I am using Orange Data Mining for some classification tasks. I don't understand why I can't use a stacked model to make predictions using a test dataset. I have read most of the documentation and I couldn't find anything on this issue.
I am using Orange 3.21.0.
Screenshot
Well, you need a model to use it for predictions. And every model needs a data to be trained on. Connect the first File widget to Stacking and it should work.
Related
Data augmentation can easily be achieved using ad hoc modules in e.g. TensorFlow. This works perfectly for classification problems, however when the objective of the network is the prediction of a geometrical feature, e.g. a landmark, a problem arises. As the image is modified, e.g. flipped, or distorted, the corresponding labels also need to be adapted.
1 - Is there any tool to do this? I am sure that this is a common problem.
2 - Would it be useful to create a data augmentation script for neural networks that predict geometrical features?
I want to understand if I need to code all of this by myself or if I am missing something that already exists. If I need to do it and it could be useful I would just create an open source thing.
You can use imgaug library https://github.com/aleju/imgaug
An example of augmentation for key points using imgaug you can find here https://github.com/aleju/imgaug#example-augment-images-and-keypoints
[![enter image description here][1]][1]I am actually reconstructing some images using dual photography. Next, I want to train a network to reconstruct clear images by removing noise (Denoising autoencoder).
The input for training the network is reconstructed images, whereas, the output is ground truth or computer based standard test images. Now the input e.g., Lena is some how not exact version of Lena with image shifted in positions and some artifacts.
If I keep input as my reconstructed image and training output as Lena test image (computer standard test image) , will it work?
I only want to know if input/output shifted or some details missing in one of them (due to some cropping) would work.
It depends on many factors like your images for training and the architecture of the network.
However, what you want to do is to make a network that learns the noise or low level information and for this purpose Generative Adversarial Networks (GAN) are very popular. You can read about them here. Maybe, after you have tried your approach and if the results are not satisfactory then try using GANs, like, DCGAN (Deep Convolution GAN).
Also, share your outcomes with the community if you would like.
Denoising Autoencoders! Love it!
There is no reason for not training your model with those images. The autoencoder, if well trained, will eventually learn the transformation if there is enough data.
However, if you have the 'positive' images, I strongly recommend you to create your own noisy images and then train in that controlled working area. You will simplify your problem and it will be easier to solve.
What is stopping you from doing just that?
I'm using cnn built by keras(tensorflow) to do visual recognition.
I wonder if there is a way to know what my own tensorflow model "see".
Google had a news showing the cat face in the AI brain.
https://www.smithsonianmag.com/innovation/one-step-closer-to-a-brain-79159265/
Can anybody tell me how to take out the image in my own cnn networks.
For example, what my own cnn model recognize a car?
We have to distinguish between what Tensorflow actually see:
As we go deeper into the network, the feature maps look less like the
original image and more like an abstract representation of it. As you
can see in block3_conv1 the cat is somewhat visible, but after that it
becomes unrecognizable. The reason is that deeper feature maps encode
high level concepts like “cat nose” or “dog ear” while lower level
feature maps detect simple edges and shapes. That’s why deeper feature
maps contain less information about the image and more about the class
of the image. They still encode useful features, but they are less
visually interpretable by us.
and what we can reconstruct from it as a result of some kind of reverse deconvolution (which is not a real math deconvolution in fact) process.
To answer to your real question, there is a lot of good example solution out there, one you can study it with success: Visualizing output of convolutional layer in tensorflow.
When you are building a model to perform visual recognition, you actually give it similar kinds of labelled data or pictures in this case to it to recognize so that it can modify its weights according to the training data. If you wish to build a model that can recognize a car, you have to perform training on a large train data containing labelled pictures. This type of recognition is basically a categorical recognition.
You can experiment with the MNIST dataset which provides with a dataset of pictures of digits for image recognition.
I'm using Google Colab for training my models.
But speed is still low.
So is there a way I can train from two different accounts and combine the training later?
No, you cannot train using 2 accounts the same model on colab. Google colab is for research purposes only. Not to train large scale production models. Colab also disconnects the kernel every 12 hour.
You can instead train the model using multiple GPU's on a single computer. Keras supports multi GPU training when using tensorflow as backend. But training on two different computers/VM is not possible. How will gradients flow during back propagation?
There is a solution though, but not an end-to-end approach. You can split your model into two different models, where the output of first model will become the input for second and second will produce the final output. For this you need a different training set for each model.
Take this example.
Suppose you are building a face recogniser where the model takes in a raw camera picture and recognises the face as yes/no.
Instead of training this big Networks you could split it into two different nets, where task for first net will be to crop the face and remove other useless things from image and second to recognise from cropped image.
This is non end-to-end model, and you can train the two models diffently on different machines with different dataset and then eventually merge it together. This is usually more powerful and easy to train.
Look up this question Tensorflow Combining Two Models End to End
Another possibility is to ensemble the two trained models. You'd have to make sure however that the data for both of the models are coming from the same distribution.
I am looking for some pre-trained deep learning model which can recognise an object in an image. Usually the images are of type used in shopping websites for products. I want to recognise what is the product in the image. I have come across some pre-trained models like VGG, Inception but they seems to be trained on some few general objects like 1000 objects. I am looking for something which is trained on more like 10000 or more.
I think the best way to do this is to build your own training set with the labels that you need to predict, then take an existing pre-trained model like VGG, remove the last fully connected layers and train the mode with your data, the process called transfer learning. Some more info here.