What is context size?
Context dimensions do not appear on the rows or columns but filter the context of the grid. While row and column dimensions display all of their list items in the grid, context dimensions limit the items in the grid by displaying only information related to the active item in the dimension.
What is window size in CNN?
In Convolutional Neural Network (CNN), a filter is select for weights sharing. For example, in the following pictures, a 3×3 window with the stride (distance between adjacent neurons) 1 is chosen.
What is the value of a context window?
Context window is the number of words to be predicted which can occur in the range of the given word. The value of a context window is double the window size that is 2*c and is represented by k. For the given image the value of the context window is 4. The dimension of an input vector is equal to |V|.
How does window size affect the quality of a word?
The ‘affected’ here means it will pull the vector of two words closer. So it depends on the material you are using for training, if the window size of 2 can capture the context of a word, but 5 is chosen, it will decrease the quality of the learnt model, and vise versa.
How does window size affect the vector of Stack Overflow?
For example “stackoverflow great website for programmers” with 5 words (suppose we save the stop words great and for here) if the window size is 2 then the vector of word “stackoverflow” is directly affected by the word “great” and “website”, if the window size is 5 “stackoverflow” can be directly affected by two more words “for” and “programmers”.
How to predict the context of an image?
The window size is the maximum context location at which the words need to be predicted. The window size is denoted by c. For example, in the given architecture image the window size is 2, therefore, we will be predicting the words at context location (t-2), (t-1), (t+1) and (t+2).