I am using word2vec (CBOW) to get similarities of words in my own corpus. I am using hierarchical softmax, and now I want to get output probabilities for each word given context word.
Currently, I am calculating probabilities for each word using this softmax formula given in the image below, but it is not efficient.
The hierarchical softmax optimizes this by using Huffman coding, I want to access these probabilities. Can anyone guide me on how can I access this using gensim?
Note: The above images are taken from this article
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Is it possible with AutoML (from H2O) to use only the Word2Vec algorithm and try out different values for the parameters to find out which parameter settings give me the most accurate vectors for my data set? So I don't want AutoML to apply the algorithms DeepLearning, GBM etc. to my dataset. Only the Word2Vec algorithm… How Do I do that?
So far I only managed to build a word2vec model with H2O.
I would like to test different Settings of the hyperparameters of Word2Vec with AutoML to evaluate which Settings are optimal...
The Word2Vec algorithm is a data transformation algorithm (converting rows of text to a matrix), not a supervised machine learning algorithm (which is what AutoML and all the algorithms inside of it do).
The typical way that Word2Vec is used is it apply Word2Vec to your text data so that your data can be used to train a supervised ML algorithm. From here you can run any supervised algorithm (GLM, Random Forest, GBM, etc) on this transformed dataset -- or my recommendation is to just pass the transformed data to AutoML, so it can find the best algorithm for you.
You will have to try out different settings for Word2Vec manually and see how well they do, given some particular supervised learning algorithm that you want to apply to your problem. Hopefully that clears up the confusion.
As I understand it, Word2Vec builds a word dictionary (or, vocabulary) based on a training corpus, and outputs a K-dim vector for each word in the dictionary. My question is, what exactly is the source of those K-Dim vectors? I'm assuming each vector is either a row or column in one of the weight matrices between the input and hidden layer, or the hidden and output layer. However, I haven't been able to find any sources to back this up, and I'm not literate enough in programming languages examine the source code and figure it out myself. Any clarifying remarks on this topic would be greatly appreciated!
what exactly is the source of those K-Dim vectors? I'm assuming each vector is either a row or column in one of the weight matrices between the input and hidden layer, or the hidden and output layer.
In the word2vec model(CBOW, Skip-gram), it outputs a feature matrix of words. This matrix is first weight matrix between input layer and projection layer(in word2vec model has no hidden layer, no activation function in it). Because when we train word in the context(in CBOW Model), we updates this weight matrix.(second - between projection and output layer - matrix also updated. however we are not using it)
in the first matrix, rows mean a vocabulary words and columns mean feature of word(K-Dimension).
if you want more information, explore it
http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/
word2vec uses machine learning to obtain word representations. It predicts a word using its context (CBOW) or vice versa (skip-gram).
In machine learning, you have a loss function that represents the error you model makes. This error depends on the model's parameters.
Training a model means minimizing the error with respect to the model's parameters.
In word2vec, these embedding matrices are the model's parameters that are being updated during the training. I hope, it helps you to understand where they come from. Indeed, they are first initialized randomly and they are changed during the training process.
You can take a look at this picture from this paper:
The W matrix that maps the input one-hot word representations to that k-dimensional vectors and the W' matrix that maps a k-dimensional representation to the output are both the model's parameters that we optimize during training.
http://i.imgur.com/dspFhlO.png
I am trying to label objects in am image using Conditional Random Fields. But I am stuck understanding this formula.
Can anyone tell me the meaning the terms of the formula and how to calculate them.
I am using MS-COCO data set which has labelled images i.e I have segmented images.
Here Z(.)= partition function and P(ci | Sj)= Probability that Sj segment of Image I belongs to class ci and q= no of pairwise spatial relations.
This is in fact the the conditional probability distribution of the labeling c={c1,c2,...,ck} for the image segments, given the segments features S={S1,S2,...,Sk}. p(ci|Si) is the probability of assigning class label ci to segment i, which can be computed using various classifiers like logistic regression, neural network, or SVM. The term B presents the aggregate pairwise function that determines how likely it is for each adjacent pair of {i,j} to take labels {ci,cj}. This term can be reallized by computing the co-occurrence statistics of different class pairs in the dataset, which is described in detail in this paper:
Object Categorization using Co-Occurrence, Location and Appearance
My objective is to detected text in an image and recognize them.
I have achieved detecting characters using stroke width transform.
What to do to recognize them?
As per my knowledge, I thought of training the svm with my dataset of letters of different fonts[images] by detecting feature point and extracting feature vectors from each and every image.[I have used SIFT Feature vector,did build the dictionary using kmean clusetering and all].
I have detected a character before, i will extract the sift feature vector for this character . and i thought of feeding this into the svm prediction function.
I dont know how to recognize using svm. I am confused! Help me and correct me where ever I went wrong with concept..
I followed this turorial for recognizing part. Can this turotial can be applicable to recognize characters.
http://www.codeproject.com/Articles/619039/Bag-of-Features-Descriptor-on-SIFT-Features-with-O
SVM is a supervised classifier. To use it, you will need to have training data that is of the type of objects you are trying to recognize.
Step 1 - Prepare training data
The training data consists of pairs of feature vectors and their corresponding class labels. In your case, it appears that you have extracted a SIFT-based "Bag-of-word" (BOW) feature vector for the characters you detected. So, for your training data, you will need to find many examples of the different characters, extract this feature vector for each of them, and associate them with a label (sometimes called a class label, and typically an integer) which you will perhaps map to a textual description (for e.g., the number 0 could be mapped to the character 'a', and so on.)
Step 2 - Training the classifier
The SVM classifier takes in as input an array/Mat of feature vectors (one per row) and their associated labels. Tune the parameters of the SVM (i.e., the regularization parameter C, and if applicable, any other parameters for kernels) on a separate validation set.
Step 3 - Predict for unseen data
At test time, given a sample that was not seen by the SVM during training, you compute a feature vector (your SIFT-based BOW vector) for the sample. Pass this feature vector to the SVM's predict function, and it will return you an integer. Remember earlier when preparing your training data, you have associated an integer with each label? This is the label predicted by the SVM for this sample. You can then map this label to a character. For e.g., if you have associated 0 with 'a', 1 with 'b' etc., you can use a vector/hashmap to map the integer to its textual counterpart.
Additional Notes
You can check out OpenCV's SVM tutorial here for details.
NOTE: Often, for beginners, the hardest part (after getting the data) is tuning the classifier. My advice is first try a simple classifier (for e.g., a linear SVM) which has few parameters to tune. A decent one would be the linear SVM, which only requires you to adjust one parameter C. Once you manage to get somewhat decent results (which gives some assurance that the rest of your code is working) you can move on to more "sophisticated" classifiers.
Lastly, the training data and feature vectors you extract are very important. The training data must be "similar" to the test data you are trying to predict. For e.g., if you are predicting characters found in road signs which comes with different fonts, lighting conditions, and pose differences, then using training data consisting of characters taken from say a newspaper/book archive may not give you good results. This is an issue of domain adaptation in machine learning.
Is there a training and optimization algorithm for 2-D (two dimensional) conditional random fields (CRF) suited for classification of imagery?
Has anyone used CRF package in R (http://crf.r-forge.r-project.org/html/CRF-package.html) for image classification? I would like to have a view of a working example code.
Thanks.
Look up on Markov Random Fields. Here's a link to a paper you might be interested in: Patric Perez: Markov Random Fields and Images (1998).
I do not think it will work alone. Since image classification is about scaling and affine transformation, so the key feature for accurate image classification is preprocessing not classification algorithm.
classification of imagery usually involves bag of words and feature pooling and stuff, whereas conditional random field is for labeling sequential data. so it might not be appropriate to use crf in this scenario.