When comparing **Theano** vs **Torch**, the Slant community recommends **Theano** for most people. In the question**“What are the best artificial intelligence frameworks?”** **Theano** is ranked 2nd while **Torch** is ranked 6th. The most important reason people chose **Theano** is:

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.

#### Ranked in these QuestionsQuestion Ranking

#### Pros

### 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.

### 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

### Pro Easy switch between CPU and GPU

It takes little more than a type cast of your inputs to go from CPU to GPU computation.

### Pro Lots of easy to combine modular pieces

Torch is a very modular framework. As such, you can choose which modules you need to implement and which modules to eliminate from your solution.

#### Cons

### 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.

### Con Not easily accessible to the academic community

Being written in Lua instead of the more widely used Python, it's not as accessible to academics as other solutions which are implemented in Python. With Python being one of the most widely used languages in scientific computing.