So tensorflow is extremely useful at creating neural networks that involve perceptron neurons. However, if one wanted to use a new type of neuron instead of the classic perceptron neuron, is this possible through augmenting tensorflow code? I can't seem to find an answer. I understand this would change the forward propagation, and more mathematical calculations, and I am willing to change all the necessary areas.
I am also aware that I can just code from scratch the layers I need, and the neurons I had in mind, but tensorflow nevertheless has GPU integration, so one can see its more ideal to manipulate their code as opposed to creating my own from scratch.
Has anyone experimented with this? My goal is to create neural network structures that use a different type of neuron than the classic perceptron.
If someone who knows where in tensorflow I could look to see where they initialize the perceptron neurons, I would very much appreciate it!
Edit:
To be more specific, is it possible to alter code in tensorflow to use a different neuron type rather than the perceptron to invoke the tensorlfow Module: tf.layers for example? Or tf.nn? (conv2D, batch-norm, max-pool, etc). I can figure out the details. I just need to know where (I'm sure they're a few locations) I would go about changing code for this.
However, if one wanted to use a new type of neuron instead of the classic perceptron neuron, is this possible through augmenting tensorflow code?
Yes. Tensorflow provides you the possibility to define a computational graph. It then can automatically calculate the gradient for that. No need to do it yourself. This is the reason why you define it symbolically. You might want to read the whitepaper or start with a tutorial.
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I'm training a neural network using keras but I'm not sure how to feed the training data into the model in the way that I want.
My training data set is effectively infinite, I have some code to generate training examples as needed, so I just want to pipe a continuous stream of novel data into the network. keras seems to want me to specify my entire dataset in advance by creating a numpy array with everything in it, but this obviously wont work with my approach.
I've experimented with creating a generator class based on keras.utils.Sequence which seems like a better fit, but it still requires me to specify a length via the __len__ method which makes me think it will only create that many examples before recycling them. Can someone suggest a better approach?
I have a pytorch model and a tensorflow model, I want to train them together on one GPU, following the process bellow: input --> pytorch model--> output_pytorch --> tensorflow model --> output_tensorflow --> pytorch model.
Is is possible to do this? If answer is yes, is there any problem which I will encounter?
Thanks in advance.
I haven't done this but it is possible but implementing is can be a little bit.
You can consider each network as a function, you want to - in some sense - compose these function to form your network, to do this you can compute the final function by just giving result of one network to the other and then use chain-rule to compute the derivatives(using symbolic differentiation from both packages).
I think a good way for implementing this you might be to wrap TF models as a PyTorch Function and use tf.gradients for computing the backward pass.
Doing gradient updates can really get hard (because some variables exist in TF's computation graph) you can turn TF variables to PyTorch Variable turn them into placeholdes in TF computation graph, feed them in feed_dict and update them using PyTorch mechanisms, but I think it would be really hard to do, instead if you do your updates inside backward method of the function you might be able to do the job(it is really ugly but might do the job).
I've implemented the Neural Network using Tensorflow. During the implementation and training, I've found several not-so-trivial bugs.
Example: during the training I had same Mini-Batch loss for different steps/epochs, but different accuracy.
Now the neural network seems to be ready and working properly. I haven't managed to train it well yet, but I am working on it.
Anyway, I would like to check somehow that I haven't done any computational errors there. I am thinking about generating some artificial data for "fake" classification problem with lets say 4 features. The classification should have a very clear human-understandable dependency between the classification output and 4 features. The idea is to try to train the NN on it and see how it performs.
What do you think?
Stanford's c231n has a couple of general tips for this, like gradient checking.
If you're just learning neural networks, why don't you try to run your implementation on some known data? Many courses provide error and loss curves form models with specified hyperparameters, so you can check whether your implementation's behavior differs significantly from correct implementation.
I'm trying to implement a fully convolutional network and train it on the Pascal VOC dataset, however after reading up on the labels in the set, I see that I need to somehow ignore the "void" label. In Caffe their softmax function has an argument to ignore labels, so I'm wondering what the mechanic is, so I can implement something similar in tensorflow.
Thanks
In tensorflow you're feeding the data in feed_dict right? Generally you'd want to just pre-process the data and remove the unwanted samples - don't give them to tensorflow for processing.
My prefered approach is a producer-consumer model where you fire up a tensorflow queue and load it with samples from a loader thread which just skips enqueuing your void samples.
In training your model dequeue samples in the model (you don't use feed_dict in the optimize step). This way you're not bothering to write out a whole new dataset with the specific preprocessing step you're interested in today (tomorrow you're likely to find you want to do some other preprocessing step).
As a side comment, I think tensorflow is a little more do-it-yourself than some other frameworks. But I tend to like that, it abstracts enough to be convenient, but not so much that you don't understand what's happening. When you implement it you understand it, that's the motto that comes to mind with tensorflow.
This is a newbie question for the tensorflow experts:
I reading lot of data from power transformer connected to an array of solar panels using arduinos, my question is can I use tensorflow to predict the power generation in future.
I am completely new to tensorflow, if can point me to something similar I can start with that or any github repo which is doing similar predictive modeling.
Edit: Kyle pointed me to the MNIST data, which I believe is a Image Dataset. Again, not sure if tensorflow is the right computation library for this problem or does it only work on Image datasets?
thanks, Rajesh
Surely you can use tensorflow to solve your problem.
TensorFlow™ is an open source software library for numerical
computation using data flow graphs.
So it works not only on Image dataset but also others. Don't worry about this.
And about prediction, first you need to train a model(such as linear regression) on you dataset, then predict. The tutorial code can be found in tensorflow homepage .
Get your hand dirty, you will find it works on your dataset.
Good luck.
You can absolutely use TensorFlow to predict time series. There are plenty of examples out there, like this one. And this is a really interesting one on using RNN to predict basketball trajectories.
In general, TF is a very flexible platform for solving problems with machine learning. You can create any kind of network you can think of in it, and train that network to act as a model for your process. Depending on what kind of costs you define and how you train it, you can build a network to classify data into categories, predict a time series forward a number of steps, and other cool stuff.
There is, sadly, no short answer for how to do this, but that's just because the possibilities are endless! Have fun!