kandi X-RAY | GuessSketch Summary
kandi X-RAY | GuessSketch Summary
Guess a Sketch - a project for Machine Learning class - using TensorFlow , python, Java and an Android App. We had 3 layers, the first one is the Guess a Sketch Android App, we created an android app with a drawing canvas based on Valerio Bozzolan’s open source project called Acrylic Paint (it is GNU license. The Android app was modified such that it doesn’t save sketches locally, but instead, send them over the internet in a POST HTTP request to the middle layer. Which in turn would return a JSON format response with labels it predicts would match this sketch. Guess a Sketch Android app would then display those predictions to the user in a list of radio buttons, There also would be an option to suggest a different label. If the predictions provided by the Middle layer were correct and the user selects one of them, then that would be a positive feedback for the system, we simply guessed it right, this positive feedback would be used later on to retrain the engine and increase the confidence of this prediction. If the user didn’t find the label they were expecting, then they can choose the “Other (Please specify)” button, which would by then asks them to enter a label for the sketch the drew. This is called “Negative feedback”, this Label along with the sketch would be sent to the middle layer and would be used later on to retrain the system and introduce a new class (label) if number of images submitted by users matches a certain threshold (30 images, for now, it’s a requirement by tensorflow), In order to help faster builds for the Android app as well as better dependency handling we used a software project management tool and build management called Gradle (Guess a Sketch Android App - Along with the predicted labels. The middle layer is Java based and uses Jersey Java library (to provide a RESTFul web service for the system. It’s deployed on Apache Tomcat (Application server and built using Maven software project management (Maven helps to build project much faster and provides dependencies resolving feature. Middle layer also uses a special logic to sanitize illegal input sent by users when asked about labels as this might cause a security threat. Functionalities of Middle layers: Guess a sketch, in this API call, the android app would send the image binary data, middle layer would forward this binary data along with a specially crafted HTTP POST request to the Guess-Sketch Engine, This engine would reply with a JSON string containing a list of predictions matching that sketch. Middle layer would then pass this JSON string back to the Android app. Positive Feedback: Android app sends the binary data of the image along with the label predicted by the Guess a sketch engine earlier, this image data along with that label would be kept inside a positive feedback folder and would be used later to retrain the system. Retraining would happen in this case by copying over all new images from the positive feedback folder to their appropriate locations inside the dataset folder and run the retraining script. Negative Feedback: Android app sends the binary data of the image along with the label entered by the user, this image data along with that label would be kept inside a negative feedback folder and would be used later to retrain the system once the number of images reaches a threshold (30 images in this case). Retraining would happen copying over this label-named new folder along with images inside it to the dataset folder and run the retraining script. Guess A Sketch Engine is python based FastCGI script that uses TensorFlow (inception engine to predict images sent by the middle layer and responds back in a JSON format string. Tensorflow initially comes untrained, we trained that system by drawing sketches for 12 different classes (refer to Dataset section above), once trained the system generates a bottleneck files, labels file and a .pb file which acts like a knowledge database that tensorflow engine can use later on to make predictions. Guess a Sketch Engine was deployed as a FastCGI script, using Apache FastCGI Module (the benefit of this approach is that we will always have parameterized number of script instances loaded in memory and waiting to serve requests. We also deploy this on Apache web server (It also used several python libraries in order to achieve the functionality needed: Flipflop (: FastCGI wrapper for WSGI applications Werkzeug (: WSGI utility library for Python that simplifies handling of HTTP Requests/Response and supports multipart/form-data uploads. Tensorflow : The tensorflow inception engine python interface JSON : to produce output in JSON format. PS: please refer to constants.java file inside the android app and inside the middle layer to change hardcoded folder paths. Didn't have time to solve these static values in a better way.
Top functions reviewed by kandi - BETA
- Creates the parent view
- Load the application s intent
- Adds view to the PictureView
- Determines if this is the first time
- Called when an options item is selected
- Refresh predictions
- Saves data to file
- Takes a screenshot of the current screen
- Try to guess a photo from the user
- Writes an uploaded input stream to a temp file
- Performs POST to supplied URL
- Region BufferedPicture
- Draws a picture to the canvas
- Resize the view
- Start the activity
- Sets the about textView
- Creates the default color picker view
- Called when the bitmap is changed
- Invoked when the double back button pressed
- Invalidate a dirty view in the parent
GuessSketch Key Features
GuessSketch Examples and Code Snippets
Trending Discussions on Machine Learning
I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.
I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.
I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition....
ANSWERAnswered 2022-Feb-17 at 10:47
You should try to export the model using torch.onnx. The page gives you an example that you can start with.
An alternative is to use TorchScript, but that requires torch libraries.
Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html
Just modifying a little your example to go over the errors I found
Notice that via tracing any if/elif/else, for, while will be unrolled
I'm trying to implement a gradient-free optimizer function to train convolutional neural networks with Julia using Flux.jl. The reference paper is this: https://arxiv.org/abs/2005.05955. This paper proposes RSO, a gradient-free optimization algorithm updates single weight at a time on a sampling bases. The pseudocode of this algorithm is depicted in the picture below.
I'm using MNIST dataset....
ANSWERAnswered 2022-Jan-14 at 23:47
Based on the paper you shared, it looks like you need to change the weight arrays per each output neuron per each layer. Unfortunately, this means that the implementation of your optimization routine is going to depend on the layer type, since an "output neuron" for a convolution layer is quite different than a fully-connected layer. In other words, just looping over
Flux.params(model) is not going to be sufficient, since this is just a set of all the weight arrays in the model and each weight array is treated differently depending on which layer it comes from.
Fortunately, Julia's multiple dispatch does make this easier to write if you use separate functions instead of a giant loop. I'll summarize the algorithm using the pseudo-code below:
This question is the same with How can I check a confusion_matrix after fine-tuning with custom datasets?, on Data Science Stack Exchange.Background
I would like to check a confusion_matrix, including precision, recall, and f1-score like below after fine-tuning with custom datasets.
Fine tuning process and the task are Sequence Classification with IMDb Reviews on the Fine-tuning with custom datasets tutorial on Hugging face.
After finishing the fine-tune with Trainer, how can I check a confusion_matrix in this case?
An image of confusion_matrix, including precision, recall, and f1-score original site: just for example output image...
ANSWERAnswered 2021-Nov-24 at 13:26
What you could do in this situation is to iterate on the validation set(or on the test set for that matter) and manually create a list of
I am trying to train a model using PyTorch. When beginning model training I get the following error message:
RuntimeError: CUDA out of memory. Tried to allocate 5.37 GiB (GPU 0; 7.79 GiB total capacity; 742.54 MiB already allocated; 5.13 GiB free; 792.00 MiB reserved in total by PyTorch)
I am wondering why this error is occurring. From the way I see it, I have 7.79 GiB total capacity. The numbers it is stating (742 MiB + 5.13 GiB + 792 MiB) do not add up to be greater than 7.79 GiB. When I check
nvidia-smi I see these processes running
ANSWERAnswered 2021-Nov-23 at 06:13
This is more of a comment, but worth pointing out.
The reason in general is indeed what talonmies commented, but you are summing up the numbers incorrectly. Let's see what happens when tensors are moved to GPU (I tried this on my PC with RTX2060 with 5.8G usable GPU memory in total):
Let's run the following python commands interactively:
I am a bit confusing with comparing best GridSearchCV model and baseline.
For example, we have classification problem.
As a baseline, we'll fit a model with default settings (let it be logistic regression):
ANSWERAnswered 2021-Nov-04 at 21:17
No, they aren't comparable.
Your baseline model used
X_train to fit the model. Then you're using the fitted model to score the
X_train sample. This is like cheating because the model is going to already perform the best since you're evaluating it based on data that it has already seen.
The grid searched model is at a disadvantage because:
- It's working with less data since you have split the
- Compound that with the fact that it's getting trained with even less data due to the 5 folds (it's training with only 4/5 of
So your score for the grid search is going to be worse than your baseline.
Now you might ask, "so what's the point of
best_model.best_score_? Well, that score is used to compare all the models used when searching for the optimal hyperparameters in your search space, but in no way should be used to compare against a model that was trained outside of the grid search context.
So how should one go about conducting a fair comparison?
- Split your training data for both models.
I created one notebook using Google AI platform. I was able to start it and work but suddenly it stopped and I am not able to start it now. I tried building and restarting the jupyterlab, but of no use. I have checked my disk usages as well, which is only 12%.
but didn't fix it.
Thanks in advance....
ANSWERAnswered 2021-Aug-20 at 14:00
You should try this Google Notebook trouble shooting section about 524 errors : https://cloud.google.com/notebooks/docs/troubleshooting?hl=ja#opening_a_notebook_results_in_a_524_a_timeout_occurred_error
I am new to Machine Learning.
Having followed the steps in this simple Maching Learning using the Brain.js library, it beats my understanding why I keep getting the error message below:
I have double-checked my code multiple times. This is particularly frustrating as this is the very first exercise!
Kindly point out what I am missing here!
Find below my code:...
ANSWERAnswered 2021-Sep-29 at 22:47
Turns out its just documented incorrectly.
In reality the export from brain.js is this:
IF we are not sure about the nature of categorical features like whether they are nominal or ordinal, which encoding should we use? Ordinal-Encoding or One-Hot-Encoding? Is there a clearly defined rule on this topic?
I see a lot of people using Ordinal-Encoding on Categorical Data that doesn't have a Direction. Suppose a frequency table:...
ANSWERAnswered 2021-Sep-04 at 06:43
You're right. Just one thing to consider for choosing
OneHotEncoder is that does the order of data matter?
Most ML algorithms will assume that two nearby values are more similar than two distant values. This may be fine in some cases e.g., for ordered categories such as:
quality = ["bad", "average", "good", "excellent"]or
shirt_size = ["large", "medium", "small"]
but it is obviously not the case for the:
color = ["white","orange","black","green"]
column (except for the cases you need to consider a spectrum, say from white to black. Note that in this case,
white category should be encoded as
black should be encoded as the highest number in your categories), or if you have some cases for example, say, categories 0 and 4 may be more similar than categories 0 and 1. To fix this issue, a common solution is to create one binary attribute per category (One-Hot encoding)
I am using sentence-transformers for semantic search but sometimes it does not understand the contextual meaning and returns wrong result eg. BERT problem with context/semantic search in italian language
by default the vector side of embedding of the sentence is 78 columns, so how do I increase that dimension so that it can understand the contextual meaning in deep.
ANSWERAnswered 2021-Aug-10 at 07:39
Increasing the dimension of a trained model is not possible (without many difficulties and re-training the model). The model you are using was pre-trained with dimension 768, i.e., all weight matrices of the model have a corresponding number of trained parameters. Increasing the dimensionality would mean adding parameters which however need to be learned.
Also, the dimension of the model does not reflect the amount of semantic or context information in the sentence representation. The choice of the model dimension reflects more a trade-off between model capacity, the amount of training data, and reasonable inference speed.
If the model that you are using does not provide representation that is semantically rich enough, you might want to search for better models, such as RoBERTa or T5.
I have a table with features that were used to build some model to predict whether user will buy a new insurance or not. In the same table I have probability of belonging to the class 1 (will buy) and class 0 (will not buy) predicted by this model. I don't know what kind of algorithm was used to build this model. I only have its predicted probabilities.
Question: how to identify what features affect these prediction results? Do I need to build correlation matrix or conduct any tests?
ANSWERAnswered 2021-Aug-11 at 15:55
No vulnerabilities reported
You can use GuessSketch like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the GuessSketch component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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