classify | based CLI for Neural Network Image Classification via Coco | Machine Learning library
kandi X-RAY | classify Summary
kandi X-RAY | classify Summary
CLI for Neural Network image classification using Coco model. This package is written to be used as a Node command-line interface (CLI).
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QUESTION
I am doing sentiment analysis, and I was wondering how to show the other sentiment scores from classifying my sentence: "Tesla's stock just increased by 20%."
I have three sentiments: positive, negative and neutral.
This is my code, which contains the sentence I want to classify:
...ANSWER
Answered 2021-Jun-15 at 14:44Because HappyTransformer does not support multi class probabilities I suggest to use another library. The library flair provides even more functionality and can give you your desired multi class probabilities, with something like this:
QUESTION
I have a dataset of around 2800 records. I have a column called as 'Trigger-catogory' which is a Multi-class classification field with one of the attribute being 'CLI-Related' The dataset has a 'Headline' column and going through the headline i want to further classify it . I have written a code as below
...ANSWER
Answered 2021-Jun-13 at 15:51use vectorized way via np.select to insert a new column:
QUESTION
I was reading a decent paper S-DCNet and I fell upon a section (page3,table1,classifier) where a convolution layer has been used on the feature map in order to produce a binary classification output as part of an internal process. Since I am a noob and when someone talks to me about classification I automatically make a synapse relating to FCs combined with softmax, I started wondering ... Is this a possible thing to do? Can indeed a convolutional layer be used to classify a binary outcome? The whole concept triggered my imagination so much that I insist on getting answers...
Honestly, how does this actually work? What is the difference between using a convolution filter instead of a fully connected layer for classification purposes?
Edit (Uncertain answer on how does it work): I asked a colleague and he told me that using a filter of the same shape as the length-width shape of the feature map at the current stage, may lead to a learnable binary output (considering that you also reduce the #channels of the feature map to a single channel). But I still don't understand the motivations behind such a technique ..
...ANSWER
Answered 2021-Jun-13 at 08:43Using convolutions as FCs can be done (for example) with filters of spatial size (1,1) and with depth of the same size as the FC input size.
The resulting feature map would be of the same size as the input feature map, but each pixel would be the output of a "FC" layer whose weights are the weights of the shared 1x1 conv filter.
This kind of thing is used mainly for semantic segmentation, meaning classification per pixel. U-net is a good example if memory serves.
Also see this.
Also note that 1x1 convolutions have other uses as well.
paperswithcode probably some of the nets there use this trick.
QUESTION
ANSWER
Answered 2021-Jun-12 at 06:42The SpaCy tokenizer seems to cache each token in a map internally. Consequently, each new token increases the size of that map. Over time, more and more new tokens inevitably occur (although with decreasing speed, following Zipf's law). At some point, after having processed large numbers of texts, the token map will thus outgrow the available memory. With a large amount of available memory, of course this can be delayed for a very long time.
The solution I have chosen is to store the SpaCy model in a TTLCache and to reload it every hour, emptying the token map. This adds some extra computational cost for reloading the SpaCy model from, but that is almost negligible.
QUESTION
I'm curious to see if anyone has any thoughts on how to accomplish this in Python with Pandas.
I have a dataframe (df1) with the credit card transaction details that contains the Point of Sale description (df1['Description']) and amount (df1['amount']). The POS description is unique to each location so you end up with several descriptions for Amazon, Shell Oil, etc.
I have another dataframe (df_lookup) that will serve as a lookup table to classify the transactions. This dataframe will include the name (df_lookup['Name']) and the category to classify each transaction df_lookup['Category']).
This is what I would like to accomplish: Compare the df1['Description'] to df_lookup['Name']. If the df1['Description'] contains the df_lookup['Name'], the corresponding df_lookup['Category'] will be added to df1 as a new column df1['Category']. Please see the examples of each dataframe and the desired outcome below.
Example of df1:
Description Amount AMAZON.COM*ajlja09ja AMZN.COM 10 AMZN Mktp US *ajlkadf 15 AMZN Prime *an9adjah 20 Shell Oil 4106541031 20 Shell Oil 4163046510 25Example of df_lookup:
Name Category AMAZON Amazon AMZN Amazon Shell Oil GasDesired Output to df1:
Description Amount Category AMAZON.COM*ajlja09ja AMZN.COM 10 Amazon AMZN Mktp US *ajlkadf 15 Amazon AMZN Prime *an9adjah 20 Amazon Shell Oil 4106541031 20 Gas Shell Oil 4163046510 25 Gas ...ANSWER
Answered 2021-Jun-09 at 18:01I came up with a solution but it could probably take long time for large DataFrames:
QUESTION
I have a large DataFrame of distances that I want to classify.
...ANSWER
Answered 2021-Jun-08 at 20:36You can vectorize the calculation using numpy:
QUESTION
I have a query:
...ANSWER
Answered 2021-Jun-07 at 03:19In SQL Server you are unable to use a calculated column in the WHERE clause, only in the ORDER BY clause.
So you either need a sub-query of some form or you have to repeat the calculation. CROSS APPLY is a neat way to accomplish this.
QUESTION
I am trying to implement GoogleNet inception network to classify images for classification project that I am working on, I used the same code before but with AlexNet network and the training was fine, but once I changed the network to GoogleNet architecture the code kept throwing the following error:
...ANSWER
Answered 2021-Jun-08 at 08:22GoogleNet is different than Alexnet, in GoogleNet your model has 3 outputs, 1 main and 2 auxiliary outputs connected in intermediate layers during training:
QUESTION
I have two data frames, df1 and df2, and want to know if something like the following is (easily) possible:
For every df1$id that matches df2$id, I want to compare df1$day against df2$day and either classify them as MATCH or NO MATCH in a new column (df1$matched) depending on whether they are identical or not.
Further clarification:
If a value in df1$id matches / appears in df2$id, I then want to look at df1$day and df2$day against that particular id. Next, I want to compare the values from df1$day and df2$day to see if they are the same or different. I then want to create a new column (matched) which classifies these values based on whether or not they are the same. Thus, for every id match between df1 and df2, the algorithm should output something like this:
Sample data:
...ANSWER
Answered 2021-Jun-07 at 14:20df1 <- read.table(text = 'id day
001 2
002 2
003 8
004 2
005 8
006 8', header = T)
df2 <- read.table(header = T, text = 'id day
004 2
005 2
006 8
007 2
008 8')
library(dplyr)
df1 %>% inner_join(df2, by = 'id') %>%
mutate(match_yn = c('no_match', 'match')[1 + (day.x == day.y)])
#> id day.x day.y match_yn
#> 1 4 2 2 match
#> 2 5 8 2 no_match
#> 3 6 8 8 match
QUESTION
When the camera opens a blank camera appears for a few seconds and it always gives the same below output and stops.
prediction: [{"className":"nematode, nematode worm, roundworm","probability":0.050750732421875},{"className":"matchstick","probability":0.043731689453125},{"className":"lighter, light, igniter, ignitor","probability":0.021453857421875}]
Any idea how I can make the real time prediction work? without getting a false prediction as above just for one time
Below is the Camera Screen code where the prediction should happen in real time camera feed when user scans a certain surrounding
...ANSWER
Answered 2021-Jun-07 at 04:03In the function handleCameraStream you stop looping the function once a prediction is found. In your case you would want to constantly run the loop as you want to make predictions on all the frames not a single one.
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