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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 | 48,129,094 | 0 | 0 | 0 | 0 | 1 | false | 8 | 2018-01-06T11:25:00.000 | -2 | 3 | 0 | Plane-plane intersection in python | 48,126,838 | -0.132549 | python,computational-geometry,intersection,plane | This is solved by elementary vector computation and is not substantial/useful enough to deserve a library implementation. Work out the math with Numpy.
The line direction is given by the cross product of the two normal vectors (A, B, C), and it suffices to find a single point, say the intersection of the two given plan... | I need to calculate intersection of two planes in form of AX+BY+CZ+D=0 and get a line in form of two (x,y,z) points. I know how to do the math, but I want to avoid inventing a bicycle and use something effective and tested. Is there any library which already implements this? Tried to search opencv and google, but with ... | 0 | 1 | 8,472 |
0 | 48,127,014 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2018-01-06T11:34:00.000 | 1 | 1 | 0 | Trying to understand neural networks | 48,126,924 | 1.2 | python,neural-network,deep-learning | Learning rate: that code does not use a learning rate, or rather it uses a learning rate of 1. Lines 48,49 just add the adjustment (gradient) value without any rate. That is an uncommon choice and can sometimes work in practice, though in general it is not advised. Technically this is the simplest version of gradien... | I'm new to coding and I've been guided to start with Python because it is good for a beginner and very versatile. I've been watching some tutorials online on how to create a neural network with Python, however I've just got stuck in this example.
I've seen and worked out tutorials where you have the learning rate and ... | 0 | 1 | 90 |
0 | 48,141,696 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2018-01-07T18:12:00.000 | 3 | 1 | 0 | use negative loss in tensorflow | 48,140,133 | 0.53705 | python,tensorflow,machine-learning | A gradient descent minimizer will typically try to find the minimum loss irrespective of the sign of the loss surface. It sounds like you either want to a) assign a large loss to encourage your model to pick something else or b) assign a fifth no-action category. | I am implementing a reinforcement agent that takes actions based on classes.
so it can take action 1 or 2 or 3 or 4.
So my question is can I use negative loss in tensorflow to stop it from outputting an action.
Example:
Let's say the agent outputs action 1 I want to very strongly dissuade it from taking action 1 in tha... | 0 | 1 | 1,647 |
0 | 48,142,657 | 0 | 0 | 0 | 0 | 2 | false | 3 | 2018-01-07T23:11:00.000 | 1 | 2 | 0 | What Neural Network to use for AI Mouse Movement | 48,142,421 | 0.099668 | python,tensorflow,machine-learning,neural-network,mouse | Using a neural network for this task seems like total over kill to me. It seems like you have 2 inputs, each which have an X and a Y coordinate with one representing the initial position and one representing the final position of the mouse.
There are a ton of ways you can introduce randomness into this path in hard to... | I am trying to make a fuction that takes in 2 (x,y) coordinates and returns and array of coordinates for where the mouse should be every 0.05 second (or about that)...
(The whole aim of the program is to have random/not smooth mouse movement to mimic a human's mouse movement)
E.G. INPUTS : [(800,600), (300,400)]
OU... | 0 | 1 | 2,533 |
0 | 48,143,029 | 0 | 0 | 0 | 0 | 2 | false | 3 | 2018-01-07T23:11:00.000 | 0 | 2 | 0 | What Neural Network to use for AI Mouse Movement | 48,142,421 | 0 | python,tensorflow,machine-learning,neural-network,mouse | If you're trying to predict where the mouse should be based on the position of something else, a simple ANN will do the job.
Are you trying to automate tasks, like have a script that controls a game? A Recurrent Neural Network like an LSTM or GRU will take history into account.
I know you're doing this as a learning ... | I am trying to make a fuction that takes in 2 (x,y) coordinates and returns and array of coordinates for where the mouse should be every 0.05 second (or about that)...
(The whole aim of the program is to have random/not smooth mouse movement to mimic a human's mouse movement)
E.G. INPUTS : [(800,600), (300,400)]
OU... | 0 | 1 | 2,533 |
0 | 48,146,211 | 0 | 0 | 0 | 0 | 2 | false | 2 | 2018-01-08T07:15:00.000 | 2 | 2 | 0 | Change sentiment of a single word | 48,145,777 | 0.197375 | python,nlp,nltk,sentiment-analysis | The models used for sentiment analysis are generally the result of a machine-learning process. You can produce your own model by running the model creation on a training set where the sentiments are tagged the way you like, but this is a significant undertaking, especially if you are unfamiliar with the underpinnings.
... | I've been working with NLTK in Python for a few days for sentiment analysis and it's a wonderful tool. My only concern is the sentiment it has for the word 'Quick'. Most of the data that I am dealing with has comments about a certain service and MOST refer to the service as being 'Quick' which clearly has Positive sent... | 0 | 1 | 915 |
0 | 48,154,073 | 0 | 0 | 0 | 0 | 2 | true | 2 | 2018-01-08T07:15:00.000 | 3 | 2 | 0 | Change sentiment of a single word | 48,145,777 | 1.2 | python,nlp,nltk,sentiment-analysis | I have fixed the problem. Found the vader Lexicon file in AppData\Roaming\nltk_data\sentiment. Going through the file I found that the word Quick wasn't even in it. The format of the file is as following:
Token Mean-sentiment StandardDeviation [list of sentiment score collected from 10 people ranging from -4 to 4]
I ed... | I've been working with NLTK in Python for a few days for sentiment analysis and it's a wonderful tool. My only concern is the sentiment it has for the word 'Quick'. Most of the data that I am dealing with has comments about a certain service and MOST refer to the service as being 'Quick' which clearly has Positive sent... | 0 | 1 | 915 |
0 | 53,168,687 | 0 | 1 | 0 | 0 | 1 | true | 1 | 2018-01-08T11:21:00.000 | 0 | 1 | 0 | Ambiguous Entity in stanfors NER | 48,149,281 | 1.2 | python-3.x,stanford-nlp,named-entity-recognition | Scrape data from sites like wikipedia,etc. and create a scoring model and then use it for context prediction. | I am working on Stanford NER, My question is regarding ambiguous entities.
For example, I have 2 sentences:
I love oranges.
Orange is my dress code for tomorrow.
How can i train these 2 sentences to give out,
first orange as Fruit, second orange as Color.
Thanks | 0 | 1 | 154 |
0 | 66,533,975 | 0 | 0 | 0 | 0 | 1 | false | 375 | 2018-01-08T14:50:00.000 | 18 | 14 | 0 | How to check if pytorch is using the GPU? | 48,152,674 | 1 | python,memory-management,gpu,nvidia,pytorch | Query
Command
Does PyTorch see any GPUs?
torch.cuda.is_available()
Are tensors stored on GPU by default?
torch.rand(10).device
Set default tensor type to CUDA:
torch.set_default_tensor_type(torch.cuda.FloatTensor)
Is this tensor a GPU tensor?
my_tensor.is_cuda
Is this model stored on the GPU?
all(p.is_cuda ... | How do I check if pytorch is using the GPU? It's possible to detect with nvidia-smi if there is any activity from the GPU during the process, but I want something written in a python script. | 0 | 1 | 569,617 |
0 | 48,159,738 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2018-01-08T23:34:00.000 | 2 | 3 | 0 | Calculating Mean & STD for Batch [Python/Numpy] | 48,159,562 | 0.132549 | python,numpy,tensorflow,batch-processing,channel | Assume you want to get the mean of multiple axis(if I didn't get you wrong). numpy.mean(a, axis=None) already supports multiple axis mean if axis is a tuple.
I'm not so sure what you mean by naive method. | Looking to calculate Mean and STD per channel over a batch efficiently.
Details:
batch size: 128
images: 32x32
3 channels (RGB)
So each batch is of size [128, 32, 32, 3].
There are lots of batches (naive method takes ~4min over all batches).
And I would like to output 2 arrays: (meanR, meanG, meanB) and (stdR, stdG,... | 0 | 1 | 3,580 |
0 | 48,221,294 | 0 | 0 | 0 | 0 | 2 | true | 1 | 2018-01-09T10:18:00.000 | 1 | 2 | 1 | Access datalake from Azure datafactory V2 using on demand HD Insight cluster | 48,165,947 | 1.2 | python,pyspark,azure-hdinsight,azure-data-factory,azure-data-lake | Currently, we don't have support for ADLS data store with HDI Spark cluster in ADF v2. We plan to add that in the coming months. Till then, you will have to contiue using the workaround as you mentioned in your post above. Sorry for the inconvenience. | I am trying to execute spark job from on demand HD Insight cluster using Azure datafactory.
Documentation indicates clearly that ADF(v2) does not support datalake linked service for on demand HD insight cluster and one have to copy data onto blob from copy activity and than execute the job. BUT this work around seems t... | 0 | 1 | 343 |
0 | 49,116,105 | 0 | 0 | 0 | 0 | 2 | false | 1 | 2018-01-09T10:18:00.000 | 0 | 2 | 1 | Access datalake from Azure datafactory V2 using on demand HD Insight cluster | 48,165,947 | 0 | python,pyspark,azure-hdinsight,azure-data-factory,azure-data-lake | The Blob storage is used for the scripts and config files that the On Demand cluster will use. In the scripts you write and store in the attached Blob storage they can write from ADLS to SQLDB for example. | I am trying to execute spark job from on demand HD Insight cluster using Azure datafactory.
Documentation indicates clearly that ADF(v2) does not support datalake linked service for on demand HD insight cluster and one have to copy data onto blob from copy activity and than execute the job. BUT this work around seems t... | 0 | 1 | 343 |
0 | 48,172,836 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-01-09T15:16:00.000 | 1 | 1 | 0 | Derivative of log plot in python | 48,171,283 | 1.2 | python,numpy,derivative | Thanks to the discussions in the comments the problem with np.gradient has been solved by updating the numpy package from version 1.12.1 to 1.13.3. This update is specially relevant if you are also getting the ValueError "distances must be scalars" when using gradient. Thus, in order to extract the order of the power-l... | We have the x and y values, and I am taking their log, by logx = np.log10(x) and logy = np.log10(y). I am trying to compute the derivative of logy w.r.t logx, so dlogy/dlogx. I used to do this successfully using numpy gradient, more precisely
derivy = np.gradient(logy,np.gradient(logx))
but for some strange reason it... | 0 | 1 | 1,101 |
0 | 48,258,953 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2018-01-09T16:52:00.000 | 1 | 1 | 0 | is it possible to use cnn models trained in cntk python in c#? | 48,173,038 | 0.197375 | c#,python,c++,cntk | Yes, this is completely possible. The CNTK framework itself is written in C++ and the C# as well as the Python interface are only wrappers onto the C++ code. So, as long you use versions which are compatible to each other, you can do that.
For instance, if you use CNTK 2.3.1 with python and also CNTK 2.3.1 with C# the... | I want to use CNTK python to train a CNN model and then use the trained model when i am programming in c# or c++. Is it possible to use CNTK python
trained model in C# or c++? | 0 | 1 | 149 |
0 | 48,188,480 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2018-01-09T22:47:00.000 | 0 | 1 | 0 | Do I need to train my neural network everytime I run it? | 48,177,771 | 0 | python,neural-network,training-data | If you're not using an already ready library it 'saves' the training only if you write a part of code to save it. The simplest way is to generate a TXT with a list of all the weights after the training and load it with a specific function. | I am trying machine learning for the first time, and am playing around with a handwriting recognition NN (in Python). I just wanted to know whether or not I need to train the model every time I run it, or if it 'saves' the training. Thanks in advance. | 0 | 1 | 966 |
0 | 48,187,560 | 0 | 1 | 0 | 0 | 1 | false | 2 | 2018-01-10T04:10:00.000 | 0 | 2 | 0 | Prevent underflow in floating point division in Python | 48,180,177 | 0 | python,floating-point,precision,underflow | Division, like other IEEE-754-specified operations, is computed at infinite precision and then (with ordinary rounding rules) rounded to the closest representable float. The result of calculating x/y will almost certainly be a lot more accurate than the result of calculating np.exp(np.log(x) - np.log(y) (and is guarant... | Suppose both x and y are very small numbers, but I know that the true value of x / y is reasonable.
What is the best way to compute x/y?
In particular, I have been doing np.exp(np.log(x) - np.log(y) instead, but I'm not sure if that would make a difference at all? | 0 | 1 | 6,827 |
0 | 48,302,888 | 0 | 0 | 0 | 0 | 2 | false | 1 | 2018-01-10T13:51:00.000 | 1 | 3 | 0 | Migrate Halcon code to OpenCV | 48,188,940 | 0.066568 | python,opencv,computer-vision,halcon | It depends on which Halcon functionalities are you using and why you want to do it. The question appears to be very general. I would recommend you to convert your Halcon Program to C++ and write a wrapper function to pass arguments to/from your openCV program.This would be the simplest option to provide interaction bet... | I am developing a solution using a comercial computer vision software called Halcon. I am thinking on migrating or convert my solution to OpenCV in Python. I will like to start developing my other computer vision solution in Halcon because the IDE is incredible, and them generate a script to migrate them to OpenCV.
Doe... | 0 | 1 | 4,998 |
0 | 48,305,091 | 0 | 0 | 0 | 0 | 2 | false | 1 | 2018-01-10T13:51:00.000 | 1 | 3 | 0 | Migrate Halcon code to OpenCV | 48,188,940 | 0.066568 | python,opencv,computer-vision,halcon | This is unfortunately not possible because Halcon itself is not an open source library and every single function is locked.
The reason behind is runtime licencing. | I am developing a solution using a comercial computer vision software called Halcon. I am thinking on migrating or convert my solution to OpenCV in Python. I will like to start developing my other computer vision solution in Halcon because the IDE is incredible, and them generate a script to migrate them to OpenCV.
Doe... | 0 | 1 | 4,998 |
0 | 48,197,823 | 0 | 0 | 0 | 0 | 3 | false | 0 | 2018-01-10T22:36:00.000 | 0 | 3 | 0 | Data Science Model and Training - Understanding | 48,197,227 | 0 | python,machine-learning,artificial-intelligence,jupyter-notebook,data-science | It depends on the model. For example linear regression, training will give you the coefficients of the slope and the intercept (generally). These are the "model parameters". When deployed, traditionally, these coefficients get fed into a different algorithm (literally y=mx+b), and then when queried "what should y be, w... | Coming from a programming background where you write code, test, deploy, run.. I'm trying to wrap my head around the concept of "training a model" or a "trained model" in data science, and deploying that trained model.
I'm not really concerned about the deployment environment, automation, etc.. I'm trying to understan... | 0 | 1 | 225 |
0 | 50,946,421 | 0 | 0 | 0 | 0 | 3 | false | 0 | 2018-01-10T22:36:00.000 | 0 | 3 | 0 | Data Science Model and Training - Understanding | 48,197,227 | 0 | python,machine-learning,artificial-intelligence,jupyter-notebook,data-science | A trained model(pickled) or whatever you want to use, contains the features on which it has been trained at least. Take for example a simple distance based model, you design a model based on the fact that (x1,x2,x3,x4) features are important and if any point if comes into contact with the model should give back the cal... | Coming from a programming background where you write code, test, deploy, run.. I'm trying to wrap my head around the concept of "training a model" or a "trained model" in data science, and deploying that trained model.
I'm not really concerned about the deployment environment, automation, etc.. I'm trying to understan... | 0 | 1 | 225 |
0 | 54,176,655 | 0 | 0 | 0 | 0 | 3 | false | 0 | 2018-01-10T22:36:00.000 | 1 | 3 | 0 | Data Science Model and Training - Understanding | 48,197,227 | 0.066568 | python,machine-learning,artificial-intelligence,jupyter-notebook,data-science | A trained model will contain the value of its parameters. If you tuned only a few parameters, then only they will contain the new adjusted value. Unchanged parameters will store the default value. | Coming from a programming background where you write code, test, deploy, run.. I'm trying to wrap my head around the concept of "training a model" or a "trained model" in data science, and deploying that trained model.
I'm not really concerned about the deployment environment, automation, etc.. I'm trying to understan... | 0 | 1 | 225 |
0 | 48,223,162 | 0 | 0 | 0 | 0 | 1 | true | 2 | 2018-01-12T03:24:00.000 | 8 | 1 | 0 | Difference between tf.layers.conv1d vs tf.layers.conv2d | 48,219,121 | 1.2 | python,tensorflow,neural-network,conv-neural-network | tf.layers.conv1d is used when you slide your convolution kernels along 1 dimensions (i.e. you reuse the same weights, sliding them along 1 dimensions), whereas tf.layers.conv2d is used when you slide your convolution kernels along 2 dimensions (i.e. you reuse the same weights, sliding them along 2 dimensions).
So the t... | What is the difference in the functionalities of tf.layers.conv1d and tf.layers.conv2d in tensorflow and how to decide which one to choose? | 0 | 1 | 4,923 |
0 | 50,122,396 | 0 | 0 | 0 | 0 | 1 | false | 8 | 2018-01-12T03:54:00.000 | 4 | 1 | 0 | Why doesn't Keras need the gradient of a custom loss function? | 48,219,296 | 0.664037 | python,python-3.x,tensorflow,deep-learning,keras | To my understanding, as long as each operator that you will use in your Error function has already a predefined gradient. the underlying framework will manage to calculate the gradient of you loss function. | To my understanding, in order to update model parameters through gradient descend, the algorithm needs to calculate at some point the derivative of the error function E with respect of the output y: dE/dy. Nevertheless, I've seen that if you want to use a custom loss function in Keras, you simply need to define E and y... | 0 | 1 | 1,747 |
0 | 48,220,586 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2018-01-12T06:01:00.000 | 0 | 2 | 0 | Word2vec Re-training with new corpus, how the weights will be updated for the existing vocabulary? | 48,220,414 | 0 | python,word2vec | If I understand this option correctly, you are resetting all the weights of the shared words and then train them on the C2 data... This would mean that all the information on the shared words from C1 is lost, which would seem like a big loss to me. (I dont know the corpus sizes). Also, how different are the two corpora... | Scenerio: A word2vec model is trained on corpus C1 with vocabulary V1. If we want to re-train the same model with another corpus C2 having vocabulary V2 using train() API, what will happen out of these two:
For model, weights for V1 intersection V2 will be reset and re-training for with corpus C2 will come up with all... | 0 | 1 | 224 |
0 | 51,941,579 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2018-01-12T06:01:00.000 | 0 | 2 | 0 | Word2vec Re-training with new corpus, how the weights will be updated for the existing vocabulary? | 48,220,414 | 0 | python,word2vec | Why not initiate each of the word2vec parameters with random generated numbers for each run? I could do this and with careful selection of the random numbers for each parameter (numFeatures, contextWindow, seed) I was able to get random similarity tuples which I wanted for my usecase. Simulating an ensemble architectur... | Scenerio: A word2vec model is trained on corpus C1 with vocabulary V1. If we want to re-train the same model with another corpus C2 having vocabulary V2 using train() API, what will happen out of these two:
For model, weights for V1 intersection V2 will be reset and re-training for with corpus C2 will come up with all... | 0 | 1 | 224 |
0 | 48,231,802 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-01-12T17:26:00.000 | 1 | 1 | 0 | What's the input_size for the RNN Model in Keras | 48,231,233 | 1.2 | python,machine-learning,deep-learning,keras,rnn | The input_dim is just the shape of the input you pass to this layer. So:
input_dim = 7
There are other options, such as:
input_shape=(7,) -- This argument uses tuples instead of integers, good when your input has more than one dimension
batch_input_shape=(batch_size,7) -- This is not usually necessary, but you ... | I'm just starting with deep learning, and I've been told that Keras would be the best library for beginners.
Before that, for the sake of learning, I built a simple feed forward network using only numpy so I could get the feel of it.
In this case, the shape of the weight matrix was (len(X[0]), num_neurons). The number ... | 0 | 1 | 328 |
0 | 50,582,786 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-01-12T19:48:00.000 | 0 | 1 | 0 | Python - Gaussian Kernel for colour images | 48,233,118 | 1.2 | python,kernel,convolution,gaussian,gaussianblur | You have to create a gaussian filter of the dimension you want e.g. 3x3 or 11x11.
Then do the convolution on each channel of colour.
If you want to do it using Fourir, you have to apply psf2otf (a matlab function that it is also in Python by users) and multiply both matrixs pointwise (on each channel). | Hello I'm working with images with Python. I want to convolve an image with a gaussian filter.
The image is an array that it have the shape (64,64,3) 64x64 pixels and 3 channels of colour. How will it be the gaussian filter? which dimension? Do you know a function to define it and make the convolution with the image? | 0 | 1 | 1,583 |
0 | 62,427,224 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2018-01-13T02:22:00.000 | 0 | 3 | 0 | How to perform .describe() method on variables that have boolean data type in pandas | 48,236,383 | 0 | python,pandas | Try using this:
df.describe(include=['object','bool']).T
the T here is used for the transpose purpose | I am trying to get the summary statistics of the columns of a data frame with data type: Boolean.
When I run:df.describe() it only gives me the summary statistics for numerical (in this case float) data types. When I change it to df.describe(include=['O']), it gives me only the object data type.
In either case, the su... | 0 | 1 | 5,123 |
0 | 48,242,730 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2018-01-13T16:49:00.000 | 0 | 1 | 0 | How to write arrays as column elements in a Data file in python and read it later in C? | 48,242,064 | 0 | python,c,arrays,data-files | I'd say write it to a text file. Per line put one first column number, followed by the second column's list of floats. Put a space between each element.
Assuming you know the maximum number of floats in the second column's array, and the maximum character length of each float, you can use fgets() to read the file line ... | I have two column dataset where each element of first column corresponds to an array. So basically my second column elements are arrays and first column elements are just numbers. I need to write it in a file using Python and later read it in C. I know HDF5 is the best way to store arrays but I was wondering if there i... | 0 | 1 | 135 |
0 | 48,259,989 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-01-14T16:16:00.000 | 0 | 1 | 0 | how could I copy tensorflow module from one computer to another? | 48,251,568 | 0 | python,ubuntu,tensorflow | You could copy the contents of your python site-packages across. But if you are generally in the situation where internet access is expensive, you might find it more practical to use a caching proxy for all of your internet traffic. | So I have computers all running on ubuntu and only one of them has python tensorflow module. I want to install tensorflow to all of them but it would be inefficient to connect every computer to the internet and install them all over again.so is there a possible efficient way to just copy paste some files from the compu... | 0 | 1 | 160 |
0 | 48,278,872 | 0 | 0 | 0 | 1 | 1 | false | 53 | 2018-01-15T14:07:00.000 | 5 | 8 | 0 | Load S3 Data into AWS SageMaker Notebook | 48,264,656 | 0.124353 | python,amazon-web-services,amazon-s3,machine-learning,amazon-sagemaker | Do make sure the Amazon SageMaker role has policy attached to it to have access to S3. It can be done in IAM. | I've just started to experiment with AWS SageMaker and would like to load data from an S3 bucket into a pandas dataframe in my SageMaker python jupyter notebook for analysis.
I could use boto to grab the data from S3, but I'm wondering whether there is a more elegant method as part of the SageMaker framework to do this... | 1 | 1 | 73,507 |
0 | 48,267,194 | 0 | 0 | 0 | 0 | 1 | true | 24 | 2018-01-15T15:25:00.000 | 44 | 2 | 0 | Keras: find out the number of layers | 48,265,926 | 1.2 | python,machine-learning,keras,deep-learning,keras-layer | model.layers will give you the list of all layers. The number is consequently len(model.layers) | Is there a way to get the number of layers (not parameters) in a Keras model?
model.summary() is very informative, but it is not straightforward to get the number of layers from it. | 0 | 1 | 16,915 |
0 | 48,280,113 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-01-16T11:12:00.000 | 1 | 1 | 0 | Sequential Rule Mining using Apriori Algorithm and Pandas | 48,279,902 | 0.197375 | python,pandas,apriori | message is string type, and elif "what is" in message: seems to be correct in syntax.
Have you checked whether the indentation is correct? Sometimes the bug can be a very simple thing.. | I am performing Sequential Rule Mining using Apriori Algorithm and FPA, I have the dataset in excel as shown below, I want to know, how should I load my data into pandas dataframe what I am using is the following read_excel command, but the data contains ---> between items and lies in single column as shown below.
How ... | 0 | 1 | 683 |
0 | 48,287,827 | 0 | 0 | 0 | 0 | 1 | true | 4 | 2018-01-16T18:36:00.000 | 3 | 1 | 0 | sort very large data with dask? | 48,287,766 | 1.2 | python,pandas,dataframe,sorting,dask | Yes, by calling set_index on the column that you wish to sort. On a single machine it uses your hard drive intelligently for excess space. | I need to sort a data table that is well over the size of the physical memory of the machine I am using. Pandas cannot handle it because it needs to read the entire data into memory. Can dask handle that?
Thanks! | 0 | 1 | 1,393 |
0 | 48,289,327 | 0 | 1 | 0 | 0 | 1 | true | 0 | 2018-01-16T19:47:00.000 | 2 | 1 | 0 | Python regex match human names with abbr (dots) in text | 48,288,763 | 1.2 | python,regex | If you are ok with the following assertions:
Names and surnames always begin with a capital letter
For names reduced to one capital letter, this letter is always immediately followed by a dot
Names can be separated with either a comma or the "and" word
These names end with a final dot
Then you can use this regex: ^... | I want to use regex to match patterns in paragraphs like the following:
©2016 Rina Foygel Barber and Emil Y. Sidky. Many optimization problems arising in high-dimensional statistics decompose naturally into a sum of several terms, where the individual terms are relatively simple but the composite objective function ca... | 0 | 1 | 179 |
0 | 48,303,455 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-01-17T13:57:00.000 | 1 | 1 | 0 | dtype changes after set_value/at | 48,302,876 | 0.197375 | python,pandas | I think the problem is related to the fact that I was trying to assign a None to a bool Series, then it just tries to convert to a different type (why not object?)
Fixed changing the dtype to object first: dataframe.foo = dataframe.foo.astype(object).
Works like a charm now. | I'm facing a weird issue on Pandas now, not sure if a pandas pitfall or just something I'm missing...
My pd.Series is just
foo
False
False
False
> a.foo.dtype
dtype('bool')
When I use a dataframe.set_value(index, col, None), my whole Series is converted to dtype('float64') (same thing applies to a.at[index, col] = No... | 0 | 1 | 37 |
0 | 48,479,898 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-01-17T14:01:00.000 | 0 | 1 | 0 | Python Gurobi, report of statistics presolve | 48,302,947 | 0 | python,attributes,gurobi | If m is the model, i create new model for presolve (mpre = m.presolve()), and then i use mpre.getAttr(GRB.Attr.NumVars), mpre.getAttr(GRB.Attr.NumConstrs) and mpre.getAttr(GRB.Attr.NumConstrs), mpre.getAttr(GRB.Attr.NumNZs). | I try to get number of rows, columns and nonzeros values after presolve. I know about getAttr(GRB.Attr.NumVars), getAttr(GRB.Attr.NumConstrs)...but they give the statics before presolve. Can any one help me.
Thanks | 0 | 1 | 71 |
0 | 48,314,822 | 0 | 1 | 0 | 0 | 1 | true | 0 | 2018-01-18T05:43:00.000 | 1 | 1 | 0 | I have trouble with Interpreter in Pycharm | 48,314,549 | 1.2 | python,opencv,pycharm,interpreter | Go to project interpreter settings in preferences in Pycharm, and use the + sign there to add the module to your interpreter.
This can happen when pip is installing to a directory that is not part of your project's python interpreter's PATH.
Installing via the Pycharm preferences menu always solves for me, although th... | I am python newbie. I am doing 1 project testing of Keras. I already installed opencv by pip install opencv-python but i can't find opencv in interpreter of Pycharm and have an error when i import cv2. My interpreter is usr/bin/python2.7 | 0 | 1 | 335 |
0 | 48,341,100 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-01-18T13:13:00.000 | 0 | 1 | 0 | Use of one-hot encoder to build decision trees | 48,322,232 | 0 | python-3.x,decision-tree,one-hot-encoding | Based on my tests, One-hot encoding results in continuous variables (amounts, in my case) having been assigned higher feature importance.
Also, a single level of a categorical variable must meet a very high bar in order to be selected for splitting early in the tree building. This apparently degrades predictive perfor... | I need to build decision trees on categorical data.
I understood that scikit-learn was only able to deal with numerical values, and the recommended approach is then to use on-hot encoding, preferrably using the Panda Dummies.
So, I build a sample dataset where all attributes and labels are categorical. At this stage,... | 0 | 1 | 792 |
0 | 48,517,556 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2018-01-18T15:04:00.000 | 0 | 1 | 0 | Use opencv in python idle | 48,324,322 | 0 | python-3.x,opencv3.0 | It could be an issue of having multiple python versions on your machine, you should select the python interpreter that is global to your system "that which utilises" pip in terminal. | I installed opencv in python3 using pip. It runs well in terminal, but when I try to run it in idle, it cannot import cv2. What could be the solution?
I am using vim as my python idle. | 0 | 1 | 787 |
0 | 48,326,851 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2018-01-18T17:08:00.000 | 0 | 1 | 0 | While trying to import pandas library to pycharm there is an error message | 48,326,786 | 0 | python-3.x,pycharm | It's telling you that you need Microsoft Visual C++ 10.0 as pandas library has dependencies that refer to that version of Microsoft Visual C++. It provides you the url to download and install it. Use it, then import pandas library. | the error message
error: Microsoft Visual C++ 10.0 is required. Get it with "Microsoft Windows SDK 7.1": www.microsoft.com/download/details.aspx?id=8279 | 0 | 1 | 244 |
0 | 48,331,105 | 0 | 0 | 0 | 0 | 2 | false | 1 | 2018-01-18T21:58:00.000 | 1 | 2 | 0 | Errors processing large images with SIFT OpenCV | 48,331,004 | 0.099668 | python,opencv,image-processing,feature-detection,sift | I would split the image into smaller windows. So long as you know the windows overlap (I assume you have an idea of the lateral shift) the match in any window will be valid.
You can even use this as a check, the translation between feature points in any part of the image must be the same for the transform to be valid | I want to use OpenCV Python to do SIFT feature detection on remote sensing images. These images are high resolution and can be thousands of pixels wide (7000 x 6000 or bigger). I am having trouble with insufficient memory, however. As a reference point, I ran the same 7000 x 6000 image in Matlab (using VLFEAT) without ... | 0 | 1 | 649 |
0 | 48,355,204 | 0 | 0 | 0 | 0 | 2 | false | 1 | 2018-01-18T21:58:00.000 | 0 | 2 | 0 | Errors processing large images with SIFT OpenCV | 48,331,004 | 0 | python,opencv,image-processing,feature-detection,sift | There are a few flavors how to process SIFT corner detection in this case:
process single image per unit/time one core;
multiprocess 2 or more images /unit time on single core;
multiprocess 2 or more images/unit time on multiple cores.
Read cores as either cpu or gpu. Threading result in serial processing instead of ... | I want to use OpenCV Python to do SIFT feature detection on remote sensing images. These images are high resolution and can be thousands of pixels wide (7000 x 6000 or bigger). I am having trouble with insufficient memory, however. As a reference point, I ran the same 7000 x 6000 image in Matlab (using VLFEAT) without ... | 0 | 1 | 649 |
0 | 48,336,589 | 0 | 0 | 0 | 1 | 1 | true | 2 | 2018-01-19T03:11:00.000 | 3 | 1 | 0 | What mean "Inf" in csv? | 48,333,572 | 1.2 | python,postgresql,csv | inf (meaning infinity) is a correct value for floating point values (real and double precision), but not for numeric.
So you will either have to use one of the former data types or fix the input data. | I am working on copying csv file content into postgresql database.
While copying into the database, I get this error:
invalid input syntax for type numeric: "inf"
My question is:
I think "inf" means "infinitive" value, is it right? what does "inf" correctly mean? If it is kinda error, is it possible to recover origin... | 0 | 1 | 1,132 |
0 | 48,701,185 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-01-19T07:23:00.000 | 0 | 1 | 0 | Do I have to do feature selection prior to applying my machine learning algorithm? | 48,335,994 | 1.2 | python,algorithm,classification,knn,supervised-learning | There is no definite answer to this. The current trend is not do feature selection and let the classifier decide which features to use. Take current image datasets for example which also have 1000+ features (depending on the image resolution). They are fed to a CNN usually without any preprocessing. However, this is no... | My question is,
Does the machine learning algorithm takes care of selecting the best features in my data ? or shall I do feature selection and scaling prior to my machine learning algorithm.
I am aware of few supervised classification machine learning algorithms such as kNN, Neural Networks, Adaboast etc.
But is th... | 0 | 1 | 124 |
0 | 48,346,637 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-01-19T15:09:00.000 | 0 | 1 | 0 | PAGMO/PYGMO: Anyone understand the options for Corana’s Simulated Annealing? | 48,344,081 | 0 | python,minimization,simulated-annealing | Gonna answer my own question here. I climbed into the actual .cpp code and found the answers.
In Corana's method, you select how many total iterations N of annealing you want. Then the minimization is a nested series of loops where you vary the step sizes, number of step-size adjustments, and temperature values at user... | I'm using the PYGMO package to solve some nasty non-linear minimization problems, and am very interested in using their simulated_annealing algorithm, however it has a lot of hyper-parameters for which I don't really have any good intuition. These include:
Ts (float) – starting temperature
Tf (float) – final temperatu... | 0 | 1 | 175 |
0 | 48,351,965 | 0 | 1 | 0 | 0 | 1 | false | 1 | 2018-01-20T01:58:00.000 | 0 | 3 | 0 | Python - Sort a list by a certain element in a tuple in a dictionary | 48,351,902 | 0 | python | For each dict item in the list you want to sort, you want to take the item's value keyed by 'info', which is a list, and sort on the second item (addressed as [1], counting from zero.
So: data.sort(key=lambda item: item['info'][1]) | data = [{'info': ('orange', 400000, 'apple'), 'photo': None}, {'info': ('grape', 485000, 'watermelon'), 'photo': None}]
I want to sort data by the 2nd element (400000, 485000) in the tuple in the dictionary. How do I do this?
I followed another answer and my closest attempt was data.sort(key=lambda tup: tup[1]), but t... | 0 | 1 | 55 |
0 | 54,622,059 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2018-01-20T07:00:00.000 | 0 | 3 | 0 | Aws Glue - S3 - Native Python | 48,353,544 | 0 | python,python-3.x,amazon-redshift,aws-glue | AWS Glue should be able to process all the files in a folder irrespective of the name in a single job. If you don’t want the old file to be processed again move it using boto3 api for s3 to another location after each run. | Within AWS Glue how do I deal with files from S3 that will change every week.
Example:
Week 1: “filename01072018.csv”
Week 2: “filename01142018.csv”
These files are setup in the same format but I need Glue to be able to change per week to load this data into Redshift from S3. The code for Glue uses native Python as the... | 0 | 1 | 292 |
0 | 48,372,244 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-01-21T09:14:00.000 | -1 | 1 | 0 | Clustering a correlation matrix into multiple matrices of maximum correlations in Python | 48,365,313 | -0.197375 | python,numpy,scipy,cluster-analysis,correlation | The obvious choice here is hierarchical agglomerative clustering.
Beware that most tools (e.g., sklearn) expect a distance matrix. But it can be trivially implemented for a similarity matrix instead. Then you can use correlation. This is textbook stuff. | Say I calculated the correlations of prices of 500 stocks, and stored them in a 500x500 correlation matrix, with 1s on the diagonal.
How can I cluster the correlations into smaller correlation matrices (in Python), such that the correlations of stocks in each matrix is maximized? Meaning to say, I would like to cluste... | 0 | 1 | 1,454 |
0 | 48,369,514 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-01-21T16:56:00.000 | 0 | 1 | 0 | Allow soft placement in tensorflow | 48,369,319 | 0 | python-2.7,tensorflow | This error probably occurs because you are using python 2.7. Where as tensorflow is for use with python 3.5 and python 3.6 | I just upgraded from tf 1.1 to tf 1.4 for python 2.7 and I got the following problem:
I have a graph which I am putting all its OPs in the specific device by using tf.device('device') command. But, one of the OPs is only allowed to be in CPU device, so I am using allow_soft_placement=True and it was working correctly ... | 0 | 1 | 518 |
0 | 48,370,179 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-01-21T18:14:00.000 | 2 | 1 | 0 | Reinforcement Learning - How to we decide the reward to the agent when the input to the game is only pixels? | 48,370,121 | 0.379949 | python,machine-learning,artificial-intelligence,reinforcement-learning,openai-gym | You need to make up a reward that proxies the behavior you want - and that is actually no trivial business.
If there is some numbers on a fixed part of the screen representing score, then you can use old fashioned image processing techniques to read the numbers and let those be your reward function.
If there is a minim... | I am new to RL and the best I've done is CartPole in openAI gym. In cartPole, the API automatically provides the reward given the action taken. How am I supposed to decide the reward when all I have is pixel data and no "magic function" that could tell the reward for a certain action.
Say, I want to make a self driving... | 0 | 1 | 322 |
0 | 48,451,376 | 0 | 1 | 0 | 0 | 1 | true | 0 | 2018-01-22T13:36:00.000 | 0 | 1 | 0 | Adaptive Filter input vectors and iteration | 48,382,873 | 1.2 | python,algorithm,filtering,signal-processing,perceptron | Your explanation is correct. The X input vector is multiplied recursively by the filter coefficients. It's been some time since I wrote an adaptive filter, but if I remember correctly you're multiplying M filter coefficients by the latest M input values to get an update.
So M is the order of your filter, or the num... | I am trying to implement an adaptive filter for noise cancellation, in particular a RLS filter to remove motion artifacts from a signal. To do this I am reading some literature, there is one thing I don't understand and every book or article I found just asumes I already now this.
I have a reference signal represented ... | 0 | 1 | 345 |
0 | 48,389,275 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2018-01-22T16:37:00.000 | 3 | 1 | 0 | How to find the median between two sorted arrays? | 48,386,293 | 0.53705 | python,arrays,algorithm | Here is my approach that I managed to come up with.
First of all we know that the resulting array will contain N+M elements, meaning that the left part will contain (N+M)/2 elements, and the right part will contain (N+M)/2 elements as well. Let's denote the resulting array as Ans, and denote the size of one of its part... | I'm working on a competitive programming problem where we're trying to find the median of two sorted arrays. The optimal algorithm is to perform a binary search and identify splitting points, i and j, between the two arrays.
I'm having trouble deriving the solution myself. I don't understand the initial logic. I will ... | 0 | 1 | 711 |
0 | 48,448,962 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-01-22T17:59:00.000 | 4 | 1 | 0 | Multi-Task Learning: Train a neural network to have different loss functions for the two classes? | 48,387,602 | 1.2 | python,tensorflow,keras | This is a question that's important in multi-task learning where you have multiple loss functions, a shared neural network structure in the middle, and inputs that may not all be valid for all loss functions.
You can pass in a binary mask which are 1 or 0 for each of your loss functions, in the same way that you pass i... | I have a neural net with two loss functions, one is binary cross entropy for the 2 classes, and another is a regression. Now I want the regression loss to be evaluated only for class_2, and return 0 for class_1, because the regressed feature is meaningless for class_1.
How can I implement such an algorithm in Keras?
Tr... | 0 | 1 | 2,069 |
0 | 48,401,126 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2018-01-23T11:07:00.000 | 2 | 1 | 0 | Is there any tensorflow version of numpy.view_as_windows? | 48,400,191 | 1.2 | python-3.x,numpy,tensorflow | Note: This answer does not answer the OP's exact question, but addresses the actual need of the OP as clarified in the comments (i.e., generate image patches, quickly). I just thought this would fit better here than in a badly-formatted comment.
If all you need to do is generating image patches, Tensorflow (and general... | I am working with python3 and tensorflow to generate image patches by using numpy view_as_windows but because of numpy can't run on GPU, is there any way to do it with tensorflow?
ex: view_as_windows(array2d, window_shape, stride)
Thanks | 0 | 1 | 133 |
0 | 48,505,541 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2018-01-24T05:25:00.000 | 0 | 1 | 0 | How to use mpi for python for parallel cluster computing/ hpc? | 48,415,348 | 0 | python,parallel-processing,cluster-computing,hpc,mpi4py | Actually, you may submit one script to some nodes, specified in a given file. But the results are given based on each script. You cannot combine the results of more than one script on run-time, since each result is saved in some particular file (if any job scheduler used). | I've read some tutorials and documentation on MPI for python. However, I'm still not clear on how it is supposed to be used for sending jobs to separate nodes in a cluster, then combining/processing the results. It seems that you only specify the number of different processes.
Is it possible to use MPI for sending vers... | 0 | 1 | 207 |
0 | 48,419,553 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2018-01-24T08:21:00.000 | 0 | 2 | 0 | How to modify the initial value in BasicLSTMcell in tensorflow | 48,417,764 | 0 | python,tensorflow,lstm,recurrent-neural-network | In the first iteration add some tf.assign operations to assign the values you want to the internal variables.
Make sure this only happens in the first iteration otherwise you'll overwrite any training you do.
The cell has a method called get_trainable_variables to help you if you want. | I want to initial the value for weight and bias in BasicLSTMcell in Tensorflow with my pre-trained value (I get them by .npy). But as I use get_tensor_by_name to get the tensor, it seems that it just returns a copy for me and the raw value is never changed. I need your help! | 0 | 1 | 488 |
0 | 48,605,766 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2018-01-24T10:20:00.000 | 2 | 2 | 0 | Predict a variable that is not in the input sequences with LSTM-Keras | 48,420,005 | 1.2 | python,keras,lstm | I'd say that one way for it to do this is for your network to simply predict C or have C as the label
I have been seeing this again and again. Don't confuse a NN with something more than it actually is. You simply approximate the output Y given an input X by learning a function F. That is your NN.
In your case the outp... | Assume that I have five columns in my dataset (A,B,C,D,E) and I want to build an LSTM model by training just on A,B,D,E (i.e. I want to exclude C)
My problem is that I still want to use this model to predict C. Is it possible if I didn't train my model with this variable? How can I do that?
EDIT 1
I'm working with cate... | 0 | 1 | 686 |
0 | 48,425,028 | 0 | 0 | 0 | 0 | 1 | false | 3 | 2018-01-24T14:26:00.000 | 0 | 2 | 0 | dask job killed because memory usage? | 48,424,813 | 0 | python,performance,dask | Try going through the data in chunks with:
chunksize = 10 ** 6
for chunk in pd.read_csv(filename, chunksize=chunksize):
process(chunk) | Hi I have a python script that uses dask library to handle a very large data frame, larger than the physical memory. I notice that the job get killed in the middle of a run if the memory usage stays at 100% of the computer for some time.
Is it expected? I would thought the data would be spilled to disk and there are p... | 0 | 1 | 1,009 |
0 | 48,432,486 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2018-01-24T21:39:00.000 | 0 | 1 | 0 | Why would importing pandas use up almost all my ram? | 48,432,038 | 0 | python,pandas,ram,conda | Ok, so starting from a brand new environment with latest python 3.5.4 and latest panda seems to cut it....so I think I'll close this wonderful thread for now and re-open if after reinstalling all other needed libs I end up with the same problem. | I'm fairly new to python but I'm pretty sure I didn't get this behaviour before.
A couple of days back I've noticed that if I open a new python console and simply do:
import pandas as pd
Then python.exe ram usage grows steadily in about 5 seconds to reach about 96% utilisation (ie about 15.5G of my 16G total ram).
That... | 0 | 1 | 61 |
0 | 48,448,182 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-01-25T16:35:00.000 | 1 | 1 | 0 | Can I use ffmpeg to output jpgs to a numpy array in python without writing the files to disk etc? | 48,447,814 | 0.197375 | python,numpy,ffmpeg | You are exactly right, JPEG images are compressed (this is even a lossy compression, PNG would be a format with lossless compression), and JPEG files are much smaller than the data in uncompressed form.
When you load the images to memory, they are in uncompressed form, and having several GB of data with 14400 images is... | I have to read thousands of images in memory.This has to be done.When i extract frames using ffmpeg from a video,the disk space for the 14400 files =92MB and are in JPG format.When I read those images in python and append in a python list using libraries like opencv,scipy etc the same 14400 files=2.5 to 3GB.Guess the d... | 0 | 1 | 420 |
0 | 48,455,544 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2018-01-26T02:48:00.000 | 0 | 1 | 0 | Pip is correctly installing libraries to the proper directory, but I cannot import those packages properly in program | 48,455,129 | 0 | python,python-3.x,pandas,python-import | SOLVED. So when I did:
import sys
sys.path.append(path to pandas library)
it worked! so now I can fully use pandas. I guess I will just have to do this anytime I download a new library and it doesn't work. Thank you for all the help | I installed (with pip) MatPlotLib and Pandas, and they are both not working properly in programs. Here is the strange thing...
When I type the following into the interactive environment of IDLE
import pandas as pd
pd.Series([1, 2, 3, 4, 5])
I get this as output: (indicating that it works properly)
0 1
1 2
2 3... | 0 | 1 | 53 |
0 | 48,471,254 | 0 | 1 | 0 | 0 | 1 | false | 2 | 2018-01-26T23:07:00.000 | 0 | 1 | 0 | How to parse EXTREMELY fuzzy dates? | 48,470,828 | 0 | python-3.x,date,fuzzy-comparison | The problem you're describing is categorically known as "Natural Language Parsing", or NLP.
Googling for Python NLP Date Parsing libraries yields several results. You should do that, and evaluate them for your needs. | I have web scraped data in a column of a pandas dataframe that represents when different pieces of art were created. This data was entered in as strings by various people in many many different formats. Some examples:
1998
circa 1995
c. 2003-5
March 2, 1904
1st quarter of 19th century
19th to 20th century
ca. late 19t... | 0 | 1 | 290 |
0 | 48,475,567 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2018-01-27T06:55:00.000 | 0 | 1 | 0 | Transposed convolution TensorFlow padding for FCN style networks | 48,473,417 | 0 | python,tensorflow,deep-learning,convolution,image-segmentation | No, it's your decision how you calculate the kernel-map in every single convolutional layer. It's a matter of designing your model. | I am implementing some variants of FCN for Segmentation. In particular, I have implemented a U-net architecture. Within the architecture, I am applying valid convolution with a 3x3 kernel and then I apply transposed convolution for upsampling with a 2x2 kernel and stride of 2.
My question is, if using valid or same pa... | 0 | 1 | 330 |
0 | 58,831,150 | 0 | 0 | 0 | 0 | 1 | false | 3 | 2018-01-27T22:44:00.000 | 0 | 2 | 0 | Error while trying to run NiftyNet quick start command | 48,481,327 | 0 | python,niftynet | Please Install tensorflow using this command
pip install tensorflow
After that install nifty net using below command
'''
pip install niftynet
'''
reinstall the python
'''
pip install python
'''
if the problem is still than please mentioned your problem in more details
please make sure your environment variable is set ... | I'm trying out NiftyNet and got stuck at the first step.
Trying to run the quickstart command
python net_download.py dense_vnet_abdominal_ct_model_zoo
python net_segment.py inference -c ~/niftynet/extensions/dense_vnet_abdominal_ct/config.ini
gives me
KeyError: "Registering two gradient with name 'FloorMod' !(Pr... | 0 | 1 | 599 |
0 | 48,482,421 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-01-28T00:08:00.000 | 2 | 1 | 0 | how to set stride to zero when using tf.layers.conv2d | 48,481,873 | 0.379949 | python,tensorflow,neural-network,conv-neural-network | A unity stride is the same as not having a stride (turning it off), as its the normal way a convolution works.
As the stride is the amount of pixels the sliding window moves, one is the minimum value, and zero would not be valid as then the sliding window wouldn't move at all. | is there a way that I can turn off stride in tensor flow when using: tf.layers.conv2d()? According to the docs, the default is (1,1) but when I try to change this to (0,0) I get an error telling me that it has to be a positive number.
Thanks. | 0 | 1 | 336 |
0 | 53,142,990 | 0 | 0 | 0 | 0 | 1 | true | 2 | 2018-01-29T20:52:00.000 | 6 | 1 | 0 | ImportError: No module named mpl_toolkits | 48,509,766 | 1.2 | python,matplotlib | I solved this by running the following imports in the following order:
import matplotlib.pyplot as plt
import mpl_toolkits
from mpl_toolkits.mplot3d import Axes3D
Note: This only worked for me on python3. So first, I had to install python3 and pip3. Then I did "pip3 install matplotlib" in Terminal. If you already have... | I have a python program and am trying to plot something using the mplot3d from mpl toolkits, but whenever I try to import the Axes3D from mpl_toolkits from mpl_toolkits.mplot3d import Axes3D
I get the following error: ImportError: No module named mpl_toolkits | 0 | 1 | 11,673 |
0 | 49,185,722 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2018-01-30T09:18:00.000 | 0 | 2 | 0 | Fine control over h5py buffering | 48,517,784 | 0 | python,hdf5,h5py | If you need consistency and avoid corrupted hdf5 files, you may like to:
1) use write-ahead-log, use append logs to write what's being added/updated each time, no need to write to hdf5 this moment.
2) periodically, or at the time you need to shutdown, you replay the logs to apply them one by one, write to the hdf5 file... | I have some data in memory that I want to store in a HDF file.
My data are not huge (<100 MB, so they fit in memory very comfortably), so for performance it seems to make sense to keep them there. At the same time, I also want to store it on disk. It is not critical that the two are always exactly in sync, as long as t... | 0 | 1 | 870 |
0 | 48,531,968 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-01-30T16:03:00.000 | 0 | 1 | 0 | TensorFlow Checkpoints to S3 | 48,525,733 | 0 | python,tensorflow,amazon-s3,amazon-sagemaker | Create an object in S3 and enable versioning to the bucket. Everytime you change the model and save it to S3, it will be automatically versioned and stored in the bucket.
Hope it helps. | I am executing a Python-Tensorflow script on Amazon Sagemaker. I need to checkpoint my model to the S3 instance I am using, but I can't find out how to do this without using the Sagemake Tensorflow version.
How does one checkpoint to an S3 instance without using the Sagemaker TF version? | 1 | 1 | 895 |
0 | 48,527,617 | 0 | 1 | 0 | 0 | 1 | false | 3 | 2018-01-30T17:42:00.000 | 1 | 2 | 0 | Jupyter Notebook and previous output | 48,527,451 | 0.099668 | python,jupyter-notebook | usually, yes, so long as the kernel is still up. the return values of all expressions evaluated are stored in the Out global list. If you are now executing statement number n, then Out[n-1] will have the last thing you successfully finished.
if your output was not returned, but rather printed. You're out of luck... | Is there any way to see the previous output without rerunning the program? For example, I left my ML algorithm to work overnight and in the morning I got the results. But, for some reason, when I pressed Enter on the original code, it started to run again and the original output disappeared. | 0 | 1 | 3,493 |
0 | 63,316,299 | 0 | 1 | 0 | 0 | 2 | false | 8 | 2018-01-30T18:02:00.000 | 0 | 2 | 0 | No module named 'bokeh.plotting'; bokeh is not a package | 48,527,785 | 0 | python,bokeh | Renaming the bokeh.py file and deleting bokeh.pyc file solved it for me. | I am trying to import curdoc. I have tried from bokeh.io import curdoc and from bokeh.plotting import curdocbut neither works.
I've tried pip install -U bokeh and pip install bokeh but it still returns no module named 'bokeh.plotting; 'bokeh' is not a package'. What is happening?
I have reverted back to 0.12.1 currentl... | 0 | 1 | 6,323 |
0 | 52,453,673 | 0 | 1 | 0 | 0 | 2 | true | 8 | 2018-01-30T18:02:00.000 | 18 | 2 | 0 | No module named 'bokeh.plotting'; bokeh is not a package | 48,527,785 | 1.2 | python,bokeh | Check your folder if any of the program named bokeh.py please rename it because it's picking bokeh.plotting from your program bokeh.py not from the library. | I am trying to import curdoc. I have tried from bokeh.io import curdoc and from bokeh.plotting import curdocbut neither works.
I've tried pip install -U bokeh and pip install bokeh but it still returns no module named 'bokeh.plotting; 'bokeh' is not a package'. What is happening?
I have reverted back to 0.12.1 currentl... | 0 | 1 | 6,323 |
0 | 48,529,697 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-01-30T18:04:00.000 | 1 | 1 | 0 | Loss is increasing from first epoch itself | 48,527,808 | 1.2 | python,optimization,neural-network,deep-learning,pytorch | Probably your learning rate is too high. Try decreasing your learning rate. A too large learning rate is the most common reason for loss increasing from the first epoch.
Also your loss is very high. It is unusual to have such a high lost. You probably have a sum in your loss function, it might be wiser to replace that... | I am training my siamese network for nlp. I have used lstm in it. and BCELoss. My loss is increasing from the first epoch. The first 36 epoch loss is
error after 0 is
272.4357
[torch.FloatTensor of size 1]
error after 1 is
271.8972
[torch.FloatTensor of size 1]
error after 2 is
271.5598
[torch.FloatTensor of ... | 0 | 1 | 1,124 |
0 | 48,550,016 | 0 | 0 | 0 | 0 | 1 | false | 3 | 2018-01-30T23:20:00.000 | 1 | 2 | 0 | How to oversample image dataset using Python? | 48,532,069 | 0.099668 | python-3.x,machine-learning,deep-learning,computer-vision,imblearn | Thanks for the clarification. In general, you don't oversample with Python. Rather, you pre-process your data base, duplicating the short-handed classes. In the case you cite, you might duplicate everything in class B, and make 5 copies of everything in class C. This gives you a new balance of 1000:600:500, likely ... | I am working on a multiclass classification problem with an unbalanced dataset of images(different class). I tried imblearn library, but it is not working on the image dataset.
I have a dataset of images belonging to 3 class namely A,B,C. A has 1000 data, B has 300 and C has 100. I want to oversample class B and C, so ... | 0 | 1 | 2,828 |
0 | 48,560,107 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-01-31T09:56:00.000 | 1 | 1 | 0 | Bigger neural network converges to bigger error than smaller | 48,539,256 | 0.197375 | python,machine-learning,neural-network,keras | What I noticed in my tests is that increasing the number of parameters require sometime to review how you prepare your input data or how you initialize your weights. I found that often increasing the number of parameteres requires to initialize the weights differently (meaning initializing with smaller values) or you n... | I am training neural networks using the great Keras library for Python. I got curious about one behaviour I don't understand.
Often even slighly bigger models converge to bigger error than smaller ones.
Why does this happen? I would expect bigger model just to train longer, but converge to smaller or same error.
I hyp... | 0 | 1 | 407 |
0 | 49,105,522 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-01-31T12:37:00.000 | 0 | 1 | 0 | Unsuccessfully Running TensorFlow Package on Windows and Python | 48,542,418 | 1.2 | python,import,packages | Just change this instruction: conda create -n tensorflow pip python=3.5
to: conda create -n tensorflow pip python=3.5 -spyder. | I have been working with Anaconda, Python 3.5 and Win 7. Having been obliged to work with TensorFlow, I installed and run it. For the first time it worked but for the second time, I got the error : "tensor flow module not found". I uninstalled and installed it again and got the same error. Switching to windows 10, pyth... | 0 | 1 | 39 |
0 | 48,544,648 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-01-31T13:23:00.000 | 0 | 1 | 0 | What is the difference between oversampling minority data and simulating minority data? | 48,543,257 | 1.2 | python,statistics | So after some research, here is my answer.
Bootstrapping is for either 1D array or 2D (also referred to as pairs bootstrap) of any columns of data (only columns) while oversampling is for the minority class in the dataset (rows + columns).
Bootstrapping is less popular for binary classification with 100+ features as i... | Oversampled data as in those created by the SMOTHE and ADASYN method.
Simulated data as in those created by statistics, using extreme mean values, bootstrapping and measuring performance with the p-value.
The application is to increase the minority class of underrepresented data using python. | 0 | 1 | 39 |
0 | 48,550,825 | 0 | 0 | 0 | 0 | 2 | true | 12 | 2018-01-31T19:41:00.000 | 15 | 3 | 0 | What does train_on_batch() do in keras model? | 48,550,201 | 1.2 | python,tensorflow,machine-learning,keras,artificial-intelligence | Yes, train_on_batch trains using a single batch only and once.
While fit trains many batches for many epochs. (Each batch causes an update in weights).
The idea of using train_on_batch is probably to do more things yourself between each batch. | I saw a sample of code (too big to paste here) where the author used model.train_on_batch(in, out) instead of model.fit(in, out). The official documentation of Keras says:
Single gradient update over one batch of samples.
But I don't get it. Is it the same as fit(), but instead of doing many feed-forward and backpro... | 0 | 1 | 13,182 |
0 | 62,452,563 | 0 | 0 | 0 | 0 | 2 | false | 12 | 2018-01-31T19:41:00.000 | 0 | 3 | 0 | What does train_on_batch() do in keras model? | 48,550,201 | 0 | python,tensorflow,machine-learning,keras,artificial-intelligence | The method fit of the model train the model for one pass through the data you gave it, however because of the limitations in memory (especially GPU memory), we can't train on a big number of samples at once, so we need to divide this data into small piece called mini-batches (or just batchs). The methode fit of keras m... | I saw a sample of code (too big to paste here) where the author used model.train_on_batch(in, out) instead of model.fit(in, out). The official documentation of Keras says:
Single gradient update over one batch of samples.
But I don't get it. Is it the same as fit(), but instead of doing many feed-forward and backpro... | 0 | 1 | 13,182 |
0 | 48,553,586 | 0 | 0 | 0 | 0 | 1 | false | 3 | 2018-01-31T23:47:00.000 | 2 | 3 | 0 | Writing dataframe to csv WITHOUT removing commas | 48,553,303 | 0.132549 | python,python-3.x,pandas,csv | df.to_csv('output.tsv', sep='\t')
Will separate the values with tabs instead of commas.
.tsv is tab separated value file | I want to write a pandas dataframe to csv. One of the columns of the df has entries which are lists, e.g. [1, 2], [3, 4], ...
When I use df.to_csv('output.csv') and I open the output csv file, the commas are gone. That is, the corresponding column of the csv has entries [1 2], [3 4], ....
Is there a way to write a ... | 0 | 1 | 7,416 |
0 | 50,587,505 | 0 | 0 | 0 | 0 | 2 | false | 5 | 2018-02-01T05:22:00.000 | 0 | 2 | 0 | PySpark casting IntegerTypes to ByteType for optimization | 48,555,860 | 0 | python,apache-spark,pyspark,spark-dataframe | In order to see whether there is any impact, you can try two things:
Write the data back to the file system. Once with the original type and anther time with your optimisation. Compare size on disk.
Try calling collect on the dataframe and look at the driver memory in your OS's system monitor, make sure to induce a ga... | I'm reading in a large amount of data via parquet files into dataframes. I noticed a vast amount of the columns either have 1,0,-1 as values and thus could be converted from Ints to Byte types to save memory.
I wrote a function to do just that and return a new dataframe with the values casted as bytes, however when lo... | 0 | 1 | 339 |
0 | 48,645,854 | 0 | 0 | 0 | 0 | 2 | false | 5 | 2018-02-01T05:22:00.000 | 0 | 2 | 0 | PySpark casting IntegerTypes to ByteType for optimization | 48,555,860 | 0 | python,apache-spark,pyspark,spark-dataframe | TL;DR It might be useful, but in practice impact might be much smaller than you think.
As you noticed:
the memory of the dataframe in the UI, I see it saved as just a transformation from the original dataframe and not as a new dataframe itself, thus taking the same amount of memory.
For storage, Spark uses in-memory ... | I'm reading in a large amount of data via parquet files into dataframes. I noticed a vast amount of the columns either have 1,0,-1 as values and thus could be converted from Ints to Byte types to save memory.
I wrote a function to do just that and return a new dataframe with the values casted as bytes, however when lo... | 0 | 1 | 339 |
0 | 48,558,428 | 0 | 0 | 0 | 0 | 1 | true | 2 | 2018-02-01T08:13:00.000 | 4 | 1 | 0 | Real-time anomaly detection from time series data | 48,558,085 | 1.2 | python,machine-learning,keras,time-series | I think you are using batch processing model(you didn't using any real-time processing frameworks and tools) so there shouldn't be any problem when making your model or classification. The problem may occur a while after you make model, so after that time your predicted model is not valid.
I suggest some ways maybe sol... | I have some problem when detecting anomaly from time series data.
I use LSTM model to predict value of next time as y_pred, true value at next time of data is y_real, so I have er = |y_pred - y_t|, I use er to compare with threshold = alpha * std and get anomaly data point. But sometime, our data is effected by admin ... | 0 | 1 | 1,811 |
0 | 48,800,308 | 0 | 0 | 0 | 0 | 1 | true | 2 | 2018-02-01T11:45:00.000 | 2 | 1 | 0 | How to generate sparse orthogonal matrix in python? | 48,561,974 | 1.2 | python,sparse-matrix,orthogonal | As a preliminary thought you could partition the matrix into diagonal blocks, fill those blocks with QR and then permute rows/columns. The resulting matrices will remain orthagonal. Alternatively, you could define some sparsity pattern for Q and try to minimize f(Q, xi) subject to QQ^T=I where f is some (preferably) ... | How can one generate random sparse orthogonal matrix?
I know there is a sparse matrices in scipy library but they are generally non-orthogonal. One can exploit QR-factorization, but it is not necessarily preserves sparsity. | 0 | 1 | 989 |
0 | 48,573,176 | 0 | 0 | 0 | 0 | 1 | true | 2 | 2018-02-01T16:05:00.000 | 5 | 2 | 0 | How to implement Sklearn Metric in Keras as Metric? | 48,567,012 | 1.2 | python,machine-learning,scikit-learn,keras | Metrics in Keras and in Sklearn mean different things.
In Keras metrics are almost same as loss. They get called during training at the end of each batch and each epoch for reporting and logging purposes. Example use is having the loss 'mse' but you still would like to see 'mae'. In this case you can add 'mae' as a m... | Tried googling up, but could not find how to implement Sklearn metrics like cohen kappa, roc, f1score in keras as a metric for imbalanced data.
How to implement Sklearn Metric in Keras as Metric? | 0 | 1 | 2,808 |
0 | 48,574,103 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-02-01T22:47:00.000 | 1 | 1 | 0 | Python np.array example | 48,572,999 | 1.2 | python,neural-network | The shape of A tells me that A is most likely an array of 60 grayscale images (batch size 60), with each image having a size of 128x128 pixels.
We have: B = np.array([A[..., n:n+5] for n in (5*4, 5*5)]). To better understand what's happening here, let's unpack this line in reverse:
for n in (5*4, 5*5): This is the same... | I am new to Python.
I am confused as to what is happening with the following:
B = np.array([A[..., n:n+5] for n in (5*4, 5*5)])
Where A.shape = (60L, 128L, 128L)
and B.shape = (2L, 60L, 128L, 5L)
I believe it is supposed to make some sort of image patch. Can someone explain to me what this does? This example is in the ... | 0 | 1 | 80 |
0 | 68,209,345 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2018-02-02T10:32:00.000 | 0 | 2 | 0 | Difference of three Naive Bayes classifiers | 48,580,762 | 0 | python,machine-learning,naivebayes | We use algorithm based on the kind of dataset we have.Bernoulli Naive bayes is good at handling boolean/binary attributes,while Multinomial Naive bayes is good at handling discrete values and Gaussian naive bayes is good at handling continuous values.
Consider three scenarios
1)consider a datset which has columns like ... | Sorry for some grammatical mistakes and misuse of words.
I am currently working with text classification, trying to classify the email.
After my research, i found out Multinomial Naive Bayes and Bernoulli Naive Bayes is more often used for text classification.
Bernoulli just cares about whether the word happens or not.... | 0 | 1 | 1,887 |
0 | 48,620,666 | 0 | 1 | 0 | 0 | 1 | false | 1 | 2018-02-02T19:11:00.000 | 1 | 1 | 0 | Installing ggplot in python 3.6.3 | 48,589,399 | 0.197375 | python,ggplot2,pip,conda | Try making a fresh conda environment and only request the installation of ggplot:
conda create --name my_new_env -c conda-forge ggplot
This installed ggplot v0.11.5 and python v3.6.3 for me just now (Ubuntu 17.10) | I am transitioning from R and would like to use ggplot in the yhat Rodeo IDE.
I have tried installing ggplot for python 3.6.3 (Anaconda 5.0.1 64-bit) in a number of ways.
When trying to install any kind of ggplot via conda i get this:
"UnsatisfiableError: The following specifications were found to be in conflict:
-gg... | 0 | 1 | 2,135 |
0 | 48,633,697 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-02-03T01:18:00.000 | 2 | 1 | 0 | Launching tensorboard error - ImportError: cannot import name weakref | 48,593,014 | 0.379949 | python,tensorflow,tensorboard | For those with the same problem I was able to fix it through the following:
1) find where your tensorflow lives pip show tensorflow and look at the location line, copy it.
2) For me it was cd /usr/local/lib/python2.7/site-packages/
3) cd tensorflow/python/lib
4) open tf_should_use.py
5) In your python editor replace li... | With python 2.7 on a mac with tensorflow and running
tensorboard --logdir= directory/wheremylog/fileis produces the following error ImportError: cannot import name weakref
I've seen several folks remedy the issue with pip install backports.weakref
but that requirement is already satisfied for me. Requirement already ... | 0 | 1 | 917 |
0 | 48,595,998 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2018-02-03T09:18:00.000 | 0 | 2 | 0 | Tensorflow: Finding index of first occurrence of elements in a tensor | 48,595,802 | 0 | python,tensorflow | One possibilty would be to use x.eval() which gives you a numpy array back,
then use numpy.unique(...) | Suppose I have a tensor, x = [1, 2, 6, 6, 4, 2, 3, 2]
I want to find the index of the first occurrence of every unique element in x.
The output should be [0, 1, 6, 4, 2].
I basically want the second output of numpy.unique(x,return_index=True). This functionality doesn't seem to be supported in tf.unique.
Is there a wo... | 0 | 1 | 778 |
0 | 48,607,299 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-02-03T17:54:00.000 | 0 | 1 | 0 | Difference between k-means clustering and creating groups by brute force approach | 48,600,277 | 1.2 | python,r,statistics,cluster-analysis | Define "better".
What you do seems to be related to "leader" clustering. But that is a very primitive form of clustering that will usually not yield competitive results. But with 1 million points, your choices are limited, and kmeans does not handle categorical data well.
But until you decide what is 'better', there pr... | I have a task to find similar parts based on numeric dimensions--diameters, thickness--and categorical dimensions--material, heat treatment, etc. I have a list of 1 million parts. My approach as a programmer is to put all parts on a list, pop off the first part and use it as a new "cluster" to compare the rest of the p... | 0 | 1 | 161 |
0 | 48,799,838 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2018-02-04T14:26:00.000 | 4 | 1 | 0 | Get specific node id from the figure plot by osmnx | 48,609,095 | 0.664037 | python,plot,openstreetmap,networkx | Just set the annotate parameter to True in the osmnx.plot.plot_graph() module. | Now I can plot the map using osmnx, but I don't know the node id of nodes in the figure. That is how can I associate the node id and the node plot in the figure? Or, Can I plot the map with node id? Thanks! | 0 | 1 | 469 |
0 | 48,779,917 | 0 | 0 | 0 | 0 | 1 | true | 2 | 2018-02-04T19:22:00.000 | 0 | 1 | 0 | How to render a plot.ly Jupyter Notebook file into Dash python Dashboard | 48,612,105 | 1.2 | python,plotly-dash | I have solved it.
It was due to miscoding. The code was not in the right place.
Thanks. | I would like to have you sharing the template in Dash python where it generate a scatter charter with the help of plot.ly stunning graphics.
I am unable to make my Dash framework running but I do the same flawlessly in Jupyter Notebook. Just was wondering if I could run it on Dash.
Your help is much appreciated.
Regard... | 1 | 1 | 458 |
0 | 48,644,352 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-02-06T13:31:00.000 | -2 | 2 | 0 | Encode array to uint8 and then back | 48,644,272 | -0.197375 | python | Try array_name.encode("uint8").decode("uint8") if this worked, then you can use the decode() method | I have an array which has intensity values from -3000 to 1000,I have thresholded all values which are less then -100 to -100 and all values greater than 400 to 400,After which i convert it to datatype np.uint8,Which makes all the values in the array to have intensity values from 0 to 255,
What i am confused about is,is... | 0 | 1 | 1,021 |
0 | 48,674,801 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2018-02-07T13:07:00.000 | 2 | 1 | 0 | Keras Training a CNN - Should I Convert Heatmap Data As Image or 2D Matrix | 48,664,649 | 1.2 | python,tensorflow,keras,conv-neural-network,data-processing | In fact - you need to reshape a single data point to have a 3D shape - as keras expects your dataset to have shape (number examples, width, height, channels). If you don't wont to make your image RGB - you can simply leave it with only one channel (and interpret it as greyscale channel). | I am interested in training a Keras CNN and I have some data in the form of 2D matrices (e.g. width x height). I normally represent, or visualize the data like a heatmap, with a colorbar.
However, in training the CNN and formatting the data input, I'm wondering if I should keep this matrix as a 2D matrix, or convert it... | 0 | 1 | 691 |
0 | 48,668,122 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2018-02-07T15:54:00.000 | 1 | 2 | 0 | How to import data into Tensorflow? | 48,668,031 | 1.2 | python,tensorflow,neural-network,deep-learning,classification | There are many ways to import images for training, you can use Tensorflow but these will be imported as Tensorflow objects, which you won't be able to visualize until you run the session.
My favorite tool to import images is skimage.io.imread. The imported images will have the dimension (width, height, channels)
Or you... | I am new to Tensorflow and to implementing deep learning. I have a dataset of images (images of the same object).
I want to train a Neural Network model using python and Tensorflow for object detection.
I am trying to import the data to Tensorflow but I am not sure what is the right way to do it.
Most of the tutorial... | 0 | 1 | 864 |
0 | 48,674,833 | 0 | 0 | 0 | 0 | 1 | true | 2 | 2018-02-07T16:27:00.000 | 4 | 1 | 0 | How to retrieve the content of a PCollection and assign it to a normal variable? | 48,668,686 | 1.2 | python,apache-beam | PCollection is simply a logical node in the execution graph and its contents are not necessarily actually stored anywhere, so this is not possible directly.
However, you can ask your pipeline to write the PCollection to a file (e.g. convert elements to strings and use WriteToText with num_shards=1), run the pipeline an... | I am using Apache-Beam with the Python SDK.
Currently, my pipeline reads multiple files, parse them and generate pandas dataframes from its data.
Then, it groups them into a single dataframe.
What I want now is to retrieve this single fat dataframe, assigning it to a normal Python variable.
Is it possible to do? | 0 | 1 | 1,669 |
0 | 48,681,568 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-02-07T18:20:00.000 | 0 | 1 | 0 | Pandas read csv adds zeros | 48,670,780 | 0 | python,pandas,csv,encoding,iso-8859-1 | Basically the column looks like this
Column_ID
10
HGF6558
059
KP257
0001 | I have a problem with reading in a csv with an id field with mixed dtypes from the original source data, i.e. the id field can be 11, 2R399004, BL327838, 7 etc. but the vast majority of them being 8 characters long.
When I read it with multiple versions of pd.read_csv and encoding='iso-8859-1' it always converts the 7 ... | 0 | 1 | 85 |
0 | 48,678,366 | 0 | 0 | 0 | 0 | 1 | false | 5 | 2018-02-07T20:57:00.000 | 2 | 3 | 0 | How to detect rotated object from image using OpenCV? | 48,673,104 | 0.132549 | c#,python,c++,opencv,object-detection | What features are you using to detect your books? Are you training a CNN and deploying it with OpenCV? In that case adding rotation image augmentation to your training would make it easy to detect rotated books.
If you are using traditional computer vision techniques instead, you can try to use some rotation invariant... | I have been training OpenCV classifier for recognition of books.The requirement is recognize book from an image. I have used 1000+ images and OpenCV is able to detect books with no rotation. However, when I try to detect books with rotations it does not work properly.So I am wondering if their anyway to detect objects ... | 0 | 1 | 5,762 |
0 | 49,749,946 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2018-02-07T22:08:00.000 | 0 | 1 | 0 | Words appearing across all topics in lda | 48,674,084 | 0 | python,gensim,lda,topic-modeling | In LDA all words are part of all topics, but with a different probability. You could define a minimum probability for your words to print, but I would be very surprised if mallet didn't come up with at least a couple of "duplicate" words across topics as well. Make sure to use the same parameters for both gensim and ma... | I am using gensim lda for topic modeling and getting the results like so:
Topic 1: word1 word2 word3 word4
Topic 2: word4 word1 word2 word5
Topic 3: word1 word4 word5 word6
However using mallet on same lda does not produce duplicate words across topics. I have ~20 documents with >1000 words each that I train the lda on... | 0 | 1 | 675 |
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