I am using CytoScape software. It has the ability to create a MCL Cluster automatically but I am having difficulties seeing the Associated Matrix. Does anyone know how to get that using Cytospace?
mcl cluster generated using clustermaker2
The clusterMaker2 app for Cytoscape performs MCL Clustering, but it does not provide output for intermediate steps, like the initial matrix of the network. However, there is an app for Cytoscape that can export a matrix version of any given network: http://apps.cytoscape.org/apps/adjexporter.
Related
When I use tensorboard to show the data, I found that there is more than one curve. I think this is related to the configuration. So could someone tell me what each curve represents?
This is not related in any way with the Cloud ML Engine. You can find
all the configurable parameters for the Engine in the docs for its REST API (training input, training output, prediction input, prediction output, model resource, version resource).
These curves from your tensorboard is something you configured in your tensorflow code, probably the training cost for several different runs, set as a summary scalar with the name "train_cost".
Is there an option to request a faster node for online prediction in ML Engine?
For example, when training I can configure any of these machines for my job:
standard,
large_model,
complex_model_s,
complex_model_m,
complex_model_l,
standard_gpu,
complex_model_m_gpu,
complex_model_l_gpu,
standard_p100,
complex_model_m_p100
See description of available clusters and machines for training here and here
I am struggling to find if it is possible to control what kind of machine runs my online prediction.
We are currently adding that capability and will let you know when it's publicly available.
ML Engine offers 4-core instance type in addition to the default serving instance type for online prediction. However the feature is still at alpha stage and it will only be available to a selected list of accounts who opted in as "Trusted Testers". Please contact cloudml-feedback#google.com if you need help to setup prediction service with faster node.
I am using Knime and I've created a Neural Network using MLP (MultiLayer Perceptron). It works fine. You may ignore all the yellow nodes as all they do is reformat the data sheet.
This works, but I would like to incorporate Cross Validation into the mix. There is a lack of working examples and so I am struggling. I am looking at X-Partitioner and X-Aggregator nodes but I have no idea how to use them in my network.
Can someone help me out?
The answer was quite simple. X-partitioner node basically replaces the Partitioner node. The X-Aggregator node is placed after the MLP Predictor. The X-Aggregator node is in charge of looping the neural network.
I am working on creating a Real-time image processor for a self driving small scale car project for uni, It uses a raspberry pi to get various information to send to the program to base a decision by.
the only stage i have left is to create a Neural network which will view the image displayed from the camera ( i already have to code to send the array of CV_32F values between 0-255 etc.
I have been scouring the internet and cannot seem to find any example code that is related to my specific issue or my kind of task in general (how to implement a neural network of this kind), so my question is is it possible to create a NN of this size in c++ without hard coding it (aka utilising openCv's capabilities): it will need 400 input nodes for each value (from 20x20 image) and produce 4 outputs of left right fwd or backwards respectively.
How would one create a neural network in opencv?
Does openCV provide a backpropogation(training) interface /function or would I have to write this myself.
once it is trained am I correct in assuming I can load the neural network using ANN_MLP load etc? following this pass the live stream frame (as an array of values) to it and it should be able to produce the correct output.
edit:: I have found this OpenCV image recognition - setting up ANN MLP. and It is very simple in comparison to what I want to do, and I am not Sure how to adapt that to my problem.
OpenCV is not a neural network framework and in turn won't find any advanced features. It's far more common to use a dedicated ANN library and combine it with OpenCV. Caffe is a great choice as a computer vision dedicated deep learning framework (with C++ API), and it can be combined with OpenCV.
Some users might see this as opinion-based-question but if you look closely, I am trying to explore use of Caffe as a purely testing platform as opposed to currently popular use as training platform.
Background:
I have installed all dependencies using Jetpack 2.0 on Nvidia TK1.
I have installed caffe and its dependencies successfully.
The MNIST example is working fine.
Task:
I have been given a convnet with all standard layers. (Not an opensource model)
The network weights and bias values etc are available after training. The training has not been done via caffe. (Pretrained Network)
The weights and bias are all in the form of MATLAB matrices. (Actually in a .txt file but I can easily write code to get them to be matrices)
I CANNOT do training of this network with caffe and must used the given weights and bias values ONLY for classification.
I have my own dataset in the form of 32x32 pixel images.
Issue:
In all tutorials, details are given on how to deploy and train a network, and then use the generated .proto and .caffemodel files to validate and classify. Is it possible to implement this network on caffe and directly use my weights/bias and training set to classify images? What are the available options here? I am a caffe-virgin so be kind. Thank you for the help!
The only issue here is:
How to initialize caffe net from text file weights?
I assume you have a 'deploy.prototxt' describing the net's architecture (layer types, connectivity, filter sizes etc.). The only issue remaining is how to set the internal weights of caffe.Net to pre-defined values saved as text files.
You can get access to caffe.Net internals, see net surgery tutorial on how this can be done in python.
Once you are able to set the weights according to your text file, you can net.save(...) the new weights into a binary caffemodel file to be used from now on. You do not have to train the net if you already have trained weights, and you can use it for generating predictions ("test").