Im using a Sklearn for my machine learning and my question is how can i see my process of my taining?
If i use Tensoflow i can see my loading process with Tensorboard. But does Sklearn have something like this?
As pointed out in the comments, you can use matplotlib. There are plenty of tutorials of how to create a plot updating in real-time during your training.
However, personally I found these options pretty cumbersome. I instead chose to use the PyTorch interface to tensorboard.
That works like a charm and you can just pass in numpy loss values.
Here's how to get started: https://pytorch.org/docs/stable/tensorboard.html
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Please could you tell me if it is feasible to transform a torch model (torch.save) into algebraic matrices/ equations that can be operated with numpy or basic Python, without the need to install torch and other related libraries (that occupy a lot of space)? In an afirmative case, could you please give me some hints or a link with explanations? Thank you very much.
I'm not aware of any way to do this without a lot of your own work. Basically you'd have to port most of the pytorch library to numpy, which would be a huge project. If space is an issue check if you can save some space by e.g using earlier torch versions or using only the CPU-versions of pytorch.
I'm aware of Tensorboard and how awesome it is, but I think that simple console output with current graph summary is better (and faster) for prototyping purpose.
And also know that I can generate tensorboard graph after simply running session with last network node as shown here.
What I'm looking for is something similar to model.summary() from Keras.
In another words: how to iterate over tensorflow graph and print out only custom high end layer with their shapes and dtypes in the same order how all these layer where generated?
It's certainly possible. If you are using tf.keras wrapper to build you can easily visualize the graph, even before model.compile() method executes.
It's keras built-in functionality called plot_model().
*This method have dependency on graphviz and pydot libraries.
for pydot installation : pip install pydot
but for graphviz installation you have follow step in this page. And also probably you have to restart the machine because of there it create system environment variables.
for tutorial on how to use this method follow this link
To plot your model with shapes and dtypes before training you could use:
tf.keras.utils.plot_model(model, show_shapes=True, expand_nested=True, show_dtype=True)
where "model" is your built model. The output of a model could looks like this:
I've been tooling around with Tensorflow and TFLearn for a few months. I've made some progress. However, I was expecting to be able to construct a functioning scikit-learn type Estimator as a TFLearn.DNN. I can fit, and I can predict, but I can't do cross-validation because evaluate is failing for me. TensorFlow is throwing:
ValueError: Cannot use the given session to evaluate tensor: the tensor's graph is different from the session's graph.
when I call evaluate. I thought the whole point of the TFLearn API was to abstract things like session management away from my code.
I have asked questions about problems I've had with TFLearn in several forums, including on the project's GitHub page. Unfortunately, I'm not getting any answers.
A few days ago, suddenly I encountered the tf.contrib.learn namespace. I'm seeing a lot of overlap between those classes and TFLearn. Then, I also found the tf.estimator class.
Finally, I just figured out that tensorflow.contrib sub-packages are third-party contributions. This leads me to wonder whether the original TFLearn is simply being absorbed into the larger TensorFlow package. Which direction is the code flowing?
I don't care what I use, as long as I get all the functionality of a scikit-learn estimator object.
I think it's best to use the official sub-modules of TensorFlow like tf.data and tf.estimator. They should be well maintained and features are added quickly.
For instance, #mrry seems in charge of tf.data and the module is very clean, easy to use and compatible with tf.estimator.
The module tf.estimator is a bit less clear, and comes from tf.contrib.learn. Don't take my word for it but I think tf.estimator will slowly replace tf.contrib.learn and it should be the official high-level API for TensorFlow (along with tf.keras).
You can find more information in the official blog post, where they explain the relationship between all modules.
I've been playing with the tensorflow standalone embedding projector (http://projector.tensorflow.org/) and found it a very helpful tool for visualization. However, when I try to replicate the t-sne result using other implementations (e.g., Rtsne, sklearn.manifold.tsne), the low dimension projection seems to be very different. Particularly, the clusters are much more spread-out in the embedding projector than that learnt using R or python packages.
I used the same perplexity, learning rate and momentum parameters. And tried both spherizing or not spherizing the data as implied in the projector.
Could anyone help to shed light on the difference between the tensorflow projector implementation of the t-sne algorithm and other implementations like Rtsne? For example, is there a similar 'exaggeration' parameter used in the projector as in Rtsne? What is the optimization algorithm? Or is there anything special in generating the visualization?
I believe the source code of the tensorflow projector is the oss_demo_bin.js file in https://github.com/tensorflow/embedding-projector-standalone. Unfortunately I'm not familiar with javascript and found it hard to interpret.
Thanks!
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!