Mobula

What is it?

Mobula is a light deep learning framework on python.

It's an efficent Python-DNN Implementation used numpy mainly, and it's aimed to learn how a neural network runs :-)

What can I do with it?

Deploy a Deep Neural Network, try to explore how it works when DNN is training or testing.
Implement some interesting layers quickly and make a test.

Benefit

Easy to Configure

Mobula needs less dependence. It is implemented by numpy mainly, so you can setup it easily.
Easy to modify

Mobula is implemented by only Python. You can modify the code easily to implement what you want.

How to install it?

pip install mobula

Layers

Mobula has implemented these layers using numpy. It's efficient relatively on Python Implementation.

The Layers supports multi-input and multi-output.

Layers with Learning

FC - Fully Connected Layer
Conv - Convolution
ConvT - Transposed Convolution
BatchNorm

Layers without Learning

Pool - Pooling
Dropout
Reshape
Crop
L0Norm, L1Norm, L2Norm (TODO)

Activation Layer

Sigmoid
ReLU
PReLU
SELU
Tanh
Softmax

Multi I/O Layer

Concat
Slice
Eltwise

Cost Layer

MSE - Mean Square Error
CrossEntropy
SigmoidCrossEntropy
SoftmaxWithLoss
SmoothL1Loss
ContrastiveLoss

Evaluation Layer (No Backward)

Accuracy (top_k)

Operators

add, subtract
multiply, matmul, dot
positive, negative, abs
exp, log
reduce_mean, reduce_max, reduce_min
equal, not_equal
greater_equal, greater, less_equal, less

Solvers

Mobula supports various solvers.

SGD
Momentum

Quick Start

Notice: Recommend using Python in Anaconda, because of Calculating Optimization numpy-mkl in Anaconda.

The detail is in Performance Analysis.

Digital Recognition

Let's construct a Convolution Nerual Network on Mobula!

We use LeNet-5 to solve Digital Recognition problem on Kaggle.

The score is above 0.99 in training for several minutes.

Firstly, you need to download the dataset train.csv and test.csv into test/ folder.

Secondly, constructing the LeNet-5.

The core code is that:


import mobula
import mobula.layers as L
import mobula.solvers as S

[...]

data, label = L.Data([X, labels], "data", batch_size = 100)
conv1 = L.Conv(data, dim_out = 20, kernel = 5)
pool1 = L.Pool(conv1, pool = L.Pool.MAX, kernel = 2, stride = 2)
conv2 = L.Conv(pool1, dim_out = 50, kernel = 5)
pool2 = L.Pool(conv2, pool = L.Pool.MAX, kernel = 2, stride = 2)
fc3   = L.FC(pool2, dim_out = 500)
relu3 = L.ReLU(fc3)
pred  = L.FC(relu3, "pred", dim_out = 10)
loss = L.SoftmaxWithLoss(pred, "loss", label = label)

# Net Instance
net = mobula.Net()

# Set Loss Layer
net.set_loss(loss)

# Set Solver
net.set_solver(S.Momentum())

# Learning Rate
net.lr = 0.2

The training and predicting codes are in examples/ folders, namely mnist_train.py and mnist_test.py.

For training the network,

python mnist_train.py

When the number of iterations is 2000, the accuracy on training set is above 0.99.

For predicting test.csv,

python mnist_test.py

At Line 53 in mnist_test.py, iter_num is the iterations of the model which is used to predict test set.

Enjoy it! :-)

wkecn/mobula