRs, which means you should have PR builds to ensure the compatibility on your own.
At last, technically speaking, the library maintainers should follow semver, so you only have to check with major upgrade PRs.
QUESTION
Noisy data presented in tabular form are given. Fit and build a model curve. Choose a functional dependence of the form:
...ANSWER
Answered 2021-May-25 at 14:44Here is the Scilab code for your first example, you will be able to adapt it to other cases. To evaluate the obtained polynomial F use Scilab function horner (see the associated help page by typing help horner).
QUESTION
I used this function to count objects in segmendted images (I loaded my pretrained weights for prediction)
...ANSWER
Answered 2021-May-18 at 12:33remove_small_objects expected a labeled image, putting: imgl=skimage.morphology.remove_small_objects(imgl, min_size=12) under imgl=measure.label(predd, background=0,connectivity=2) solved the problem.
QUESTION
Let's try to make MobileNet V. 2 locate a bright band on a noisy image. Yes, it is overkill to use a deep convolutional network for such a tack, but originally it was intended just like a smoke test to make sure the model works. We will train it on synthetic data:
ANSWER
Answered 2021-May-14 at 14:59The problem was that a network reasonably functioning in the training mode failed to work in the inference mode. What might be the cause? There are basically two layer types working differently in the two modes: dropout and batch normalization. In MobileNet V. 2, we have only batch normalization, so let's consider how it works.
In the training mode a BN layer calculates batch mean and variance and normalizes the data using these batch values. At the same time it remembers the mean and variance as a moving average weighted with a coefficient called momentum.
QUESTION
I am learning HOF at the moment:
...ANSWER
Answered 2021-May-13 at 10:52weaponsFromUniverse returns useWeapon which is a function that receives one parameter called weaponName.
When doing:
QUESTION
This is my example image:
You can see in the bottom left corner and on the edge of the main structure, there is a lot of noise and outlier green pixels. I'm looking for a way to remove them. Currently, I have tried the following:
...ANSWER
Answered 2021-May-12 at 08:32Try this:
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