Theano Tutorial: Deep Learning Made Easy

Theano Tutorial: Deep Learning Made Easy

Theano is a powerful deep learning library for Python. In this tutorial, we’ll show you how to get started with Theano and train your first deep learning model.

For more information check out our video:

Introduction to Theano

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features:

-tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions.
-transparent use of a GPU – Perform data-intensive computations much faster than on a CPU.
-efficient symbolic differentiation – Theano does your derivatives for functions with one or many inputs.
-speed and stability optimizations – Get the right answer for log(1+x) even when x is really tiny.
-dynamic C code generation – Evaluate expressions faster.
-extensive unit-testing and self-verification – Detect and diagnose many types of errors.

Setting up Theano

The Theano library is one of the most popular open source Deep Learning libraries. It is developed and maintained by the community, with many contributions coming from industry and academia. Theano allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.

This tutorial will assume that you have already set up Theano on your machine. If you have not done so already, please refer to the instructions on the website. In this tutorial, we will be using the CPU version of Theano.

Once you have Theano installed, you can verify that it is working properly by running the following command:

python -c “import theano; print(theano.config.device)”

If everything is set up correctly, you should see something like this:

cpu

Getting Started with Theano

Theano is a Python Library that allows you to define, optimize, and evaluate mathematical expressions in particular involving matrix operations efficiently. Theano features:
* tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions.
* transparent use of a GPU – Perform data-intensive computations up to 140x faster than with CPU.(float32)
* efficient symbolic differentiation – Theano does your derivatives for functions with one or many inputs.
* speed and stability optimizations – Get the right answer for log(1+x) even when x is really tiny.
Theano has been powering large-scale computational experiments since 2007. But it is also approachable enough to be used in the classroom (Symbolic Computation for Deep Learning by Yoshua Bengio).

Deep Learning with Theano

Deep learning is a branch of machine learning that is concerned with algorithms inspired by the structure and function of the brain. These algorithms are used to model high-level abstractions in data by using a deep graph with many layers of processing nodes.

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

This tutorial will teach you the basics of Theano, including how to define and optimize mathematical expressions, how to use Theano’s integrated graphics processing unit (GPU) support, and how to extend Theano with custom C code.

Theano Functions

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features:

-tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions.
-transparent use of a GPU – Perform data-intensive computations much faster than on a CPU.
-efficient symbolic differentiation – Theano does your derivatives for functions with one or many inputs.
-speed and stability optimizations – Avoid nasty bugs when computing derivatives.

Theano has been powering large-scale computationally intensive scientific investigations since 2007. But it is also approachable enough to be used in the classroom (I teach a class of intro to deep learning that uses Theano).

In this post I will walk through the most important parts of Theano by implementing a multi-layer perceptron from scratch. Check out the accompanying Github repo for the code I’ll be using in this post: https://github.com/mila/theano-tutorial

Theano Variables

In this section of the Theano Tutorial we will cover Theano variables. Theanos variables are the mathematical entities that we want to take derivatives of. In short, they are our input values that we want to use to train our machine learning models. We can think of them as placeholders for our input data. Just like in algebra, we will assign a value to each variable when we want to compute something specific. In machine learning, we often have a large number of input values, so it would be very tedious (and prone to errors) to write out each value explicitly every time we wanted to compute something. That’s where Theano variables come in – they allow us to express our computations in a much more succinct way.

Theano variables can be either scalars (0-dimensional), vectors (1-dimensional), matrices (2-dimensional), or even higher-dimensional tensors. We can create a Theano variable by calling the class theano.tensor.var:
“`
import theano
x = theano.tensor.var(name=’x’, dtype=theano.config.floatX)
“`

The name argument is optional, but it can be helpful to give your variables descriptive names so that you can keep track of what they represent when you are looking at your code later on. The dtype argument specifies the data type of the variable (e.g., float32, int32). By default, Theano will use 32-bit floating point numbers (float32). However, you can change this default by editing the file ~/.theanorc (see here for more details).

Let’s create some scalar, vector, and matrix variables and see what they look like:
“`
import numpy as np # import NumPy library for working with arrays
import theano # import Theano library
s = theano.tensor.var(name=’s’) # create scalar Theano variable
v = theano.tensor.var(name=’v’, dtype=theano.config.floatX) # create vector Theano variable
m = theano.tensor.var(name=’m’, dtype=theano.config.floatX) # create matrixThe ano variable“`

Note that we didn’t have to specify the shape of any of these variables when we created them – that’s because shape is not part of a The ano variable’s definition (unlike in NumPy arrays). Instead, shape is computed automatically based on how the variable is used in subsequent computations.

Theano Shared Variables

Shared variables are Theano’s way of representing values that are used in multiple computations. They can be thought of as placeholders for values that will be supplied when the computation is run. Shared variables can be used in symbolic expressions but they also have an internal value that can be accessed and modified by functions.

The main types of shared variables are:
– `shared()`: Creates a shared variable from a numpy array.
– `shared_like()`: Creates a shared variable from another shared variable or numpy array, with the same type and shape.
– `unbroadcast()`: Converts a broadcastable object to a non-broadcastable object, without copying data.

Theano Updates

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray). It can use GPUs and perform efficient numerical computations.

Theano Exceptions

Theano has numerous preset exception types that you can use to convey information about what went wrong in your code. When something goes wrong with Theano, an exception is raised. You can catch exceptions with try-except blocks.

Here are some common exception types that you might encounter:

TypeError: This exception is raised when you try to do an operation on two variables of different types. For example, if you try to add a string and an integer, you will get a TypeError.

ValueError: This exception is raised when you try to do an operation on two variables that are of the same type but the operation is undefined for those values. For example, if you try to divide a string by an integer, you will get a ValueError.

NameError: This exception is raised when you try to use a variable that has not been defined. For example, if you try to use a variable that does not exist, you will get a NameError.

IOError: This exception is raised when you try to open a file that does not exist. For example, if you try to open a file that does not exist, you will get an IOError.

Theano Debugging

There are several ways to debug Theano code. The most common way is to use the print function. This will print out the values of variables at different points in the code.

Another way to debug is to use the Theano debugger (dbg). This allows you to step through the code and see the values of variables at each step.

Finally, you can also use a Python debugger such as pdb or ipdb.

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