OpenDeep: a fully modular & extensible deep learning framework

Developer hub:

OpenDeep is a deep learning framework for Python built from the ground up in Theano with a focus on flexibility and ease of use for both industry data scientists and cutting-edge researchers. OpenDeep is a modular and easily extensible framework for constructing any neural network architecture to solve your problem.

Use OpenDeep to:

  • Quickly prototype complex networks through a focus on complete modularity and containers similar to Torch.
  • Configure and train existing state-of-the-art models.
  • Write your own models from scratch in Theano and plug into OpenDeep for easy training and dataset integration.
  • Use visualization and debugging tools to see exactly what is happening with your neural net architecture.
  • Plug into your existing Numpy/Scipy/Pandas/Scikit-learn pipeline.
  • Run on the CPU or GPU.

This library is currently undergoing rapid development and is in its alpha stages.

Quick example usage

Train and evaluate a Multilayer Perceptron (MLP - your generic feedforward neural network for classification) on the MNIST handwritten digit dataset:

from opendeep.models import Prototype, Dense, Softmax
from opendeep.models.utils import Noise
from opendeep.optimization.loss import Neg_LL
from opendeep.optimization import AdaDelta
from import MNIST
from theano.tensor import matrix, lvector

print "Getting data..."
data = MNIST()

print "Creating model..."
in_shape = (None, 28*28)
in_var = matrix('xs')
mlp = Prototype()
mlp.add(Dense(inputs=(in_shape, in_var), outputs=512, activation='relu'))
mlp.add(Noise, noise='dropout', noise_level=0.5)
mlp.add(Dense, outputs=512, activation='relu')
mlp.add(Noise, noise='dropout', noise_level=0.5)
mlp.add(Softmax, outputs=10, out_as_probs=False)

print "Training..."
target_var = lvector('ys')
loss = Neg_LL(inputs=mlp.models[-1].p_y_given_x, targets=target_var, one_hot=False)

optimizer = AdaDelta(model=mlp, loss=loss, dataset=data, epochs=10)

print "Predicting..."
predictions =

print "Accuracy: ", float(sum(predictions==data.test_targets)) / len(data.test_targets)

Congrats, you just:

  • set up a dataset (MNIST)
  • instantiated a Prototype container model
  • added fully-connected (dense) layers and dropout noise to create an MLP
  • trained it with an AdaDelta optimizer
  • and predicted some outputs given inputs!


Because OpenDeep is still in alpha, you have to install via Also, please make sure you have these dependencies installed first.


  • Theano: Theano and its dependencies are required to use OpenDeep. You need to install the bleeding-edge version directly from their GitHub, which has installation instructions here.
  • Six: Python 2/3 compatibility library.
  • Pillow (PIL) (optional): image manipulation functionality.
  • PyYAML (optional): used for YAML parsing of config files.
  • Bokeh (optional): if you want live charting/plotting of values during training or testing.

Install from source

  1. Navigate to your desired installation directory and download the github repository:

    git clone
  2. Navigate to the top-level folder (should be named OpenDeep and contain the file and run with develop mode:

    cd opendeep
    python develop

Using python develop instead of the normal python install allows you to update the repository files by pulling from git and have the whole package update! No need to reinstall when you get the latest files.

That’s it! Now you should be able to import opendeep into python modules.

More Information

Source code:

Documentation and tutorials:

User group: opendeep-users

Developer group: opendeep-dev

Twitter: @opendeep

We would love all help to make this the best library possible! Feel free to fork the repository and join the Google groups!

Why OpenDeep?

  • Modularity. A lot of recent deep learning progress has come from combining multiple models. Existing libraries are either too confusing or not easily extensible enough to perform novel research and also quickly set up existing algorithms at scale. This need for transparency and modularity is the main motivating factor for creating the OpenDeep library, where we hope novel research and industry use can both be easily implemented.
  • Ease of use. Many libraries require a lot of familiarity with deep learning or their specific package structures. OpenDeep’s goal is to be the best-documented deep learning library and have smart enough default code that someone without a background can start training models, while experienced practitioners can easily create and customize their own algorithms.
  • State of the art. A side effect of modularity and ease of use, OpenDeep aims to maintain state-of-the-art performance as new algorithms and papers get published. As a research library, citing and accrediting those authors and code used is very important to the library.


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