I followed the tutorial of adanet:
https://github.com/tensorflow/adanet/tree/master/adanet/examples/tutorials
and was able to apply adanet to my own binary classification problem.
But how can I predict using the train model? I have a very little knowledge of TensorFlow. Any helps would be really appreciated
You can immediately call estimator.predict on a new unlabeled example and get a prediction.
Related
I was wondering if anyone can help by providing me with some guidelines for creating a bald-or-not image classifier.
So far I have a model for face and eye detection and to sum it up, this is my main questions:
Where can I find datasets for this kind of classification without going to google and download thousands of images by hand?
What classification model (i.e. the structure of layers in the network) should be used for this?
Question 1:
You could start by looking at some of the datasets available in Kaggle or Tensor Flow Datasets to see if there is anything available.
If none, you could try using an Image scraper tool to download images quickly compared to by hand.
Question 2:
Typically Image Classification model uses Convolutional Layers and MaxPooling layers. On top of the commonly used Dense Layer for Multi-layer Perceptron.
To get started you can study the Tensor Flow tutorial for Image Classification in this link,
which classifies whether the Image is Cat or Dog.
This example can provide you with the general idea of how to build an Image Classifier.
Hope this helps you. Thanks
I am using deepLab to generate semantic segmentation masked images for a video in cityscapes datasets. So, I started with the pre-trained model xception65_cityscapes_trainfine provided on the modelzoo and trained it further on the dataset.
I am curious to know How I can start training it from scratch? and not end up just using the pre-trained model? could anyone suggest a direction on How I can achieve it?
Any contribution from the community will be helpful and appreciated.
I trained a keras model inspired by the Inception Model on Tensorflow backend.
The problem is, the ouput is always the same, for differents images I tested.
However, model.evaluate give me a high accuracy percentage, so, the model seems to work.
Have you an idea ? Thanks!
Finally found the answer.
I just forgot to preprocess my input for the prediction.
All is clear now!
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!
I just started to work on tensorflow not so long. I'm working on the seq2seq model and using seq2seq example code.
I want to modify seq2seq model code to get top-k outputs (k is 5 or 10) for the Reinforcement learning model, not to get top-1 output.
First, I think I should modify decoder part of the seq2seq somehow, but I don't know which part is to change.
Is there any references or codes for the problem?
check out https://github.com/tensorflow/tensorflow/issues/654. There are some discussions on this, but no worked example yet.
tf.contrib.seq2seq.BeamSearchDecoder would do the magic for you.