SimpleFilter | Python module for image convolution and ML classification | Machine Learning library
kandi X-RAY | SimpleFilter Summary
kandi X-RAY | SimpleFilter Summary
SimpleFilter is a Python 3 module that provides the tools necessary to build a convolutional classification network. It handles the convolutions or mutation of images and comes pre-packaged with SimpleClassifier, a k-nearest neighbor classifier that is optimized to work with the module.
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Top functions reviewed by kandi - BETA
- Cycle through the given filters
- Convert a 2D array into a single array
- Mutate an array using matplotlib
- Generate a list of values from an array
- Prepare an array with padding
- Upsets the values in the array
- Flattens conv_layers
- Converts an array to zero
- Randomize a filter
- Predict the label of the test
- Compute the Euclidean distance between two arrays
- Remove all elements from an array
- Remove all elements in the array
- Update the filter
- Create a random filter
SimpleFilter Key Features
SimpleFilter Examples and Code Snippets
Lines of Code : 16License : Permissive (MIT)
CopyEvery function is listed below with a description of its parameters, however here's a quick demonstration:
from SimpleFilter import *
sf = SimpleFilter()
image = sf.load(path/to/image,[200,200]) # This loads an image at size 200x200 px
image2 = sf import warnings
from sklearn.exceptions import ConvergenceWarning
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=ConvergenceWarning)
optimizer_function_that_creates_warning()
warnings.simplefilter('always')
warnings.simplefilter('ignore')
global dfM
global dfB
global dfQ
global dfSN1
global dfSN2
dfM.set(pd.read_excel('weekexport.xlsx', sheet_name='Foaie1', header=None, usecols="D",index_col=None))
dfB.set(pd.read_excel('weekexport.xlsx', sheet_nameHelp on built-in function implement_array_function in module numpy.core._multiarray_umath:
implement_array_function(...)
Implement a function with checks for __array_function__ overrides.
All arguments are required, and can only from warnings import simplefilter
simplefilter(action='ignore', category=FutureWarning)
import warnings
from warnings import (
filterwarnings as original_filterwarnings,
simplefilter as original_simplefilter
)
def ignore_filterwarnings(*args, **kwargs):
pass
def ignore_simplefilter(*args, **kwargs):
pass
wfrom warnings import warn, simplefilter
simplefilter('error')
def foo():
a = '20'
warn("Horrible Things", RuntimeWarning)
foo()
> (4)foo()
1 from warnings import warn
Community Discussions
Trending Discussions on SimpleFilter
QUESTION
I'm working on isochrones building them for some schools just to show accessibility. Here is my code:
...ANSWER
Answered 2022-Mar-03 at 14:34I see three sub-questions
- error "ValueError: Found no graph nodes within the requested polygon".
- no such error when using 20 sample points you have provided. Make sure you are using fully upto date omnx
- also reference: OSMNX graph from point and geometry information
- performance
- it is expected that
ox.graph_from_point()takes time. This is where time is taken. Having implemented caching using a pickle file so same results are not continuously requested
- missing map layers
- there are no missing map layers
ox.graph_from_point()has a dist parameter. This defaults to 1000m which is not sufficient for walking for 60 minutes at 4.5km/h. Have amended to define a distance that will be sufficient
QUESTION
So I have a filter on my DbContext like this:
...ANSWER
Answered 2022-Feb-18 at 15:50Try this implementation. Sorry, not not tested:
QUESTION
The first segment of code below (code # 1) generates a graph for which 1) when you hover over each point, the data associated with each point is displayed and 2) when you click on each point, the data associated with each point is saved to a list. For this code, I would also like to display the image associated with each point. Assume the dataframe df has a column 'image' which contains the image pixel/array data of each point. I found code online (code #2) that implements this image hover feature but without the click feature. I'm having a hard time combining the image hover feature with the click feature. So, basically, I'm trying to combine the click feature (click on point, it's data is saved to a list) of code # 2 into code # 1.
CODE # 1 (with click feature):
...ANSWER
Answered 2022-Jan-26 at 09:23- you want a callback that does hover and click
- on hover display image associated with point and full hover info
- on click update list of clicked points and figure title
- Assume the dataframe df has a column 'image' have created one that is a b64 encoded image
- have inserted this into the figure by using customdata (hover_data parameter in px)
- have added an additional div image
- have changed callback to behave as it did before and also contents on new div. This uses b64 encoded image, extending with necessary
"data:image/png;base64," - need to take note of this https://dash.plotly.com/vtk/click-hover and https://dash.plotly.com/advanced-callbacks
QUESTION
I want to programatically capture when statsmodels.api.OLS raises its "The smallest eigenvalue is ..." warning
This would enable me to filter a large number of OLS systems by whether or not they raise this warning
Ideally, I would like to pick off just particular warnings instead of a blanket filter for any/all warnings
My attempt (below) attempts a blanket filter using warnings.filterwarnings() , it doesn't work
How do I get warnings.filterwarnings() to work? Or is there some other module I should be looking at instead?
...ANSWER
Answered 2022-Jan-11 at 03:20You can get the smallest eigenvalue using model.eigenvals[-1], just check that it is less than 1e-10 to raise an exception. Here's the source that generates the note
QUESTION
When fine-tuning the sloBERTa Transformer model, based on CamemBERT, for a multiclass classification task with SimpleTransformers, I want to use the model argument "max_seq_length": 512, as previous work states that it gives better results than 128, but the inclusion of this argument triggers the error below. The error is the same in Kaggle and Google Colab environment, and terminating the execution and reruning it does not help. The error is triggered not matter how small the number of training epochs is, and the dataset contains only 600 instances (with text as strings, and labels as integers). I've tried lowering the max_seq_length to 509, 500 and 128, but the error persists.
The setup without this argument works normally and allows training with 90 epochs, so I otherwise have enough memory.
...ANSWER
Answered 2022-Jan-02 at 13:52This happened because max_seq_length defines the number of input neurons for the model thus increasing the number of trainable parameters which will require it to allocate more memory which might exceed your memory limits on those platforms.
Most of the time, max_seq_length is up the dataset, and sometimes adding too much could be wasteful in terms of training time and model size.
What you can do is to find the max number of words per sample in your training dataset and use that as your max_seq_length.
QUESTION
I am testing numba performance on some function that takes a numpy array, and compare:
ANSWER
Answered 2021-Dec-22 at 23:52The slower execution time of the Numba implementation is due to the compilation time since Numba compile the function at the time it is used (only the first time unless the type of the argument change). It does that because it cannot know the type of the arguments before the function is called. Hopefully, you can specify the argument type to Numba so it can compile the function directly (when the decorator function is executed). Here is the resulting code:
QUESTION
if I tune a model with the LightGBMTunerCV I always get this massive result of the cv_agg's binary_logloss. If I do this with a bigger dataset, this (unnecessary) io slows down the performance of the optimization process.
Here is the code:
...ANSWER
Answered 2021-Nov-29 at 23:38You can pass verbose_eval parameter with value None in LightGBMTunerCV().
Example:
QUESTION
I have a numba decorated function which is called by another class method. It keeps giving me deprecation warnings. To silence it I've so far tried:
(1) adding @jit(warn=False), but throws an error that the option is unavailable
(2) trying to catch the warning with warning.catch_warnings()
None seems to work, interestingly enough, if I break before the execution of the function and run in the debug console the same statement (with warnings.catch_warnings() etc.) and then run the method, it will silence it, but if I run the .py fully it doesn't silence it. Any ideas?
ANSWER
Answered 2021-Nov-25 at 13:06Executing annualize() this way
QUESTION
ANSWER
Answered 2021-Nov-16 at 15:22Starting with tf-2.0 Keras is now the official high-level API of TensorFlow packaged within it. Not sure if it is a possibility in your case, but using tf-2.0 should remove this error. Here's a Quick Start guide if needed.
For most of the simple cases, just changing the import statement to import tensorflow.keras as keras gets it going.
QUESTION
I have two features rank and ratings for different product IDs under different categories scraped from an ecommerce website on different dates.
sample dataframe available here:
...ANSWER
Answered 2021-Oct-07 at 06:55Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install SimpleFilter
You can use SimpleFilter like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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