RegionProposal | Region proposal and patient-level prediction work | Machine Learning library
kandi X-RAY | RegionProposal Summary
kandi X-RAY | RegionProposal Summary
The ultimate goal of this project is exploring high and low-resolution images techniques for performing high-level inferences over complete tissue medical slides, which presents a few challenging aspects regarding storage and processing. The high-level inferences consist of learning patterns between summarized slides and target labels for solving a specific problem. For example, it could be interesting to calculate the abundance (by area) of regions like angiogenesis, necrosis, and infiltrating lymphocytes to infer patient-level characteristics. A tissue slide can easily reach around 20GB in-memory, which turns intractable many approaches of feeding those slides entirely to a classifier. One recent method for addressing this problem is sampling regions based on low-resolution features (i.e. texture, boundaries) for extracting information about the tissue. Moreover, this method can also help with speeding up the processing, since after cropping the proposed regions, the problem would be turned into a tractable learning problem. ##Tutorials This section is divided in two parts. First, we implemented a soft-attention model utilizing Long Short Term Memories in order to propose regions at pixel level for classification similarly as in (ref). The second approach consists of using Reinforcement Learning for predicting fixed sized windows for feeding a classifier composed by Convolutional Neural Networks similarly as in (ref). Part A - Soft-Attention Model using LSTM ** TODO. Part B - Hard-Attention Model using RL (Cell-RAM) ** TODO. The Cell Recurrent Attention Model (Cell-RAM) API is implemented using Theano 0.8.2 and provides tools for defining and training a recurrent model using the algorithm REINFORCE (ref). This class defines the hyperparameters of a recurrent model and implements the basic functions for building up a custom recurrent model. This function initializes a object for the recurrent model with parameters defined in specs. ####Parameters: specs - set of parameters that defines a model. This function returns a theano variable initialized as determined. ####Parameters: init - param initialization from lasagne in_shape - param shape name - param name type - determines if the param is used in the prediction phase 'p' or reinforce phase 'r'. This function returns all parameters values defined for the model and is used for saving/loading a model. This function sets the parameters of a given model to the given values. ####Parameters: values - consists of a set of values for each parameter of the model. This function returns the value's container for each parameter. This function reset the patience parameter used in the early stopping implementation. This function decrease the patience parameter used in the early stopping implementation. This function saves the model to file. ####Parameters: fname - file name. This function loads the model from a file. ####Parameters: fname - file name. This class defines a policy (RNN) and the forward learning steps. This function initializes the policy with the parameters given in specs and defines the learnable parameters for performing classification, updating the hidden state and predicting the location of the windows. ####Parameters: specs - set of parameters. This function performs the forward propagation through-time using theano scan and initializes the hidden state and window's location. This is a helper function that receives a gaussian noise, previous location and hidden state, entire image and returns the next values for the location and hidden state. It extracts the proposed region given a window's location, updates the hidden units, predict a new window's location and performs classification on the proposed region in this order. ####Parameters: g_noise - samples of a gaussian noise used for sampling the predicted distribution for the location loc_tm1 - previous location h_tm1 - previous hidden state x - entire image. This function performs classification using a convolutional neural networks which is trained along with the reinforcement learning step and returns the probabilities for each class corresponding with the given samples. ####Parameters: x_t - proposed region at time-stamp t. This function crops the entire image accordingly to the previous predicted location and returns a stack of cropped images one for each batch. ####Parameters: loc_tm1 - previous location x - entire image. This function extracts features from the proposed region using the same CNN from the classification step, which means that the feature extraction of the proposed region and the classification phase shares the same parameters. In this step the activations right before the softmax are used as features for updating the hidden units. ####Parameters: x_t - proposed region at time-stamp t. This function updates the hidden state using gated recurrent units. ####Parameters: h_tm1 - previous hidden state x_in - extracted features from the proposed region. This function performs a dot product for estimating the next locations for the window given the current hidden state. ####Parameters: h_t - current hidden state. This class abstract the world of our problem which includes the policy, agent and the learning routines. This function initializes a instance of CRAM using the defined parameters passed in specs. ####Parameters: specs - set of parameters. This function define the theano graphs for all theano functions used in the model. This function returns the updates for the learnable parameters during the prediction step using ADAM. ####Parameters: prob - probabilities for each class for a given mini-batch y - ground-truth for a given mini-batch. This functions returns the accuracy for a given mini-batch. ####Parameters: pred - predicted classes for a giben mini-batch y - ground-truth for a given mini-batch. This function computes the loss considered during the reinforce learning step. ####Parameters: preds - predicted classes for a given mini-batch y - ground-truth for a given mini-batch. This function returns the loss used for training the classifier. ####Parameters: prob - probabilites for each class given a mini-batch y - ground-truth for a given mini-batch. This function returns the log-likelihood. ####Parameters: x_vars - samples from the predicted distribution means - means of the predicted distribution. This function collects samples from the environment. ####Parameters: y - ground-truth. This function returns the estimated gradients for the REINFORCE algorithm. ####Parameters: y - ground-truth. This function returns the gradients for the parameters in the classifier. ####Parameters: y - ground-truth. This function computes the total loss for being used during validation step regarding the classification. ####Parameters: X_val - validation samples y_val - validation ground-truth. This function computes the total loss for being used during validation step regarding th region proposal. This function initialize the score for the Early Stopping implementation. This function checks if the model should stop or not. This function returns the current best score achieved during learning. This function updates the score when a better score is found. This function loads a trained model from a file.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Step forward forward .
- Builds theano matrix
- The main function .
- Compute r gradients for each sample .
- Initialize parameters .
- Wrapper for sgd method .
- Train a model using SGD .
- Convert images to images .
- Reads an image file .
- Forward step .
RegionProposal Key Features
RegionProposal Examples and Code Snippets
Community Discussions
Trending Discussions on Machine Learning
QUESTION
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.
...ANSWER
Answered 2022-Feb-17 at 10:47You 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
ONNX is much more portable and you can use in languages such as C#, Java, or Javascript https://onnxruntime.ai/ (even on the browser)
A running exampleJust 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
QUESTION
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.
...ANSWER
Answered 2022-Jan-14 at 23:47Based 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:
QUESTION
This question is the same with How can I check a confusion_matrix after fine-tuning with custom datasets?, on Data Science Stack Exchange.
BackgroundI 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
...ANSWER
Answered 2021-Nov-24 at 13:26What 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 y_true and y_pred.
QUESTION
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
ANSWER
Answered 2021-Nov-23 at 06:13This 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:
QUESTION
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):
ANSWER
Answered 2021-Nov-04 at 21:17No, 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
X_trainsample. - 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
X_valper fold).
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.
QUESTION
I am not able to access jupyter lab created on google cloud
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%.
I tried the diagnostic tool, which gave the following result:
but didn't fix it.
Thanks in advance.
...ANSWER
Answered 2021-Aug-20 at 14:00You 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
QUESTION
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:
...ANSWER
Answered 2021-Sep-29 at 22:47Turns out its just documented incorrectly.
In reality the export from brain.js is this:
QUESTION
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:
...ANSWER
Answered 2021-Sep-04 at 06:43You're right. Just one thing to consider for choosing OrdinalEncoder or 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"]orshirt_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 0 and 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)
QUESTION
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.
code:
...ANSWER
Answered 2021-Aug-10 at 07:39Increasing 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.
QUESTION
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?
Table example:
...ANSWER
Answered 2021-Aug-11 at 15:55You could build a model like this.
x = features you have. y = true_lable
from that you can extract features importance. also, if you want to go the extra mile,you can do Bootstrapping, so that the features importance would be more stable (statistical).
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install RegionProposal
You can use RegionProposal like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
