I m implementing Deep autoencoder using RBM. I understand that, for unfolding the network, we need to use the transposed weights of the encoder for the decoder. But I'm not sure which biases should we use for the decoder. I appreciate it if anyone can elaborate it for me or send me a link for pseudocode.
Related Questions in MACHINE-LEARNING
- C++ using std::vector across boundaries
- Linked list without struct
- Connecting Signal QML to C++ (Qt5)
- how to get the reference of struct soap inherited in C++ Proxy/Service class
- Why we can't assign value to pointer
- Conversion of objects in c++
- shared_ptr: "is not a type" error
- C++ template using pointer and non pointer arguments in a QVector
- C++ SFML 2.2 vectors
- Lifetime of temporary objects
Related Questions in NEURAL-NETWORK
- C++ using std::vector across boundaries
- Linked list without struct
- Connecting Signal QML to C++ (Qt5)
- how to get the reference of struct soap inherited in C++ Proxy/Service class
- Why we can't assign value to pointer
- Conversion of objects in c++
- shared_ptr: "is not a type" error
- C++ template using pointer and non pointer arguments in a QVector
- C++ SFML 2.2 vectors
- Lifetime of temporary objects
Related Questions in UNSUPERVISED-LEARNING
- C++ using std::vector across boundaries
- Linked list without struct
- Connecting Signal QML to C++ (Qt5)
- how to get the reference of struct soap inherited in C++ Proxy/Service class
- Why we can't assign value to pointer
- Conversion of objects in c++
- shared_ptr: "is not a type" error
- C++ template using pointer and non pointer arguments in a QVector
- C++ SFML 2.2 vectors
- Lifetime of temporary objects
Related Questions in RBM
- C++ using std::vector across boundaries
- Linked list without struct
- Connecting Signal QML to C++ (Qt5)
- how to get the reference of struct soap inherited in C++ Proxy/Service class
- Why we can't assign value to pointer
- Conversion of objects in c++
- shared_ptr: "is not a type" error
- C++ template using pointer and non pointer arguments in a QVector
- C++ SFML 2.2 vectors
- Lifetime of temporary objects
Popular Questions
- How do I undo the most recent local commits in Git?
- How can I remove a specific item from an array in JavaScript?
- How do I delete a Git branch locally and remotely?
- Find all files containing a specific text (string) on Linux?
- How do I revert a Git repository to a previous commit?
- How do I create an HTML button that acts like a link?
- How do I check out a remote Git branch?
- How do I force "git pull" to overwrite local files?
- How do I list all files of a directory?
- How to check whether a string contains a substring in JavaScript?
- How do I redirect to another webpage?
- How can I iterate over rows in a Pandas DataFrame?
- How do I convert a String to an int in Java?
- Does Python have a string 'contains' substring method?
- How do I check if a string contains a specific word?
Popular Tags
Trending Questions
- UIImageView Frame Doesn't Reflect Constraints
- Is it possible to use adb commands to click on a view by finding its ID?
- How to create a new web character symbol recognizable by html/javascript?
- Why isn't my CSS3 animation smooth in Google Chrome (but very smooth on other browsers)?
- Heap Gives Page Fault
- Connect ffmpeg to Visual Studio 2008
- Both Object- and ValueAnimator jumps when Duration is set above API LvL 24
- How to avoid default initialization of objects in std::vector?
- second argument of the command line arguments in a format other than char** argv or char* argv[]
- How to improve efficiency of algorithm which generates next lexicographic permutation?
- Navigating to the another actvity app getting crash in android
- How to read the particular message format in android and store in sqlite database?
- Resetting inventory status after order is cancelled
- Efficiently compute powers of X in SSE/AVX
- Insert into an external database using ajax and php : POST 500 (Internal Server Error)
I believe Geoff Hinton makes all of his source code available on his website. He is the go-to guy for the RBM version of this technique.
Basically, if you have an input matrix M1 with dimension 10000 x 100 where 10000 is the number of samples you have and 100 is the number of features and you want to transform it into 50 dimensional space you would train a restricted boltzman machine with a weight matrix of dimensionality 101 x 50 with the extra row being the bias unit that is always on. On the decoding side then you would take you 101 x 50 matrix, drop the extra row from the bias making it a 100 x 50 matrix, transpose it to 50 x 100 and then add another row for the bias unit making it 51 x 100. You can then run the entire network through backpropogation to train the weights of the overall network.