Im using the MXNet implementation of the TFT model, and I want to get the feature importance for every timestep from the trained model. Unfortunately, there is no such implemented functionwhich would satisfy my demand. According to the original article for TFT, there is a way to get the feature importance by getting the weigths off of the variable selection network. Howewer, it's softmax function gives back an embedded, 3 dimensional matrix. Im stuck with this problem, due to the lack of documentation about TFT/MXNet.
Any help is highly appricated.
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I am currently testing out custom object detection using the Tensorflow API. But I don't quite seem to understand the theory behind it.
So if I for example download a version of MobileNet and use it to train on, lets say, red and green apples. Does it forget all the things that is has already been trained on? And if so, why does it then benefit to use MobileNet over building a CNN from scratch.
Thanks for any answers!
Does it forget all the things that is has already been trained on?
Yes, if you re-train a CNN previously trained on a large database with a new database containing fewer classes it will "forget" the old classes. However, the old pre-training can help learning the new classes, this is a training strategy called "transfert learning" of "fine tuning" depending on the exact approach.
As a rule of thumb it is generally not a good idea to create a new network architecture from scratch as better networks probably already exist. You may want to implement your custom architecture if:
You are learning CNN's and deep learning
You have a specific need and you proved that other architectures won't fit or will perform poorly
Usually, one take an existing pre-trained network and specialize it for their specific task using transfert learning.
A lot of scientific literature is available for free online if you want to learn. you can start with the Yolo series and R-CNN, Fast-RCNN and Faster-RCNN for detection networks.
The main concept behind object detection is that it divides the input image in a grid of N patches, and then for each patch, it generates a set of sub-patches with different aspect ratios, let's say it generates M rectangular sub-patches. In total you need to classify MxN images.
In general the idea is then analyze each sub-patch within each patch . You pass the sub-patch to the classifier in your model and depending on the model training, it will classify it as containing a green apple/red apple/nothing. If it is classified as a red apple, then this sub-patch is the bounding box of the object detected.
So actually, there are two parts you are interested in:
Generating as many sub-patches as possible to cover as many portions of the image as possible (Of course, the more sub-patches, the slower your model will be) and,
The classifier. The classifier is normally an already exisiting network (MobileNeet, VGG, ResNet...). This part is commonly used as the "backbone" and it will extract the features of the input image. With the classifier you can either choose to training it "from zero", therefore your weights will be adjusted to your specific problem, OR, you can load the weigths from other known problem and use them in your problem so you won't need to spend time training them. In this case, they will also classify the objects for which the classifier was training for.
Take a look at the Mask-RCNN implementation. I find very interesting how they explain the process. In this architecture, you will not only generate a bounding box but also segment the object of interest.
I'm trying to incorporate image normalization in my keras model to run on Google's cloud TPU. Therefore I inserted a line into my code:
with strategy.scope():
input_shape=(128,128,3)
image_0 = Input(shape=input_shape)
**image_1 = tf.image.per_image_standardization(image_0)**
...
There was nor error thrown, but according the documentation of google tf.image.per_image_standardization
is not a supported function. Does anybody know if it works anyhow, or does anybody have an idea how to check if it works?
From the TensorFlow Model Garden reference for ResNet, the mean and standard deviation of a dataset is often calculated beforehand and each batch is standardized via mean subtract and dividing by the standard deviation. See here for a reference (this uses ImageNet statistics).
I would suggest creating a separate script that calculates the mean and standardization and doing the same. Could you also point to the documentation where tf.image.per_image_standardization is not supported? I don't see why this wouldn't work, but you shouldn't apply it as a layer like in the provided code snippet. It should be in the data preprocessing pipeline like in the above reference.
I have a question about TensorFlow's estimators in tf.estimator, in particular DNNClassifier. It says in the documentation:
max_steps: Number of total steps for which to train model. If None, train forever or train until input_fn generates the OutOfRange error or StopIteration exception
In the doc on datasets for estimators it mentions that for training you need to use the shuffle(), repeat(), and batch_size methods, so that the iterator on the data set does stop after it's gone through the data once.
Does this mean that the pre-made estimators such as DNNClassifier have no stopping criterion based on the learning rate or changes in the loss? Is it really the case you can only have these models stop training based on how you specify the input function or by giving a maximum number of steps?
TensforFlow will not presume to know upon what learning rate or loss it should stop at. This is reasonable because they're problem-dependent. You could reasonably argue that it could infer a sensible limit based on round-off error for the given data types (if they're consistent, e.g. float32) but then many problems should be stopped earlier. So there is no sensible, broadly applicable default.
However, you can control this behaviour yourself using callbacks. TensorFlow includes the EarlyStopping callback. You can find the (python) documentation for it here.
Currently, I am working on deep neural network for image detection and I founded a model called YOLO Network, and it's very powerful to make objects detections, but I have a question:
How can we design and concept our own model? Do we use a brut force for that, for example "I use 2 convolutional and 1 pooling layer and 1 fully connected layer" after that if the result is'nt good I change the number of layers and change the parameter until I find the best model, Please if there is anyone who knows some informations about that, show me how ?
I use Tensorflow.
Thanks,
There are a couple of papers addressing this issue. For example in http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Szegedy_Rethinking_the_Inception_CVPR_2016_paper.pdf some general principles are mentioned, like preserving information by not having too rapid changes in any cut of the graph seperating the output from the input.
Another paper is https://arxiv.org/pdf/1606.02228.pdf where specific hyperparameter combinations are tried.
The remainder are just what you observe in practice and depends on your dataset and on your requirement. Maybe you have performance requirements because you want to deploy to mobile or you need more than 90 % accuracy. Then you will have to choose your model accordingly.
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!