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TensorFlow
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9
Experiences
Pros
7
Cons
1
Specs
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Pro
Ensured continued support
TensorFlow is developed and maintained by Google. It's the engine behind a lot of features found in Google applications, such as: recognizing spoken words translating from one language to another improving Internet search results Making it a crucial component in a lot of Google applications. As such, continued support and development is ensured in the long-term, considering how important it is to the current maintainers.
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Con
Not fully open source
For now, Google has only open sourced parts of the AI engine, namely some algorithms that run atop it. The advanced hardware infrastructure that drives this engine is not "open source".
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Pro
Visualization suite available
Google has made a powerful suite of visualizations available for both network topology and performance.
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Pro
Easily spin up sessions without restarting the program
TensorFlow can run with multiple GPUs. This makes it easy to spin up sessions and run the code on different machines without having to stop or restart the program.
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Pro
Python has a lot of powerful scientific libraries available
Other than having an easy syntax, using Python also gives developers a wide range of some of the most powerful libraries for scientific calculations (NumPy, SciPy, Pandas) without having to switch languages.
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Pro
Safe choice
Safest choice out there.
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Pro
Great debugging potential
You can introduce and retrieve the results of discretionary data on any edge of the graph. You can also combine this with TensorBoard (suite of visualization tools) to get pretty and easy to understand graph visualizations, making debugging even simpler.
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Pro
Written in Python, which is regarded as a really pleasant language to read and develop in
TensorFlow is written in Python, with the parts that are crucial for performance implemented in C++. But all of the high-level abstractions and development is done in Python.
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Specs
License:
Apache License 2.0
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35
3
Theano
All
3
Experiences
Pros
2
Cons
1
Top
Pro
Well adapted for numerical tasks
Theano is a Python library which is very well adapted for numerical tasks often encountered when dealing with deep learning. What makes it well adapted for those tasks is the fact that it combines several paradigms for numerical computations, namely: matrix operations symbolic variable and function definitions Just-in-time compilation to CPU or GPU machine code
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Con
Somewhat low level on its own
Theano is one of the oldest deep learning libraries out there and a lot of other widely used libraries have been built on top of it. But Theano heavily relies on the mathematical side of deep learning and data discovery, having similar features to NumPy or Matlab. This is why it's usually used with other libraries in order to achieve a higher level of abstraction.
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Pro
Optimized for both CPU and GPU
Since all variables are actually symbolic variables, you need to define a function and fill in the values in order to get a value. For example: # X, y and w are a matrix and vectors respectively # E is a scalar that depends on the above variables # to get the value of E we must define: Efun = theano.function([X,w,y], E,allow_input_downcast=True) While this seems like an unnecessary step, it's actually not. Since Theano now has a representation of the whole expression graph for the Efun function, it can compile and optimize the code so that it can run on both CPU and GPU.
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23
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Keras
All
7
Experiences
Pros
5
Cons
1
Specs
Top
Pro
Runs on top of Theano, TensorFlow or CNTK
You can choose the back-end for Keras. Simply change the backend field to "theano", "tensorflow", or "cntk". Theano was discontinued in 2017, so TensorFlow or CNTK would be the better choice.
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Con
Little customization compared to other frameworks
Keras is a high-level API. It's difficult to customize your model past a point. If you want to build something beyond the application-level, use Theano or TensorFlow. (Keras runs on top of either one of these anyways)
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Pro
Simple to use
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Pro
Can be used to write really short pieces of code
Keras enforces minimalism as much as possible. Because of this, it's possible to write a small Neural Network in just a couple of lines of code.
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Pro
Really straightforward for someone who is familiar with deep learning
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Pro
Quite modular
When using Keras you don't have to pull every part of the framework on your project. For example, you can only use training algorithms and not layer implementations. So it works more like a collection of libraries.
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Specs
License:
MIT
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Infer.NET
All
5
Experiences
Pros
3
Cons
2
Top
Con
No commercial license
Infer.NET is free for academic use. However, at this time, commercial use of Infer.NET is limited to Microsoft. No other commercial licenses are available.
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Pro
Supports multiple inference algorithms
Infer.NET supports expectation propagation (including belief propagation as a special case), variational message passing (also known as variational Bayes), max product (for discrete models), and block Gibbs sampling.
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Con
Closed source
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Pro
Versatile
You can use Infer.NET to solve many different kinds of machine learning problems, from standard problems like classification, recommendation or clustering through to customised solutions to domain-specific problems.
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Pro
Cross platform
Can be used on Windows (.NET), OSX or Linux (using mono)
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