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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 | 49,714,303 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-04-07T23:44:00.000 | 1 | 1 | 0 | Graphs from two sets of data files with different number of datapoints | 49,713,065 | 0.197375 | python,r,pandas,matplotlib,graph | Ok, addressing the points as best I can without an example of what you tried that didn't work...
How to import to sets of data and how to choose specific column in each of them (would it be exactly the same as if dealing with one file)?
You'd import each file separately. Assuming your file has headers, something like... | I want to produce a graph from two sets of data files (txt and csv) and I have encountered a couple of issues using either R or Python and would be super super grateful if somebody could help :)
How to import to sets of data and how to choose specific column in each of them (would it be exactly the same as if dealing ... | 0 | 1 | 52 |
0 | 51,268,892 | 1 | 0 | 0 | 0 | 1 | true | 1 | 2018-04-08T03:04:00.000 | 0 | 1 | 0 | Cutoff in Closeness/Betweenness Centrality in python igraph | 49,713,991 | 1.2 | python,igraph | The cutoff really depends on the application and on the netwrok parameters (# nodes, # edges).
It's hard to talk about closeness threshold, since it depends greatly on other parameters (# nodes, # edges,...).
One thing you can know for sure is that every closeness centrality is somewhere between 2/[n(n-1)] (which is ... | I am currently working a large graph, with 1.5 Million Nodes and 11 Million Edges.
For the sake of speed, I checked the benchmarks of the most popular graph libraries: iGraph, Graph-tool, NetworkX and Networkit. And it seems iGraph, Graph-tool and Networkit have similar performance. And I eventually used iGraph.
With t... | 0 | 1 | 676 |
0 | 49,734,613 | 0 | 0 | 0 | 0 | 1 | true | 14 | 2018-04-09T02:59:00.000 | 6 | 2 | 0 | How are PyTorch's tensors implemented? | 49,724,954 | 1.2 | python,python-3.x,rust,pytorch,tensor | Contiguous array
The commonly used way to store such data is in a single array that is laid out as a single, contiguous block within memory. More concretely, a 3x3x3 tensor would be stored simply as a single array of 27 values, one after the other.
The only place where the dimensions are used is to calculate the mappi... | I am building my own Tensor class in Rust, and I am trying to make it like PyTorch's implementation.
What is the most efficient way to store tensors programmatically, but, specifically, in a strongly typed language like Rust? Are there any resources that provide good insights into how this is done?
I am currently buil... | 0 | 1 | 1,685 |
0 | 49,731,077 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-04-09T10:35:00.000 | -1 | 1 | 0 | convert list of tuple () to numpy matrix | 49,730,922 | -0.197375 | python,numpy,tuples | try this:
a = [(1,2),(2,49),(3,45)]
print map(list,a) | I want to convert a list of tuple like a = [(1,2),(2,49),(3,45)]
to numpy matrix : [[1,2],[2,49],[3,45]]
Can anyone help me?
thanks in advance | 0 | 1 | 1,397 |
0 | 49,732,736 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-04-09T12:00:00.000 | 1 | 1 | 0 | Image becomes blue masked after applying tf.image.crop_to_bounding_box and tf.image.encode_jpeg | 49,732,469 | 1.2 | python,tensorflow,computer-vision | Your problem here is not related to Tensorflow at all but to OpenCV.
For some reason, OpenCV loads images in a BGR format and not RGB as Tensorflow expects them to be.
Invert your first and third channels in your image before passing it to Tensorflow and everything should be fine. | I am trying to crop and save images from tensorflow object detection API output with following code I created using previous stack overflow questions.
But after saving the image seemed to have high density of blue color.
The original image has normal color. Since both cropping and encoding refer image channel for forma... | 0 | 1 | 197 |
0 | 49,733,409 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2018-04-09T12:41:00.000 | 0 | 3 | 0 | Solving csv files with quoted semicolon in Pandas data frame | 49,733,229 | 0 | python-3.x,pandas,csv,data-cleaning,quoting | The sep parameter of pd.read_csv allows you to specify which character is used as a separator in your CSV file. Its default value is ,. Does changing it to ; solve your problem? | So I am facing the following problem:
I have a ; separated csv, which has ; enclosed in quotes, which is corrupting the data.
So like abide;acdet;"adds;dsss";acde
The ; in the "adds;dsss" is moving " dsss" to the next line, and corrupting the results of the ETL module which I am writing. my ETL is taking such a csv fro... | 0 | 1 | 2,337 |
0 | 49,733,693 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-04-09T13:02:00.000 | 0 | 1 | 0 | finding the maximum value of a column in pandas | 49,733,632 | 1.2 | python,pandas,dataframe | Just df.index[df[your_column_name].argmax()] :) | sorry for very basic question but i can't find a direct answer.
I want to check a column to find the maximum value. for now I assume there is only one maximum value, its unique.
I then want to find the index name for that row.
Is there a way to do this? | 0 | 1 | 66 |
0 | 49,743,935 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2018-04-10T01:27:00.000 | 0 | 2 | 0 | Plot 2D image in 3D axes | 49,743,901 | 0 | python,matplotlib | If your image is a coloured image you must first ensure that it is an indexed image. This means that you can only have 2d matrix (and not 3 matricies for the RGB components). Command rgb2ind can help.
Then you can directly show you image in a 3D way. Use the mesh or surf command.
You can also adjust perspective with an... | Using matplotlib is it possible to take a 2D image of something and place it in a 3D figure? I'd like to take a 2D image and place it at z position of 0. I want to then move the other pixels in the image along the z-axis separately based on a calculation I am making. | 0 | 1 | 1,911 |
0 | 49,800,463 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-04-10T09:49:00.000 | 1 | 1 | 0 | What does the key " b'batch_label' " in cifar-10 unpickle by python use for? | 49,750,513 | 1.2 | python,machine-learning | The cifar10 dataset you are donwloading is splitted in serveral batches.
Each batch has its own id (batch_label).
data is the actual batch if images, while filenames is the name of the images encoded. labels, of course, is the set of labels associated with the data | When I unpickle cifar-10 by python, I get a dictionary with 4 keys:'batch_label','data','filenames','labels'. But I don't konw what the key 'batch_label' represent for. It's a 'bytes' type data, length is 21. I konw I needn't to know it when I train a network. But I'm still curious about it. Thanks for any reply. ^_^ | 0 | 1 | 233 |
0 | 49,756,505 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-04-10T14:34:00.000 | 0 | 1 | 0 | python not using latest numpy version | 49,756,369 | 0 | python,linux,path | You should use a virtual environment. Your system most likely will default to the most up to date version.
Try installing:
pip install virtualenv
then run:
virtualenv -p python2.7 environment_name.
Source the virtualenv with:
source environment_name/bin/activate
then pip install numpy=1.14.2.
Then you will have a m... | I have a problem.
I need numpy 1.14.2, and my machine (Linux Mint 17.3) has only 1.8.2 installed.
I then installed 1.14.2 through pip. But when I load it in ipython, it still says that it is 1.8.2.
Using yolk I saw that 1.14.2 is actually installed, but marked as non-active.
numpy - 1.14.2 - non-active... | 0 | 1 | 343 |
0 | 49,762,442 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2018-04-10T20:29:00.000 | 0 | 2 | 0 | Manipulate Python Data frame to plot line charts | 49,762,381 | 0 | python,pandas,matplotlib,visualization | If you have a pandas DataFrame, let's call it df, for which your columns are X, Y, Z, etc. and your rows are the years in order, you can simply call df.plot() to plot each column as a line with the y axis being the values and the row name giving the x-axis. | I have the following data frame:
Parameters Year 2016 Year 2017 Year 2018....
0) X 10 12 13
1) Y 12 12 45
2) Z 89 23 97
3
.
.
.
I want to make a line chart with the column headers starting from Year 2016 to be on the x-axis and each line ... | 0 | 1 | 403 |
0 | 49,784,641 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-04-11T21:08:00.000 | 0 | 2 | 0 | Python - Subtracting the Elements of Two Arrays | 49,784,499 | 0 | python,arrays,math,subtraction | I can't replicate this but it looks like the numbers are 8 bit and wrapping some how | I am new to Python programming and stumbled across this feature of subtracting in python that I can't figure out. I have two 0/1 arrays, both of size 400. I want to subtract each element of array one from its corresponding element in array 2.
For example say you have two arrays A = [0, 1, 1, 0, 0] and B = [1, 1, 1,... | 0 | 1 | 5,861 |
0 | 49,856,665 | 0 | 0 | 0 | 0 | 2 | false | 5 | 2018-04-12T07:02:00.000 | 0 | 3 | 0 | How to reconnect to the ongoing process on GoogleColab | 49,790,025 | 0 | python,deep-learning,keras,jupyter-notebook,google-colaboratory | It seems there's no normal way to do this. But you can save your model to Google Drive with current training epoch number, so when you see something like "my_model_epoch_1000" on your google drive, you will know that the process is over. | I recently started to use Google Colab to train my CNN model. It always needs about 10+ hours to train once. But I cannot stay in the same place during these 10+ hours, so I always poweroff my notebook and let the process keep going.
My code will save models automatically. I figured out that when I disconnect from the ... | 0 | 1 | 3,496 |
0 | 49,854,222 | 0 | 0 | 0 | 0 | 2 | false | 5 | 2018-04-12T07:02:00.000 | 0 | 3 | 0 | How to reconnect to the ongoing process on GoogleColab | 49,790,025 | 0 | python,deep-learning,keras,jupyter-notebook,google-colaboratory | first question: restart runtime from runtime menu
second question: i think you can use tensorboard to monitor your work. | I recently started to use Google Colab to train my CNN model. It always needs about 10+ hours to train once. But I cannot stay in the same place during these 10+ hours, so I always poweroff my notebook and let the process keep going.
My code will save models automatically. I figured out that when I disconnect from the ... | 0 | 1 | 3,496 |
0 | 49,797,871 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2018-04-12T13:18:00.000 | 3 | 1 | 0 | How to eliminate a dummy dimension of ndarray? | 49,797,656 | 1.2 | python,numpy | You can't eliminate that 0 dimension. A dimension of length 0 is not a "dummy" dimension. It really means length 0. Since the total number of elements in the array (which you can check with a.size) is the product of the shape attribute, an array with shape (0, 1325, 3) contains 0 elements, while an array with shape ... | How can I eliminate a dummy dimension in python numpy ndarray?
For example, suppose that A.shape = (0, 1325, 3),
then how can eliminate '0' dimension so that A.shape = (1325,3).
Both 'np.sqeeze(A)' or 'A.reshape(A.shape[1:])' don't work. | 0 | 1 | 118 |
0 | 51,196,523 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-04-12T20:17:00.000 | 2 | 1 | 0 | tensorflow Object detection, Bounding Boxes are not visible on the own dataset | 49,805,206 | 0.379949 | python,tensorflow,ubuntu-16.04,object-detection | Try passing the bounding boxes to a print statement. Do you get any valid output? If so then it is probably nothing to do with your model and your bounding boxes are created just fine. What is your mAP? If it is very early on in the training process then your accuracy is probably too poor and do not meet a minimum thre... | I am trying the run the TF object detection locally on my own dataset. Every step is happening perfectly except the Visualization of the BB on a test image.
First I have run the Pascal VOC dataset on the Faster R-CNN Inception ResNet v2, modified the scripts as per the VOC dataset and Then followed the instructions fro... | 0 | 1 | 1,054 |
0 | 51,225,714 | 0 | 0 | 0 | 0 | 2 | false | 9 | 2018-04-12T20:45:00.000 | 2 | 11 | 0 | Could not find a version that satisfies the requirement numpy == 1.9.3 | 49,805,576 | 0.036348 | python,python-3.x,pandas,numpy,quandl | Just Install Python 3.5 or higher. | Trying to install quandl and need pandas, so I tried pip install pandas and get:
Could not find a version that satisfies the requirement numpy==1.9.3 (from versions: 1.10.4, 1.11.0, 1.11.1rc1, 1.11.1, 1.11.2rc1, 1.11.2, 1.11.3, 1.12.0b1, 1.12.0rc1, 1.12.0rc2, 1.12.0, 1.12.1rc1, 1.12.1, 1.13.0rc1, 1.13.0rc2, 1.13.0, 1.... | 0 | 1 | 57,246 |
0 | 51,056,520 | 0 | 0 | 0 | 0 | 2 | false | 9 | 2018-04-12T20:45:00.000 | 1 | 11 | 0 | Could not find a version that satisfies the requirement numpy == 1.9.3 | 49,805,576 | 0.01818 | python,python-3.x,pandas,numpy,quandl | Just use this command :
pip install pandas==0.19.0 .
An older version which will not cry with this error !! | Trying to install quandl and need pandas, so I tried pip install pandas and get:
Could not find a version that satisfies the requirement numpy==1.9.3 (from versions: 1.10.4, 1.11.0, 1.11.1rc1, 1.11.1, 1.11.2rc1, 1.11.2, 1.11.3, 1.12.0b1, 1.12.0rc1, 1.12.0rc2, 1.12.0, 1.12.1rc1, 1.12.1, 1.13.0rc1, 1.13.0rc2, 1.13.0, 1.... | 0 | 1 | 57,246 |
0 | 49,812,019 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2018-04-13T03:56:00.000 | 0 | 2 | 1 | Google Cloud - What products for time series data cleaning? | 49,809,084 | 1.2 | python,apache-spark,google-cloud-platform,google-cloud-dataflow,google-cloud-dataproc | I would use PySpark on Dataproc, since Spark is not just realtime/streaming but also for batch processing.
You can choose the size of your cluster (and use some preemptibles to save costs) and run this cluster only for the time you actually need to process this data. Afterwards kill the cluster.
Spark also works very ... | I have around 20TB of time series data stored in big query.
The current pipeline I have is:
raw data in big query => joins in big query to create more big query datasets => store them in buckets
Then I download a subset of the files in the bucket:
Work on interpolation/resampling of data using Python/SFrame, because so... | 0 | 1 | 522 |
0 | 57,368,807 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2018-04-13T07:24:00.000 | 2 | 2 | 0 | How to install tensorflow-gpu for both python2 and python3 | 49,811,510 | 0.197375 | python-3.x,tensorflow | If you have already installed tensorflow 1.2, CUDA 8.0 and CuDNN 5.1 for python2.7. Then you can:
yum install python3-pip
(now you have python3 and pip3, however the python version may not be 3.5)
python3 -m pip install --upgrade tensorflow-gpu==1.2
(make sure the installed version is exactly same as that of python... | I already have installed tensorflow 1.2, CUDA 8.0 and CuDNN 5.1 for python2.7. Now I want to use it for python3.5 but importing tensorflow fails. How do I install tensorflow for python3 again. And do I have to do the CUDA and CuDNN episodes again? | 0 | 1 | 1,667 |
0 | 50,134,947 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2018-04-13T14:26:00.000 | 2 | 1 | 0 | cannot import tensorflow - "Import Error: cannot import name 'self_check' " | 49,819,253 | 0.379949 | python,tensorflow,anaconda | pip install --upgrade tensorflow
or
you can uninstall the tensorflow and re-install it. | I'm trying to use tensorflow-gpu. Using Anaconda, I installed the libraries and active both (tensorflow and tensorflow-gpu) I have also installed Keras in this anaconda environment as well
Next I launch Spider IDE within my Anaconda environment and run my py script, that is when I get the following error:
cannot import... | 0 | 1 | 2,441 |
0 | 49,908,382 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2018-04-13T19:58:00.000 | 1 | 1 | 0 | Regularization Application in the Proximal Adagrad Optimizer in Tensorflow | 49,824,281 | 1.2 | python,optimization,tensorflow,neural-network,regularized | Regularization applies neither to forward or backpropagation but to the weight updates.
You can use different optimizers for different layers by explicitly passing the variables to minimize to each optimizer. | I have been trying to implement l1-regularization in Tensorflow using the l1_regularization_strength parameter in the ProximalAdagradOptimizer function from Tensorflow. (I am using this optimizer specifically to get a sparse solution.) I have two questions regarding the regularization.
Does the l1-regularization used ... | 0 | 1 | 425 |
0 | 49,838,147 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2018-04-13T21:32:00.000 | 1 | 1 | 0 | Hypothesis search tree | 49,825,415 | 0.197375 | python-hypothesis | TLDR: don't worry, this is a common use-case and even a naive strategy works very well.
The actual search process used by Hypothesis is complicated (as in, "lead author's PhD topic"), but it's definitely not a depth-first search! Briefly, it's a uniform distribution layered on a psudeo-random number generator, with a ... | I have a object with many fields. Each field has different range of values. I want to use hypothesis to generate different instances of this object.
Is there a limit to the number of combination of field values Hypothesis can handle? Or what does the search tree hypothesis creates look like? I don't need all the combi... | 0 | 1 | 75 |
0 | 49,838,288 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-04-14T01:27:00.000 | 3 | 1 | 0 | Have a Strategy that does not uniformly choose between different strategies | 49,827,010 | 0.53705 | python-hypothesis | Hypothesis does not allow users to control the probability of various choices within a strategy. You should not use undocumented interfaces either - hypothesis.internal is for internal use only and could break at any time!
I strongly recommend using C = st.one_of(A, B) and trusting Hypothesis with the details. | I'd like to create a strategy C that, 90% of the time chooses strategy A, and 10% of the time chooses strategy B.
The random python library does not work even if I seed it since each time the strategy produces values, it generates the same value from random.
I looked at the implementation for OneOfStrategy and they use... | 0 | 1 | 172 |
0 | 62,990,659 | 0 | 0 | 0 | 0 | 2 | false | 1 | 2018-04-14T19:26:00.000 | 1 | 2 | 0 | Using memory mapped buffers for scipy sparse | 49,835,342 | 0.099668 | python,numpy,scipy,sparse-matrix | This can partially work, until you try to do much with the array. There's a very good chance the subarrays will be fully read into memory if you subset, or you'll get an error.
An important consideration here is that the underlying code is written assuming the arrays are typical in-memory numpy arrays. Cost of random a... | I have to handle sparse matrix that can occasionally be very big, nearing or exceeding RAM capacity. I also need to support mat*vec and mat*mat operations.
Since internally a csr_matrix is 3 arrays data, indices and indptr is it possible to create a csr matrix from numpy memmap. | 0 | 1 | 517 |
0 | 50,985,982 | 0 | 0 | 0 | 0 | 2 | true | 1 | 2018-04-14T19:26:00.000 | -2 | 2 | 0 | Using memory mapped buffers for scipy sparse | 49,835,342 | 1.2 | python,numpy,scipy,sparse-matrix | This works without any problems. | I have to handle sparse matrix that can occasionally be very big, nearing or exceeding RAM capacity. I also need to support mat*vec and mat*mat operations.
Since internally a csr_matrix is 3 arrays data, indices and indptr is it possible to create a csr matrix from numpy memmap. | 0 | 1 | 517 |
0 | 49,841,568 | 0 | 0 | 0 | 0 | 1 | false | 58 | 2018-04-15T11:19:00.000 | 6 | 2 | 0 | What does calling fit() multiple times on the same model do? | 49,841,324 | 1 | python,machine-learning,scikit-learn | You can use term fit() and train() word interchangeably in machine learning. Based on classification model you have instantiated, may be a clf = GBNaiveBayes() or clf = SVC(), your model uses specified machine learning technique.
And as soon as you call clf.fit(features_train, label_train) your model starts training u... | After I instantiate a scikit model (e.g. LinearRegression), if I call its fit() method multiple times (with different X and y data), what happens? Does it fit the model on the data like if I just re-instantiated the model (i.e. from scratch), or does it keep into accounts data already fitted from the previous call to f... | 0 | 1 | 28,564 |
0 | 54,985,155 | 0 | 0 | 0 | 0 | 1 | true | 3 | 2018-04-15T19:47:00.000 | 0 | 2 | 0 | AttributeError: module 'tensorflow' has no attribute 'Session' | 49,846,106 | 1.2 | tensorflow,python-3.6 | With the new version of python3.6 my problem is solved.
I removed my previous python3.6 and installed the newest version. Then installed tensorflow through pip and it works. | When I call any function in python3.6, I get the error below; however, it works fine in python3.4. Any idea?
import tensorflow as tf
tf.Session()
Traceback (most recent call last):
File "", line 1, in
AttributeError: module 'tensorflow' has no attribute 'Session'
Here is my System information
OS Platform and D... | 0 | 1 | 18,023 |
0 | 49,849,810 | 0 | 0 | 0 | 0 | 3 | false | 0 | 2018-04-16T04:34:00.000 | 0 | 4 | 0 | Regarding image classification using CNN | 49,849,715 | 0 | python,tensorflow,classification,conv-neural-network | This means that you are clearly overfitting.
Your model is not able to generalize well to the images that you enter manually.
There are a few things that you could do.
You could try regularization so that your model does not overfit.
Try getting more data
Data augmentation
I hope that by introducing regularization, y... | I built a image classification model using convolutional neural network for 5 classes.It is giving the training accuracy as 100% and testing accuracy as 82%. But when I gave an image manually for predicting, the model is not able to classify them correctly.For 10 images the model is classifying only 3-4 images correctl... | 0 | 1 | 116 |
0 | 51,365,356 | 0 | 0 | 0 | 0 | 3 | false | 0 | 2018-04-16T04:34:00.000 | 0 | 4 | 0 | Regarding image classification using CNN | 49,849,715 | 0 | python,tensorflow,classification,conv-neural-network | Your model is not able to generalize properly. the training has gone very quickly through all the images in the training set and even started making predictions on the same images.
Try adding images and of different sizes.
Try flipping the original images horizontally to create transformed images that might a... | I built a image classification model using convolutional neural network for 5 classes.It is giving the training accuracy as 100% and testing accuracy as 82%. But when I gave an image manually for predicting, the model is not able to classify them correctly.For 10 images the model is classifying only 3-4 images correctl... | 0 | 1 | 116 |
0 | 70,926,699 | 0 | 0 | 0 | 0 | 3 | false | 0 | 2018-04-16T04:34:00.000 | -1 | 4 | 0 | Regarding image classification using CNN | 49,849,715 | -0.049958 | python,tensorflow,classification,conv-neural-network | Besides that, probably your training set has way easier images than your test set (those image that you give manually). That can happen even if you are feeding the network from the same dataset.
To have better results on unseen images, you must have to reach a good generalisation level. Try what is said previously abov... | I built a image classification model using convolutional neural network for 5 classes.It is giving the training accuracy as 100% and testing accuracy as 82%. But when I gave an image manually for predicting, the model is not able to classify them correctly.For 10 images the model is classifying only 3-4 images correctl... | 0 | 1 | 116 |
0 | 63,833,958 | 0 | 0 | 0 | 0 | 1 | false | 17 | 2018-04-16T10:13:00.000 | 3 | 3 | 0 | Select the inverse index in pd.Dataframe | 49,854,796 | 0.197375 | python,pandas,dataframe,indexing | Assuming my_index are the row indices you want to ignore, you could drop these where they exist in the dataframe df:
df = df.drop(my_index, errors='ignore') | How to select the inverse index in pd.DataFrame by using loc or iloc?
I tried df.loc[!my_index,my_feature] but fail.
And df.loc[[ind for ind in df.index.tolist() if ind not in my_index],my_feature] looks too dull. Any better idea? | 0 | 1 | 12,631 |
0 | 49,864,136 | 0 | 1 | 0 | 0 | 1 | false | 16 | 2018-04-16T18:04:00.000 | 5 | 2 | 0 | numpy.product vs numpy.prod vs ndarray.prod | 49,863,633 | 0.462117 | python,numpy,numpy-ndarray | This is what I could gather from the source codes of NumPy 1.14.0. For the answer relevant to the current Master branch (NumPy 1.15.0), see the answer of miradulo.
For an ndarray, prod() and product() are equivalent.
For an ndarray, prod() and product() will both call um.multiply.reduce().
If the object type is not n... | I'm reading through the Numpy docs, and it appears that the functions np.prod(...), np.product(...) and the ndarray method a.prod(...) are all equivalent.
Is there a preferred version to use, both in terms of style/readability and performance? Are there different situations where different versions are preferable? If ... | 0 | 1 | 13,184 |
0 | 49,869,744 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-04-17T03:51:00.000 | 0 | 1 | 0 | Training changing input size RNN on Tensorflow | 49,869,622 | 0 | python,numpy,tensorflow,machine-learning,rnn | As a general guideline, GPU boosts performance only if you have calculation intensive code and little data transfer. In other words, if you train your model one instance at a time (or on small batch sizes) the overhead for data transfer to/from GPU can even make your code run slower! But if you feed in a good chunk of ... | I want to train an RNN with different input size of sentence X, without padding. The logic used for this is that I am using Global Variables and for every step, I take an example, write the forward propagation i.e. build the graph, run the optimizer and then repeat the step again with another example. The program is ex... | 0 | 1 | 42 |
0 | 49,905,936 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-04-17T13:23:00.000 | 0 | 1 | 0 | python: loadmat - ValueError: negative dimensions are not allowed | 49,879,324 | 1.2 | python,scipy,mat | I found out that this was a binary file (was written with Matlab's fwrite).
The solution was saving the file as text, and then loading it in python not using loadmat. | Trying to read a matlab file into python. I am using the function loadmat from scipy.io to read it, and I'm getting back the error ValueError: negative dimensions are not allowed.
How can I fix this issue? | 0 | 1 | 131 |
0 | 63,543,545 | 0 | 1 | 0 | 0 | 1 | false | 10 | 2018-04-17T14:07:00.000 | 1 | 3 | 0 | What is difference between plot and iplot in Pandas? | 49,880,314 | 0.066568 | python,pandas,matplotlib,plotly | Correct answer provided.I tried to run this code in pycharm IDE but could not. jupyter notebook is required to graph iplot. | What is the difference between plot() and iplot() in displaying a figure in Jupyter Notebook? | 0 | 1 | 28,800 |
0 | 49,885,159 | 0 | 0 | 0 | 0 | 1 | false | 5 | 2018-04-17T15:47:00.000 | 6 | 2 | 0 | Keras Tensorflow Binary Cross entropy loss greater than 1 | 49,882,424 | 1 | python,tensorflow,keras | Yes, its correct, the cross-entropy is not bound in any specific range, its just positive (> 0). | Library: Keras, backend:Tensorflow
I am training a single class/binary classification problem, wherein my final layer has a single node, with activation of sigmoid type. I am compiling my model with a binary cross entropy loss. When I run the code to train my model, I notice that the loss is a value greater than 1. Is ... | 0 | 1 | 10,194 |
0 | 64,196,727 | 0 | 1 | 0 | 0 | 2 | false | 8 | 2018-04-18T00:56:00.000 | 0 | 5 | 0 | Issues importing mlxtend python | 49,889,524 | 0 | python,python-import,importerror,mlxtend | Try this:
conda install -c conda-forge mlxtend
Don't mix pip and Conda environments. Last time I did this I broke my Python and needed to reinstall everything again.
If you must deal with different environments using pip and conda because of many projects, use pyenv. | I'm new to python, so apologies if this is a silly question.
I'm trying to use mlxtend, and have installed it using pip. Pip confirms that it is installed (when I type "pip install mlxtend" it notes that the requirement is already satisfied). However, when I try and import mlxtend in python using "import mlxtend as ml... | 0 | 1 | 19,607 |
0 | 58,758,122 | 0 | 1 | 0 | 0 | 2 | false | 8 | 2018-04-18T00:56:00.000 | 3 | 5 | 0 | Issues importing mlxtend python | 49,889,524 | 0.119427 | python,python-import,importerror,mlxtend | I had the same issue while using Anaconda, I tried to install it with Anaconda, however, Notebook didn't see it installed. You can also try to install it in CMD by just typing
pip install mlxtend --user or pip3 install mlxtend --user
After installing it with CMD, Notebook didn't give error for my case. Just reply if th... | I'm new to python, so apologies if this is a silly question.
I'm trying to use mlxtend, and have installed it using pip. Pip confirms that it is installed (when I type "pip install mlxtend" it notes that the requirement is already satisfied). However, when I try and import mlxtend in python using "import mlxtend as ml... | 0 | 1 | 19,607 |
1 | 49,896,647 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-04-18T02:37:00.000 | 0 | 1 | 0 | Different return from Java and Python cv2.findContours | 49,890,287 | 1.2 | c#,python,opencv,opencv-contour | I've finally found the answer to this phenomenon. The python program is using OpenCV 3.4, while the Java bindings of my C# program is using the older OpenCV 3.1 . The FindContours method may be found in both versions, but apparently returns different values. | I'm translating a Python program which uses OpenCV to C# with Java bindings. I tested both Python and C# programs using the same image, and I realized the findContours method returns different contours between the 2 programs.
Python: _, contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPL... | 0 | 1 | 194 |
0 | 49,894,197 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-04-18T07:32:00.000 | 0 | 1 | 0 | tensorflow: CNN for non square image | 49,893,741 | 0 | python,tensorflow | I don't exactly agree with reshaping a rectangular picture as you destroy the relationship between neighbour pixels. Instead, you have several options to apply CNNs on a non-quadratic image:
1.) Use padding. During preprocessing, you could fill in pixels to get a quadratic image. Through this you can apply the quadrati... | tensorflow version 1.5.0rc1
python version:3.5
When reshape a rectangular image to [height,width]
by using tf.reshape(x,[-1,x,y,1])
eg. tf.reshape(x,[-1,14,56,1]) run conv2d returns:
InvalidArgumentError (see above for traceback): Input to reshape is a
tensor with 358400 values, but the requested shape requires ... | 0 | 1 | 1,803 |
0 | 49,898,817 | 0 | 0 | 0 | 0 | 1 | false | 8 | 2018-04-18T11:36:00.000 | 1 | 5 | 0 | Pandas reading csv files with partial wildcard | 49,898,742 | 0.039979 | python,pandas | Loop over each file and build a list of DataFrame, then assemble them together using concat. | I'm trying to write a script that imports a file, then does something with the file and outputs the result into another file.
df = pd.read_csv('somefile2018.csv')
The above code works perfectly fine. However, I'd like to avoid hardcoding the file name in the code.
The script will be run in a folder (directory) that con... | 0 | 1 | 14,334 |
0 | 49,908,880 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-04-18T20:48:00.000 | 4 | 1 | 0 | Sum of values accessed through numpy memmap is wrong | 49,908,788 | 1.2 | python,numpy,memory | numpy.memmap is for treating raw data in a file as a numpy array. Your filename is 'file.npy', so that is not "raw" data. It is a NPY file, which has a header containing meta-information about the array stored in it.
To memory-map a NPY file, use the mmap_mode argument of numpy.load(). | I have a large (9.3 GB) .npy file containing a uint8 values in an (67000, 9, 128, 128) ndarray. I created it using np.save() and when loading it using x = np.memmap('file.npy', "uint8", shape=(67000, 9, 128, 128), mode="r"), np.sum(x[0,0,:,0]) returns 13783. The "problem" is that when I try loading it with np.load("fil... | 0 | 1 | 379 |
0 | 49,911,007 | 0 | 1 | 0 | 0 | 1 | false | 3 | 2018-04-18T21:14:00.000 | 1 | 1 | 0 | Count number of nodes per level in a binary tree | 49,909,109 | 0.197375 | python,python-3.x,tree,binary-tree,tree-traversal | The search ordering doesn't really matter as long as you only count each node once. A depth-first search solution with recursion would be:
Create a map counters to store a counter for each level. E.g. counters[i] is the number of nodes found so far at level i. Let's say level 0 is the root.
Define a recursive function... | I've been searching for a bit now and haven't been able to find anything similar to my question. Maybe i'm just not searching correctly. Anyways this is a question from my exam review. Given a binary tree, I need to output a list such that each item in the list is the number of nodes on a level in a binary tree at the ... | 0 | 1 | 1,773 |
0 | 49,911,299 | 0 | 0 | 0 | 0 | 1 | false | 8 | 2018-04-19T01:13:00.000 | 6 | 2 | 0 | How to restrict output of a neural net to a specific range? | 49,911,206 | 1 | python,neural-network,keras,regression | Normalize your outputs so they are in the range 0, 1. Make sure your normalization function lets you transform them back later.
The sigmoid activation function always outputs between 0, 1. Just make sure your last layer has sigmoid activation to restrict your output into that range. Now you can take your outputs and tr... | I'm using Keras for a regression task and want to restrict my output to a range (say between 1 and 10)
Is there a way to ensure this? | 0 | 1 | 8,004 |
0 | 49,915,763 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2018-04-19T07:12:00.000 | 3 | 2 | 0 | I get the following error whenever I try to run conda install tensorflow | 49,914,882 | 0.291313 | python,tensorflow,anaconda,jupyter-notebook,conda | You have to run the conda info tensorflow and conda info numba to see each dependencies for each package and then you have to install those package like conda install package=version to fix the problem. | This is the error :
Solving environment: failed
UnsatisfiableError: The following specifications were found to be in conflict:
- numba -> numpy[version='>=1.14,<1.15.0a0']
- tensorflow
Use "conda info " to see the dependencies for each package. | 0 | 1 | 1,294 |
0 | 52,469,364 | 0 | 0 | 0 | 0 | 1 | false | 10 | 2018-04-19T13:34:00.000 | 3 | 2 | 0 | Scale a numpy array with from -0.1 - 0.2 to 0-255 | 49,922,460 | 0.291313 | python,image,numpy | You can also use uint8 datatype while storing the image from numpy array.
import numpy as np
from PIL import Image
img = Image.fromarray(np.uint8(tmp))
tmp is my np array of size 255*255*3. | I have an numpy array in python that represent an image its size is 28x28x3 while the max value of it is 0.2 and the min is -0.1. I want to scale that image between 0-255. How can I do so? | 0 | 1 | 18,784 |
0 | 49,935,903 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2018-04-19T19:44:00.000 | 2 | 1 | 0 | Clear approach for assigning semantic tags to each sentence (or short documents) in python | 49,929,066 | 1.2 | python-2.7,nlp,nltk,gensim,semantic-analysis | Lack of labeled data means you cannot apply any semantic classification method using word vectors, which would be the optimal solution to your problem. An alternative however could be to construct the document frequencies of your token n-grams and assume importance based on some smoothed variant of idf (i.e. words that... | I am looking for a good approach using python libraries to tackle the following problem:
I have a dataset with a column that has product description. The values in this column can be very messy and would have a lot of other words that are not related to the product. I want to know which rows are about the same product,... | 0 | 1 | 184 |
0 | 50,009,358 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-04-20T06:37:00.000 | 0 | 1 | 0 | Is there a way to nullify a specific feature in test set while evaluating a tensorflow model? | 49,935,491 | 0 | python,tensorflow,machine-learning,deep-learning,regression | For deep models there's no general input-independent way to do this kind of feature ablation you want (take a pretrained model and just change that feature's representation on the test set).
Instead I recommend you do training time ablation: train different variations of your model with different feature combinations a... | Idea behind nullifying/ignoring a feature from test set is to understand how important is it considered by the model to predict the target variable (by comparing the evaluation metric's value). For numerical variables, I thought of setting them to 0, assuming the multiplication (with weights) would be 0 and thus it wou... | 0 | 1 | 46 |
0 | 49,936,800 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2018-04-20T07:36:00.000 | 0 | 1 | 0 | How can a specific model be cleared from the memory? | 49,936,467 | 0 | python,keras | you can use del[] to explicitly delete the variables you no longer need, this triggers python's garbage collector. so put all the variables associated with that model inside del[] and python would free the memory. | I created several Keras models (Model_1, Model_2...Model_N) in one code. I would like to clear only one specified model (e.g. Model_1).
I guess K.clear_session(), which will clear all models from the memory, is not useful in this case. Is there any solution?
Thank you in advance. | 1 | 1 | 36 |
0 | 49,943,047 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-04-20T12:04:00.000 | 1 | 1 | 0 | Tuning hyperparameters Inception model with checkpoints | 49,941,265 | 1.2 | python,tensorflow,deep-learning | Both the methods are okay but you have to see your training loss after adjusting them. If they are converging in both the cases then it's fine otherwise adjust accordingly.
However, people adopt these two methods as far as I know 1. Keep a higher learning rate initially and keep a decay factor, thus reducing your learn... | I have a question concerning tuning hyperparameters for the Inception ResNet V2 model (or any other DL model), which I can't really wrap my head around.
Right now, I have certain set certain hyperparameters, such as learning_rate, decay_factor and decay_after_nr_epochs. My model saves checkpoints, so it can continue at... | 0 | 1 | 470 |
0 | 52,770,137 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-04-20T14:13:00.000 | 0 | 2 | 0 | LightGBM Python API. Best_iteration and best_score for custom evaluation function (feval) | 49,943,689 | 0 | python-3.x,lightgbm | You can use objective:"multi_error", or also you can combine objectives as
objective: "multi_error", "multi_logloss"
Multi_error will directly focus on the accuracy. | I'm using lightgbm.train with valid_sets, early_stopping_rounds and feval function for multiclass problem with "objective": "multiclass". I want to find best_iteration and best_score for my custom evaluation function. But it finds them for multi_logloss metrics, which is corresponding to specified objective. So the que... | 0 | 1 | 472 |
0 | 49,947,979 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2018-04-20T18:38:00.000 | 1 | 1 | 0 | Python pickle for Machine Learning | 49,947,850 | 0.197375 | python | Pickling is just the python form of serialization. Serialization only preserves raw data, such as strings, integers, floats, lists, and similar things.
If you pickle a model, you can unpickle it and use it later, but if you don't have the libraries (keras, tensorflow, or whatever you need), you cannot use it on that m... | I have built a predictive model in Windows to train the dataset using python 3.6.1. I have used pickle to save the train model as a Pickle file
Now I have written a another python script to read and load the train pickle file to predict the test data.
I have got results successful in Windows.
Now I want to move the Tr... | 0 | 1 | 984 |
0 | 51,285,200 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-04-20T22:54:00.000 | 1 | 1 | 0 | Using Cartopy by respecting 1 to 1 km aspect ratio | 49,950,672 | 1.2 | python-3.x,cartopy | The aspect ratio of a Mercator projection isn't actually 1°:1°, but is instead 1:1 in cartesian "Mercator units". The Mercator projection doesn't preserve distance along the meridians, and therefore the higher (or lower) you go in latitude in the Mercator projection, the longer (in physical distance) 1 Mercator unit is... | I'm using cartopy to plot images on geographic axes. I used the Mercator projection to plot my data. However I realized that the equal aspect ratio is based on degrees and not in km. How can I do to force the map to respect a 1 km x 1 km aspect ratio instead of a 1° x 1° aspect ratio.
Thanks | 0 | 1 | 160 |
0 | 50,278,726 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2018-04-20T23:01:00.000 | 0 | 1 | 0 | Viewing a large (12.5GB) HDF5 file written using h5py on HDFView 2.14 | 49,950,725 | 1.2 | python,out-of-memory,hdf5,h5py,hdf | you can edit your hdfview.bat and add more memory on the switch
this is the line on .bat file:
start "HDFView" "%JAVABIN%\javaw.exe" %JAVAOPTS% -Xmx1024M -Djava.library.path="%INSTALLDIR%\lib;%INSTALLDIR%\lib\ext" -Dhdfview.root="%INSTALLDIR%" -cp "%INSTALLDIR%\lib\fits.jar;%INSTALLDIR%\lib\netcdf.jar;%INSTALLDIR%\lib\... | I have a very large HDF5 file created using Python/ h5py. I cannot open the file on HDFView 2.14, when I try to open the file nothing happens. Any suggestions on how I can open/ view the file? It contains just 5 datasets, but each dataset has 778 million rows.. hence the problem.
Thank you! | 0 | 1 | 626 |
0 | 49,956,444 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-04-21T10:32:00.000 | 1 | 1 | 0 | RNN Implementation | 49,954,852 | 0.197375 | python-3.x,recurrent-neural-network,pytorch,rnn | One hot assumes each of your vector should be different in one place. So if you have 97 unique character then i think you should use a 1-hot vector of size ( 97 + 1 = 98). The extra vector maps all the unknown character to that vector. But you can also use a 256 length vector. So you input will be:
B x N x V ( B = batc... | I am going to implement RNN using Pytorch . But , before that , I am having some difficulties in understanding the character level one-hot encoding which is asked in the question .
Please find below the question
Choose the text you want your neural network to learn, but keep in mind that your
data set must be quite l... | 0 | 1 | 147 |
0 | 49,968,114 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-04-22T02:32:00.000 | 0 | 1 | 0 | k-means clustering multi column data in python | 49,961,977 | 1.2 | python,cluster-analysis,k-means,data-science | You don't need to reformat anything.
Each row is a 60 dimensional vector of continous values with a comparable scale (coordinates), as needed for k-means.
You can just run k-means on this.
But assuming that the measurements were taken in sequence, you may observe a strong correlation between rows, so I wouldn't expect ... | I Have data-set for which consist 2000 lines in a text file.
Each line represents x,y,z (3D coordinates location) of 20 skeleton joint points of human body (eg: head, shoulder center, shoulder left, shoulder right,......, elbow left, elbow right). I want to do k-means clustering of this data.
Data is separated by 'sp... | 0 | 1 | 394 |
0 | 49,968,205 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2018-04-22T16:07:00.000 | 3 | 2 | 0 | How to train a model when there is too much training data? | 49,968,000 | 0.291313 | python,tensorflow,machine-learning,neural-network,keras | Option 1 is far superior, supposing the data has the same quality throughout.
The reason being is that it helps avoid overfitting. One of the biggest issues in ML is that you have a limited amount of data to train on, and the networks tend to overfit that is learn the specific of the set of data instead of generalizing... | I have an RNN and way too much training data. Iterating through the data in its entirety will take years. Right now I have two options for training:
1. Do one pass over as much data as possible
2. Find a select portion of the data and train for multiple epochs on it
Which one is better and why? | 0 | 1 | 418 |
0 | 49,968,311 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2018-04-22T16:07:00.000 | 2 | 2 | 0 | How to train a model when there is too much training data? | 49,968,000 | 0.197375 | python,tensorflow,machine-learning,neural-network,keras | The question is asked in general terms, so the following is also in general terms. I hope this contributes something to helping you with your specific problem.
One approach would be a variant of cross validation. You randomly select some portion of the data, and evaluate the result on a second portion of the data. Re... | I have an RNN and way too much training data. Iterating through the data in its entirety will take years. Right now I have two options for training:
1. Do one pass over as much data as possible
2. Find a select portion of the data and train for multiple epochs on it
Which one is better and why? | 0 | 1 | 418 |
0 | 51,364,465 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-04-23T14:25:00.000 | 0 | 1 | 0 | Python : Image Clustering | 49,983,588 | 0 | python,arrays,image,cluster-analysis | If you want save data for subsequent uses other than this one you should use csv. Otherwise you could load images directly in arrays, in this case take a look at spark. | can I convert all the images to an array saved in one file csv then use those arrays for clustering,
or do I have to convert one by one image into array form then merge them for used in clustering? | 0 | 1 | 122 |
1 | 49,984,333 | 0 | 0 | 1 | 0 | 2 | false | 0 | 2018-04-23T15:01:00.000 | 3 | 4 | 0 | C++ Vector in Python for Perfomance | 49,984,220 | 0.148885 | python,c++,c,arrays | Both the array and the low level operations on it would have to be in C++; switching on a per element basis will have little benefit.
There are many python modules that have internal C/C++ implementations. Simply wrapping a C or C++ style array would be pointless, as the built in python data types can basically be tha... | I'm creating an image detection module, and I do a lot of math calculations around arrays.
I know that C/C++’s array iterates faster than Python’s
I can't move my project to C/C++, so I wanted to create an array module in C/C++ and call it in Python.
What I want to know:
1) Is this viable? Or calling a module from anot... | 0 | 1 | 111 |
1 | 49,984,679 | 0 | 0 | 1 | 0 | 2 | false | 0 | 2018-04-23T15:01:00.000 | 0 | 4 | 0 | C++ Vector in Python for Perfomance | 49,984,220 | 0 | python,c++,c,arrays | Try boost.python.
If you can port all the computational heavy stuff to C++, it'll be quite fast but if you need to switch continuously between C++ and python, you won't get much improvement. | I'm creating an image detection module, and I do a lot of math calculations around arrays.
I know that C/C++’s array iterates faster than Python’s
I can't move my project to C/C++, so I wanted to create an array module in C/C++ and call it in Python.
What I want to know:
1) Is this viable? Or calling a module from anot... | 0 | 1 | 111 |
0 | 49,994,600 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-04-23T15:21:00.000 | 0 | 1 | 0 | Feasibility of converting all python pandas/numpy code to base python | 49,984,571 | 0 | python,pandas,numpy | I'm only going to address the 2nd point. Reimplementing all of numpy/pandas is certainly a very large and useless task. But you're not reimplementing all of it, you only need some parts, and if it's only a few functions, than it's certainly possible.
I'd start from a working script, replace arrays by python lists, and ... | General python question-
I have built a script using numpy and pandas libraries. I have now been told that I cannot use any libraries- only base python to code. This is because apparently open source libraries are not approved.
Does this restriction make sense? Isn't base python as open source as pandas/numpy librar... | 0 | 1 | 53 |
0 | 50,057,446 | 0 | 0 | 0 | 0 | 1 | true | 9 | 2018-04-23T18:49:00.000 | 8 | 1 | 0 | How do I merge two trained neural network weight matrices into one? | 49,988,009 | 1.2 | python,matrix,machine-learning,neural-network,mnist | In general if you combine the weights / biases entirely after training, this is unlikely to produce good results. However, there are ways to make it work.
Intuition about combining weights Consider the following simple example: You have a MLP with one hidden layer. Any two instances of the MLP can produce identical... | I have two identical neural networks running on two separate computers (to reduce the time taken to train the network), each having a subset of a complete data set (MNIST).
My question is; can I combine the two weight matrices of both networks into one matrix, while remaining a proper accuracy? I have seen several arti... | 0 | 1 | 3,859 |
0 | 52,720,246 | 0 | 0 | 1 | 0 | 1 | false | 0 | 2018-04-24T09:50:00.000 | 0 | 1 | 0 | which imresize is fastest in Python | 49,998,519 | 0 | python,image,performance,interpolation | cv2.resize is slightly faster than scipy.misc.imresize | Currently, we need a interpolation in a Image. I have used the scipy.misc.imresize. This function has two drawback:
it can only output interger matrix, but I need a float result.
the speed of scipy.misc.imresize is a little slow | 0 | 1 | 259 |
0 | 50,051,554 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-04-24T13:49:00.000 | 0 | 1 | 0 | How to read back the "random-seed" from a saved model of Dynet | 50,003,397 | 1.2 | python,lstm,dynet | You can't read back the seed parameter. Dynet model does not save the seed parameter. The obvious reason is, it is not required at testing time. Seed is only used to set fixed initial weights, random shuffling etc. for different experimental runs. At testing time no parameter initialisation or shuffling is required. So... | I have a model already trained by dynet library. But i forget the --dynet-seed parameter when training this model.
Does anyone know how to read back this parameter from the saved model?
Thank you in advance for any feedback. | 0 | 1 | 78 |
0 | 50,011,442 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2018-04-24T15:30:00.000 | 0 | 1 | 0 | Sampling data such that distribution is preserved | 50,005,578 | 0 | python-3.x,pandas | Increase your sample size (n>>100). The data you are sampling from is itself a random sample. Creating a subset through random selection is itself a random process. If one of the data classes has a low frequency then the problem is that your sample size (100) is too low.
If you change the replace flag to 'True' and do ... | vsample_data = credit_card.sample(n=100, replace='False')
print(vsample_data)
Here, I was trying to sample 100 data points from a data set but not able to get correct sample data such that it preserves the original distribution of the credit card fraud data-set i.e Class-0( Non- Fraud) and Class-1(Fraud). | 0 | 1 | 537 |
0 | 50,310,718 | 1 | 0 | 1 | 0 | 1 | false | 0 | 2018-04-25T00:38:00.000 | 0 | 1 | 0 | can't import more than 50 contacts from csv file to telegram using Python3 | 50,012,489 | 0 | python,csv,telegram,telethon | You can not import a large number of people in sequential. ُThe telegram finds you're sperm.
As a result, you must use sleep between your requests | Trying to Import 200 contacts from CSV file to telegram using Python3 Code. It's working with first 50 contacts and then stop and showing below:
telethon.errors.rpc_error_list.FloodWaitError: A wait of 101 seconds is required
Any idea how I can import all list without waiting?? Thanks!! | 0 | 1 | 287 |
0 | 50,215,479 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2018-04-25T07:59:00.000 | 1 | 2 | 0 | spacy: add lemmatizer lookup for Dutch (nl) language | 50,016,956 | 0.099668 | python,spacy,lemmatization | In theory, your approach is correct – if you copy exactly how it's implemented in German and other languages that implement the lookup, it should work.
I suspect your problem here is actually a different one: According to the error message, it can't actually find the spacy.lang.nl.lemmatizer module, so spaCy now fails ... | I'am using Spacy 2.0.11 with Dutch language model nl_core_news_sm (nl). How can I add the lemmatization lookup similar to the implementation for German (de)?
I tried the following steps:
add lookup to init.py in the language folder (nl)
add lemmatizer.py in the language folder (nl)
This resulted in the following ... | 0 | 1 | 1,755 |
0 | 50,017,530 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2018-04-25T08:16:00.000 | 0 | 2 | 0 | How to calculate a 95 credible region for a 2D joint distribution? | 50,017,251 | 0 | python,numpy,scipy,confidence-interval,credible-interval | If you are interested in finding a pair x_1, x_2 of real numbers such that
P(X_1<=x_1, X_2<=x_2) = 0.95 and your distribution is continuous then there will be infinitely many of these pairs. You might be better of just fixing one of them and then finding the other | Suppose we have a joint distribution p(x_1,x_2), and we know x_1,x_2,p. Both are discrete, (x_1,x_2) is scatter, its contour could be drawn, marginal as well. I would like to show the area of 95% quantile (a scale of 95% data will be contained) of the joint distribution, how can I do that? | 0 | 1 | 305 |
0 | 50,017,700 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2018-04-25T08:16:00.000 | 1 | 2 | 0 | How to calculate a 95 credible region for a 2D joint distribution? | 50,017,251 | 0.099668 | python,numpy,scipy,confidence-interval,credible-interval | As the other points out, there are infinitely many solutions to this problem. A practical one is to find the approximate center of the point cloud and extend a circle from there until it contains approximately 95% of the data. Then, find the convex hull of the selected points and compute its area.
Of course, this will ... | Suppose we have a joint distribution p(x_1,x_2), and we know x_1,x_2,p. Both are discrete, (x_1,x_2) is scatter, its contour could be drawn, marginal as well. I would like to show the area of 95% quantile (a scale of 95% data will be contained) of the joint distribution, how can I do that? | 0 | 1 | 305 |
0 | 57,815,167 | 0 | 1 | 0 | 0 | 1 | false | 5 | 2018-04-25T12:18:00.000 | 0 | 2 | 0 | How to parallelise python input pipeline in Distributed Tensorflow | 50,022,168 | 0 | python,tensorflow,tensorflow-datasets | Recently Google has released Tensorflow Extended (TFX). It essentially consists of:
A set of operators which each use Apache Beam to do data distribution (They call them components).
A standardization of both data and parameter format (what they call protobuf)
Automated dependency management of operators (workflow/orc... | I have a non trivial input pipeline, which consists of reading ground truth and raw data and performing preprocessing on them, written in Python. It takes a long time to run the input pipeline for a single sample so I have multiple processes (from python multiprocessing package) running in parallel and queues to perfor... | 0 | 1 | 932 |
0 | 50,028,462 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-04-25T17:27:00.000 | 1 | 1 | 0 | lstm autoencoder time series data input shape | 50,028,127 | 1.2 | python,keras | Sample: an individual "sequence", not connected or related to any other sequence
Timesteps: the length of your sequences, each sequence has a start and an end, between them the sequence has steps
Features (the last dimension): different parallel variables measured in a same sequence.
Only you can organize ... | I looked into different cases but could not understand what I have to choose. My code is also working but my prediction looks strange only having 2 parallel lines. I have an LSTM Autoencoder for regression of time series. Autoencoder because I need to reduce the dimensions. Mydata looks like: 400 samples with each of t... | 0 | 1 | 484 |
0 | 50,339,467 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-04-26T08:08:00.000 | 2 | 1 | 0 | What tensorflow distribution to represent a list of categorical data | 50,037,919 | 1.2 | python,tensorflow,machine-learning | Does tf.contrib.distributions.Categorical satisfy your needs? Samples should be from (0 to n - 1), where n represents the category.
Example:
# logits has shape [N, M], where M is the number of classes
dist = tf.contrib.distributions.Categorical(logits=logits)
# Sample 20 times. Should give shape [20, N].
samples = dis... | I want to construct a variational autoencoder where one sample is an N*M matrix where for each row, there are M categories. Essentially one sample is a list of categorical data where only one category can be selected - a list of one-hot vectors.
Currently, I have a working autoencoder for this type of data - I use a so... | 0 | 1 | 794 |
0 | 65,922,663 | 0 | 0 | 0 | 0 | 1 | false | 4 | 2018-04-26T08:31:00.000 | 0 | 2 | 0 | Do gensim Doc2Vec distinguish between same Sentence with positive and negative context.? | 50,038,347 | 0 | python,nlp,gensim,doc2vec | use textblob and set the sentiment and polarity for each sentence. tokenize the sentences using nlp | While learning Doc2Vec library, I got stuck on the following question.
Do gensim Doc2Vec distinguish between the same Sentence with positive and negative context?
For Example:
Sentence A: "I love Machine Learning"
Sentence B: "I do not love Machine Learning"
If I train sentence A and B with doc2vec and find cosine simi... | 0 | 1 | 806 |
0 | 50,044,405 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-04-26T11:37:00.000 | 1 | 1 | 0 | spotfire - Disappear lines when value in filters are selected | 50,042,009 | 0.197375 | python,line,spotfire,tibco | there isn't a way to react to filter changes as far as I know, but you can "recreate" the filter as a property control and execute the script when that property value changes.
typical example is for a listbox/dropdown property control representing a listbox filter. | I am actually new to python scripting and I have a requirement where we have a line chart (each line shows tasks and y axis the time it took to complete).
Using lines and curves I have added average, upper control and lower control limits to the line chart. I also have a filter which shows the tasks.
whenever a task is... | 0 | 1 | 339 |
0 | 50,046,198 | 0 | 0 | 0 | 0 | 2 | false | 1 | 2018-04-26T14:36:00.000 | 1 | 2 | 0 | ModuleNotFoundError: No module named 'matplotlib._path' | 50,045,758 | 0.099668 | python,matplotlib,anaconda | A temporary solution is to add the line sys.path.append(/path/to/located/package).
A permanent solution is to add the path to .bashrc | While I installed properly the matplotlib and seaborn. I was able to import matplotlib but when I was trying to import the seaborn I got the following error message. ModuleNotFoundError: No module named 'matplotlib._path' . Same if I was trying to import matplotlib.pyplot.
After spending a lot of time googling and tryi... | 0 | 1 | 925 |
0 | 52,482,106 | 0 | 0 | 0 | 0 | 2 | true | 1 | 2018-04-26T14:36:00.000 | 0 | 2 | 0 | ModuleNotFoundError: No module named 'matplotlib._path' | 50,045,758 | 1.2 | python,matplotlib,anaconda | I finally started using anaconda and installing everything in one environment. In this way, I save and import everything that I want in this environment without any confusion. | While I installed properly the matplotlib and seaborn. I was able to import matplotlib but when I was trying to import the seaborn I got the following error message. ModuleNotFoundError: No module named 'matplotlib._path' . Same if I was trying to import matplotlib.pyplot.
After spending a lot of time googling and tryi... | 0 | 1 | 925 |
0 | 52,635,273 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-04-26T15:29:00.000 | 0 | 1 | 0 | Reading symmetrical Harwell-Boeing matrix in Python | 50,046,865 | 0 | python,scipy,sparse-matrix,eigenvalue,finite-element-analysis | I found a solution using BeBOP Sparse Matrix Converter to convert to MarketMatrix and then importing this into Python. | SciPy can read only read unsymmetric matrices with hb_read. Anyone know if it is possible to read symmetric matrices in Python (3)? | 0 | 1 | 202 |
0 | 50,056,349 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-04-26T15:30:00.000 | 1 | 2 | 0 | Deciding to the clustering algorithm for the dataset containing both categorical and numerical variables | 50,046,876 | 0.099668 | python,machine-learning,cluster-analysis,dimensionality-reduction | The best thing to try is hierarchical agglomerative clustering with a distance metric such as Gower's.
Mixed data with different scales usually does not work in any statistical meaningful way. You have too many weights to choose, so no result will be statistically well founded, but largely a result of your weighting. S... | I am a newbie in machine learning and trying to make a segmentation with clustering algorithms. However, Since my dataset has both categorical variables (such as gender, marital status, preferred social media platform etc) as well as numerical variables ( average expenditure, age, income etc.), I could not decide which... | 0 | 1 | 330 |
0 | 50,048,886 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-04-26T16:27:00.000 | 0 | 1 | 0 | Benefit of applying tf.image.per_image_standardization() over batch_norm layer in Tensorflow? | 50,047,909 | 0 | python,tensorflow,deep-learning | They both do similar things, and one could indeed use batch norm to normalize input images.
However, I would not use batch norm for this purpose:
It is clearer to use tf.image.per_image_standardization for image normalization than batch norm.
Batch normalization is a broader concept than a per-channel normalization. L... | What is the benefit in applying tf.image.per_image_standardization() before the first layer of the deep neural network over adding the Batch_Norm layer as the first layer?
In order to normalize the [0.0, 255.0] float value image pixels before feeding into the network, which method would be suitable?
tf.image.per_imag... | 0 | 1 | 661 |
0 | 50,177,881 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2018-04-27T09:10:00.000 | 0 | 1 | 0 | Collaborative Filtering using categorical features | 50,059,020 | 0 | python,recommendation-engine,collaborative-filtering,multiclass-classification | Association rule mining will be helpful here. It calculates the relative likelihood that items will appear together in a user's history. It's different from, and differently useful from, collaborative filtering recommendation techniques. | I am trying to build a recommender system using collaborative filtering. The issues I am facing are :
The User-Item dataset has mostly categorical variables, so cant find the best way to calculate similarity matrix. Euclidean / Cosine distance will not work here, trying with Jaccard distance.
The dataset does not have... | 0 | 1 | 895 |
0 | 50,067,974 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-04-27T12:43:00.000 | 0 | 1 | 0 | Image Lossy Compression to Different Formats | 50,062,696 | 0 | python,compression,jpeg | There is no "quality" setting in JPEG. Some encoders use quality as a simplified interface method for selecting quantization tables. You mentioned PNG. You can control the compression settings. The difference is that in JPEG compression setting trade off variations from the original against the output stream size. In P... | I have looked for this everywhere and already installed a few softwares that did not help me.
The thing is, I am studying and researching image and video quality analysis and part of the process is applying my work to different types of lossy compression formats.
Regarding the image part of the work, to do so for JPEG ... | 0 | 1 | 305 |
0 | 50,063,200 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2018-04-27T12:58:00.000 | 0 | 2 | 0 | Select columns periodically on pandas DataFrame | 50,062,936 | 0 | python,pandas,dataframe | df.iloc[:,[i*17 for i in range(0,65)]] | I'm working on a Dataframe with 1116 columns, how could I select just the columns in a period of 17 ?
More clearly select the 12th, 29th,46th,63rd... columns | 0 | 1 | 81 |
0 | 50,069,637 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-04-27T15:05:00.000 | 0 | 3 | 0 | Delimit array with different strings | 50,065,295 | 0 | python-3.x,numpy,csv | I ended up using the Pandas solution provided by Scott. For some reason I am not 100% clear on, I cannot simply convert the array from string to float with float(array). I created an array of equal size and iterated over the size of the array, converting each individual element to a float and saving it to the other a... | I have a text file that contains 3 columns of useful data that I would like to be able to extract in python using numpy. The file type is a *.nc and is NOT a netCDF4 filetype. It is a standard file output type for CNC machines. In my case it is sort of a CMM (coordinate measurement machine). The format goes somethi... | 0 | 1 | 84 |
0 | 50,068,729 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-04-27T18:54:00.000 | 0 | 1 | 0 | How to append a contents of text file to a column in dataframe (pandas python) | 50,068,665 | 0 | python,pandas | You can concatenate the files to one file with a delimiter of your choice (make sure that the delimiter is not included in any of the text files), and then use pd.read_csv() to read the concatenated file. | There are bunch of text files in a directory. I want to add each text file to the last column in a row. Can this be achieved using pandas?
Eg:
Directory1>
File1.txt
File2.txt
File3.txt.... (Size of these files are close to 70KB)
Dataframe:
Column1 Column2 column3
Value1 Value2 (Trying to add Fil... | 0 | 1 | 149 |
0 | 50,070,463 | 0 | 1 | 0 | 0 | 1 | false | 1 | 2018-04-27T21:14:00.000 | 0 | 3 | 0 | Import tensorflow contrib learn python learn | 50,070,398 | 0 | python,python-3.x,tensorflow,scikit-learn,deep-learning | It looks like you have an error in your import statement. Try
from tensorflow.contrib.learn import DNNClassifier | I am new to tensorflow. When I am using import tensorflow.contrib.learn.python.learn for using the DNNClassifier it is giving me an error: module object has no attribute python
Python version 3.4
Tensorflow 1.7.0 | 0 | 1 | 1,041 |
0 | 50,088,667 | 0 | 0 | 0 | 0 | 1 | false | 3 | 2018-04-29T13:47:00.000 | 1 | 2 | 0 | How to preprocess audio data for input into a Neural Network | 50,087,271 | 0.099668 | python,audio,machine-learning,deep-learning,speech-recognition | I would split each wav by the areas of silence. Trim the silence from beginning and end. Then I'd run each one through a FFT for different sections. Smaller ones at the beginning of the sound. Then I'd normalise the frequencies against the fundamental. Then I'd feed the results into the NN as a 3d array of volumes, fre... | I'm currently developing a keyword-spotting system that recognizes digits from 0 to 9 using deep neural networks. I have a dataset of people saying the numbers(namely the TIDIGITS dataset, collected at Texas Instruments, Inc), however the data is not prepared to be fed into a neural network, because not all the audio d... | 0 | 1 | 3,097 |
0 | 50,091,280 | 0 | 1 | 0 | 0 | 1 | false | 1 | 2018-04-29T20:58:00.000 | -1 | 1 | 0 | How would i generate a random number in python without duplicating numbers | 50,091,226 | -0.197375 | python,python-3.x,random | Generate a random number
check if there are any duplicates, if so go back to 1
you have a number with no duplicates
OR
Generate it one digit at a time from a list, removing the digit from the list at each iteration.
Generate a list with numbers 0 to 9 in it.
Create two variables, the result holding value 0, and multi... | I was wondering how to generate a random 4 digit number that has no duplicates in python 3.6
I could generate 0000-9999 but that would give me a number with a duplicate like 3445, Anyone have any ideas
thanks in advance | 0 | 1 | 537 |
0 | 53,323,802 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-04-30T15:12:00.000 | 0 | 1 | 0 | Keras Neural Network. Preprocessing | 50,103,377 | 0 | python,scikit-learn,neural-network,keras | Usually, if you are doing regression you should use a linear' activation in the last layer. A sigmoid function will 'favor' values closer to 0 and 1, so it would be harder for your model to output intermediate values.
If the distribution of your targets is gaussian or uniform I would go with a linear output layer. De-... | I have this doubt when I fit a neural network in a regression problem. I preprocessed the predictors (features) of my train and test data using the methods of Imputers and Scale from sklearn.preprocessing,but I did not preprocessed the class or target of my train data or test data.
In the architecture of my neural netw... | 0 | 1 | 99 |
0 | 50,125,190 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-05-01T12:12:00.000 | 0 | 1 | 0 | Using DBSCAN to find data that are far from high density clusters? | 50,116,265 | 1.2 | python,scikit-learn,dbscan | For anomaly detection use an anomaly detection algorithm instead.
There are siblings derived from DBSCAN for exactly this purpose. For example, LOF and LoOP and kNN and ... | Conscious that dbscan clusters don't necessarily have cluster centres, but for an anomaly detection task, I want to spot data that are outliers/away from the normal clusters. Is there a way to do this using sklearn's dbscan? | 0 | 1 | 62 |
0 | 50,117,662 | 0 | 0 | 0 | 0 | 1 | true | 2 | 2018-05-01T13:33:00.000 | 2 | 1 | 0 | Multi-label classification methods for large dataset | 50,117,450 | 1.2 | python,scikit-learn,multilabel-classification | 20 minutes for this size of a job doesn't seem that long, neither does 4 hours for training.
I would really try vowpal wabbit. It excels at this sort of multilabel problem and will probably give unmatched performance if that's what you're after. It requires significant tuning and will still require quality training da... | I realize there's another question with a similar title, but my dataset is very different.
I have nearly 40 million rows and about 3 thousand labels. Running a simply sklearn train_test_split takes nearly 20 minutes.
I initially was using multi-class classification models as that's all I had experience with, and realiz... | 0 | 1 | 450 |
0 | 50,123,218 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-05-01T20:11:00.000 | 0 | 1 | 0 | Bokeh- refer to child within a row within a layout | 50,123,144 | 0 | python,reference,bokeh | Oftentimes, posting a question quickly boosts my research ability, and it is no different here. To refer to p2 in:
layout1=layout(row(p1,p2), p3)
it is:
layout1.children[0].children[1]
Now I can speak to all my grandchildren! | How would one refer to p2 in :
layout1=layout(row(p1,p2), p3)
where p1, p2, and p3 are plots?
I've attempted layout1.children[0][1] but to no avail | 0 | 1 | 7 |
0 | 50,219,125 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-05-02T14:41:00.000 | 0 | 2 | 0 | How to window or reset streaming operations in tensorflow? | 50,137,369 | 0 | python,tensorflow | The metrics in tf.contrib.eager.metrics (which work both with and without eager execution) have a init_variable() op you can call if you want to reset their internal variables. | Tensorflow provides all sorts of nice streaming operations to aggregate statistics along batches, such as tf.metrics.mean.
However I find that accumulating all values since the beginning often does not make a lot of sense. For example, one could rather want to have statistics per epoch, or any other time window that ma... | 0 | 1 | 275 |
0 | 50,140,995 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-05-02T15:18:00.000 | 0 | 1 | 0 | How to find if there are wrong values in a pandas dataframe? | 50,138,110 | 0 | python,pandas,dataframe | "120 cm" is a string, not an integer, so that's a confusing example. Some ways to find "unexpected" values include:
Use "describe" to examine the range of numerical values, to see if there are any far outside of your expected range.
Use "unique" to see the set of all values for cases where you expect a small number of... | I am quite new in Python coding, and I am dealing with a big dataframe for my internship.
I had an issue as sometimes there are wrong values in my dataframe. For example I find string type values ("broken leaf") instead of integer type values as ("120 cm") or (NaN).
I know there is the df.replace() function, but theref... | 0 | 1 | 1,435 |
0 | 50,139,776 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-05-02T16:17:00.000 | 1 | 1 | 0 | Bokeh or Holoviews: BarChart by downsampling date time to months/years/etc | 50,139,193 | 1.2 | python-3.x,pandas,bokeh,holoviews | You will need to do your own downsampling. Bokeh does not generally do any sort of data aggregations itself. The few exceptions are:
hexbin to generate data binned on a hex tile grid
passing a pandas GroupBy to initialize a CDS will create a CDS with columns for the aggregates
And even in the second case, arguably i... | I have a pandas dataframe (wrapped in holoviews typically) that has three columns. Col1 is a datetime, Col2 are categorical strings (i.e. one of 'Cat', 'Dog', 'Bird'), and Col3 being an integer count.
I'm trying to find a way to use the bokeh library to downsample the datetime to months, quarters, years, etc. simila... | 0 | 1 | 151 |
0 | 55,172,665 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2018-05-02T22:13:00.000 | 0 | 1 | 0 | Keras: Getting different values shown by fit_generator (verbose=1) and the metrics in the history object | 50,144,314 | 0 | python,tensorflow,machine-learning,keras | Even the metrics shown in verbose mode (val_loss, val_acc, etc...) has rounding and sometimes the rounding conflict with EarlyStopping or ModelCheckPoint callbacks behaviour.
I think that this isn't really a problem, just a summary point of view. If you really need these numbers with all decimals places, the history is... | I'm new using Keras and I'm building a model to classify medical images.
The dataset is very large and I am using fit_generator() function to optimize RAM space.
When the model trains by batches, it shows statistics of each batch such as loss, precision, etc; and finally when it ends with all the batches at the end o... | 0 | 1 | 172 |
0 | 50,218,148 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-05-03T03:16:00.000 | 2 | 2 | 0 | AWS DLAMI - GPUs not showing up for Keras | 50,146,392 | 1.2 | python,amazon-web-services,tensorflow,amazon-ec2,keras | This was resolved by uninstalling, then reinstalling tensorflow-gpu through the conda environment provided by the AMI. | I have created a DLAMI (Deep Learning AMI (Amazon Linux) Version 8.0 - ami-9109beee on g2.8xlarge) and installed jupyter notebook to create a simple Keras LSTM. When I try to turn my Keras model into a GPU model using the multi_gpu_model function, I see the following error logged:
Ignoring visible gpu device (device: ... | 0 | 1 | 683 |
0 | 50,984,130 | 0 | 1 | 0 | 0 | 3 | false | 1 | 2018-05-03T05:14:00.000 | 1 | 3 | 0 | Install opencv python package in Anaconda | 50,147,385 | 0.066568 | python-2.7,opencv,image-processing,ide,anaconda | The good thing with anaconda is that it should make your life easier without getting your hands too dirty. If you use only basic libs, only install packages with anaconda commands to be sure not to corrupt your python environment.
if you're on linux, use conda install -c conda-forge opencv
on windows, do the same with ... | Can someone provide the steps and the necessary links of the dependencies to install external python package "opencv-python" for image processing?
I tried installing it in pycharm, but it was not able to import cv2(opencv) and was throwing version mismatch with numpy!
Please help! | 0 | 1 | 9,045 |
0 | 54,570,301 | 0 | 1 | 0 | 0 | 3 | false | 1 | 2018-05-03T05:14:00.000 | 0 | 3 | 0 | Install opencv python package in Anaconda | 50,147,385 | 0 | python-2.7,opencv,image-processing,ide,anaconda | Create a virtual env. Following which run following instals
pip install numpy scipy matplotlib scikit-learn jupyter
pip install opencv-contrib-python
pip install dlib
You may verify the install via this script in Python environment-
import cv2
cv2.version | Can someone provide the steps and the necessary links of the dependencies to install external python package "opencv-python" for image processing?
I tried installing it in pycharm, but it was not able to import cv2(opencv) and was throwing version mismatch with numpy!
Please help! | 0 | 1 | 9,045 |
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