In Keras (tensorflow backend) I can weight the classes via a parameter, and there is another parameter that allows the weighting of the samples (as it was referenced in this question.)
In my case, I need to do both simultaneously: each sample should have an individual weight for each of the two classes that I have. My problem is that I don't just have one weight per sample for both classes, but the two classes need individual weights for each sample.
How can I achieve this?
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I am using Google's Dopamine framework to train a specific reinforcement learning use-case. I am using an auto encoder to pre-train the convolutional layers of the Deep Q Network and then transfer those pre-trained weights in the final network.
To that end, I have created a separate model (in this case an auto-encoder) which I train and save the resulting model and weights.
The DQN model is created using Keras's model sub-classing method and the model used to save the trained convolutional layers weights was build using the Sequential API. My issue is with when trying to load the pre-trained weights to my final DQN model. Based on whether I use the load_model() or load_weights() functionality from Tensorflow's API I get two different overall behaviors of my network and I would like to understand why. Specifically I have the two following scenarios:
Loading the weights with theload_weights() method to the final model. The weights are the weights of the encoder plus one additional layer(added just before saving the weights) to fit the architecture of the final network implemented in dopamine where they are loaded.
First load the saved model with load_model() and then when defining the new model in the __init__() method, extract the relevant layers from the loaded model and then use them for the final model.
Overall, I would expect the two approaches to yield similar results with regards to the average reward achieved per episode , when I use the same pre-trained weights. However the two approaches differ ( 1. yield higher average reward than 2. although using the same pre-trained weights) and I don't understand why.
Furthermore, in order to validate this behavior I have tried loading random weights with the two aforementioned approaches in order to see a change in behavior. In both cases, based on which of the two aforementioned loading methods I am using, I end up with very similar resulting behavior with the respected case when loading the trained weights. It's seems like the pre-trained weights in each respected case have no effect on the overall resulting training behavior. Although, this might be irrelevant to the issue I am trying to investigate here as it might be the case that the pre-trained weights don't offer any benefit overall which is also possible.
Any thoughts and ideas on this would be much appreciated.
I am new to deep learning, and I am doing a research using CNNs. I need to train a CNN model on a dataset of images (landmark images) and test the same model using a different dataset (landmark images too). One of the motivations is to see the ability of the model to generalize. But the problems is: Since the dataset used for train and test is not the same, the classes are not the same! Possibly, the number of classes too, which means that the predictions made on the test dataset are not trust worthy (Since the weights of the output layer have been calculated based on different classes belonging to train dataset). Is there any way to evaluate a model on a different dataset without affecting test accuracy?
The performance of a neural network on one dataset will not generally be the same as its performance on another. Images in one dataset can be more difficult to distinguish than those in another. As a rule of thumb: if your landmark datasets are similar, it's likely that performance will be similar. However, this is not always the case: subtle differences between the datasets can result in significantly different performance.
You can account for the potentially different performance on the two datasets by training another network on the other dataset. This will give you a baseline of what to expect when you try to generalize your network to it.
You can apply your neural network trained for one set of classes to another set of classes. There are two main approaches to this:
Transfer learning. This is where the last layer of your trained network is replaced with a new layer(s) that is trained, by itself, to classify the new images. (Use for many classes. Can use for few classes.)
All-Transfer learning. Rather than replacing the last layer, add a new layer after it and only train the final layers. (Use for few classes.)
Both approaches are much quicker than training a neural network from scratch.
I assume that you are facing a classification problem.
What do you explicitly mean? Do you have classes A B and C in your train-dataset and the same classes in your test-dataset with a different labeling, or do you have completly different classes in your test-dataset with respect to your train-dataset?
You can solve the first problem by creating a mapping from trainlabel to testlabel or vice versa.
The second one depends on what you are trying to achieve... If you want the model to predict classes, which were never trained, you wont get any outcome.
How to implement Multi-target classification in tensorflow? Say I have a list of features f1,f2,...,fn and i want to predict the class or a value of three targets t1,t2,and t3. So each target belongs to a single class only.
It sounds like you're interested in multinomial logistic regression. In TensorFlow, the most important function for this is tf.nn.softmax_cross_entropy_with_logits_v2.
This site gives a good idea how the softmax function makes it possible to classify a point in multiple categories.
I recently came across this link for learning tensorflow object detection
https://www.youtube.com/watch?v=Rgpfk6eYxJA&t=993s
However I have few doubts and want suggestion on how to proceed.
1) How should I train different objects using the same model( I mean what should my data set contain if I want to train cats,dogs as objects.
2) and once I have trained it for dogs and then continue training on cars will the model detect dogs?
Your dataset should contain a large variety of examples for every object (class) you wish to detect. It sounds like you're misunderstanding the training process by assuming that you train it on each class of objects in sequence, this is incorrect. When you train the model you will be taking a random batch of samples (maybe 64 for example) across all classes.
Training simultaneously on all or many of the classes makes sense, you have one model that has to perform equally well on all classes. So when you train the model you compute the error of the parameters with respect to a random selection of classes and average the error to come up with each update step, yielding a model that performs well across classes.
Notice that it's quite common to run into class imbalance issues. If you have only a few samples of cats, and millions of samples of dogs you will disproportionately penalize the network for misclassifying dogs as cats and the network will simply always predict dog to hedge its bet. Ideally, you will have a roughly equal balance of data per class, if not, there are books and tutorials galore on the strategies to deal with this.
I was training keras with tensorflow for person detection. After the training, when the testing was done so many images contains redundant labeling of person. ie; for a single person in an image, multiple labeling as a person was shown. What is the actual reason behind this?
My training set contains nearly 2000 images, a single class person, batch=32, epoch=100, threshold=0.55 and testing images=250.
Overtraining of samples may lead to redundancy and if you are using different angles of an image, for example if you train for detecting people and you are providing samples of human from different angles, then it may show errors on detection in real cases. If this is not the issue, then non- maximal suppression will be the better option.