kerosene | Deep Learning framework for fast and clean research | Machine Learning library

by banctilrobitaille Python Version: v0.2.3 License: MIT

X-Ray Key Features Code Snippets(1)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | kerosene Summary

kerosene is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. kerosene has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Kerosene is a high-level deep Learning framework for fast and clean research development with Pytorch - see the doc for more details.. Kerosene let you focus on your model and data by providing clean and readable code for training, visualizing and debugging your achitecture without forcing you to implement rigid interface for your model.

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Security

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Support

kerosene has a low active ecosystem.

It has 9 star(s) with 0 fork(s). There are 2 watchers for this library.

It had no major release in the last 12 months.

There are 21 open issues and 18 have been closed. On average issues are closed in 27 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of kerosene is v0.2.3

Quality

kerosene has no bugs reported.

Security

kerosene has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

kerosene is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

kerosene releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed kerosene and discovered the below as its top functions. This is intended to give you an instant insight into kerosene implemented functionality, and help decide if they suit your requirements.

Compute the Tversky
Flattens a tensor
Create a metric from the confusion matrix
Train the model
Initialize on epoch
Called when epoch end
Fire a given temporal event
Create a model from a dictionary
Parse the training config file
Create IOUU metric
Performs a single training step
Compute metrics for the given pred and target
Compute the training loss for the given pred and target
Backward the loss
Read a section from the config file
Return the state of the optimizer
Return a dictionary of the monitored monitors
Returns the epoch monitors
Computes the KL divergence of inputs
Performs a prediction step
Register an event handler
Evaluate a single step
Step the scheduler
Updates test loss stats
Updates the validation loss
Update training loss

Get all kandi verified functions for this library.

kerosene Key Features

No Key Features are available at this moment for kerosene.

kerosene Examples and Code Snippets

,MNIST Example

Python

Lines of Code : 23

License : Permissive (MIT)

Copy

if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    CONFIG_FILE_PATH = "config.yml"

    model_trainer_config, training_config = YamlConfigurationParser.parse(CONFIG_FILE_PATH)

    train_loader = DataLoader(torchvision.dataset

Community Discussions

Trending Discussions on kerosene

python read CSV with commas as separators but interpret commas inside quotes as thousands

ValueError: is not in list

Is it possible to use narrow types with Fsharp?

How to set value of checkbox to true using two arrays?

position_stack does not work with facet_wrap

convert 1 table into 2 tables in MSSQL

having problems with importing variables from other files in the same folder.

I have this type of json format data

Pip installing kerosene gives unicodeDecodeError

R: Search separate words in entire data frame to create a subset

QUESTION

python read CSV with commas as separators but interpret commas inside quotes as thousands

Asked 2021-Jan-04 at 21:58

I am tried to read in data from a govt. website using:

df = pd.read_csv("https://ir.eia.gov/wpsr/table9.csv",encoding = 'unicode_escape', error_bad_lines=False,thousands=',')

but for some reason it doesn't parse correctly and still returns strings. Should it be converting to float/int or do I need an additional step/would it be better to utilize .replace(',','') for the columns in question?

Sample from csv file ...

ANSWER

Answered 2021-Jan-04 at 21:58

A value in a dataframe with a comma (e.g. 2,238) is not interpreted as a numeric value. The thousands parameter in pandas.read_csv() allows numeric columns with , to correctly be converted to a numeric dtype where the , is the thousands separator, instead of a decimal point.
There is an issue with the data, which is preventing the columns from being converted to numeric data types.
- "Stocks (Million Barrels) ","Lower Atlantic (PADD 1C)","1.728","1.758","1.128","1.319","– –","– –"
  - "– –" is actually '\x96 \x96'
- "Ultra Low Sulfur Distillate Reclassification ","< 15 ppm Distillate, Downgraded to 15 to 500 ppm","–","–","–","–","–","–"
  - "–" is actually '\x96'

Source https://stackoverflow.com/questions/65569552

QUESTION

ValueError: is not in list

Asked 2020-Aug-25 at 09:20

I am trying to print a corresponding value to the index of a list from another list like so:

print(safeDis[chem.index(self.drop2)])

but when doing this i get the above error. I believe i had this working in a previous iteration but i cannot find the one that was.

...

ANSWER

Answered 2020-Aug-25 at 09:20

The problem was that self.drop2 is an object of OptionMenu, not the value of it. To get the value returned by it, use the get() method on its variable defined (self.clicked2.get())

So it should be:

Source https://stackoverflow.com/questions/63565250

QUESTION

Is it possible to use narrow types with Fsharp?

Asked 2020-May-20 at 18:58

I would think this is something trivial but I have spent some time on it and still, there is no clean way of doing it:

I have a type like:

...

ANSWER

Answered 2020-May-20 at 10:56

What you want to do is deal with a sum type in arbitrary way over subsets of value constructors.

You can explicitly model the subsets and embed them in the superset:

Source https://stackoverflow.com/questions/61878366

QUESTION

How to set value of checkbox to true using two arrays?

Asked 2020-Feb-06 at 06:09

I have two arrays

...

ANSWER

Answered 2020-Feb-06 at 05:58

You can achieve this by changing your input tag to

Source https://stackoverflow.com/questions/60088410

QUESTION

position_stack does not work with facet_wrap

Asked 2019-Mar-13 at 17:15

I am plotting some shares by piecharts. I want to put the labels in the middle of each fraction, but even when I use position_stack, it only gives me the labels as desired for the first graph (Alberta) but not for the rest.

...

ANSWER

Answered 2019-Mar-13 at 17:15

I think the issue is that the geom_text layer doesn't quite know what to stack, and in particular doesn't know the order to stack. This is because the Fuel column determines the stacking order for the bars, but isn't mapped at all for the geom_text layer.

The simplest fix is to move the aesthetic mapping into ggplot() rather than individual layers. This way it will be inherited by subsequent layers (also avoids the duplication of the x and y aesthetics in each layer). By the magic of ggplot, even though geom_text doesn't use the fill mapping for fill, it will still know to group/order by that aesthetic for the position.

I've made that change, changed position_stack to position_fill for the text layer, and replaced geom_bar(stat = "identity") with geom_col which is now the preferred method for that idiom.

Source https://stackoverflow.com/questions/55146069

QUESTION

convert 1 table into 2 tables in MSSQL

Asked 2019-Feb-11 at 04:26

I have a sheet have data like this:

VendorName Description FuelType
Avery Wood Wood
Beta LP Gas LP Gas
Clever Oil,Kerosene,LP Gas Oil
Clever Oil,Kerosene,LP Gas Kerosene
Clever Oil,Kerosene,LP Gas LP Gas

But now, I need to convert them like this

this one called vendor table

VendorName:
Avery
Beta
Clever

this one called vendor fueltype table

VendorName fueltype
Avery Wood
Beta LP Gas
Clever Oil
Clever Kerosene
Clever LP Gas

I feel I can directly use the select from insert into, to move the sheet I have to the fueltype table, but somehow I couldn't think of a good way to make all the record in the second table. I am thinking there is something that if there are duplicated name in table, select the first one, or something like that.

Can anyone give some advice?

...

ANSWER

Answered 2019-Feb-11 at 02:08

Based on what you have given us, the following should work:

Source https://stackoverflow.com/questions/54622755

QUESTION

having problems with importing variables from other files in the same folder.

Asked 2018-Nov-28 at 12:16

Tried to import i Dictionary from another file in the same folder.

this is the "fluid" module:

...

ANSWER

Answered 2018-Nov-28 at 12:16

fluiddensitydict is already defined as a dictionary however you are calling as a function. You can iterate through both Keys and values and print the value of your dictionary which has the same key as your input() value: like this:

Source https://stackoverflow.com/questions/53518686

QUESTION

I have this type of json format data

Asked 2018-Jun-22 at 12:08

{
    "fertilizer":[{"pg1":"-21.259515860749435","pg2":"24.741169305724725","lastyearlastmonth":"764.119","currentmonth":"601.671","currentyearytd":"5735.1","lastyearytd":"4597.6","pname":"Urea","mmonth":"11","period":"11","myear":"2017"},{"pg1":"-20.53085432388131","pg2":"9.258986807905458","lastyearlastmonth":"631.435","currentmonth":"501.796","currentyearytd":"2227.9","lastyearytd":"2039.1","pname":"DAP","mmonth":"11","period":"11","myear":"2017"},{"pg1":"67.37546062508531","pg2":"51.07126222636238","lastyearlastmonth":"36.635","currentmonth":"61.318","currentyearytd":"648.7","lastyearytd":"429.4","pname":"CAN","mmonth":"11","period":"11","myear":"2017"},{"pg1":"-49.542998848640515","pg2":"7.7561388653683245","lastyearlastmonth":"112.91","currentmonth":"56.971","currentyearytd":"636.3","lastyearytd":"590.5","pname":"NP","mmonth":"11","period":"11","myear":"2017"},{"pg1":"-53.39393939393939","pg2":"-2.1138211382113776","lastyearlastmonth":"4.95","currentmonth":"2.307","currentyearytd":"60.2","lastyearytd":"61.5","pname":"NPK","mmonth":"11","period":"11","myear":"2017"},{"pg1":"-14.652234166073644","pg2":"-5.41561712846349","lastyearlastmonth":"26.699","currentmonth":"22.787","currentyearytd":"75.1","lastyearytd":"79.4","pname":"SSP","mmonth":"11","period":"11","myear":"2017"},{"pg1":"123.88827636898196","pg2":"239.6551724137931","lastyearlastmonth":"2.721","currentmonth":"6.092","currentyearytd":"39.4","lastyearytd":"11.6","pname":"SOP","mmonth":"11","period":"11","myear":"2017"},{"pg1":"58.21359223300969","pg2":"134.07407407407408","lastyearlastmonth":"2.575","currentmonth":"4.074","currentyearytd":"31.6","lastyearytd":"13.5","pname":"MOP","mmonth":"11","period":"11","myear":"2017"},{"pg1":null,"pg2":null,"lastyearlastmonth":"0","currentmonth":"0","currentyearytd":"0","lastyearytd":"0","pname":"MAP","mmonth":"11","period":"11","myear":"2017"},{"pg1":null,"pg2":null,"lastyearlastmonth":"0","currentmonth":"0","currentyearytd":"0","lastyearytd":"0","pname":"TSP","mmonth":"11","period":"11","myear":"2017"},{"pg1":"-56.21508379888268","pg2":"-0.6802721088435351","lastyearlastmonth":"2.864","currentmonth":"1.254","currentyearytd":"14.6","lastyearytd":"14.7","pname":"AS","mmonth":"11","period":"11","myear":"2017"},{"pg1":"-20.609372141004858","pg2":"20.81536932387274","lastyearlastmonth":"1584.91","currentmonth":"1258.27","currentyearytd":"9468.82","lastyearytd":"7837.43","pname":"Total","mmonth":"11","period":"11","myear":"2017"}],
    "automobile":[{"pg1":"11.009369676320272","pg2":"22.30648472406714","lastyearlastmonth":"14088","currentmonth":"15639","currentyearytd":"50641","lastyearytd":"41405","pname":"PC","mmonth":"9","period":"3","myear":"2017"},{"pg1":"2597.0588235294117","pg2":"1874.4827586206895","lastyearlastmonth":"34","currentmonth":"917","currentyearytd":"2863","lastyearytd":"145","pname":"SUV","mmonth":"9","period":"3","myear":"2017"},{"pg1":"15.686274509803921","pg2":"14.290401968826908","lastyearlastmonth":"1938","currentmonth":"2242","currentyearytd":"6966","lastyearytd":"6095","pname":"LCV","mmonth":"9","period":"3","myear":"2017"},{"pg1":"58.64978902953587","pg2":"34.17569193742479","lastyearlastmonth":"474","currentmonth":"752","currentyearytd":"2230","lastyearytd":"1662","pname":"Truck","mmonth":"9","period":"3","myear":"2017"},{"pg1":"-60.57692307692307","pg2":"-34.74320241691843","lastyearlastmonth":"104","currentmonth":"41","currentyearytd":"216","lastyearytd":"331","pname":"Bus","mmonth":"9","period":"3","myear":"2017"},{"pg1":"74.47245017584994","pg2":"99.92364469330617","lastyearlastmonth":"3412","currentmonth":"5953","currentyearytd":"15710","lastyearytd":"7858","pname":"Tractor","mmonth":"9","period":"3","myear":"2017"},{"pg1":"14.790290676232942","pg2":"27.133399110577265","lastyearlastmonth":"119308","currentmonth":"136954","currentyearytd":"444541","lastyearytd":"349665","pname":"2 Wheel","mmonth":"9","period":"3","myear":"2017"},{"pg1":"33.41478313989004","pg2":"30.228058051140287","lastyearlastmonth":"4911","currentmonth":"6552","currentyearytd":"18844","lastyearytd":"14470","pname":"3 Wheel","mmonth":"9","period":"3","myear":"2017"},{"pg1":"17.04856787048568","pg2":"26.917829782768393","lastyearlastmonth":"16060","currentmonth":"18798","currentyearytd":"60470","lastyearytd":"47645","pname":"Total Cars","mmonth":"9","period":"3","myear":"2017"}],
    "oilmkting":[{"pg1":"-100","pg2":"-100.00000","lastyearlastmonth":"0.102","currentmonth":"0","currentyearytd":"0.0","lastyearytd":"0.4","pname":"100LL","mmonth":"10","period":"4","myear":"2017"},{"pg1":"-5.129426108196923","pg2":"-0.04207","lastyearlastmonth":"59.803999999999995","currentmonth":"56.73639801025391","currentyearytd":"237.6","lastyearytd":"237.7","pname":"JP-1","mmonth":"10","period":"4","myear":"2017"},{"pg1":"1194.9860724233984","pg2":"104.22535","lastyearlastmonth":"1.436","currentmonth":"18.596","currentyearytd":"58.0","lastyearytd":"28.4","pname":"JP-8","mmonth":"10","period":"4","myear":"2017"},{"pg1":"8.688345498555524","pg2":"15.40422","lastyearlastmonth":"575.4939999999999","currentmonth":"625.494907043457","currentyearytd":"2580.9","lastyearytd":"2236.4","pname":"MS","mmonth":"10","period":"4","myear":"2017"},{"pg1":"179.72428859212695","pg2":"209.85915","lastyearlastmonth":"3.8990000000000005","currentmonth":"10.906450012207031","currentyearytd":"44.0","lastyearytd":"14.2","pname":"HOBC","mmonth":"10","period":"4","myear":"2017"},{"pg1":null,"pg2":null,"lastyearlastmonth":"0","currentmonth":"0","currentyearytd":"0.0","lastyearytd":"0.0","pname":"E-10","mmonth":"10","period":"4","myear":"2017"},{"pg1":"52.374035671418994","pg2":"11.97917","lastyearlastmonth":"7.263999999999999","currentmonth":"11.068449951171875","currentyearytd":"43.0","lastyearytd":"38.4","pname":"Kerosene","mmonth":"10","period":"4","myear":"2017"},{"pg1":"1.599296460019584","pg2":"18.32285","lastyearlastmonth":"836.0709999999998","currentmonth":"849.4422539062501","currentyearytd":"3142.3","lastyearytd":"2655.7","pname":"HSD","mmonth":"10","period":"4","myear":"2017"},{"pg1":"9.73656730601859","pg2":"61.22449","lastyearlastmonth":"1.526","currentmonth":"1.6745800170898437","currentyearytd":"7.9","lastyearytd":"4.9","pname":"LDO","mmonth":"10","period":"4","myear":"2017"},{"pg1":"4.033703464379442","pg2":"-6.30181","lastyearlastmonth":"866.287","currentmonth":"901.2304487304688","currentyearytd":"3413.8","lastyearytd":"3643.4","pname":"FO","mmonth":"10","period":"4","myear":"2017"},{"pg1":"5.2411828169554875","pg2":"7.54227","lastyearlastmonth":"2351.8830000000007","currentmonth":"2475.149487670898","currentyearytd":"9527.6","lastyearytd":"8859.4","pname":"Total","mmonth":"10","period":"4","myear":"2017"}],
    "cement":[{"pg1":"4.941027677946538","pg2":"12.29975","lastyearlastmonth":"3554.888","currentmonth":"3730.536","currentyearytd":"22242.2","lastyearytd":"19806.1","pname":"Domestic","mmonth":"12","period":"6","myear":"2017"},{"pg1":"-11.24660806265538","pg2":"-17.33874","lastyearlastmonth":"369.258","currentmonth":"327.729","currentyearytd":"2406.6","lastyearytd":"2911.4","pname":"Exports","mmonth":"12","period":"6","myear":"2017"},{"pg1":"3.417788227043532","pg2":"8.50138","lastyearlastmonth":"3924.146","currentmonth":"4058.265","currentyearytd":"24648.8","lastyearytd":"22717.5","pname":"Total Sales","mmonth":"12","period":"6","myear":"2017"}]

}

...

ANSWER

Answered 2018-Jun-22 at 11:58

Try this

Source https://stackoverflow.com/questions/50986743

QUESTION

Pip installing kerosene gives unicodeDecodeError

Asked 2018-Apr-25 at 16:57

I'm trying to execute pip3 install kerosene from a nvidia-docker container. I get the error:

...

ANSWER

Answered 2018-Apr-25 at 16:57

The bug seems to be fixed in fuel 2 years ago but it's not included in the package at PyPI. Install from Github:

Source https://stackoverflow.com/questions/50025311

QUESTION

R: Search separate words in entire data frame to create a subset

Asked 2018-Feb-07 at 20:34

I have a data.frame with 11717 obs. of 15 variables. See below:

...

ANSWER

Answered 2018-Feb-07 at 20:34

I am assuming that by "same sentence" you mean that the words your are searching for are within the same string in one column?

If so, given how I read your description of the problem, I am also assuming you just want to subset the data frame by the rows that contain both words whether they appear in the same sentence or in different columns of the same row. In either case it appears you just want to extract those rows that have both words.

If so, then one way you can do this is to concatenate all the columns together per row into one long string/sentence per row of the data frame and then grepl for your key words in the longer string (using one grepl per word/phrase). This worked for me on ~100k rows quickly (although I reduced the number of columns):

Source https://stackoverflow.com/questions/48672432

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install kerosene

You can download it from GitHub.
You can use kerosene 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.

Support

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