GUI and Desktop Applications int64 0 1 | A_Id int64 5.3k 72.5M | Networking and APIs int64 0 1 | Python Basics and Environment int64 0 1 | Other int64 0 1 | Database and SQL int64 0 1 | Available Count int64 1 13 | is_accepted bool 2
classes | Q_Score int64 0 1.72k | CreationDate stringlengths 23 23 | Users Score int64 -11 327 | AnswerCount int64 1 31 | System Administration and DevOps int64 0 1 | Title stringlengths 15 149 | Q_Id int64 5.14k 60M | Score float64 -1 1.2 | Tags stringlengths 6 90 | Answer stringlengths 18 5.54k | Question stringlengths 49 9.42k | Web Development int64 0 1 | Data Science and Machine Learning int64 1 1 | ViewCount int64 7 3.27M |
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0 | 56,572,030 | 0 | 1 | 0 | 0 | 6 | false | 33 | 2019-02-15T19:21:00.000 | -2 | 11 | 0 | Numpy is installed but still getting error | 54,715,835 | -0.036348 | python,numpy,tensorflow | For me it was about the consoles used.
My cygwin terminal - NOPE, dos - NOPE, but console opened from Anaconda or Spyder etc... all commands (pip install etc) worked. | I am trying to run jupyter notebook and getting following error.
I am using Win 7 with anaconda python 3.7.
ImportError: Something is wrong with the numpy installation. While importing we detected an older version of numpy in ['c:\users\paperspace\anaconda3\envs\tensorflow10\lib\site-packages\numpy']. One method of fi... | 0 | 1 | 64,552 |
0 | 55,652,317 | 0 | 1 | 0 | 0 | 6 | false | 33 | 2019-02-15T19:21:00.000 | 22 | 11 | 0 | Numpy is installed but still getting error | 54,715,835 | 1 | python,numpy,tensorflow | Run
pip3 uninstall numpy
until you receive a message stating no files available with numpy to uninstall and then you can freshly install numpy using
pip install numpy
and that will fix the issue. | I am trying to run jupyter notebook and getting following error.
I am using Win 7 with anaconda python 3.7.
ImportError: Something is wrong with the numpy installation. While importing we detected an older version of numpy in ['c:\users\paperspace\anaconda3\envs\tensorflow10\lib\site-packages\numpy']. One method of fi... | 0 | 1 | 64,552 |
0 | 56,306,185 | 0 | 1 | 0 | 0 | 6 | false | 33 | 2019-02-15T19:21:00.000 | 4 | 11 | 0 | Numpy is installed but still getting error | 54,715,835 | 0.072599 | python,numpy,tensorflow | This Means a Duplicated.
Try pip uninstall numpy or pip3 uninstall numpy then sudo apt-get install python3-numpy
FOR (DEBIAN DIST) | I am trying to run jupyter notebook and getting following error.
I am using Win 7 with anaconda python 3.7.
ImportError: Something is wrong with the numpy installation. While importing we detected an older version of numpy in ['c:\users\paperspace\anaconda3\envs\tensorflow10\lib\site-packages\numpy']. One method of fi... | 0 | 1 | 64,552 |
0 | 70,216,694 | 0 | 1 | 0 | 0 | 6 | false | 33 | 2019-02-15T19:21:00.000 | 0 | 11 | 0 | Numpy is installed but still getting error | 54,715,835 | 0 | python,numpy,tensorflow | Your issue maybe similar to mine below.
How to reproduce it
Started a virtual environment named "jupyter_ve" with numpy=1.21.0 where I launched jupyter.
Opened a notebook using a virtual environment named "project_ve" with numpy=1.20.0 where I launched my notebook kernel using "project_ve"
In Notebook import fa... | I am trying to run jupyter notebook and getting following error.
I am using Win 7 with anaconda python 3.7.
ImportError: Something is wrong with the numpy installation. While importing we detected an older version of numpy in ['c:\users\paperspace\anaconda3\envs\tensorflow10\lib\site-packages\numpy']. One method of fi... | 0 | 1 | 64,552 |
0 | 55,599,428 | 0 | 1 | 0 | 0 | 6 | false | 33 | 2019-02-15T19:21:00.000 | 3 | 11 | 0 | Numpy is installed but still getting error | 54,715,835 | 0.054491 | python,numpy,tensorflow | Use conda update --all
This works. | I am trying to run jupyter notebook and getting following error.
I am using Win 7 with anaconda python 3.7.
ImportError: Something is wrong with the numpy installation. While importing we detected an older version of numpy in ['c:\users\paperspace\anaconda3\envs\tensorflow10\lib\site-packages\numpy']. One method of fi... | 0 | 1 | 64,552 |
0 | 70,205,626 | 0 | 0 | 0 | 0 | 1 | false | 71 | 2019-02-15T20:09:00.000 | 0 | 5 | 0 | How to do gradient clipping in pytorch? | 54,716,377 | 0 | python,machine-learning,deep-learning,pytorch,gradient-descent | Well, I met with same err. I tried to use the clip norm but it doesn't work.
I don't want to change the network or add regularizers. So I change the optimizer to Adam, and it works.
Then I use the pretrained model from Adam to initate the training and use SGD + momentum for fine tuning. It is now working. | What is the correct way to perform gradient clipping in pytorch?
I have an exploding gradients problem. | 0 | 1 | 99,166 |
0 | 55,090,778 | 0 | 1 | 0 | 0 | 1 | true | 2 | 2019-02-16T15:09:00.000 | 0 | 1 | 0 | Visual studio can't see Anaconda's modules | 54,724,459 | 1.2 | python,python-3.x,visual-studio,numpy,anaconda | Resolved it myself
In the PATH variable
C:\ProgramData\Anaconda3
C:\ProgramData\Anaconda3\Library\mingw-w64\bin
C:\ProgramData\Anaconda3\Library\usr\bin
C:\ProgramData\Anaconda3\Library\bin
C:\ProgramData\Anaconda3\Scripts
should go first | I have 2 Python environments (Python 3.6 by Visual studio 2017 and Python 3.7 by Anaconda)
When I run a script I get an error that import numpy
Traceback (most recent call last): File
"C:\ProgramData\Anaconda3\lib\site-packages\numpy\core__init__.py",
line 16, in
from . import multiarray ImportError: DLL ... | 0 | 1 | 469 |
0 | 54,726,295 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2019-02-16T18:25:00.000 | 1 | 1 | 0 | Retraining or Continuing Training | 54,726,275 | 1.2 | python,tensorflow | If you load your model before the training instructions, then you are continuing to train the same model. Tensorflow will just load the pre-trained weights of your model and continue updating them during the new training session. | Ive started experimenting with tensorflow lately and there is one thing I'm not quite sure i get right:
If i have a set of training data, train the model on it for N number of epochs and than use model.save("Multi_stage_test.model") to save the model.
After running the program again with the same training data and w... | 0 | 1 | 84 |
0 | 54,730,825 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2019-02-17T05:40:00.000 | 0 | 1 | 0 | Automated feature generation for time series problems - Featuretools | 54,730,480 | 0 | python,featuretools | Featuretools can certainly help with this. Can you provide more information on the dataset and specific prediction problem so we can help? | I'm trying to use featuretools to generate features to help me predict the number of museum visits next month.
Can featuretools generate features for time series? Should I changed the data so that the id is the month or can featuretools do it automatically? | 0 | 1 | 468 |
0 | 54,782,462 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2019-02-17T21:13:00.000 | 1 | 1 | 0 | Is it possible to train model from multiple datasets for each class? | 54,737,739 | 1.2 | python,tensorflow,object-detection-api | The short answer is yes, it might be problematic, but with some effort you can make it possible.
If you have two urban datasets, and in one you only have annotations for traffic lights, and in the second you only have annotations for cars, then each instance of car in the first dataset will be learned as false example,... | I'm pretty new to object detection. I'm using tensorflow object detection API and I'm now collecting datasets for my project
and model_main.py to train my model.
I have found and transformed two quite large datasets of cars and traffic lights with annotations. And made two tfrecords from them.
Now I want to train a p... | 0 | 1 | 1,404 |
0 | 54,742,112 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2019-02-18T05:59:00.000 | 2 | 1 | 0 | Filtering and filling Nan with mode in Pandas | 54,741,230 | 0.379949 | python,pandas | df.loc[df["columnname"]==0,"columnname"]=np.nan
df["columnname"]=df.groupby("groupbycolumnname").columnname.transform(lambda x: x.fillna(x.mode())) | I have many qualitative variables in one column1 and corresponding qualitative values in another column which have blank values. I want to replace the blanks with the MODE value of each qualitative variable in column 1. For example if I have varibles such as Accountant, Engineer, Manager, etc. in column 1 and if I have... | 0 | 1 | 90 |
0 | 54,777,057 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2019-02-19T09:11:00.000 | 4 | 1 | 0 | How to use pretrained word2vec vectors in doc2vec model? | 54,762,478 | 1.2 | python,machine-learning,nlp,word2vec,doc2vec | You might think that Doc2Vec (aka the 'Paragraph Vector' algorithm of Mikolov/Le) requires word-vectors as a 1st step. That's a common belief, and perhaps somewhat intuitive, by analogy to how humans learn a new language: understand the smaller units before the larger, then compose the meaning of the larger from the sm... | I am trying to implement doc2vec, but I am not sure how the input for the model should look like if I have pretrained word2vec vectors.
The problem is, that I am not sure how to theoretically use pretrained word2vec vectors for doc2vec. I imagine, that I could prefill the hidden layer with the vectors and the rest of t... | 0 | 1 | 1,101 |
0 | 54,767,990 | 0 | 1 | 0 | 0 | 1 | false | 1 | 2019-02-19T12:19:00.000 | 0 | 1 | 0 | Python - Manual - libary installation Failure on Windows with Spacy, Thinc and msgpack-numpy python 3.7 | 54,766,199 | 0 | python,numpy | After many tries, I figured out the simple mistake I was making...
Collecting msgpack-numpy<0.4.4.0 (from thinc==6.12.0) Does not mean, I need 0.4.4.0 it means I need a package less than 0.4.4.0.
So I found msgpack_numpy-0.4.3.2-py2.py3-none-any.whl
It is confusing to list packages as a less than or equal requir... | All, I am pursuing a path of manual installation of python libraries, one that unfortunately, I cannot deviate from and it has become challenging because some of the libraries are just not easily found from pypi.org. This is a Windows 10 set up using Anaconda for python 3.7
My goal is to install SPACY and I have tri... | 0 | 1 | 132 |
0 | 54,767,624 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2019-02-19T13:27:00.000 | 0 | 1 | 0 | data structures to recognize particular elements / shapes with similar patterns on the image in python | 54,767,421 | 1.2 | python,algorithm,image-processing,data-structures | If you're planning on using something like PyTorch later on (which would tie in to the sort of neural-network functionality you're pursuing), I'd become familiar with how NumPy operates, as it bridges pretty well into Torch data structures. If it helps, a lot of SciPy and Matplotlib functions work beautifully with Nump... | I am having a trouble picking the right data structure as/library . I lack in experience in the area of image processing / pattern recognition . The aim is to building a simple prototype to learn recognizing particular shapes from construction plans. I would be great full for any indication about the data structure as ... | 0 | 1 | 47 |
0 | 54,779,427 | 0 | 0 | 0 | 1 | 1 | false | 0 | 2019-02-19T16:30:00.000 | 0 | 1 | 0 | What is a good AWS solution (DB, ETL, Batch Job) to store large historical trading data (with daily refresh) for machine learning analysis? | 54,770,900 | 0 | mysql,database,amazon-web-services,amazon-dynamodb,mysql-python | If you want to use AWS EMR, then the simplest solution is probably just to run a daily job that dumps data into a file in S3. However, if you want to use something a little more SQL-ey, you could load everything into Redshift.
If your goal is to make it available in some form to other people, then you should definitel... | I want to build a machine learning system with large amount of historical trading data for machine learning purpose (Python program).
Trading company has an API to grab their historical data and real time data. Data volume is about 100G for historical data and about 200M for daily data.
Trading data is typical time s... | 0 | 1 | 82 |
0 | 59,461,549 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2019-02-19T19:16:00.000 | 0 | 3 | 0 | Decimal class rounding in Pandas | 54,773,447 | 0 | python,pandas,decimal | To round your decimal to 2 significant figures for example:
round(df['yourSeries'].astype('float'), 2)
Or if you just want an int:
round(df['yourSeries'].astype('float')) | i have problems rounding Decimals() inside a Pandas Dataframe. The round() method does not work and using quantize() neither. I've searched for a solution with no luck so far.
round() does nothing, i asume it is meant for float numbers
quantize() won't work because it is not a DataFrame function
Any thoughts? | 0 | 1 | 881 |
0 | 54,805,518 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-02-20T15:02:00.000 | 0 | 1 | 0 | Calculating object labelling consensus area | 54,789,340 | 0 | python,computational-geometry | IMO the simplest is to fill the shapes using polygon filling / circle filling (this is simple, you can roll your own) / path filling (from a seed). Then finding the area of overlap is an easy matter. | Scenario: four users are annotating images with one of four labels each. These are stored in a fairly complex format - either as polygons or as centre-radius circles. I'm interested in quantifying, for each class, the area of agreement between individual raters – in other words, I'm looking to get an m x n matrix, wher... | 0 | 1 | 21 |
0 | 58,782,823 | 0 | 1 | 0 | 0 | 1 | false | 2 | 2019-02-20T22:36:00.000 | 2 | 1 | 0 | Dataset with lots of zero value as missing value. What should I do? | 54,796,340 | 0.379949 | python,data-science,mining | Well there are a few options.
Take an average of the non zero values and fill all the zeros with the average. This yields 'tacky' results and is not best practice a few outliers can throw off the whole.
Use the median of the non zero values, also not a super option but less likely to be thrown off by outliers.
Binnin... | I am currently working on IMDB 5000 movie dataset for a class project. The budget variable has a lot of zero values.
They are missing entries. I cannot drop them because they are 22% of my entire data.
What should I do in Python? Some suggested binning? Could you provide more details? | 0 | 1 | 1,215 |
0 | 54,799,272 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-02-21T02:43:00.000 | 0 | 2 | 0 | Matrix shapes difference between lectures | 54,798,437 | 0 | python,machine-learning,deep-learning | Both ways are fine, it only matters to be consistent, i.e., that all matrix operations are correct. Depending on the shape of the matrix you might have matrix*vector or vector_transposed*matrix, or some variety along these lines.
Playing around with different representations might actually help the understanding in the... | I'm currently learning deep-learning from two lectures. What gets me confused is that there is a notation difference between two lectures when they shape an input matrix X.
In Coursera's lecture, they make a matrix X in shape of (number of features, number of samples), so that they stack the samples vertically. Otherw... | 0 | 1 | 39 |
0 | 54,801,198 | 0 | 1 | 0 | 0 | 2 | true | 1 | 2019-02-21T06:09:00.000 | 3 | 2 | 0 | How to know if the Variable is Categorical or Numerical if all it contains is Digits? | 54,800,350 | 1.2 | python,pandas,dataframe,statistics,data-analysis | The short answer, is: your knowledge of the problem domain / application domain will tell you.
There are some differences that you look for, but to apply these differences, you will still have to use some domain knowledge (sometimes common sense).
The following are some differences which will help you distinguish:
For... | I have a dataset which has several Variables.
I want to determine that how can we judge for a variable if it is categorical or numerical other than the method of unique value counts, as for instance one of my variable Disease Type has 31 Unique Values whereas other Variable Distance from Office has 25 Unique Values, b... | 0 | 1 | 1,458 |
0 | 54,801,115 | 0 | 1 | 0 | 0 | 2 | false | 1 | 2019-02-21T06:09:00.000 | 0 | 2 | 0 | How to know if the Variable is Categorical or Numerical if all it contains is Digits? | 54,800,350 | 0 | python,pandas,dataframe,statistics,data-analysis | <dataframename>.info() will give the total count of each variable along with whether it is non-null and its datatype like float64,object,int64 etc | I have a dataset which has several Variables.
I want to determine that how can we judge for a variable if it is categorical or numerical other than the method of unique value counts, as for instance one of my variable Disease Type has 31 Unique Values whereas other Variable Distance from Office has 25 Unique Values, b... | 0 | 1 | 1,458 |
0 | 54,821,940 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2019-02-22T06:24:00.000 | 2 | 2 | 0 | I want all numpy arrays to be forced to be 2 dimensional | 54,821,206 | 0.197375 | python,arrays,numpy | As noted above, the np.matrix class has semantics quite similar to a matlab array.
However, if you goal is to learn numpy as a marketable skill, I would strongly recommend you fully embrace the concept of an ndarray; while there is some historical truth to calling numpy a port of matlab, it is a bit of an insult, as th... | I am coming over from Matlab, and while everything was mostly ported over really well (the community has to be thanked for this, a Matlab license costs way over $1000). There is one thing that I cannot for the life of me find out.
In Matlab, all arrays are 2D (until recently, where they gave you other options). Such th... | 0 | 1 | 114 |
0 | 54,832,931 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2019-02-22T12:17:00.000 | 0 | 2 | 0 | OpenCV can't be found but can be imported | 54,826,979 | 1.2 | python,opencv,ubuntu | I found out that I hadn't uninstalled in the build folder, problem solved! | I've installed opencv from source using a virtualenv, however I faced some errors and needed to reinstall it. I tried removing all the files with sudo find / -name "opencv" -exec rm {} \; and checked if the package was removed with pkg-config --modversion opencv, and it said it could not be found, but when I open the t... | 0 | 1 | 362 |
0 | 54,833,867 | 0 | 0 | 0 | 0 | 1 | false | 6 | 2019-02-22T19:18:00.000 | 2 | 1 | 0 | Can I use a machine learning model as the objective function in an optimization problem? | 54,833,739 | 0.379949 | python,machine-learning,optimization,scikit-learn,scipy | Yes; a linear regression model is a straightforward linear function of coefficients (one of which is the "intercept" or "bias").
The problem you have now is that a more complex model isn't quite so simple. You need to load the model into an appropriate engine. To "call" the model, you feed that engine the input vecto... | I have a data set for which I use Sklearn Decision Tree regression machine learning package to build a model for prediction purposes. Subsequently, I am trying to utilize scipy.optimize package to solve for the minimized solution based on a given constraint.
However, I am not sure if I can take the decision tree model... | 0 | 1 | 904 |
0 | 54,839,647 | 0 | 1 | 1 | 0 | 1 | true | 0 | 2019-02-23T08:18:00.000 | 0 | 1 | 0 | IronPython.Runtime.Exceptions.ImportException: 'No module named pandas' | 54,839,602 | 1.2 | c#,python,pandas | Sorry you cant access third party functionalities/packages (like Pandas here)
The reason:
Pandas / numpy use great parts of c code, that is a no good for IronPython (.NET).!!!!
If you need to use the power of pandas, i suggest you to create a dialog (send/reveive datas or by file or by calling an external python prog)... | I am running C# project which use result of python file. I'm using IronPython to run python file in visual studio. But the error above come up. Please help me. Many thanks! | 0 | 1 | 1,617 |
0 | 54,842,514 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2019-02-23T13:41:00.000 | 0 | 5 | 0 | Which loss function should I use in my LSTM and why? | 54,842,219 | 0 | python,python-3.x,tensorflow,keras | You'll want to use a logistic activation. This pushes each logit between 0 and 1, which represents the probability of that category.
Then use categorical cross entropy. This will not make your model a single class classifier since you are using the logistic activation rather than the softmax activation.
As a rule of ... | I try to understand Keras and LSTMs step by step. Right now I build an LSTM there the input is a sentence and the output is an array of five values which can each be 0 or 1.
Example:
Input sentence: 'I hate cookies'
Output example: [0,0,1,0,1]
For this, I am using keras library.
Now I am not sure which loss function I ... | 0 | 1 | 7,333 |
0 | 70,772,878 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2019-02-23T13:41:00.000 | 0 | 5 | 0 | Which loss function should I use in my LSTM and why? | 54,842,219 | 0 | python,python-3.x,tensorflow,keras | When it comes to regression problem in deep learning mean square error MSE is the most preferred loss function but when it comes to categorical problem where you want your output to be 1 or 0, true or false the cross binary entropy is preferable | I try to understand Keras and LSTMs step by step. Right now I build an LSTM there the input is a sentence and the output is an array of five values which can each be 0 or 1.
Example:
Input sentence: 'I hate cookies'
Output example: [0,0,1,0,1]
For this, I am using keras library.
Now I am not sure which loss function I ... | 0 | 1 | 7,333 |
0 | 54,928,019 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2019-02-23T14:52:00.000 | 0 | 1 | 0 | Read existing gurobi .lp-file and add constraints to it | 54,842,821 | 0 | python,gurobi | Without knowing your error message, I assume the problem is that you have not defined x.
The quickest way to fix this would probably be to reconstruct the variable name (that you defined earlier when building the model) from the SI values and then access the variables with getVarByName.
If this is slow because the mod... | I have a little problem using Gurobi in Python. I have a .lp-file, where my linear programm is saved. To these constraints I want to add some additional constraints. Loading and optimizing works without any problems, but I just cannot add new constraints to my model. I do not know what I am doing wrong...
I hope there ... | 0 | 1 | 392 |
0 | 54,850,244 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2019-02-23T15:12:00.000 | 3 | 1 | 0 | How to run predict() on "precomputed" data for clustering in python | 54,842,990 | 0.53705 | python,cluster-analysis | Most clustering algorithms, including AP, have no well-defined way to "predict" on new data. K-means is one of the few cases simple enough to allow a "prediction" consistent with the initial clusters.
Now sklearn has this oddity of trying to squeeze everything into a supervised API. Clustering algorithms have a fit(X, ... | I have my own precomputed data for running AP or Kmeans in python. However when I go to run predict() as I would like to run a train() and test() on the data to see if the clusterings have a good accuracy on the class or clusters, Python tells me that predict() is not available for "precomputed" data.
Is there another... | 0 | 1 | 439 |
0 | 58,692,607 | 0 | 1 | 0 | 0 | 3 | false | 0 | 2019-02-23T16:30:00.000 | 1 | 6 | 0 | Pip Install Tensorflow Failed | 54,843,623 | 0.033321 | python,tensorflow,object-detection-api | I had the same issue on windows 10, I find out that tensorflow work with python x64 installation and the command I used is the follow:
pip install --upgrade tf | I am trying to install tensorflow in python using pip command as
pip install tensorflow
, but unfortunately, I received the following error:
Could not find a version that satisfies the requirement tensorflow (from versions: ) No matching distribution found for tensorflow
I have also tried to install tensorflow using th... | 0 | 1 | 10,204 |
0 | 54,843,717 | 0 | 1 | 0 | 0 | 3 | false | 0 | 2019-02-23T16:30:00.000 | 1 | 6 | 0 | Pip Install Tensorflow Failed | 54,843,623 | 0.033321 | python,tensorflow,object-detection-api | Are you sure that you are capitalizing/spelling properly? The command prompt is case-sensitive. The input I use is:
cd C:\path\to\the\directory\python\is\installed\in (cd, space, the path to the directory) then:
python -m pip install TensorFlow
It should work afterwards. | I am trying to install tensorflow in python using pip command as
pip install tensorflow
, but unfortunately, I received the following error:
Could not find a version that satisfies the requirement tensorflow (from versions: ) No matching distribution found for tensorflow
I have also tried to install tensorflow using th... | 0 | 1 | 10,204 |
0 | 54,843,877 | 0 | 1 | 0 | 0 | 3 | false | 0 | 2019-02-23T16:30:00.000 | 2 | 6 | 0 | Pip Install Tensorflow Failed | 54,843,623 | 0.066568 | python,tensorflow,object-detection-api | I think Tensorflow does not currently have support for Python 3.7 and if you have Python 3.7 currently installed this might be the cause of the error message Could not find a version that satisfies the requirement tensorflow (from versions: ) No matching distribution found for tensorflow
You can downgrade to Python 3.6... | I am trying to install tensorflow in python using pip command as
pip install tensorflow
, but unfortunately, I received the following error:
Could not find a version that satisfies the requirement tensorflow (from versions: ) No matching distribution found for tensorflow
I have also tried to install tensorflow using th... | 0 | 1 | 10,204 |
0 | 54,846,520 | 0 | 0 | 0 | 0 | 1 | true | 3 | 2019-02-23T21:20:00.000 | 1 | 1 | 0 | What is the input format for word2vec features in SVM classification task? | 54,846,314 | 1.2 | python,classification,svm,word2vec | Vector of many features
From the perspective of an SVM, each dimension of a word vector would be a separate numeric feature - each dimension in that vector represents a numeric metric representing something different.
The same applies for non-SVM classifiers. For example, if you'd have a neural network, and your input ... | I am doing a binary classification task using linear SVM in scikit learn. I use nominal features and word vectors. I obtained the word vectors using the pretrained Google word2vec, however, I am not sure how SVM can handle word vectors as a feature.
It seems that I need to "split" each vector in 300 separate features (... | 0 | 1 | 947 |
0 | 54,921,745 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-02-24T05:49:00.000 | 0 | 1 | 0 | Why am I getting cv2 error in raspbianOS and not raspberrypi shell | 54,849,139 | 0 | python | Please try to run your code after executing the below command.
python -m pip install opencv-contrib-python | I recently installed and compiled openCV on my raspberrypi.
Now if I use the command import cv2 in my raspian OS (either in the python shell or the IDE) I get an error
no module named cv2 found but the same command works in the raspberrypi shell.
How do I resolve this??
File "home/pi/Desktop/FR.py",line 2,in m... | 0 | 1 | 252 |
0 | 54,855,155 | 0 | 0 | 0 | 0 | 1 | true | 2 | 2019-02-24T09:50:00.000 | 2 | 1 | 0 | Intent classification with large number of intent classes | 54,850,657 | 1.2 | python,tensorflow,nlp,text-classification | First, word2vec and GloVe are, almost, dead. You should probably consider using more recent embeddings like BERT or ELMo (both of which are sensitive to the context; in other words, you get different embeddings for the same word in a different context). Currently, BERT is my own preference since it's completely open-so... | I am working on a data set of approximately 3000 questions and I want to perform intent classification. The data set is not labelled yet, but from the business perspective, there's a requirement of identifying approximately 80 various intent classes. Let's assume my training data has approximately equal number of each ... | 0 | 1 | 1,227 |
0 | 54,856,783 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2019-02-24T21:29:00.000 | 0 | 1 | 0 | How to revert keras model to previous epoch weights after train_on_batch nan update | 54,856,754 | 1.2 | python,tensorflow,keras,deep-learning,nan | since you have not pasted your code and weights I can't tell you much, but I suspect this problem may be due to dropout or regularisation, if you are using any of the two techniques set the parameters or percentage of dropouts properly as per your network, a high percentage in a small network will lead this sort of pro... | I'm having trouble resetting my keras model to the weights it had in the previous epoch after I hit a train_on_batch update that makes some of the weights nans.
I have tried to save the model weights after each training step and then to load the "good" (non-nan) weights back into the keras model after a nan training up... | 0 | 1 | 896 |
0 | 54,862,189 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2019-02-24T21:57:00.000 | 0 | 1 | 0 | Lucas Kanade: How to calculate distance between tracked points | 54,856,954 | 1.2 | python,opencv,computer-vision,computer-science | In new_pts points have the same index. But they can be not founded - see to the status array: if status[i] == 1 then new_pts[i] contains a new coordinates of the old_pts[i].
For the more robustness it can to search direct flow (goodFeaturesToTrack(frame1) -> LK flow), backward flow (goodFeaturesToTrack(frame2) -> LK fl... | I'm using lucas-kanade opencv implementation to track objects between frames. I want to be able to do the following two things:
Calculate the distance moved by each point between frames
Track bounding boxes for each object across frames
I have obtained the features to track using cv2.goodFeaturesToTrack(). I also add... | 0 | 1 | 241 |
0 | 54,870,187 | 0 | 0 | 0 | 0 | 1 | true | 3 | 2019-02-25T10:27:00.000 | 1 | 1 | 0 | Is there a way to impose a constraint in tensor flow, could I enforce some rule along the way? | 54,864,100 | 1.2 | python-3.x,tensorflow,constraints | If by "generated by TensorFlow" you mean generated by a neural network, I don't think it is possible to do that in general. You can't really guarantee that the output of a neural network never violates such hard constraints in general, especially at test time.
Here's what you could do:
Add a loss term, something like ... | Is there some way to a constraint on the data generated by tensor flow, for example if my model produced two outputs can you impose some sort of constraint on these, like if a and b where the outputs could you pre-enforce something like (a+b)/2<10? So the model wouldn't break this rule?
Thanks in advance | 0 | 1 | 236 |
0 | 54,865,791 | 0 | 1 | 0 | 0 | 1 | false | 1 | 2019-02-25T11:49:00.000 | 0 | 2 | 0 | Python packages installing error in deep-learning environment | 54,865,526 | 0 | python,pip,deep-learning | terminal one as administrator if using Windows and use ubuntu before command use sudo. | When I am installing the new package in my deep-learning environment it gives me this error:
Could not install packages due to an EnvironmentError: [WinError 32]
The process cannot access the file because it is being used by another
process: Consider using the --user option or check the permissions.
Please help ... | 0 | 1 | 170 |
0 | 54,871,249 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2019-02-25T16:55:00.000 | 1 | 2 | 0 | Can Image Recognition deal with classes, where the deciding quality is not directly visible? | 54,871,092 | 1.2 | python,keras,image-recognition | It could and also could not. For a CNN to provide good results with no contextual input, it would mean there must be some for of correlation between the input and the output. So lets say some apps have designs that correlate with age-rating, then yes its possible, otherwise its not unless you give the network something... | I have the following problem and am not quite sure if it is solvable by image recognition (and convolutional neural networks).
I have a dataset of 400k pictures divided into 5 classes. The pictures are screenshots of apps, which are put into the 5 classes depending on what age rating they received.
For example: I have ... | 0 | 1 | 36 |
0 | 54,871,548 | 0 | 1 | 0 | 0 | 1 | false | 1 | 2019-02-25T17:17:00.000 | 1 | 4 | 0 | How to standardise different date formats in pandas? | 54,871,446 | 0.049958 | python,pandas | Try removing the format parameter and setting infer_datetime_format=Truein the arguments you pass to pd.to_datetime | I have a dataset in csv format which contains dates in a column. I have imported this dataset into python pandas, and this date column is shown as an object. I need to convert this column to date time but i have a problem. This date column has date format in two formats
1. 11/7/2013 11:51
2. 13-07-2013 08:33:16
I need... | 0 | 1 | 2,154 |
0 | 68,398,101 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-02-25T18:23:00.000 | 0 | 2 | 0 | Plot a density histogram with Plotly | 54,872,488 | 0 | python,plotly | Try this:
go.Histogram(x=some_vec, histnorm="probability density") | I'm looking for a way to plot a density histogram with Plotly. As a density=True with a numpy histogram. My variable is a continuous one from 0 to 20. I already have a count on yaxis with bins. So I'm looking for replace theses counts by percentage (o density). | 0 | 1 | 207 |
0 | 54,880,674 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-02-26T06:07:00.000 | 1 | 3 | 0 | Is AUC a better metric than accuracy in case of imbalenced datasets in machine learning,If not which is the best metric? | 54,879,340 | 0.066568 | python,machine-learning,artificial-intelligence,roc,auc | Neither are good for imbalanced datasets. Use the area under the precision recall curve instead. | Is auc better in handling imbalenced data. As in most of the cases if I am dealing with imbalenced data accuracy is not giving correct idea. Even though accuracy is high, model has poor perfomance. If it's not auc which is the best measure to handle imbalenced data. | 0 | 1 | 1,508 |
0 | 55,558,787 | 0 | 0 | 1 | 0 | 2 | false | 0 | 2019-02-26T08:57:00.000 | 0 | 3 | 0 | how to increase fps for raspberry pi for object detection | 54,881,654 | 0 | python,opencv,raspberry-pi,object-detection,yolo | The raspberry pi not have the GPU procesors and because of that is very hard for it to do image recognition at a high fps . | I'm having low fps for real-time object detection on my raspberry pi
I trained the yolo-darkflow object detection on my own data set using my laptop windows 10 .. when I tested the model for real-time detection on my laptop with webcam it worked fine with high fps
However when trying to test it on my raspberry pi, whi... | 0 | 1 | 1,551 |
0 | 66,489,611 | 0 | 0 | 1 | 0 | 2 | false | 0 | 2019-02-26T08:57:00.000 | 0 | 3 | 0 | how to increase fps for raspberry pi for object detection | 54,881,654 | 0 | python,opencv,raspberry-pi,object-detection,yolo | My detector on raspberry pi without any accelerator can reach 5 FPS.
I used SSD mobilenet, and quantize it after training.
Tensorflow Lite supplies a object detection demo can reach about 8 FPS on raspberry pi 4. | I'm having low fps for real-time object detection on my raspberry pi
I trained the yolo-darkflow object detection on my own data set using my laptop windows 10 .. when I tested the model for real-time detection on my laptop with webcam it worked fine with high fps
However when trying to test it on my raspberry pi, whi... | 0 | 1 | 1,551 |
0 | 55,735,532 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2019-02-26T18:14:00.000 | 0 | 2 | 0 | Running Facenet using OpenVINO | 54,891,713 | 0 | python,tensorflow,openvino | You can use Python APIs to integrate OV inference into your Python application. Please see inference_engine/samples/python_samples folder for existing Python samples. | I am stuck at a problem using OpenVINO. I am trying to run the facenet after converting model using OpenVINO toolkit but I am unable to use .npy and .pickle for complete face recognition. I am successful in converting .pb file to .bin and .xml file using OpenVino toolkit. | 0 | 1 | 636 |
0 | 55,158,313 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2019-02-26T18:14:00.000 | 0 | 2 | 0 | Running Facenet using OpenVINO | 54,891,713 | 0 | python,tensorflow,openvino | openvino converts the model to intermediate representation, which is compatible across multiple hardwares. It can improve the performance of your model too. Since you have already mentioned that yopu were ableto convert your model to IR format, the next phase is inference for which you can use the .xml and .bin files.
... | I am stuck at a problem using OpenVINO. I am trying to run the facenet after converting model using OpenVINO toolkit but I am unable to use .npy and .pickle for complete face recognition. I am successful in converting .pb file to .bin and .xml file using OpenVino toolkit. | 0 | 1 | 636 |
0 | 54,894,944 | 0 | 1 | 0 | 0 | 1 | true | 1 | 2019-02-26T21:46:00.000 | 1 | 2 | 0 | Finding Largest Subset of Data where Average Matches Criteria | 54,894,635 | 1.2 | python,optimization,weighted-average | Start by translating the desired range to 0, just for convenience. I'll translate to the lower bound, although the midpoint is also a good choice.
This makes your data set [10, 1, -10, 20, -12]. The set sum is 9; you need it to be in the range 0 to upper_bound * len(data).
This gives you a tractable variation of the ... | I'm trying to find the largest subset sum of a particular data set, where the average of a field in the data set matches predetermined criteria.
For example, say I have a people's weights (example below) and my goal is to find the largest weight total where the average weight of the resulting group is between 200 and 2... | 0 | 1 | 35 |
0 | 54,897,167 | 0 | 0 | 0 | 0 | 1 | true | 3 | 2019-02-26T21:57:00.000 | 3 | 1 | 0 | Why should I use tf.data? | 54,894,799 | 1.2 | python,numpy,tensorflow,machine-learning | The tf.data module has specific tools which help in building a input pipeline for your ML model. A input pipeline takes in the raw data, processes it and then feeds it to the model.
When should I use tf.data module?
The tf.data module is useful when you have a large dataset in the form of a file such as .csv or .tfr... | I'm learning tensorflow, and the tf.data API confuses me. It is apparently better when dealing with large datasets, but when using the dataset, it has to be converted back into a tensor. But why not just use a tensor in the first place? Why and when should we use tf.data?
Why isn't it possible to have tf.data return th... | 0 | 1 | 1,075 |
0 | 54,920,367 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-02-27T17:56:00.000 | 1 | 1 | 0 | Does the Google News Word2Vec model take up storage every time you run it? | 54,911,712 | 0.197375 | python,nlp,gensim,word2vec,word-embedding | Just loading a model won't usually use any more disk storage. (An exception: if load or use needs addressable memory beyond your RAM, you may start using virtual memory, which might show up as less disk space depending on your OS. But, with these sorts of models, you want to avoid relying on any virtual memory, as basi... | This may seem like an odd question but I'm new to this so thought I'd ask anyway.
I want to use this Google News model over various different files on my laptop. This means I will be running this line over and over again in different Jupyter notebooks:
model=word2vec.KeyedVectors.load_word2vec_format("GoogleNews-vecto... | 0 | 1 | 149 |
0 | 54,914,559 | 0 | 1 | 0 | 0 | 1 | true | 4 | 2019-02-27T20:19:00.000 | 12 | 1 | 0 | what's the difference between "import keras" and "import tensorflow.keras" | 54,913,830 | 1.2 | python-3.x,tensorflow,keras | Tensorflow.keras is an version of Keras API implemented specifically for use with Tensorflow. It is a part of Tensorflow repo and from TF version 2.0 will become main high level API replacing tf.layers and slim.
The only reason to use standalone keras is to maintain framework-agnostic code, i.e. use it with another ba... | I was wondering, what's the difference between importing keras from tensorflow using import tensorflow.keras or just pip installing keras alone and importing it using import keras as both seemed to work nicely so far, the only difference I noticed is that i get Using TensorFlow backend. in the command line every time I... | 0 | 1 | 1,437 |
0 | 60,148,794 | 0 | 0 | 0 | 0 | 1 | false | 5 | 2019-02-28T03:04:00.000 | 5 | 2 | 0 | Viewing Graph from saved .pbtxt file on Tensorboard | 54,917,785 | 0.462117 | python,tensorflow,tensorboard | Open tensorboard and use the "Upload" button on the left to upload the pbtxt file will directly open the graph in tensorboard. | I just have a graph.pbtxt file. I want to view the graph in tensorboard. But I am not aware of how to do that. Do I have to write any python script or can I do it from the terminal itself? Kindly help me to know the steps involved. | 0 | 1 | 5,032 |
0 | 56,553,227 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2019-02-28T10:14:00.000 | 0 | 1 | 1 | Model Deployment On GCP Without Cloud Storage Bucket | 54,923,211 | 0 | python,google-cloud-ml | For model deployment you need a Google Cloud Storage using AI Platform.
Another option is to use AI platform training (local or in GCP), output the model (SavedModel format) to a local folder or Cloud Storage and from there using TF Serving in Compute Engine Instance. | I have developed a Tensorflow based machine learning model on local Machine. And I want to deploy it in Google Cloud Platform (Cloud ML Engine) for predictions.
The model reads input data from Google Bigquery and the output predictions has to be written in Google Bigquery only.
There are some data preparation scripts ... | 1 | 1 | 147 |
0 | 54,934,592 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2019-02-28T15:02:00.000 | 0 | 1 | 0 | Python: Image processing create wrinkled paper effect | 54,928,574 | 0 | python,image-processing | Instead of using transparency, assuming you have two images of the same dimensions, one bright with the wrinkled paper and one with a dark text on a white background, you could try to take for every pixel the minimum of the values of the corresponding pixels in the two images.
In this way you should succeed in merging ... | Maybe it is a little bit hard to describe my problem. I am searching for an algorithm in Python, to create wrinkled paper effect on a white image with some text.
My first try was adding some real wrinkled paper image (with transparency) to the image with text. This looks nice, but hast the side effect, that the text i... | 0 | 1 | 408 |
0 | 54,950,068 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-02-28T16:24:00.000 | 0 | 1 | 0 | Intersection of interpol1d objects | 54,930,134 | 0 | python,python-3.x,scipy | Use scipy.optimize.brentq for bracketed root-finding:
brentq(lambda x: interp1d(xx, yy)(x) - interp1d(xxx, yyy)(x), -1, 1) | I have 2 cumulative distributions that I want to find the intersection of. To get an underlying function, I used the scipy interpol1d function. What I’m trying to figure out now, is how to calculate their intersection. Not sure how I can do it. Tried fsolve, but I can’t find how to restrict the range in which to search... | 0 | 1 | 58 |
0 | 54,930,980 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-02-28T17:06:00.000 | 0 | 1 | 0 | model can predict new image | 54,930,833 | 0 | python-3.x,tensorflow,jupyter-notebook | By just looking at what you have posted you should replace image_resized.reshape(1,50,50,3) with image_resized.reshape(1,28,28,3) | ValueError Traceback (most recent call last) in () ----> 1 prediction = model.predict(image_resized.reshape(1,50,50,3)) 2 print('Prediction Score:\n',prediction[0]) ValueError: cannot reshape array of size 2352 into shape (1,50,50,3) | 0 | 1 | 30 |
0 | 54,935,427 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-02-28T21:02:00.000 | 0 | 1 | 0 | Tensorflow data pipeline: Slow with caching to disk - how to improve evaluation performance? | 54,934,207 | 0 | python,tensorflow,machine-learning,tensorflow-datasets | prefetch(1) means that there will be only one element prefetched, I think you may want to have it as big as the batch size or larger.
After first cache you may try to put it second time but without providing a path, so it would cache some in the memory.
Maybe your HDD is just slow? ;)
Another idea is you could just ma... | I've built a data pipeline. Pseudo code is as follows:
dataset ->
dataset = augment(dataset)
dataset = dataset.batch(35).prefetch(1)
dataset = set_from_generator(to_feed_dict(dataset)) # expensive op
dataset = Cache('/tmp', dataset)
dataset = dataset.unbatch()
dataset = dataset.shuffle(64).batch(256).prefetch(1)
to_f... | 0 | 1 | 257 |
0 | 71,724,209 | 0 | 0 | 0 | 0 | 1 | false | 3 | 2019-03-01T16:32:00.000 | 0 | 3 | 0 | What is the real number of CNN layers in yolov3? | 54,948,738 | 0 | python,conv-neural-network,object-detection,yolo | YOLO v3 has 107 layers in total, you should also count shortcut layers, route layers, upsample layers, and YOLO layers(32 in total). So, there are 75+32=107 layers in total. When you see indexes in shortcut or route layers, you will find that we count from 0. Therefore, yolo layers are in 82,94,106 layers. | I'm really confused with the architecture of yolov3. I've read the documentation and paper about it. Some people say that it has 103 convolutional layers, some others say that it has 53 layers. But when you count the convolutional layers in the .cfg file (after downloading it) it comes to about 75! ...What is missed he... | 0 | 1 | 5,589 |
0 | 54,958,225 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2019-03-01T18:33:00.000 | 0 | 1 | 0 | Reordering numpy 4D-array | 54,950,408 | 1.2 | python,numpy,matrix-multiplication,numpy-ndarray,array-broadcasting | If your matrix is called matrix, matrix.shape = (1,2,1,4) (as in my example above) does the trick. NumPy will automatically notices if your new shape is "out of bounds", and automatically reorder the data correctly if it's not.
EDIT: You can also use newMatrix = numpy.reshape(matrix, (1,2,1,4)) to create a new matrix a... | I'm having troubles understanding how to manage and modify numpy matrices. I find it very difficult to "picture" the matrices in my head.
I have a (4x2x1x1) matrix which I want to make into a (1x2x1x4) matrix, such that I can apply matrix multiplication with another matrix which have the shape (3x2x1x1).
Thanks in adv... | 0 | 1 | 130 |
0 | 54,953,548 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-03-01T22:38:00.000 | 1 | 2 | 0 | Add pixel wise independent noise to image | 54,953,272 | 0.099668 | python,numpy,image-processing,scikit-learn,noise-generator | You have several options. If you want to take random samples with replacement, just use one of the numpy's builtin random modules (i.e., numpy.random.random). You could also use numpy.random.pareto for more dramatic/bursty noise. These methods generate independent samples.
If you have a distribution in the form of a se... | My question is simple: I have an image and I want to add pixel wise independent noise to the image. The noise can be derived from from any distribution such as Gaussian. What are the available modules in numpy/scikit-learn to do the same?
I do not have any code but I am learning about modules such as numpy.random.norma... | 0 | 1 | 561 |
0 | 54,958,224 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-03-02T11:44:00.000 | 0 | 1 | 0 | Pandas - select lowest value to date | 54,958,144 | 0 | python,pandas | Without seeing your dataset it's hard to help you directly. The problem does boil down to the following. You need to select the range of data you want to work with (so select rows for the date range and columns for the user/speed).
That would look something like x = df.loc[["2-4-2018","2-4-2019"], ['users', 'speed']]... | I'm new to Pandas.
I've got a dataframe where I want to group by user and then find their lowest score up until that date in the their speed column.
So I can't just use df.groupby(['user'])['speed'].transform('min) as this would give the min of all values not just form the current row to the first.
What can I use to ge... | 0 | 1 | 43 |
0 | 54,962,810 | 0 | 1 | 0 | 0 | 1 | false | 1 | 2019-03-02T17:22:00.000 | 0 | 1 | 0 | Why can python lists hold more data than numpy arrays? | 54,961,068 | 0 | python,arrays,numpy,memory,out-of-memory | Allocate the memory for a numpy array and never create a list in the first place.
memmap should not be neccessary as the original list fits in memory. | Sorry, I feel like this may be a basic question, but I did not find any "solutions" for this.
I am filling a python list with a lot of data, and finally want to convert it to a numpy.array for further processing.
However, when I call numpy.asarray(my_list), I get an out of memory error. Why does that happen? Is it bec... | 0 | 1 | 85 |
0 | 54,964,640 | 0 | 0 | 0 | 0 | 1 | false | 3 | 2019-03-02T23:40:00.000 | 0 | 2 | 0 | pandas read csv by taking samples | 54,964,173 | 0 | python,pandas,csv | Use skiprows with a complex selector:
Example would be
rows=1000
prob=.01
print len(pd.read_csv(filename, usecols=[0], skiprows=lambda x: x in random.sample(xrange(0,num_rows), int((1.0-prob)*num_rows ))))
There are also other ways of doing it by reading in chunks and then just skipping rows.
e.g. you could use chunk... | I have a large CSV file and I just want to take a 1% sample from it. Is there a good way to read the samples directly into pandas data frame without having to read the whole file and then discard 99% of the data? | 0 | 1 | 1,045 |
0 | 58,200,454 | 0 | 0 | 0 | 0 | 1 | true | 3 | 2019-03-03T14:12:00.000 | 1 | 2 | 0 | How to add augmented images to original dataset using Pytorch? | 54,969,705 | 1.2 | python,pytorch | Why do you want it? Generally speaking, it is enough to increase the number of epochs over the dataset, and your model will see the original and the augmented version of every image at least once (assuming a relatively high number of epochs).
Explanation:
For instance, if your augmentation has a chance of 50% to be app... | From my understanding, RandomHorizontalFlip etc. replace image rather than adding new images to dataset. How do I increase my dataset size by adding augmented images to dataset using PyTorch?
I have gone through the links posted & haven't found a solution. I want to increase the data size by adding flipped/rotated imag... | 0 | 1 | 2,045 |
0 | 56,727,042 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-03-03T14:26:00.000 | 0 | 2 | 0 | Face Recognition with one shot learning | 54,969,832 | 0 | python,tensorflow,conv-neural-network | As my knowledge, CNN needs lots of data for training the model.So we cant implement one shot learning features on CNN | I'm a newbie at deep learning. I started with face recognition example and I found that there are 2 types of model base on data for pre-trained.
1. One-shot learning with siamese network: Which we can use few data for train the model.
2. Convolutional neural network: Need numerous data for train the model.
Could we com... | 0 | 1 | 420 |
0 | 54,971,460 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2019-03-03T17:06:00.000 | 1 | 2 | 0 | different element data types within numpy array? | 54,971,408 | 0.099668 | python,numpy | From the docs:
dtype : data-type, optional
The desired data-type for the array. If not given, then the type will
be determined as the minimum type required to hold the objects in the
sequence. This argument can only be used to ‘upcast’ the array. For
downcasting, use the .astype(t) method.
So if you set dtype a... | Just like list in python where [1,"hello", {"python": 10}] it can have all different types within, can numpy array have this as well?
when numpyarray.dtype => dtype('float64') is it implying all elements are of type float? | 0 | 1 | 2,473 |
0 | 54,974,116 | 0 | 0 | 0 | 0 | 1 | false | 3 | 2019-03-03T20:40:00.000 | 1 | 2 | 0 | Keras: using weights when fitting | 54,973,519 | 0.099668 | python,tensorflow,keras | I think there might be some confusion with what "sample_weight" means in the context of model.fit. When you call model.fit, you are minimizing a loss function. That loss function measures the error between your model prediction and the true values. Some of the samples in your dataset might be more important to you, so ... | I have a question about using weights in keras. I have some data as events and for each of them there is an associated weight. Therefore, when I do the training of my keras model I was using the sample_weight argument to pass that information.
I then notice that if I want to use the model.predict method there isn't an ... | 0 | 1 | 1,419 |
0 | 54,983,029 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2019-03-04T11:07:00.000 | 2 | 1 | 0 | Tensorflow cannon import name 'gradients_util' | 54,981,982 | 0.379949 | python,tensorflow | You must use version of benchmark compatible with your TensorFlow version.
Determine it using pip show tensorflow.
Then go to cloned repository of benchmarks, and checkout to required branch, for example git checkout cnn_tf_v1.13_compatible.
Previous comments suggested to use TensorFlow 2.0, but I don't think author of... | After doing
pip install tensorflow-gpu
I am trying to
import gradients_util from tensorflow.python.ops
but I get
cannot import name 'gradients_util'
error. How can I fix this? | 0 | 1 | 1,486 |
0 | 55,593,835 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2019-03-04T13:30:00.000 | 0 | 1 | 0 | After Pytorch Upgrade , my model is giving almost random output | 54,984,345 | 1.2 | python,machine-learning,pytorch,torchvision | It was because of drop out layer. Model.eval disables the dropout layer. Pretty simple.
But Now in Pytorh upgrade, if Dropout is not defined specifically in model init function, it will not get disable during eval.
Atleast this was reason for my case. | I trained, tested and still using a model in "Pytorch 0.4.1". It was, and is still working fine (output is what it should be) if I use pitch 0.4.1.
But as i upgrade to version 1.0.1, every time, I try to evaluate same input image, I get different output (Its a regression).
I tried to see what has been changed in those ... | 0 | 1 | 58 |
0 | 55,231,544 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2019-03-05T00:20:00.000 | 0 | 1 | 0 | Typical Number of generations for NEAT (Neuro Evolution of Augmenting Topologies)? | 54,993,604 | 0 | python-3.x,es-hyperneat | There are a couple other hyperparameters to consider before deciding NEAT cannot produce a usable neural network for your problem. You will have to make sure that your population is also large enough. Obviously a larger dataset is more helpful, but that is limited. Finally, changes such as mutation rates, aggregation o... | Does anyone have an estimate of the number of generations one should search before concluding that the NEAT-algorithm is not able to reach the minima?
I am running NEAT on a very small dataset of cancer patients (~5K rows). And after 5000 generations, the concordance index for prediction of survival index is not improv... | 0 | 1 | 210 |
0 | 54,993,847 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-03-05T00:37:00.000 | 0 | 1 | 0 | Regression analysis for linear regression | 54,993,739 | 0 | python,machine-learning,sklearn-pandas | If your x values are categorical then it does not necessarily make much sense binding them to a uniform grid. Who's to say category A and B should be spaced apart the same as B and C. Assuming that they are will only lead to incorrect representation of your results.
As your choice of scale is the unknowns, you would be... | I have a regression model where my target variable (days) quantitative values ranges between 2 to 30. My RMSE is 2.5 and all the other X variables(nominal) are categorical and hence I have dummy encoded them.
I want to know what would be a good value of RMSE? I want to get something within 1-1.5 or even lesser but I a... | 0 | 1 | 53 |
0 | 55,007,697 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2019-03-05T04:09:00.000 | 0 | 2 | 0 | error UnicodeDecodeError: 'utf-8' codec when reading CSV | 54,995,240 | 0 | python-3.x | Thank you both. It seems to work. I read it successfully and until know, variables are responding fine to my calculations. Seems SOLVED. | I recently download the PISA 2012 Student database from PISA. I follow the instructions and successfully read it on SAS. Then I exported as CSV to read it in Python 3, using proc export, but I keep getting this error when trying to read it in python pandas: UnicodeDecodeError: 'utf-8' codec can't decode byte 0xc1 in po... | 0 | 1 | 183 |
0 | 55,010,897 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2019-03-05T20:15:00.000 | 3 | 1 | 0 | Pandas `to_csv` String column got converted | 55,010,853 | 1.2 | python,pandas | CSV doesn't have data types. Excel has no way of knowing what you want, so it tries to interpret it. If you are using Excel, click the data tab and 'from csv' and you can specify dtypes on reading it.
Otherwise open the csv file in notepad and you'll see that the data is there. | I have a dataframe with a column of user ids converted from int to string
df['uid'] = df['uid'].astype(str)
However when I write to csv, the column got rounded to the nearest integer in format 1E+12 (the value is still correct when you select the cell).
But to_excel outputs the column correctly, can someone explain a b... | 0 | 1 | 419 |
0 | 55,014,376 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-03-05T22:16:00.000 | 0 | 1 | 0 | Tf-idf for SO posts (where tag can only occur once ) | 55,012,475 | 0 | python,nlp,tf-idf | If you want to filter out tags that are so common, you can use conditional probability. eg: python is so common on posts taged pytorch, so P(python|pytorch) will be hight, likes:0.9. You can find a threshold to filter those tags.
Association rule learning is more suitable and more complex than the above. | Using the stackoverflow data dump, I am analyzing SO posts that are tagged with pytorch or keras. Specifically, I count how many times each co tag occurs (ie the tags that aren't pytorch in a pytorch tagged post).
I'd like to filter out the tags that are so common they've lost real meaning for my analysis (like the py... | 0 | 1 | 29 |
0 | 55,375,736 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-03-06T04:46:00.000 | -1 | 1 | 0 | nyoka package install conflict with keras/tensorflow version | 55,015,742 | -0.197375 | python-3.x,tensorflow,keras,pmml | Which version of Nyoka are you using? The issue was there prior to version 3.0.1. The latest version of nyoka (3.0.6) does not have tensorflow as a dependency. Could you try with the latest version? | I have installed tensorflow-gpu and keras on my gpu machine for deep learning training. The tensorflow version is 1.12. However, nyoka (pmml converter package of python) has conflict because of tensorflow dependencies. I think it uses tensorflow 1.2. Can there be any workaround for it? | 0 | 1 | 171 |
0 | 55,028,215 | 0 | 1 | 0 | 0 | 2 | true | 1 | 2019-03-06T16:39:00.000 | 5 | 4 | 0 | Why does 2- - - -1=3? | 55,028,071 | 1.2 | python | 2 - - - - 1 is the same as 2 - ( - ( - ( - 1))) what is the same as
2 - ( - (1)) = 2 + 1 = 3
As soon as number of minuses is even you actually do "+". | I recently ran into a bug using python (v3.6.8) and pandas (v0.23.4) where I was trying to subtract a date offset. However, I accidentally typed two -- signs and it ended up adding the date offset instead. I did some more experimenting and found that 2--1 will return 3. This makes sense since you could interpret that a... | 0 | 1 | 115 |
0 | 55,028,501 | 0 | 1 | 0 | 0 | 2 | false | 1 | 2019-03-06T16:39:00.000 | 2 | 4 | 0 | Why does 2- - - -1=3? | 55,028,071 | 0.099668 | python | mathematically, it's correct. by why would a programming language allow that? maybe i just lack imagination, but i can't think of any reason why you would want to explicitly string together plus or minus signs. and if you did do that, it is likely a typo as in the original post. if it's done through variables, then it ... | I recently ran into a bug using python (v3.6.8) and pandas (v0.23.4) where I was trying to subtract a date offset. However, I accidentally typed two -- signs and it ended up adding the date offset instead. I did some more experimenting and found that 2--1 will return 3. This makes sense since you could interpret that a... | 0 | 1 | 115 |
0 | 55,385,124 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2019-03-06T20:47:00.000 | 0 | 1 | 0 | Using non-square input matrix for convolutional autoencoder | 55,031,885 | 0 | python,keras,deep-learning,conv-neural-network,autoencoder | it is hard to tell what is the issue here since no sample code is provided . However most propably your Matrix is diminsions are odd ( example 9×9 ) or becomes odd while pooling. tofix this issue you need to either pad your input to make the matrix diminsions even . our crop the decoder lsyers of your autoencoder to h... | Is it possible to train convolutional autoencoder (CAE) with non-square (rectangular) input matrix? All the tutorials and resources I have studied on CAE seems to use squared images. The data I am working with is not image. I have hundreds of single cells and for each cell there is a matrix (genomic data) with thousand... | 0 | 1 | 469 |
0 | 55,154,500 | 1 | 0 | 0 | 0 | 1 | true | 0 | 2019-03-07T00:56:00.000 | 0 | 1 | 0 | Google colab Transport endpoint is not connected | 55,034,469 | 1.2 | python,neural-network,jupyter-notebook,jupyter,google-colaboratory | In google colab, you can use their GPU service for upto 12 hours, after that it will halt your execution, if you ran it for 3-4 hours, it will just stop displaying Data continuously on your browser window(if left Idle), and refreshing the window will restore that connection.
In case you ran it for 34 hours, then it wil... | I am tuning hyperparameters for a neural network via gridsearch on google colab. I got a "transport endpoint is not connected" error after my code executed for 3 4 hours. I found out that this is because google colab doesn't want people to use the platform for a long time period(not quite sure though).
However, funnily... | 0 | 1 | 4,543 |
0 | 56,431,054 | 0 | 0 | 0 | 0 | 3 | false | 0 | 2019-03-07T06:59:00.000 | 0 | 3 | 0 | TensorFlow: Sample Integers from Gumbel Softmax | 55,037,810 | 0 | python,tensorflow,tensorflow-probability | You can do a dot product of the relaxed one hot vector with a vector of [1 2 3 4 ... n]. The result is going to give you the desired scalar.
For instance if your one hot vector is [0 0 0 1], then dot([0 0 0 1],[1 2 3 4]) will give you 4 which is what you are looking for. | I am implementing a program to sample integers from a categorical distribution, where each integer is associated with a probability. I need to ensure that this program is differentiable, so that back propagation can be applied. I found tf.contrib.distributions.RelaxedOneHotCategorical which is very close to what I am t... | 0 | 1 | 768 |
0 | 67,752,253 | 0 | 0 | 0 | 0 | 3 | false | 0 | 2019-03-07T06:59:00.000 | 0 | 3 | 0 | TensorFlow: Sample Integers from Gumbel Softmax | 55,037,810 | 0 | python,tensorflow,tensorflow-probability | You can't get what you want in a differentiable manner because argmax isn't differentiable, which is why the Gumbel-Softmax distribution was created in the first place. This allows you, for instance, to use the outputs of a language model as inputs to a discriminator in a generative adversarial network because the acti... | I am implementing a program to sample integers from a categorical distribution, where each integer is associated with a probability. I need to ensure that this program is differentiable, so that back propagation can be applied. I found tf.contrib.distributions.RelaxedOneHotCategorical which is very close to what I am t... | 0 | 1 | 768 |
0 | 55,052,713 | 0 | 0 | 0 | 0 | 3 | false | 0 | 2019-03-07T06:59:00.000 | 0 | 3 | 0 | TensorFlow: Sample Integers from Gumbel Softmax | 55,037,810 | 0 | python,tensorflow,tensorflow-probability | The reason that RelaxedOneHotCategorical is actually differentiable is connected to the fact that it returns a softmax vector of floats instead of the argmax int index. If all you want is the index of the maximal element, you might as well use Categorical. | I am implementing a program to sample integers from a categorical distribution, where each integer is associated with a probability. I need to ensure that this program is differentiable, so that back propagation can be applied. I found tf.contrib.distributions.RelaxedOneHotCategorical which is very close to what I am t... | 0 | 1 | 768 |
0 | 55,039,173 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-03-07T08:21:00.000 | 1 | 2 | 0 | Python Select variables in multiple linear regression | 55,039,091 | 0.099668 | python,linear-regression | You are probably looking for a k-fold validation model.
The idea is to randomly select your features, and have a way to validate them against each other.
The idea is to train your model with your feature selection on (k-1) partitions of your data. And validate it against the last partition. You do it for each partition... | I have a dependent variable y and 6 independent variables. I want to make a linear regression out of it. I use sklearn library to do it.
The problem is some of my independent variables have correlation more than 0.5. So I can't have them in my model at the same time
I searched throw internet but didn't find any solut... | 0 | 1 | 1,223 |
0 | 55,041,022 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-03-07T09:41:00.000 | 0 | 1 | 0 | Text analysis for unstructured data | 55,040,536 | 0 | python,classification,naivebayes | The Naive Bayes classifier is a supervised learning method and requires you to train it using labelled data in which you know the targets in advance. You can then use it on unlabelled data to predict future values but you can't train it on data with no target values.
It's hard to recommend a different method without kn... | I have a Question
i do have large amount of Unstructured text data , which i want to classify into different -different sectors .
i am using a Naive Bayes classifier for it
Now, my question is what should i pass in Y?? because i don't have a Target values
and as per the syntax i have to pass it .
mnb = MultinomialN... | 0 | 1 | 68 |
0 | 55,043,641 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-03-07T09:48:00.000 | 0 | 1 | 0 | How can I set the branch method used by glpk solver through pyomo? | 55,040,678 | 0 | python,pyomo,glpk | To use --first as branch method
import pyomo.environ as pe
opt_solver = pe.SolverFactory("glpk")
opt_solver.options["first"] = "" | glpsol.exe --help provides the following options:
Options specific to the MIP solver:
--nomip consider all integer variables as continuous (allows solving MIP as pure LP)
--first branch on first integer variable
--last branch on last integer variable
--mostf branch on most fractional variable
--drtom branch using heur... | 0 | 1 | 215 |
0 | 55,050,551 | 0 | 1 | 0 | 0 | 1 | true | 1 | 2019-03-07T18:21:00.000 | 2 | 1 | 0 | Multi-type storage array in Python | 55,050,445 | 1.2 | python,arrays,numpy,types,storage | Use np.dtype(object): np.array(board, dtype=np.dtype(object))
As for empty cells: just set them to None.
Edit: as some people have suggested, you might not need a numpy array at all. A list of lists or a dict with tuple indexes might solve your problem just fine and remove the overhead of using numpy. | I'm programming a game in python3.6. There is some pawns on the board which are instance of the class 'pawn'. There is also boulders on the board which are instance of the class boulder. I just want to store these pawns and boulders in an array, like a numpy.array.
I have 2 problems :
There is different type of objec... | 0 | 1 | 861 |
0 | 67,356,441 | 0 | 0 | 0 | 0 | 1 | false | 103 | 2019-03-07T18:58:00.000 | 2 | 4 | 0 | Can I run a Google Colab (free edition) script and then shut down my computer? | 55,050,988 | 0.099668 | python,google-colaboratory | use multiprocess in python make one other function and start while loop there! that while loop won't let it sleep! | Can I run a google colab (free edition) script and then shut down my computer?
I am training several deeplearning models with crossvalidation, and therefore I would like to know if I can close the window or the computer with the training running at the same time in the cloud. | 0 | 1 | 96,447 |
0 | 55,065,070 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-03-08T14:10:00.000 | 0 | 1 | 0 | comparing two columns in the same csv file | 55,064,953 | 0 | python,numpy-slicing | I don't understand the question, and would be very confusing for others too.
However, it seems like a simple loop and condition code. Try using PANDAS with Python, the library to process the CSV in case this helps you. | I have a CSV file with two columns.
One has values like size XL, size L, size M and size S. In the other I only have XL and L.
What I want to do is that when my loop finds XL in the first column it overwrites that cell value with XL and when it doesn't find XL in the next cell it should just skip it.
In the next ite... | 0 | 1 | 61 |
0 | 55,085,727 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2019-03-10T07:57:00.000 | 0 | 1 | 0 | PySpark RDD SortByKey() Not Working Properly | 55,085,647 | 1.2 | python,python-2.7,apache-spark,pyspark | You are sorting strings - as shown by the u prefix on the output (for Unicode, not that it really matters). Therefore it is working as 10 comes before 2 when tested as a string.
Map all your values to integers before working with them and you should be fine. | I want to sort an RDD that I have, which contains a key range of 0-49995 such that (0, value), ... , (49995, value).
I want to sort it in ascending order, and I am using the SortByKey() function, but it seems like it is not working properly because this is the result that I am getting:
test0.sortByKey(True).take(5)
... | 0 | 1 | 493 |
0 | 55,093,212 | 0 | 1 | 0 | 0 | 1 | true | 0 | 2019-03-10T22:26:00.000 | 1 | 1 | 0 | While Loop in Python - Array Manipulation | 55,093,027 | 1.2 | python,python-3.x,numpy,for-loop | Just before for a_class,a_class_weight in zip(classes, class_weights):, you're initializing total_fold_array to [].
That loop executes for exactly as many times as there are elements in classes.
Each iteration of that loop appends a curr_fold_array to total_fold_array.
That is why, at the end of that loop, you have as ... | I am working on the following Python codes.
I am hoping to accomplish the following:
Create a total_fold_array which will hold 5 items (folds)
For each fold, create an array of data from a larger dataset based off of the logic (which I know is correct) inside of my for...zip loop
To help you understand:
The classses ... | 0 | 1 | 910 |
0 | 55,095,505 | 0 | 0 | 0 | 0 | 1 | false | 9 | 2019-03-11T04:56:00.000 | 3 | 2 | 0 | In sklearn regression, is there a command to return residuals for all records? | 55,095,437 | 0.291313 | python,scikit-learn | One option is to use fit() to get predictions and residual is simply the difference between the actual value and predictions. | I know this is an elementary question, but I'm not a python programmer. I have an app that is using the sklearn kit to run regressions on a python server.
Is there a simple command which will return the predictions or the residuals for each and every data record in the sample? | 0 | 1 | 12,667 |
0 | 55,103,222 | 0 | 0 | 0 | 0 | 1 | false | 6 | 2019-03-11T13:21:00.000 | 1 | 3 | 0 | How to save and load my neural network model after training along with weights in python? | 55,102,781 | 0.066568 | python,scikit-learn,pickle | So you write it down yourself. You need some simple steps:
In your code for neural network, store weights in a variable. It could be simply done by using self.weights.weights are numpy ndarrays. for example if weights are between layer with 10 neurons to layer with 100 neurons, it is a 10 * 100(or 100* 10) nd array.
U... | I have trained a single layer neural network model in python (a simple model without keras and tensorflow).
How canI save it after training along with weights in python, and how to load it later? | 0 | 1 | 10,072 |
0 | 55,223,274 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2019-03-12T08:57:00.000 | 0 | 1 | 0 | spaCy 2.0: Loading Training Data from excel file Custom NER Model issues | 55,117,481 | 0 | python,xlrd,spacy | Please write the code after 4 space you will get proper format in stack overflow or ctrl+k then write the code. | I have made a custom NER model using spaCy by loading the training data from a text file in the prescribed format and the model is working fine, However If I am trying to load training data from excel file we get the output model but does not getting the entities(no output and also no error).
Model from the training da... | 0 | 1 | 371 |
0 | 55,122,132 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2019-03-12T12:23:00.000 | 0 | 2 | 0 | tf.gradient acting like tfp.math.diag_jacobian | 55,121,377 | 0 | python,tensorflow,diagonal,gradient | I suppose I have found a solution:
my_grad = tf.gradients(tf.reduce_sum(loss), input)
ensures, that the cross dependencies i!=j are ignored - that works really nicely and fast.. | I try to calculate noise for input data using the gradient of the loss function from the input-data:
my_grad = tf.gradients(loss, input)
loss is an array of size (n x 1) where n is the number of datasets, m is the size of the dataset, input is an array of (n x m) where m is the size of a single dataset.
I need my_grad ... | 0 | 1 | 132 |
0 | 55,127,590 | 0 | 0 | 0 | 0 | 1 | true | 2 | 2019-03-12T16:58:00.000 | 0 | 2 | 0 | extract feature names from trained model | 55,126,903 | 1.2 | python,scikit-learn,xgboost | Its mandatory that prediction dataset should contain only those columns which are present in training dataset. It even makes sense not to include extra columns because the weights are learnt based on your training dataset. Including any extra column apart from training dataset doesn't provide any value or improve your ... | I have a pre-trained XGBoost model read from a pickle file. When I was trying to make predictions on a new dataset with some columns outside of the feature set of the model, I received the error message:
training data did not have the following fields: column1, column2,...
I am okay with excluding these columns not e... | 0 | 1 | 1,723 |
0 | 55,143,748 | 0 | 1 | 0 | 0 | 1 | true | 0 | 2019-03-13T01:30:00.000 | 1 | 1 | 0 | Dask on single OSX machine - is it parallel by default? | 55,133,058 | 1.2 | python,dask,dask-distributed | Yes, Dask is parallel by default.
Unless you specify otherwise, or create a distributed Client, execution will happen with the "threaded" scheduler, in a number of threads equal to your number of cores. Note, however, that because of the python GIL (only one python instruction executed at a time), you may not get as m... | I have installed Dask on OSX Mojave. Does it execute computations in parallel by default? Or do I need to change some settings?
I am using the DataFrame API. Does that make a difference to the answer?
I installed it with pip. Does that make a difference to the answer? | 0 | 1 | 136 |
0 | 55,141,944 | 0 | 1 | 0 | 0 | 2 | false | 0 | 2019-03-13T12:14:00.000 | 0 | 3 | 0 | How to multiply diagonal elements by each other using numpy? | 55,141,674 | 0 | python,numpy,matrix | You could use the identity matrix given by numpy.identity(n) and then multiply it by a n dimensional vector. | For the purpose of this exercise, let's consider a matrix where the element m_{i, j} is given by the rule m_{i, j} = i*j if i == j and 0 else.
Is there an easy "numpy" way of calculating such a matrix without having to resort to if statements checking for the indices? | 0 | 1 | 1,790 |
0 | 55,141,958 | 0 | 1 | 0 | 0 | 2 | true | 0 | 2019-03-13T12:14:00.000 | 1 | 3 | 0 | How to multiply diagonal elements by each other using numpy? | 55,141,674 | 1.2 | python,numpy,matrix | You can use the numpy function diag to construct a diagonal matrix if you give it the intended diagonal as a 1D array as input.
So you just need to create that, like [i**2 for i in range (N)] with N the dimension of the matrix. | For the purpose of this exercise, let's consider a matrix where the element m_{i, j} is given by the rule m_{i, j} = i*j if i == j and 0 else.
Is there an easy "numpy" way of calculating such a matrix without having to resort to if statements checking for the indices? | 0 | 1 | 1,790 |
0 | 55,161,676 | 0 | 0 | 0 | 0 | 1 | false | 5 | 2019-03-14T11:07:00.000 | -1 | 1 | 0 | what's the mean of input_length in K.ctc_batch_cost() | 55,160,939 | -0.197375 | python,tensorflow,keras | CTC loss for one example is calculated on a 2D array (T,C). C must be equal to number of character + 1(blank characters). C contains the probability distribution of the characters at a time stamps. T will be the number of time stamps.
T should be of length 2* max_string_length. All possible encoding of y_true with len... | I have download a code for ocr using Keras, which applied the CRNN network and use the CTC loss as the loss function.
However, I'm really new to CTC loss and just have trouble with the usage of K.ctc_batch_cost(), especially the meaning of input_length. In the document of keras,
Arguments of tf.keras.backend.ctc_bat... | 0 | 1 | 1,540 |
0 | 55,169,008 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-03-14T15:52:00.000 | 0 | 2 | 0 | Faster pytorch dataset file | 55,166,874 | 0 | python,machine-learning,dataset,pytorch,lmdb | One trivial solution can be pre-processing your dataset and saving multiple smaller crops of the original 3D volumes separately. This way you sacrifice some disk space for more efficient IO.
Note that you can make a trade-off with the crop size here: saving bigger crops than you need for input allows you to still do ra... | I have the following problem, I have many files of 3D volumes that I open to extract a bunch of numpy arrays.
I want to get those arrays randomly, i.e. in the worst case I open as many 3D volumes as numpy arrays I want to get, if all those arrays are in separate files.
The IO here isn't great, I open a big file only to... | 0 | 1 | 1,082 |
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