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0 | 59,488,669 | 0 | 1 | 0 | 0 | 2 | false | 0 | 2019-12-26T12:25:00.000 | 1 | 2 | 0 | How many training data(sentences) are required for custom NER using spacy python?[Just rought idea] | 59,488,470 | 0.099668 | python,machine-learning,spacy,named-entity-recognition | For developing custom ner model at least 50-100 occurrences of each entity will be required along with their proper context. Otherwise if you have less data than your custom model will overfit on that. So, depending upon your data you will require atleast 200 to 300 sentences. | I want to know let's say I have 10 custom entities to recognize how much annotated training sentences should I give (Any rough idea) ??
Thank You, in Advance!! :)
I am new to this, please help | 0 | 1 | 1,690 |
0 | 59,490,154 | 0 | 1 | 0 | 0 | 2 | false | 0 | 2019-12-26T12:25:00.000 | 1 | 2 | 0 | How many training data(sentences) are required for custom NER using spacy python?[Just rought idea] | 59,488,470 | 0.099668 | python,machine-learning,spacy,named-entity-recognition | For the custom NER model from Spacy, you will definitely require around 100 samples for each entity that too without any biases in your dataset.
All this is as per my experience.
Suggestion -:
Spacy Custom model you can explore, but for production level or some good project, you can't be totally dependent on that only,... | I want to know let's say I have 10 custom entities to recognize how much annotated training sentences should I give (Any rough idea) ??
Thank You, in Advance!! :)
I am new to this, please help | 0 | 1 | 1,690 |
0 | 59,493,316 | 0 | 0 | 0 | 0 | 1 | false | 3 | 2019-12-26T20:22:00.000 | 2 | 2 | 0 | Does tf.keras.losses.categorical_crossentropy return an array or a single value? | 59,493,127 | 0.197375 | python,tensorflow,keras,loss-function | Most usual losses return the original shape minus the last axis.
So, if your original y_pred shape was (samples, ..., ..., classes), then your resulting shape will be (samples, ..., ...).
This is probably because Keras may use this tensor in further calculations, for sample weights and maybe other things.
In a custom ... | I'm using a custom training loop. The loss that is returned by tf.keras.losses.categorical_crossentropy is an array of I'm assuming (1,batch_size). Is this what it is supposed to return or a single value?
In the latter case, any idea what I could be doing wrong? | 0 | 1 | 747 |
0 | 63,206,321 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2019-12-26T20:34:00.000 | 0 | 3 | 0 | Access output of intermediate layers in Tensor-flow 2.0 in eager mode | 59,493,222 | 0 | python,tensorflow,deep-learning,tensorflow2.0 | The most straightforward solution would go like this:
mid_layer = model.get_layer("layer_name")
you can now treat the "mid_layer" as a model, and for instance:
mid_layer.predict(X)
Oh, also, to get the name of a hidden layer, you can use this:
model.summary()
this will give you some insights about the layer input/outp... | I have CNN that I have built using on Tensor-flow 2.0. I need to access outputs of the intermediate layers. I was going over other stackoverflow questions that were similar but all had solutions involving Keras sequential model.
I have tried using model.layers[index].output but I get
Layer conv2d has no inbound node... | 0 | 1 | 3,974 |
0 | 59,494,808 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2019-12-27T00:23:00.000 | 3 | 1 | 0 | Does correlation important factor in Unsupervised learning (Clustering)? | 59,494,747 | 1.2 | python,machine-learning,correlation,unsupervised-learning,feature-engineering | My assumption here is that you're asking this question because in cases of linear modeling, highly collinear variables can cause issues.
The short answer is no, you don't need to remove highly correlated variables from clustering for collinearity concerns. Clustering doesn't rely on linear assumptions, and so collinea... | I am working with the dataset of size (500, 33).
In particular the data set contains 9 features say
[X_High, X_medium, X_low, Y_High, Y_medium, Y_low, Z_High, Z_medium, Z_low]
Both visually & after correlation matrix calculation I observed that
[X_High, Y_High, Z_High] & [ X_medium, Y_medium, Z_medium ] & [X_low, Y_... | 0 | 1 | 1,456 |
0 | 59,678,549 | 0 | 0 | 1 | 0 | 1 | false | 0 | 2019-12-27T09:10:00.000 | 0 | 1 | 0 | TextBlob Naive Bayes classifier for neutral tweets | 59,498,416 | 0 | python,nltk,sentiment-analysis,naivebayes,textblob | If you have only two classes, Positive and Negative, and you want to predict if a tweet is Neutral, you can do so by predicting class probabilities.
For example, a tweet predicted as 80% Positive remains Postive. However, a tweet predicting as 50% Postive could be Neutral instead. | I am doing a small project on sentiment analysis using TextBlob. I understand there are are 2 ways to check the sentiment of tweet:
Tweet polarity: Using it I can tell whether the tweet is positive, negative or neutral
Training a classifier: I am using this method where I am training a TextBlob Naive Bayes classifier ... | 0 | 1 | 304 |
0 | 59,501,847 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-12-27T12:55:00.000 | 1 | 2 | 0 | Sentiment Classification using Doc2Vec | 59,501,121 | 0.099668 | python,nlp,gensim,doc2vec | To get a vector for an unseen document, use vector = model.infer_vector(["new", "document"])
Then feed vectorinto your classifier: preds = clf.predict([vector]). | I am confused as to how I can use Doc2Vec(using Gensim) for IMDB sentiment classification dataset. I have got the Doc2Vec embeddings after training on my corpus and built my Logistic Regression model using it. How do I use it to make predictions for new reviews? sklearn TF-IDF has a transform method that can be used on... | 0 | 1 | 217 |
0 | 59,694,232 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-12-27T16:40:00.000 | 1 | 1 | 0 | Is there a way find nearest neighbors with BallTree or KDTree using cosine similarity? | 59,503,600 | 0.197375 | python,scikit-learn,knn,nearest-neighbor,cosine-similarity | try to normalize your matrix and use euclidian metric. | I have very sparse and huge rating data which I should find top k neighbors for each session. I need to compare approximate and exact nearest neighbor algorithms but since the data is very big and sparse the computation of the exact method is taking days to compute with brute force. I want to use KD Trees or Ball Trees... | 0 | 1 | 302 |
0 | 59,507,255 | 0 | 1 | 0 | 0 | 1 | true | 1 | 2019-12-27T19:50:00.000 | 0 | 1 | 0 | Remove synonyms of TFIDF results in python | 59,505,444 | 1.2 | python,nlp,tf-idf,cosine-similarity | First you'd want to make a choice between Stems and Lemmas (neither are Roots, mind you). Google the difference for more on that.
You mention antonyms, but most are determined by prefix (e.g. important vs (un)important). So the Stemmer should leave most antonyms unchanged.
As for synonyms, let's assume you're thinkin... | I am currently working on a project where get the top 10 most relevant words of set of document using tfidf in python. However, there are results where are get the same word and its plurial or adverb or so. To go around this problem, I decided to use stemming, but this leads to a problem where words and their antonyms ... | 0 | 1 | 281 |
0 | 59,506,950 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-12-27T22:57:00.000 | 0 | 2 | 0 | PCA analysis incites memory allocation problem. How to solve this without reducing image resolution or number of images | 59,506,774 | 0 | python,image,pca,feature-extraction | As this problem stated, you have to reconfigure your system to make more RAM available to the run-time system.
However, I suspect that you can finesse the problem by iterating through the images, rather than loading all of them into one large NumPy array. Rather than using the monolithic processing tools, you'll need ... | using PCA (principle component analysis) to extract features from a set of 4K images giving me the memory error
File "/home/paul90/.local/lib/python3.6/site-packages/sklearn/decomposition/_pca.py", line 369, in fit_transform
U, S, V = self._fit(X)
MemoryError: Unable to allocate array with shape (23339520, 40) and ... | 0 | 1 | 323 |
0 | 59,512,325 | 0 | 1 | 0 | 0 | 1 | true | 0 | 2019-12-28T14:59:00.000 | 0 | 1 | 0 | Skilearn ImportError: DLL load failed: The specified module could not be found | 59,512,003 | 1.2 | python,scikit-learn,scipy | I am leaving this up to help others, I found the solution, had to unistall completely, reinstall and changing the reg file to allow long path download may have played a role in the beginning. | I am attempting to verify version of my Sklearn to verify the proper installation, earlier I was having an issue where I had to change my registry value of Computer\HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\FileSystem@LongPathEnabled value to 1.
Then I was able to install the files how ever, now when I attemp... | 0 | 1 | 112 |
0 | 59,518,465 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-12-28T20:33:00.000 | 0 | 2 | 0 | How can i train a Image classifier model with tensorflow in Python and use the trained model in Java application? | 59,514,643 | 0 | java,python,android,sockets,tensorflow | python trained model server serve http api ,accept the image , return classification result | I'm trying to build a client-Server Application where client is an android device and server is a windows Pc.it would be easy to do socket programming in java for building the complete application.At first, I tried to build the server side completely in python because of the image classifier I wrote in python. but it g... | 1 | 1 | 60 |
0 | 59,529,321 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-12-30T10:18:00.000 | 0 | 1 | 0 | How can I see the possible values on a column | 59,529,170 | 0 | python,pandas,data-analysis | for numeric columns use: describe()
for categorical columns use : value_counts() | I am working with Colab, I have a very large column and I want to see the possible values that are in this column. | 0 | 1 | 164 |
0 | 59,534,703 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2019-12-30T17:22:00.000 | 2 | 1 | 0 | Difference between Series & Data Frame | 59,534,544 | 1.2 | python,pandas,dataframe,series | You can think of Series as a column in a DataFrame while the actual DataFrame is the table if you think of it in terms of sql | If we perform value_counts function on a column of a Data Frame, it gives us a Series which contains unique values' counts.
The type operation gives pandas.core.series.Series as a result. My question is that what is the basic difference between a Series & a Data Frame? | 0 | 1 | 819 |
0 | 59,546,917 | 0 | 1 | 0 | 0 | 1 | true | 0 | 2019-12-31T17:17:00.000 | 0 | 1 | 0 | Python data correlation | 59,546,892 | 1.2 | python | no difference, if method='pearson' | Can anyone tell me what is the difference between the pearson correlation method and the normal corr() method? I expect it to be the same output, is that right? | 0 | 1 | 41 |
0 | 59,571,636 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2020-01-01T18:18:00.000 | 1 | 1 | 0 | Replace finite values with '1' in a 3 dimension xarray | 59,554,967 | 0.197375 | gis,python-xarray | You can use the xr.where function.
xr.where(data > 0, 1, data) | I am working on lake ice thickness in the northern hemisphere. My final data set is an xarray with dimensions [365,360,720] - (days,lat,lon) and a data varibale 'icethickness'. This data variable has 3 kinds of values. A finite value for ice thickness, zero for water and 'nan' for oceans.
I want to convert all the fini... | 0 | 1 | 73 |
0 | 59,555,564 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2020-01-01T18:42:00.000 | 1 | 2 | 0 | Randomly select one/more functions from collection and apply combinations | 59,555,146 | 0.099668 | python,function | How about this functools.reduce(lambda acc, f: f(acc), random.sample(funs, n), image)? | I have a list of n functions: f1(),f2()..fn() etc and need to select n randomly and apply one/more of those in sequence to an object. What's the Pythonic way to do this?
The use-case is to generate augmentations for images (for training an ML model) from a set of images, and apply (one/more) augmentation functions to... | 0 | 1 | 45 |
0 | 59,556,331 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2020-01-01T20:51:00.000 | 5 | 1 | 1 | Invalid token when trying to login into Jupyter notebook with Docker jupyter/tensorflow-notebook | 59,556,013 | 1.2 | python,docker,jupyter-notebook | The answer turned out to be quite easy: there was an other Jupyter notebook server I wasn't aware of running. Closing it did the trick. | I pulled a (seemingly popular, supported by Jupyter) jupyter-tensorflow Docker image using
docker pull jupyter/tensorflow-notebook
and started it successfully with
docker run -p 8888:8888 jupyter/tensorflow-notebook
However, upon navigating to http://127.0.0.1:8888/?token=177a...., I am prompted for a password or toke... | 0 | 1 | 1,754 |
0 | 59,563,487 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2020-01-02T12:21:00.000 | 3 | 1 | 0 | Embedd existing ML model in apache flink | 59,563,265 | 1.2 | python,machine-learning,apache-flink,flinkml | There are two possible solutions depending on the model You are using:
Possibly the simples idea is to create external service that will call the model and return the results and then simply call the service with AsyncFunction
Use some library, again depending on Your model to load the pre-trained model inside a Proce... | we are training machine learning models offline and persist them in python pickle-files.
We were wondering about the best way to embedd those pickeled-models into a stream (e.g. sensorInputStream > PredictionJob > OutputStream.
Apache Flink ML seems to be the right choice to train a model with stream-data but not to re... | 1 | 1 | 825 |
0 | 59,569,070 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2020-01-02T19:29:00.000 | 1 | 2 | 0 | Finding corresponding eigenvalues to a set of eigenvector | 59,568,960 | 0.099668 | python,matrix,eigenvalue,eigenvector | Think about the definition of an eigenvector. An eigenvector v of a linear transformation represented by matrix A is a vector that only changes in magnitude, not direction, when that linear transformation is applied to it. The scalar change in magnitude of the eigenvector is its eigenvalue. You have a linear transforma... | I solved most of this assignment using Python, but not sure how to input (or even understand) the remaining information. Aren't you supposed to find the eigenvector given some eigenvalue, thus it appears in reverse in the following:
Let the symmetric 3 × 3 matrix A be A = ((1 2 1), (2 5 0), (1 0 5))
If A has the follo... | 0 | 1 | 196 |
0 | 59,652,333 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2020-01-02T20:40:00.000 | 0 | 1 | 1 | What's a compatible GLIBC version for tensorflow 2.0 for python3.6.8 on CentOS 6.9? | 59,569,722 | 1.2 | python,tensorflow,python-3.6,centos6,tensorflow2.0 | Just tested and tensorflow 2.0 works fine with GLIBC 2.12.
It was tensorflow 1.14 that's not compatible with GLIBC 2.12. | The server currently has CentOS 6.9 with GLIBC 2.12 and tensorflow 1.14.
tensorflow is throwing error saying that it needs GLIBC 2.15.
Now I want to upgrade tensorflow to 2.0 but I want to know what's a working GLIBC version for tensorflow 2.0 on CentOS 6.9 before doing so.
I didn't seem to find any source to state thi... | 0 | 1 | 308 |
0 | 59,575,035 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2020-01-02T22:49:00.000 | 0 | 2 | 0 | Which clustering method is the standard way to go for text analytics? | 59,571,031 | 0 | python,cluster-analysis,text-mining | in my view, You can use LDA(latent Dirichlet allocation, it is more flexible in comparison to other clustering techniques because of having Alpha and Beta vectors that can adjust to the contribution of each topic in a document and word in a topic. It can help if the documents are not of similar length or quality. | Assume you have lot of text sentences which may have (or not) similarities. Now you want to cluster similar sentences for finding centroids of each cluster. Which method is the prefered way for doing this kind of clustering? K-means with TF-IDF sounds promising. Nevertheless, are there more sophisticated algorithms or ... | 0 | 1 | 51 |
0 | 59,572,241 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2020-01-03T01:31:00.000 | 0 | 1 | 0 | Is it a common practice to save and load NN models for multiple sessions in practice? | 59,572,172 | 0 | python,tensorflow,oop,neural-network | Yes, of course this is common practice. If you're trying to work with a large net that takes hours to train, it would be totally impractical to do anything other than save weights for future use. In fact, if you're at all concerned about reproducibility of your results, you should save any noteworthy iterations of your... | I just started learning TF and realized that if I create a model of a NN, and then if I were to create a seession to do something with the NN such as getting an output value given the input value,
and then after the session close, if I were to do something else with the NN again, then create a session for that, then it... | 0 | 1 | 126 |
0 | 59,953,057 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2020-01-03T03:03:00.000 | 0 | 4 | 0 | How could I run tensorflow on windows 10? I have the gpu Geforce gtx 1650. Can I run tensorflow on it? if yes, then how? | 59,572,687 | 0 | python,python-3.x,tensorflow | Here are the steps for installation of tensorflow:
Download and install the Visual Studio.
Install CUDA 10.1
Add lib, include and extras/lib64 directory to the PATH variable.
Install cuDNN
Install tensorflow by pip install tensorflow | I want to do some ML on my computer with Python, I'm facing problem with the installation of tensorflow and I found that tensorflow could work with GPU, which is CUDA enabled. I've got a GPU Geforce gtx 1650, will tensorflow work on that.
If yes, then, how could I do so? | 0 | 1 | 2,593 |
0 | 67,939,485 | 0 | 1 | 0 | 0 | 1 | false | 1 | 2020-01-03T08:08:00.000 | 3 | 5 | 0 | ModuleNotFoundError: No module named 'numpy' | 59,575,113 | 0.119427 | python-3.x,numpy | If you are using PyCharm, open Pycharm Go to File->Setting->Project->Python Interpreter->Click on '+'->search and select required package->install package | I tried importing numpy to carry out some array operations in Python:
import numpy *
But I got this error message:
ModuleNotFoundError: No module named 'numpy'
What do i do?? | 0 | 1 | 14,026 |
0 | 59,582,216 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2020-01-03T12:28:00.000 | 1 | 1 | 0 | What is the benefit of NLP sentence segmentation over Python algorithm? | 59,578,665 | 0.197375 | python-3.x,nlp | The sentence segmentation routines from the NLP libraries like SpaCy, NLTK, etc. handle edge cases much better and are more robust to handling punctuation and context. For example, if you choose to split sentences by treating a '.' as a sentence boundary, how would you handle a sentence like - "There are 0.5 liters of ... | I have a task in NLP to do a sentence segmentation, but I wonder, what are the advantages of using built-in NLP sentence segmentation algorithms, such as Spacy, NLTK, BERT etc, over Python '.' separator or similar algorithm?
Is it the speed? or accuracy? or less line of code?
How different or strong these algorithms ov... | 0 | 1 | 164 |
0 | 59,590,347 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2020-01-04T06:18:00.000 | 0 | 1 | 0 | Is there anyway to run GPT2 without GPU and TensorFlow | 59,588,423 | 0 | python,tensorflow,nlp,gpu | What I would do :
sess.run([var for var in tf.trainable_variables]) to get trained parameters and save them as numpy array.
rebuild a numpy only version of gpt2.
But :
This will take a substantial amount of work to rebuild the model
You need to install tensorflow at least once to get the trained variables.
As ... | GPT2 is an excellent OpenAI project for NLP. The developer requirement stated we need to use tensor Flow and GPU. I only want to use (not to train) the existing trained parameters. Is there any way to use GPT2 without the expensive hardware with GPU and without the need to install of Tensor Flow? | 0 | 1 | 1,003 |
0 | 59,593,303 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2020-01-04T17:28:00.000 | 1 | 1 | 0 | Most efficient way to mask an opencv bgr with a boolean array | 59,593,132 | 1.2 | python,numpy,opencv | Answer is
img[~mask,:] = [0,0,0]
That ,: takes care of the other dimension so you don't get mismatch issues. | I have an image loaded with opencv with img.shape = (208, 117, 3). I also have a boolean numpy array with mask.shape = (208, 117). How to I make all pixels in the img (0,0,0) wherever the mask has False, otherwise leave the pixels as they are? | 0 | 1 | 696 |
0 | 59,603,091 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2020-01-05T18:01:00.000 | 0 | 1 | 0 | exporting and indexing csv file with pandas | 59,602,713 | 0 | python,pandas,dataframe,indexing | While writing your files to csv, you can use set index = True in the to_csv method. This ensures that the index of your dataframe is written explicitly to the csv file | I've created a csv file with the column names and saved it using pandas library. This file will be used to create a historic record where the rows will be charged one by one in different moments... what I'm doing to add rows to this csv previously created is transform the record to a DataFrame and then using to_csv() I... | 0 | 1 | 81 |
0 | 59,613,792 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2020-01-05T22:07:00.000 | 2 | 1 | 0 | jedi support in spyder 4 | 59,604,624 | 1.2 | python,spyder | (Spyder maintainer here) Spyder 4.0.0 and 4.0.1 only work with Jedi 0.14.1. Newer nor older versions are supported, so be sure to have that exact version installed.
By the way, although not required, Kite completions are better for scientific packages than Jedi ones. | I updated to Spyder 4 (4.0.0?) to get the latest and greatest features. At this time I found out that Spyder now has the option of using Kite for autocompletion. After trying Kite for a bit I wanted to revert back to the previous autocompletion setup. I thought this was possible by unchecking to use Kite in the prefere... | 0 | 1 | 1,717 |
0 | 59,617,838 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2020-01-06T13:48:00.000 | 1 | 1 | 0 | Placing examples in a mini-batch on the rows or columns of a matrix? | 59,613,281 | 1.2 | python-3.x,tensorflow2.0 | Quoting @ShanqingCai
Putting examples in a batch along the rows of a matrix (i.e., the first axis of the input tensor) is the prevailing way of training deep learning models. Virtually any tensorflow2 / keras example you can find follows that pattern. Putting them along any non-first axis is much rarer.
As said, the ... | I am using tensorflow 2.0 to train a model. I am deciding about whether I should put multiple examples in a batch along the rows or columns of a matrix. Obviously this will affect how I design the model as well. Is there any practical advice on which is better? | 0 | 1 | 56 |
0 | 59,617,330 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2020-01-06T14:54:00.000 | 2 | 1 | 0 | RandomForestRegressor for classification problems | 59,614,227 | 1.2 | python,scikit-learn,data-science,random-forest | The Random Forest regressor does differ somewhat from the Random Forest classifier when it comes to ensembling the decision trees:
The classifier uses the mode of the predicted classes of the decision trees
The regressor uses the mean of the predicted values of the decision trees
Due to this difference the models can... | I've been doing Applied Machine Learing in Python course on coursera and on Assignment of week 4 I`ve found something interesting. During my first attempt to complete the assignment I tried using RandomForestClassifier from sklearn to predict labels, but the model was overfitting and was showing poor test accuracy resu... | 0 | 1 | 78 |
0 | 59,625,798 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2020-01-07T09:36:00.000 | 4 | 2 | 0 | What is the replacement of Placeholder in Tensorflow 2.0 | 59,625,668 | 0.379949 | python,tensorflow2.0 | There is no replacement for placeholder in Tf2 as its default mode is eager execution , if want to use placeholder in tf2 than use tf.compat.v1 syntax and disable v2 behavior | I am new to Tensorflow and I have used tensorflow.placeholder() in tensorflow 1.0. But is there any replacement of placeholder. | 0 | 1 | 4,307 |
0 | 59,661,270 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2020-01-07T15:42:00.000 | 0 | 1 | 0 | Image transmission between processes too slow in python's multiprocessing | 59,631,595 | 0 | python-3.x,ipc,python-multiprocessing,opencv4 | EDIT: I found this question, which has been answered, and addressed my problem using sharedmem module | I'm trying to send images (4000, 3000, 3) between two processes.
My first process acquires the image with a camera, attaches to it some metadata, another image and then sends the whole thing to the second process, which processes it.
I want to have a maximal delay of 0.2 seconds between the moment the image is acquired... | 0 | 1 | 282 |
0 | 65,552,040 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2020-01-08T03:52:00.000 | 0 | 2 | 0 | How to detect image brightness and sharpness in python? | 59,639,187 | 0 | python-3.x,image-processing,ocr,python-tesseract | *Basic recommendation, using the gray images is better on OCR
You can use the BRISQUE image quality assessment for scoring the image quality, it's available as a library. check it out, a smaller score means good quality. | I tried applying tesseract ocr on image but before applying OCR I want to improve the quality of the image so that OCR efficiency increase,
How to detect image brightness and increase or decrease the brightness of the image as per requirement.
How to detect image sharpness | 0 | 1 | 328 |
0 | 59,644,182 | 0 | 0 | 0 | 0 | 1 | false | 5 | 2020-01-08T10:17:00.000 | 1 | 2 | 0 | The sklearn.* module is deprecated in version 0.22 and will be removed in version 0.24 | 59,643,694 | 0.099668 | python,scikit-learn,deprecation-warning | It's just a warning, for now -- until you upgrade sklearn to version 0.24. Then your code will need to be modified before it works. It's giving you a heads-up about this, so you can fix your code ahead of time. The modifications described below should work with your current version; you don't need to wait to upgrade be... | I am migrating a piece of software from Python 2.7 to Python 3.
One problem that arises is:
The sklearn.neighbors.kde module is deprecated in version 0.22 and
will be removed in version 0.24. The corresponding classes / functions
should instead be imported from sklearn.neighbors. Anything that
cannot be importe... | 0 | 1 | 8,393 |
0 | 59,683,042 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2020-01-08T20:05:00.000 | 1 | 1 | 0 | Neural network filtering out everything else than cats/dogs | 59,653,108 | 1.2 | python-3.x,tensorflow,keras,conv-neural-network | Labelling next class as 'other' or something like that is the simplest way to do that.
What I found, that because of many different types of images in 'other' class, training sample should be much bigger than just when differing between cats and dogs. | The question is simple, however I cannot find solution:
How to recognise cats and dogs and filter out everything other?
Another words: I have a big database with images of cats and dogs and all other photos mixed together, is there a way to say in output: cat -or- dog -or- something else?
Either CNNs are not constructe... | 0 | 1 | 39 |
0 | 59,658,376 | 0 | 0 | 0 | 0 | 3 | false | 0 | 2020-01-09T06:04:00.000 | 0 | 3 | 0 | Is there any rules of thumb for the relation of number of iterations and training size for lightgbm? | 59,658,070 | 0 | python,lightgbm | In a similar problem in deep learning with Keras: I do it by using an early stopper and cross validation with train and validation data, and let the model optimize itself using validation data during trainings.
After each training, I test the model with test data and examine the mean accuracies. In the mean time after... | When I train a classification model using lightgbm, I usually use validation set and early stopping to determine the number of iterations.
Now I want to combine training and validation set to train a model (so I have more training examples), and use the model to predict the test data, should I change the number of ite... | 0 | 1 | 764 |
0 | 59,661,123 | 0 | 0 | 0 | 0 | 3 | false | 0 | 2020-01-09T06:04:00.000 | 1 | 3 | 0 | Is there any rules of thumb for the relation of number of iterations and training size for lightgbm? | 59,658,070 | 0.066568 | python,lightgbm | As you said in your comment, this is not comparable to the Deep Learning number of epochs because deep learning is usually stochastic.
With LGBM, all parameters and features being equals, by adding 10% up to 15% more training points, we can expect the trees to look alike: as you have more information your split values ... | When I train a classification model using lightgbm, I usually use validation set and early stopping to determine the number of iterations.
Now I want to combine training and validation set to train a model (so I have more training examples), and use the model to predict the test data, should I change the number of ite... | 0 | 1 | 764 |
0 | 59,810,976 | 0 | 0 | 0 | 0 | 3 | false | 0 | 2020-01-09T06:04:00.000 | 0 | 3 | 0 | Is there any rules of thumb for the relation of number of iterations and training size for lightgbm? | 59,658,070 | 0 | python,lightgbm | I'm not aware of a well-established rule of thumb to do such estimate. As Florian has pointed out, sometimes people rescale the number of iterations obtained from early stopping by a factor. If i remember correctly, typically the factor assumes a linear dependence of the data size and the optimal number of trees. I.e. ... | When I train a classification model using lightgbm, I usually use validation set and early stopping to determine the number of iterations.
Now I want to combine training and validation set to train a model (so I have more training examples), and use the model to predict the test data, should I change the number of ite... | 0 | 1 | 764 |
0 | 59,674,053 | 0 | 0 | 0 | 0 | 1 | false | 8 | 2020-01-09T09:41:00.000 | 3 | 3 | 0 | Holoviews charts sharing axis when combined and outputted | 59,661,074 | 0.197375 | python,holoviews,holoviz | Sander's response is correct and will solve your specific problem, but in this case it may not be addressing the root cause. HoloViews only links axes that are the same, and it sounds like you're plotting different quantities on the y axis in each plot. In that case, the real fix is to put in a real name for the y axis... | I'm using Holoviews to construct a dashboard of charts. Some of these charts have percentages in the y axis where as others have sums/counts etc. When I try to output all the charts I have created to a html file, all the charts change their y axis to match the axis of the first chart of my chart list.
For example:
Ch... | 1 | 1 | 1,953 |
0 | 59,682,913 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2020-01-09T20:33:00.000 | 0 | 1 | 0 | Passing a specific Xarry Data Variable into a New Numpy Array | 59,671,677 | 0 | python,numpy,python-xarray | Try ds.x.values, where ds is the name of your dataset. .x (which is equivalent to ds["x"]) gets the x dataarray from your dataset and .values returns the numpy array with its values. | I have a xarray dataset, with data variables of:
Data variables:
hid (particle) float32 ...
d (particle) float32 ...
x (particle) float32 ...
y (particle) float32 ...
z (particle) float32 ...
image (hologram_number, xsize, ysize) uint8 ...
I am wondering if th... | 0 | 1 | 46 |
0 | 59,743,781 | 0 | 0 | 0 | 0 | 1 | true | 3 | 2020-01-10T00:03:00.000 | 5 | 1 | 0 | Should I use tf.add or + to add two tensors in Tensorflow? | 59,673,839 | 1.2 | python-3.x,tensorflow2.0 | In my understanding they are equivalent and just executes the __add__ magic function. | I am using Tensorflow 2.0 for Python 3.
Suppose I have two tensor variables, x and y, and I want to compute their element-wise sum x + y. Should I just write x + y, or tf.add(x, y)? If they are not equivalent, when should I use one or the other? | 0 | 1 | 1,972 |
0 | 59,691,150 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2020-01-10T06:40:00.000 | 1 | 1 | 0 | Get text size using opencv python | 59,676,639 | 0.197375 | python,opencv,image-processing | You can first use a text detector or a contour detector to get the height of one line of text. That can be used to get the size of text in pixels. But after that, you need some reference to convert it to font size (which is usually defined in points).
All of this can be done in OpenCV. If you post an image with text, ... | im trying to get the font size of text in an image, is there any library in python or can it be done using opencv? Thanks in advance | 0 | 1 | 1,072 |
0 | 59,677,083 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2020-01-10T07:19:00.000 | 0 | 3 | 0 | convert float64 to int (excel to pandas) | 59,677,058 | 0 | python,pandas | int(yourVariable) will cast your float64 to a integer number.
Is this what you are looking for? | I have imported excel file into python pandas. but when I display customer numbers I get in float64 format i.e
7.500505e+09 , 7.503004e+09
how do convert the column containing these numbers | 0 | 1 | 497 |
0 | 59,678,540 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2020-01-10T09:01:00.000 | 1 | 1 | 0 | Camera Calibration basic doubts | 59,678,398 | 0.197375 | python,opencv,image-processing,camera,camera-calibration | Camera calibration is supposed to do for the same camera. Purpose of calibrating a camera is to understand how much distortion the image has and to correct it before we use it to take actual pics. Even if you do not have the original camera, If you have the checkerboard images taken from that camera it is sufficient. O... | I am starting out with computer vision and opencv. I would like to try camera calibration for the images that I have to see how it works. I have a very basic doubt.
Should I use the same camera from which the distorted images were captured or I can use any camera to perform my camera calibration? | 0 | 1 | 67 |
0 | 59,684,408 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2020-01-10T14:04:00.000 | 1 | 1 | 0 | random binary matrix with restrictions | 59,683,287 | 1.2 | python,constraints,binary-matrix | I don't think you need any constraint to fulfill the last conditions. Columns = 8, which is just half of 16. You can just simply copy the first 8 rows to the last 8 rows and reverse all the 0 and 1, then the column sum would be 8 and the first three conditions are met. | I want to create a binary 16*15 matrix with certain conditions. I use binary strings to make the matrix. I want my matrix to be as described:
-The first and last two elements of each row must be alternative.
-the sum of each row must be 8 or 7.
-in each row, there should not be consecutive 1s or 0s. (one couple(00 or 1... | 0 | 1 | 122 |
0 | 59,687,911 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2020-01-10T19:21:00.000 | 0 | 2 | 0 | How to predict a continuous variable without any output data ? All i have is Input data | 59,687,810 | 0 | python,machine-learning,predict,continuous | I may be misunderstanding but it appears that you don't have the necessary information to make the prediction.
My understanding is that you have category information but no other associations. For some categories you might be able to hard code your prediction based on expert opinion. Predicting a ping sweep is basicall... | I am working on a cyber security project wherein we have to prioritize vulnerabilities based on the existing features which are mostly categorical variables (also including couple of ordinal variables).
The objective here is to detect vulnerability that is most likely to be exploited, and thereby prioritizing it. Henc... | 0 | 1 | 95 |
0 | 59,691,992 | 0 | 1 | 0 | 0 | 1 | true | 1 | 2020-01-11T05:01:00.000 | 0 | 1 | 0 | not able to install scikit-learn on pycharm on windows | 59,691,800 | 1.2 | python,scikit-learn,pycharm | In PyCharm go to File -> Settings -> Project -> Project Interpreter. Click on the + sign on the right hand side of the window. Type the name of the library you want to install and PyCharm will add it for you.
When PyCharm is unable to make that installation, it usually has to do with your virtual environment not being ... | I am trying to install scikit learn on Pycharm but it always shows an error:
" numpy.distutils.system_info.NotFoundError: No lapack/blas resources found. "
Also, pycharm is not able to recognize pre-installed libraries(basically I installed some libraries eg. numpy scikit learn using pip in cmd)
it is not able to inst... | 0 | 1 | 1,024 |
0 | 59,693,366 | 0 | 0 | 0 | 0 | 1 | true | 2 | 2020-01-11T08:50:00.000 | 0 | 1 | 0 | NLP AI logic - dialogue sequences with multiple parameters per sequence architecture | 59,692,913 | 1.2 | python,tensorflow,keras,deep-learning,artificial-intelligence | From the way I understand your question, you want to find emotions/actions based on a particular sentence. Sentence A has emotions as labels and Sentence B has actions as labels. Each of the labels has 4 different values with a total of 8 values. And you are confused about how to implement labels as input.
Now, you can... | I have a dataset of dialogues with various parameters (like if it is a question, an action, what emotion it conveys etc ). I have 4 different "informations" per sentence.
let s say A replys to B
A has an additive parameter in a different list for its possible emotions (1.0.0.0) (angry.happy.sad.bored) - an another list... | 0 | 1 | 61 |
0 | 59,707,606 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2020-01-11T12:41:00.000 | -1 | 2 | 0 | Fitting with funtional parameter constraints in Python | 59,694,481 | -0.099668 | python,curve-fitting,non-linear-regression | Like James Phillips, I was going to suggest SciPy's curve_fit. But the way that you have defined your function, one of the constraints is on the function itself, and SciPy's bounds are defined only in terms of input variables.
What, exactly, are the forms of your functions? Can you transform them so that you can use ... | I have some data {x_i,y_i} and I want to fit a model function y=f(x,a,b,c) to find the best fitting values of the parameters (a,b,c); however, the three of them are not totally independent but constraints to 1<b , 0<=c<1 and g(a,b,c)>0, where g is a "good" function. How could I implement this in Python since with curve... | 0 | 1 | 672 |
0 | 59,699,915 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2020-01-11T13:32:00.000 | 0 | 1 | 0 | rtx 2070s failed to allocate gpu memory from device:CUDA_ERROR_OUT_OF_MEMORY: out of memory | 59,694,874 | 0 | python,tensorflow | Tensorflow memory management can be frustrating.
Main takeaway: whenever you see OOM there is actually not enough memory and you either have to reduce your model size or batch size. TF would throw OOM when it tries to allocate sufficient memory, regardless of how much memory has been allocated before.
On the start, TF ... | tf 2.0.0-gpu
CUDA 10.0
RTX2070super
hi. i got a problem regarding allocating gmemory. The initial allocation of memory is 7GB like this.
Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6994 MB memory)
2020-01-11 22:19:22.983048: I tensorflow/stream_executor/platform/default/dso_loader.cc:44... | 0 | 1 | 1,026 |
0 | 59,703,393 | 0 | 1 | 0 | 0 | 1 | true | 0 | 2020-01-11T17:50:00.000 | 0 | 1 | 0 | error message of No module named 'tensorflow' in GCP AI Platform Notebook | 59,697,085 | 1.2 | tensorflow,google-cloud-platform,installation,python-import,gcp-ai-platform-notebook | You have probably selected an instance type that doesn't have tensorflow pre-installed.
After you install a Python dependency you will have to restart the Python Kernel for updates to take effect by clicking on Kernel->Restart Kernel.... | I launched a notebook with GCP AI Platform. Then, I tried to install tensorflow by:
import tensorflow as tf
There is an error message of
No module named 'tensorflow'
I tried to install it by:
!pip install -U --user tensorflow==1.14.0
But the same error message appeared. As it is a GCP platform, I wonder why I need ... | 0 | 1 | 358 |
0 | 59,744,476 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2020-01-11T18:22:00.000 | 0 | 1 | 0 | How to feed variable-length of speech feature to RNN(LSTM) for Speech Recognition? | 59,697,342 | 0 | python,speech-recognition,lstm,recurrent-neural-network,speech-to-text | it depends on how your system built is it end-to-end training or did u use hand-engineering features such MFCC? one more note the main use of RNN's is to have a variable-length input. | I am trying to build a Speech Recognition System, which is a squence-to-sequence model. But I got confused about how to feed the extracted feature(fbank with the dimension of 40) to LSTM. As far as I have found, there are different methods to feed the data as input into LSTM. However, I have a doubt to fully understand... | 0 | 1 | 327 |
0 | 59,707,182 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2020-01-11T22:23:00.000 | 1 | 1 | 0 | Average a dimension of a chunk (xarray) | 59,699,168 | 1.2 | python-xarray | X.groupby('time.year').mean(dim='time') should do the trick as long as time is a datetime64 object. | Let X be a variable I have stored in multiple files. In each files, X has dimension of (time, depth, latitude, longitude). I wish to do a yearly average, and each file store a year of data.
To be efficient, I open the data with X = xarray.open_mfdataset('path'+'year_*_X.nc').
I do not want to average all the time axis,... | 0 | 1 | 139 |
0 | 59,709,870 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2020-01-12T15:18:00.000 | 0 | 2 | 0 | Which data to plot to know what model suits best for the problem? | 59,705,218 | 0 | python,machine-learning,plot,prediction | If you are using off-the-shelf packages like sklearn, then many simple models like SVM, RF, etc, are just one-liners, so in practice, we usually try several such models at the same time. | I'm sorry, i know that this is a very basic question but since i'm still a beginner in machine learning, determining what model suits best for my problem is still confusing to me, lately i used linear regression model (causing the r2_score is so low) and a user mentioned i could use certain model according to the curve... | 0 | 1 | 56 |
0 | 59,706,610 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2020-01-12T15:18:00.000 | 1 | 2 | 0 | Which data to plot to know what model suits best for the problem? | 59,705,218 | 0.099668 | python,machine-learning,plot,prediction | This question is too general, but I will try to give an overview of how to choose the model. First of all you should that there is no general rule to choose the family of models to use, it is more a choosen by experiminting different model and looking to which one gives better results. You should also now that in gener... | I'm sorry, i know that this is a very basic question but since i'm still a beginner in machine learning, determining what model suits best for my problem is still confusing to me, lately i used linear regression model (causing the r2_score is so low) and a user mentioned i could use certain model according to the curve... | 0 | 1 | 56 |
0 | 59,742,768 | 0 | 1 | 0 | 0 | 1 | true | 0 | 2020-01-13T00:59:00.000 | 0 | 1 | 0 | Importing the multiarray numpy extension module failed. Most likely you are trying to import a failed build of numpy | 59,709,490 | 1.2 | python,numpy,heroku | i solve it
my solution was
Run locally 1- python -m pip install python-dev-tools
then 2- pip freeze > requirements.txt
3-git add .
4-git commit -m "v5"
5- heroku git:remote -a "your appname on heroku"
6-git push heroku master | my project run local on pycharm without error but when i deployed it on heroku
error message appears :Importing the multiarray numpy extension module failed.
i changed numpy version more than one but it does not work
Django Version: 2.2.5
Exception Type: ImportError
Exception Value: Importing the multiarray n... | 1 | 1 | 322 |
0 | 59,722,344 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2020-01-13T18:30:00.000 | 0 | 1 | 0 | How to predict the player using random forest ML | 59,722,197 | 1.2 | python,machine-learning | To address such a problem, I would suggest creation of a custom target variable.
Firstly, the transformation of names of players into dummy variables seems reasonable (Just make sure, the unique player is identified with the same first and last name combinations thereby, avoiding duplications and thus, having the corr... | I have to predict the winner of the Australian Open 2020. My dataset has these features: Location / Tournament / Date / Series / Court / Surface / Round / Winner / Loser etc.
I trained my model using just these features 'Victory','Series','Court','Surface','WinRank','LoseRank','WPts','LPts','Wsets','Lsets','Weather' a... | 0 | 1 | 30 |
0 | 59,902,346 | 0 | 0 | 0 | 0 | 1 | true | 2 | 2020-01-14T07:18:00.000 | 0 | 3 | 0 | Importing libraries in AWS Lambda function code from S3 bucket | 59,729,056 | 1.2 | python,amazon-web-services,amazon-s3,deep-learning,aws-lambda | Ultimately, I changed my approach from using Lambda to using EC2.
I deployed the whole code with libraries on an EC2 instance and then triggered it using Lambda. On EC2, it can also be deployed on Apache server to change port mapping. | I have to deploy a Deep Learning model on AWS Lambda which does object detection. It is triggered on addition of image in the S3 bucket.
The issue that I'm facing is that the Lambda function code uses a lot of libraries like Tensorflow, PIL, Numpy, Matplotlib, etc. and if I try adding all of them in the function code o... | 0 | 1 | 1,106 |
0 | 59,742,877 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2020-01-14T21:39:00.000 | 0 | 1 | 0 | How to calculate the standard deviation of noise for an image? | 59,742,157 | 0 | python,image,opencv,image-processing,noise | I would try to pursue this by taking the image before denoise and applying a bitwise_and to it from the denoised image. This would give you just the noise. Then you could take the standard deviation and compare between the denoising algorithms. | I'm currently testing out various denoising algorithms on noisy images. Is there a way of measuring the standard deviation of the noise in the images that I'm denoising (before and after) with OpenCV(python) or any easier method? | 0 | 1 | 565 |
0 | 59,773,204 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2020-01-15T06:45:00.000 | 2 | 1 | 0 | catboost classifier for class imbalance? | 59,746,304 | 1.2 | python,classification,catboost | For scale_pos_weight you would use negative class // positive class. in your case it would be 11 (I prefer to use whole numbers).
For class weight you would provide a tuple of the class imbalance. in your case it would be: class_weights = (1, 11)
class_weights is more flexible so you could define it for multi-class tar... | I am using catboost classifier for my binary classification model where I have a highly imbalance dataset of 0 -> 115000 & 1 -> 10000.
Can someone please guide me in how to use the following parameters in catboostclassifier:
1. class_weights
2. scale_pos_weight ?
From the documentation, I am under the impression that... | 0 | 1 | 3,731 |
0 | 59,761,188 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2020-01-15T22:40:00.000 | 0 | 1 | 0 | keras model prediction is nan after saving and loading | 59,760,569 | 0 | python,tensorflow,keras,google-colaboratory | As far as I know keras has its own function to save the model such as model.save('file.h5'), and the joblib library is used to save sklearn models. | I trained a neural network with google colab.
I saved the neural network using joblib.dump()
I then loaded the model on my PC using joblib.load()
I made a prediction on the exact same sample, using the same model, on both colab and my PC. On colab, it has an output of [[0.51]]. On my pc, it has an output of [[nan]].
... | 0 | 1 | 358 |
0 | 61,222,708 | 0 | 0 | 0 | 0 | 1 | true | 2 | 2020-01-16T12:26:00.000 | 1 | 1 | 0 | How to use "ignore" class with tensorflow object detection API? | 59,769,698 | 1.2 | python,tensorflow,object-detection,object-detection-api | Yes, you need to have another Class which is the object you don't want to detect.
If you don't have this Other Class which includes everything that is not to be detected. The model will compare it to the existing class which is almost identical to the cards of interest.
Some of the factors are:
Similarity of Shape
Si... | I have trained tensorflow object detection model (for num_steps:50000) using SSD (mobilenet-v1) on custom dataset. I got mAP@.50IOU ~0.98 and loss ~1.17. The dataset consist of uno playing card images (skip, reverse, and draw four). On all these cards, model performs pretty well as I have trained model only on these 3 ... | 0 | 1 | 687 |
0 | 59,771,196 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2020-01-16T13:30:00.000 | 1 | 3 | 0 | Neural Networks Weight matrices explained | 59,770,747 | 0.066568 | python,neural-network | By your description I suppose that you have two layers, where the first layer outpits a tensor of batch x 40 and the second layer a batch x 1 tensor, meanwhile the input is a tensor of batch x 80.
Then the weights dimentions are:
W1: 80x40 -> first layer
W2: 40x1-> out layer | Hi guys was wondering if anyone could help me with what I would actually answer for this question on a upcoming exam I have on AI in Python. The question confuses me as I thought I would usually need more info to answer but it is not provided. the question asked is
A Python class is used to represent a neural network ... | 0 | 1 | 1,373 |
0 | 59,771,216 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2020-01-16T13:30:00.000 | 2 | 3 | 0 | Neural Networks Weight matrices explained | 59,770,747 | 0.132549 | python,neural-network | Let's say :
X input vector, (size 80*1)
H hidden layer vector, (size 40*1)
Y output vector, (size 1*1)
You have :
H = W1 * X
Y = W2 * H
So :
W1 has size (40*80)
W2 has size (1*40)
Note : size (m*n) means m rows, n columns | Hi guys was wondering if anyone could help me with what I would actually answer for this question on a upcoming exam I have on AI in Python. The question confuses me as I thought I would usually need more info to answer but it is not provided. the question asked is
A Python class is used to represent a neural network ... | 0 | 1 | 1,373 |
0 | 59,771,829 | 0 | 1 | 0 | 0 | 1 | true | 0 | 2020-01-16T14:25:00.000 | 1 | 1 | 0 | Anaconda Jupyter shows eroor when importing tensorflow | 59,771,753 | 1.2 | python,anaconda | try:
$ conda install tensorflow==2.0.0 | I am having the following error when importing TensorFlow:
ERROR:root:Internal Python error in the inspect module.
Below is the traceback from this internal error.
I believe it is related to .dll
ImportError: DLL load failed: The specified module could not be found.
I am using Python version 3.7.4.
Any advi... | 0 | 1 | 146 |
0 | 59,773,876 | 0 | 0 | 0 | 0 | 1 | false | 4 | 2020-01-16T16:15:00.000 | 1 | 1 | 0 | How to get descriptive statistics of all columns in python | 59,773,763 | 0.197375 | python-3.x,pandas,data-analysis | probably, some of your columns where in some type other than numerical. Try train.apply(pd.to_numeric) then train.describe() | I have a dataset with 200000 rows and 201 columns. I want to have descriptive statistics of all the variables.
I tried:
'''train.describe()'''
But this is only giving the output for the first and last 8 variables. This there any method I can use to get the statistics for all of the columns. | 0 | 1 | 678 |
0 | 59,810,718 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2020-01-16T19:31:00.000 | 0 | 1 | 0 | How to make feature vectors size equal for training neural networks? | 59,776,583 | 1.2 | python-3.x,wav,feature-extraction,mfcc | Adding zeros or zero padding is the most common method of making very short audio signals longer as well as it can be used to match the lengths of audio data before feature extraction.
In my understanding, this does not affect the outcome of the analysis, specially as you are using a neural network. | I am training a neural network, but the feature vectors do not have the same size.
This problem may be fixed by adding some zeros or removing some values, but the greater problem would be data loss or generating meaningless data.
So, is there any approach to make them equal size, without mentioned weaknesses? Maybe tr... | 0 | 1 | 160 |
0 | 59,803,137 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2020-01-18T17:16:00.000 | 0 | 3 | 0 | Set column value as the mean of a group in pandas | 59,803,001 | 0 | python,pandas | df['my_label_mean_temperature']= df.groupby('label', as_index=False)['temperature'].mean() | I have a data frame with columns X Y temperature Label
label is an integer between 1 and 9
I want to add an additional column my_label_mean_temperature which will contain for each row the mean of the temperatures of the rows that has the same label.
I'm pretty sure i need to start with my_df.groupby('label') but not s... | 0 | 1 | 38 |
0 | 59,807,451 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2020-01-19T04:55:00.000 | 2 | 1 | 0 | How can I use machine learning for time series problem | 59,807,263 | 0.379949 | python,machine-learning,time-series,prediction | You are looking for a ML model for time-series data.
This is a huge field but I`ll try to write a few important notes:
Try to generate a dataframe where each row is a timestamp and each column is a feature.
Now you can generate rolling features - for example rolling mean/std of your features, using a few different t... | Hello I have a time series data that basically behaves in a sawtooth manner. After each maintenance period, the signal always goes up before going down until a maintenance happens which will cause the signal to increase again. I am trying to predict the signal and see what happens to the signal if I schedule future mai... | 0 | 1 | 58 |
0 | 60,538,738 | 0 | 1 | 0 | 0 | 1 | true | 0 | 2020-01-19T05:09:00.000 | 0 | 2 | 0 | Find unique number of sequence and reads | 59,807,333 | 1.2 | python | As it is, the problem isn't defined well enough - it is underconstrained.
(I'm going to be using case-sensitive sequences of [A-Za-z] for examples, since using unique characters makes the reasoning easier, but the same things apply to [1-4] and [ACGT] as well; For the same reason, I'm allowing only single-character dif... | I have a data frame which contains multiple number sequences, i.e.:
1324123
1235324
12342212
4313423
221231;
...
these numbers met the following requirement: the number of each digit is from 1 - 4.
What I want to do is find all unique sequences and their reads. Regarding the unique sequence, two-digit differences are a... | 0 | 1 | 122 |
0 | 67,048,383 | 0 | 1 | 0 | 0 | 3 | false | 13 | 2020-01-19T11:16:00.000 | 0 | 6 | 0 | How to install TensorFlow with Python 3.8 | 59,809,495 | 0 | python,python-3.x,tensorflow,python-3.8 | Currently it does support python 3.8 all we need to do is create a new environment ,select 'update index' , select uninstalled and one can find tensorflow for installing | Whenever I try to install TensorFlow with pip on Python 3.8, I get the error that TensorFlow is not found. I have realized later on that it is not supported by Python 3.8.
How can I install TensorFlow on Python 3.8? | 0 | 1 | 55,356 |
0 | 61,622,264 | 0 | 1 | 0 | 0 | 3 | false | 13 | 2020-01-19T11:16:00.000 | 3 | 6 | 0 | How to install TensorFlow with Python 3.8 | 59,809,495 | 0.099668 | python,python-3.x,tensorflow,python-3.8 | Tensorflow does not support Python 3.8 at the moment. The latest supported Python version is 3.7. A solution is to install Python 3.7, this will not affect your codes since Python 3.7 and 3.8 are very similar. Right now Python 3.7 is supported by more frameworks like TensorFlow. Soon Python 3.8 will have more supported... | Whenever I try to install TensorFlow with pip on Python 3.8, I get the error that TensorFlow is not found. I have realized later on that it is not supported by Python 3.8.
How can I install TensorFlow on Python 3.8? | 0 | 1 | 55,356 |
0 | 69,379,579 | 0 | 1 | 0 | 0 | 3 | false | 13 | 2020-01-19T11:16:00.000 | 0 | 6 | 0 | How to install TensorFlow with Python 3.8 | 59,809,495 | 0 | python,python-3.x,tensorflow,python-3.8 | Instead of pip or conda command, I used pip3 command and it worked. | Whenever I try to install TensorFlow with pip on Python 3.8, I get the error that TensorFlow is not found. I have realized later on that it is not supported by Python 3.8.
How can I install TensorFlow on Python 3.8? | 0 | 1 | 55,356 |
0 | 59,871,447 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2020-01-20T02:58:00.000 | 0 | 1 | 0 | Does it make sense to use sample_weights for balanced datasets? | 59,816,484 | 1.2 | python-3.x,scikit-learn,classification | ANSWER
After a couple rounds of testing and more research, I've discovered that yes, it does make sense to add more weight to the 0's with a balanced binary classification dataset, if your goal is to decrease the chance of over-predicting the 1's. I ran two separate training sessions using a weight of 2 for 0's and 1 f... | I have limited knowledge about sample_weights in the sklearn library, but from what I gather, it's generally used to help balance imbalanced datasets during training. What I'm wondering is, if I already have a perfectly balanced binary classification dataset (i.e. equal amounts of 1's and 0's in the label/Y/class colum... | 0 | 1 | 81 |
0 | 59,817,648 | 0 | 1 | 0 | 0 | 1 | false | 3 | 2020-01-20T05:37:00.000 | 0 | 2 | 0 | My Dataset is showing a string when it should be a curly bracket set/dictionary | 59,817,518 | 0 | python,string,list,dictionary,type-conversion | what you have is a set of words, curly brackets are for Dictionary use such as {'Alex,'19',Marry','20'} its linking it as a key and value which in my case it name and age, rather than that you can use to_list command in python maybe it suits your needs. | My dataset has a column where upon printing the dataframe each entry in the column is like so:
{"Wireless Internet","Air conditioning",Kitchen}
There are multiple things wrong with this that I would like to correct
Upon printing this in the console, python is printing this:'{"Wireless Internet","Air conditioning",Kitc... | 0 | 1 | 182 |
0 | 68,297,432 | 0 | 1 | 0 | 0 | 5 | false | 143 | 2020-01-20T12:26:00.000 | 2 | 16 | 0 | Could not load dynamic library 'cudart64_101.dll' on tensorflow CPU-only installation | 59,823,283 | 0.024995 | python,python-3.x,tensorflow,keras,tensorflow2.0 | download CUDA Toolkit 11.0 RC
To solve the issue,
I just find cudart64_101.dll on my disk
( C:\Program Files\NVIDIA Corporation\NvStreamSrv) and add it as variable environment that is add value (C:\Program Files\NVIDIA\Corporation\NvStreamSrv)cudart64_101.dll to user's environment variable Path). | I just installed the latest version of Tensorflow via pip install tensorflow and whenever I run a program, I get the log message:
W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
Is this bad? How do I fix the error? | 0 | 1 | 357,438 |
0 | 72,103,706 | 0 | 1 | 0 | 0 | 5 | false | 143 | 2020-01-20T12:26:00.000 | 0 | 16 | 0 | Could not load dynamic library 'cudart64_101.dll' on tensorflow CPU-only installation | 59,823,283 | 0 | python,python-3.x,tensorflow,keras,tensorflow2.0 | This could be caused by the version of python you are running as well, I was using the python 3.7 from the microsoft store and I run into this error, switching to python 3.10 fixed it. | I just installed the latest version of Tensorflow via pip install tensorflow and whenever I run a program, I get the log message:
W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
Is this bad? How do I fix the error? | 0 | 1 | 357,438 |
0 | 62,364,379 | 0 | 1 | 0 | 0 | 5 | false | 143 | 2020-01-20T12:26:00.000 | 2 | 16 | 0 | Could not load dynamic library 'cudart64_101.dll' on tensorflow CPU-only installation | 59,823,283 | 0.024995 | python,python-3.x,tensorflow,keras,tensorflow2.0 | Tensorflow gpu 2.2 and 2.3 nightly
(along CUDA Toolkit 11.0 RC)
To solve the same issue as OP, I just had to find cudart64_101.dll on my disk (in my case C:\Program Files\NVIDIA Corporation\NvStreamSrv) and add it as variable environment (that is add value C:\Program Files\NVIDIA\Corporation\NvStreamSrv)cudart64_101.dl... | I just installed the latest version of Tensorflow via pip install tensorflow and whenever I run a program, I get the log message:
W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
Is this bad? How do I fix the error? | 0 | 1 | 357,438 |
0 | 65,183,422 | 0 | 1 | 0 | 0 | 5 | false | 143 | 2020-01-20T12:26:00.000 | 3 | 16 | 0 | Could not load dynamic library 'cudart64_101.dll' on tensorflow CPU-only installation | 59,823,283 | 0.037482 | python,python-3.x,tensorflow,keras,tensorflow2.0 | I installed cudatoolkit 11 and copy dll
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.1\bin to C:\Windows\System32.
It fixed for PyCharm but not for Anaconda jupyter:
[name: "/device:CPU:0" device_type: "CPU" memory_limit: 268435456
locality { } incarnation: 6812190123916921346 , name: "/device:GPU:0"
device_... | I just installed the latest version of Tensorflow via pip install tensorflow and whenever I run a program, I get the log message:
W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
Is this bad? How do I fix the error? | 0 | 1 | 357,438 |
0 | 60,424,108 | 0 | 1 | 0 | 0 | 5 | false | 143 | 2020-01-20T12:26:00.000 | -4 | 16 | 0 | Could not load dynamic library 'cudart64_101.dll' on tensorflow CPU-only installation | 59,823,283 | -1 | python,python-3.x,tensorflow,keras,tensorflow2.0 | A simpler way would be to create a link called cudart64_101.dll to point to cudart64_102.dll. This is not very orthodox but since TensorFlow is looking for cudart64_101.dll exported symbols and the nvidia folks are not amateurs, they would most likely not remove symbols from 101 to 102. It works, based on this assumpti... | I just installed the latest version of Tensorflow via pip install tensorflow and whenever I run a program, I get the log message:
W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
Is this bad? How do I fix the error? | 0 | 1 | 357,438 |
0 | 59,824,522 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2020-01-20T13:22:00.000 | 0 | 4 | 0 | Uninstall tensorflow 2.1.0 | 59,824,224 | 0 | python,tensorflow | This problem means that you use different pythons in your terminal and your script.
You mentioned PyCharm. By default, it creates a virtual environment for a new project. It also can use your global python. In any case, open a terminal in PyCharm (it should be in the bottom of the window or View -> Tool Windows -> Term... | I am having problems with uninstalling TensorFlow.
I have a Python script that uses TensorFlow. I want it to use TensorFlow1.15, but it is currently using TensorFlow 2.1.0. I deleted TensorFlow via my cmd: pip uninstall tensorflow and pip uninstall tensorflow-gpu. When I run these commands again it says that TensorFlow... | 0 | 1 | 7,509 |
0 | 59,837,712 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2020-01-21T08:57:00.000 | 0 | 1 | 0 | Reducing tuple values with reduceByKey in Pyspark | 59,837,355 | 0 | python,pyspark,mapreduce | Ok, I found the issue.
When reducing for example (id, (x, x, 1)), (id, (y, y, 1)), (id, (z, z, 1)), in the first reduce you would obtain (id, 2), (id, (z, z, 1)) so when trying to reducing again, the number 2 in the first element is non subscriptable, I must keep the data structure during the process. | I'm starting to work with the MapReduce paradigm with Pyspark, I'm stuck with a problem and I don't know if it a programming error or that I shouldn't do it this way.
I have data from which I am extracting the following information with map for each line:
(id, (date, length, counter)), I did it this way to extract all ... | 0 | 1 | 158 |
0 | 59,838,258 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2020-01-21T09:34:00.000 | 0 | 1 | 0 | How to classify data into N classes + one "garbage" class (or leave some data out)? | 59,838,018 | 0 | python,machine-learning,scikit-learn,classification,noise | I would recommend to
make a binary classifier that classifies into
"garbage"/"non-garbage" classes
make a regular N-classes classifier that classifies "non-garbage"
inputs | I am trying to classify images of cells into N classes ("cell 1", "cell 2" ...) but some of the images are just noise and I would like to either not classify them or put them into a "garbage" class.
I have tried the latter, but it is not very successful and I suspect it is because my "garbage" class is very heterogenou... | 0 | 1 | 205 |
0 | 59,847,592 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2020-01-21T16:17:00.000 | 0 | 1 | 0 | API calls from NLTK, Gensim, Scikit Learn | 59,845,191 | 1.2 | python,api,nlp,nltk,gensim | Generally with NLTK, gensim, and scikit-learn, algorithms are implemented in their source code, and run locally on your data, without sending data elsehwere for processing.
I've never noticed any documentation/functionality of these packages mentioning a reliance on an remote/cloud service, nor seen users discussing t... | I plan to use NLTK, Gensim and Scikit Learn for some NLP/text mining. But i will be using these libraries to work with my org data. The question is while using these libraries 'do they make API calls to process the data' or is the data taken out of the python shell to be processed. It is a security question, so was won... | 0 | 1 | 67 |
0 | 64,513,424 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2020-01-22T10:21:00.000 | 1 | 2 | 0 | Alternative way to run wav2letter Facebook AI Research Speech to Text model on Windows Machine | 59,857,456 | 0.099668 | python-3.x,deep-learning,speech-recognition,speech-to-text | Wav2Letter has different train- and inference-time dependencies.
I am assuming you will be performing training on CUDA backend. If so, you need ArrayFire and Flashlight.
For inference, besides basic dependancies (such as cereal for serialization, etc.) you don't need either. The FAIR team has provided their own impleme... | I'm trying to implement Speech-to-text using wav2letter. As far as I have researched the model uses the Arrayfire tensor library with dependency on flashlight ML library.
Now, the flashlight library is built for Linux based system.
Is there any way to run this model on the Windows-based system. | 0 | 1 | 626 |
0 | 59,859,213 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2020-01-22T11:17:00.000 | 0 | 1 | 0 | Is Tensorflows object detection API producing incorrect ymin and ymax bounding box coordinates? | 59,858,497 | 0 | python,tensorflow,object-detection,bounding-box | It turns out I'd over looked the fact that python's coordinate system has 0,0 in the top left and the API accounts for this by making top = ymin and bottom = ymax within visualization_utils.py. So a ymax = 1073 would indeed be near the bottom of the frame. | I'm running the API over a series of video frames to track objects through a scene and I'm extracting the bounding box coordinates for each object in order to calculate the centre of each bounding box. It would appear, however, that there is an offset in the ymin and ymax coordinates.
The scene is of a person walking ... | 0 | 1 | 141 |
0 | 59,859,426 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2020-01-22T11:33:00.000 | 4 | 1 | 0 | How can I deploy my features in a Machine Learning algorithm? | 59,858,819 | 0.664037 | python,machine-learning,sentiment-analysis,feature-selection | basically, to make things very "simple" and "shallow", all algorithm takes some sort of a numeric vector represent the features
the real work is to find how to represent the features as vector which yield the best result, this depends by the feature itself and on the algorithm using
for example to use SVM which basical... | I’m way new to ML so I have a really rudimentary question. I would appreciate it if one clarifies it for me.
Suppose I have a set of tweets which labeled as negative and positive. I want to perform some sentiment analysis.
I extracted 3 basic features:
Emotion icons
Exclamation marks
Intensity words(very, really e... | 0 | 1 | 83 |
0 | 59,859,181 | 0 | 0 | 0 | 0 | 2 | true | 0 | 2020-01-22T11:37:00.000 | 0 | 3 | 0 | How to avoid pandas from adding indexes in dataframes when using CSV files | 59,858,908 | 1.2 | python,pandas,dataframe,indexing | Use this : df = pd.read_csv("nameOfFile.csv", index_col="nameOfColToUseAsIndex") and put the name of your first column in "nameOfColToUseAsIndex". | I'm using dataframes and CSV files to manipulate data. Most of the time, my dataframes, or the one provided by the API I'm using don't have indexes. If they have indexes, especially when writing and reading CSV files, I just remove them by using the name of the column, "unnamed:0".
But this time, to_CSV places indexes ... | 0 | 1 | 1,352 |
0 | 59,859,039 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2020-01-22T11:37:00.000 | 2 | 3 | 0 | How to avoid pandas from adding indexes in dataframes when using CSV files | 59,858,908 | 0.132549 | python,pandas,dataframe,indexing | Writing pandas dataframes into file via to_csv() method has optional parameter index, which you can set to false to prevent it from writing its own index:
df.to_csv('filename.csv', index=False) | I'm using dataframes and CSV files to manipulate data. Most of the time, my dataframes, or the one provided by the API I'm using don't have indexes. If they have indexes, especially when writing and reading CSV files, I just remove them by using the name of the column, "unnamed:0".
But this time, to_CSV places indexes ... | 0 | 1 | 1,352 |
0 | 59,860,211 | 0 | 0 | 0 | 0 | 2 | false | 1 | 2020-01-22T12:43:00.000 | 0 | 2 | 0 | Training model on custom data | 59,860,042 | 0 | python,tensorflow | It has low probability that your model implemented to a camera has no accuracy because you have only 32 images.
Anyway before you've had about up to 10% loss (It seems to be about 90% accuracy), so it should work I think the problem is not in the amount of images.
After training your model you need to save the coeffi... | I am trying to try an object detection model on a custom data set. I want it to recognize a specififc piece of metal from my garage. I took like 32 photos and labelled them. The training goes well, but up to 10% loss. After that it goes very slow, so I need to stop it. After that, I implemented the model on camera, but... | 0 | 1 | 42 |
0 | 59,860,321 | 0 | 0 | 0 | 0 | 2 | false | 1 | 2020-01-22T12:43:00.000 | 0 | 2 | 0 | Training model on custom data | 59,860,042 | 0 | python,tensorflow | Just labeling will not help in object detection. What you are doing is image classification but expecting results of object detection.
Object detection requires bounding box annotations and changes in the loss function which is to be fed to the model during each backpropagation step.
You need some tools to do data ann... | I am trying to try an object detection model on a custom data set. I want it to recognize a specififc piece of metal from my garage. I took like 32 photos and labelled them. The training goes well, but up to 10% loss. After that it goes very slow, so I need to stop it. After that, I implemented the model on camera, but... | 0 | 1 | 42 |
0 | 59,940,960 | 0 | 0 | 0 | 0 | 2 | false | 1 | 2020-01-22T19:16:00.000 | 0 | 2 | 0 | Tensorflow Hanging on Google Compute Engine Using nohup | 59,866,803 | 0 | python,tensorflow,google-compute-engine | Try to use absolute paths in your execution instead of relative paths | I am trying to run a TensorFlow model that I estimate will take roughly 11 hours. As such, I would like to use nohup so I can exit my terminal keep the process running. I use the following command to do so:
nohup python3 trainModel.py > log.txt &
My model appears to be running as normal, but gets hung up with the last ... | 0 | 1 | 89 |
0 | 59,869,881 | 0 | 0 | 0 | 0 | 2 | false | 1 | 2020-01-22T19:16:00.000 | 0 | 2 | 0 | Tensorflow Hanging on Google Compute Engine Using nohup | 59,866,803 | 0 | python,tensorflow,google-compute-engine | could you send the exit status code of the execution? echo $?
This will help to get an exact idea of the error
Furthermore, can you try to send the standard error output to the log.txt file, for instance
nohup python3 trainModel.py 2> log.txt &
Standard output will be redirected to the nohup file and log.txt will con... | I am trying to run a TensorFlow model that I estimate will take roughly 11 hours. As such, I would like to use nohup so I can exit my terminal keep the process running. I use the following command to do so:
nohup python3 trainModel.py > log.txt &
My model appears to be running as normal, but gets hung up with the last ... | 0 | 1 | 89 |
0 | 59,973,866 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2020-01-23T01:16:00.000 | 0 | 1 | 0 | Model Serving with sklearn and gRPC | 59,870,409 | 0 | python,scikit-learn,thread-safety,grpc,grpc-python | I don't know anything about sklearn specifically, but in general, gRPC is thread-safe, and it's fine to make many concurrent calls to the same method. | Say I have an trained RandomForestClassifier model from sklearn. We're using gRPC to serve that model and provide predictions in real-time in a high traffic situation. From what I understand, gRPC can support multiple threads run concurrently. Can they all simultaneously make calls to RF's predict method without run... | 0 | 1 | 127 |
0 | 59,874,911 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2020-01-23T08:55:00.000 | 1 | 1 | 0 | Is there pre-trained doc2vec model in Python 3.7? | 59,874,635 | 1.2 | python,python-3.7 | The answer is, I'm afraid, no. Not at this time. Also, I think the python 2 models that are available are word2vec, not doc2vec | Is there any pre-trained doc2Vec models, trained in python 3 on news articles data.
Most of the models that I am finding are in older versions. And, it's difficult to implement in python 3.7. | 0 | 1 | 54 |
0 | 59,884,342 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2020-01-23T16:00:00.000 | 0 | 1 | 0 | Constant accuracy over epochs | 59,882,584 | 0 | python,tensorflow,machine-learning,deep-learning,artificial-intelligence | In order to fully answer this question (specific to your case) we'd need to know what loss function you are using and how you measure accuracy.
In general, this can certainly happen for a variety of reasons. The easiest reason to illustrate is with a simple classifier. Suppose you have a 2-class classification proble... | I am training a gan and I am the accuracy doesn't change over epoch meanwhile the loss is deacresing.
Is there something wrong or it is normal because it's a gan?
Thank you in advance. | 0 | 1 | 88 |
0 | 59,888,228 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2020-01-23T22:58:00.000 | 1 | 1 | 0 | How should metrics be added to a multi-headed TensorFlow estimator? | 59,888,227 | 0.197375 | python,tensorflow,machine-learning,metrics | Build a class around the model creation that holds the model configuration and use a member function for the metric_fn parameter.
class ModelBuilder:
# constructor storing configuration options in self
def __init__(self, labels, other_config_args):
self.labels = labels
...
# Function for building the esti... | I previously created metrics for a TensorFlow Classifier referencing predictions['logits'] to calculate the metrics. I have changed the model from a Classifier to an Estimator in order to enable multi-objective learning (using MultiHead). However, this has caused Python to throw an error since now the elements of predi... | 0 | 1 | 373 |
0 | 61,153,120 | 0 | 0 | 0 | 0 | 3 | false | 0 | 2020-01-25T09:49:00.000 | 0 | 4 | 0 | Thonny : installing tensorflow and importing it | 59,908,131 | 0 | python,tensorflow,machine-learning,data-science,thonny | Tensorflow can be installed in Thonny by
Tools -> Open System Shell
pip install --upgrade tensorflow | I am having trouble importing and installing tensorflow. I can't install it via that Thonny manage package option nor via the command window for windows operators. I get the same error for both ways:
ERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none) Error: No matching distr... | 0 | 1 | 1,448 |
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