dnnet

Implementation of Deep Neural Network with numpy. Now dnnet can run with GPU through cupy.

dnnet provides high-level API to define and run neural network model. User can turn on/off GPU layer-wise, that is, you can compute convolution layer with GPU, activation layer with CPU, and dropout layer with CPU, for example.

Brief tour of dnnet; Introduce small examples, supported methodologies
Installation
Example; Run sample scripts
Use in your project

Brief tour of dnnet

Quick glance of usage

User can create a instance of NeuralNetwork, add layers one by one,
finalize model, set optimizer, execute model fitting, and save model.

In the below, some arguments are not specified to simplify the example.

from dnnet.neuralnet import NeuralNetwork
from dnnet.training.optimizer import AdaGrad
from dnnet.training.weight_initialization import DefaultInitialization, He
from dnnet.training.loss_function import MultinomialCrossEntropy
from dnnet.layers.activation import Activation, ActivationLayer
from dnnet.layers.affine import AffineLayer
from dnnet.layers.batch_norm import BatchNormLayer
from dnnet.layers.convolution import ConvolutionLayer
from dnnet.layers.dropout import DropoutLayer


# Load x, y here

model = NeuralNetwork(input_shape=(1, 28, 28), dtype=np.float32)

model.add(ConvolutionLayer(filter_shape=(32, 3, 3))
model.add(BatchNormLayer())
model.add(ActivationLayer(activation=Activation.Type.relu))
model.add(DropoutLayer(drop_ratio=0.25))

model.add(AffineLayer(output_shape=10)
model.add(ActivationLayer(activation=Activation.Type.softmax)
model.compile()

optimizer = AdaGrad(learning_rate=1e-3, weight_decay=1e-3)
learning_curve = model.fit(
    x=x, y=y, epochs=5, batch=size=100, optimizer=optimizer,
    loss_function=MultinomialCrossEntropy())
model.save(path='./data/output', name='my_cnn.dat')

User can also load model, and predict output.

model.load(path='./data/output', name='my_cnn.dat')
y_pred = model.predict(x_unknown)

GPU is easily enabled. Do the follows at the top of your script.

from dnnet.config import Config
Config.enable_gpu()

If GPU is enabled but you'd like to turn it off for some specific layers, you can use force_cpu flag. Here, ConvolutionLayer and AffineLayer don't have the flag.

from dnnet.config import Config
Config.enable_gpu()

# Do something here.

# AffineLayer uses GPU.
model.add(AffineLayer(output=512, weight_initialization=He()))
# BatchNormLayer uses CPU regardless of Config.enable_gpu().
model.add(BatchNormLayer(force_cpu=True))

Supported Methods

Layers

Affine
Convolution
Activation
Pool
Batch Normalization
Dropout

Activation Functions

Sigmoid
ReLU
ELU
Tanh
Softmax

Optimization Methods

SGD
Momentum
AdaGrad
Adam
AdaDelta
RMSProp

Weight Initialization Methods

Xavier's method
He's method
Default

Loss Functions

MultinomialCrossEntropy for multinomial classification.
BinomialCrossEntropy for binary classification.
SquaredError for regression.

Installation

Requisites

python 3.4 or later
numpy 1.12.0 or later
matplotlib

If you'd like to use GPU, you need to install the follows additionally.

CUDA (eg. CUDA 10.0)
CuDNN (eg. CuDNN7.6.5)
cupy (eg. cupy-cuda100==7.0.0)

Install dnnet by pip.

pip install dnnet

Install dnnet from source.

dnnet doesn't require any complicated path-settings.
You just download scripts from github, place it wherever you like,
and you add some lines like below in your scripts.

import sys
sys.path.append('<path-to-dnnet-root-dir>')

from dnnet.neuralnet import NeuralNetwork

Setup environment from scratch (Optional)

In this section, setting up python environment from scratch is described.
"From scratch" means that you're supposed to use brand-new computer,
no python packages (even python itself!) and relevant libraries are installed.

It may also be useful when you start new python project. In this case,
you will partially execute the following steps.

Setup Python Virtual Environment

Assumption

Use python3
Make directory for pyenv in "/home//Documents"
Root directory of your python virtual env is in "/home//Work/py352_ws"
"/home//Work/py352_ws/" is your working directory

Setup procedure

Install required packages

$ sudo apt-get install git gcc make openssl libssl-dev libbz2-dev libreadline-dev libsqlite3-dev zlib1g-dev libffi-dev

Install tkinter(This is required to use matplotlib in virtualenv)

$ sudo apt-get install python3-tk python-tk tk-dev

Install pyenv

   $ cd ~
   $ git clone git://github.com/yyuu/pyenv.git ./pyenv
   $ mkdir -p ./pyenv/versions ./pyenv/shims

Set paths Add the following description in ~/.bashrc

export PYENV_ROOT=${HOME}/Documents/pyenv
if [ -d "${PYENV_ROOT}" ]; then
  export PATH=${PYENV_ROOT}/bin:$PATH
  eval "$(pyenv init -)"
fi

And then execute the follows.

   $ exec $SHELL -l
   $ . ~/.bashrc

Install pyenv-virtualenv

   $ cd $PYENV_ROOT/plugins
   $ git clone git://github.com/yyuu/pyenv-virtualenv.git

Install python 3.5.2

   $ pyenv install 3.5.2

Setup local pyenv

   $ mkdir -p ~/Work/py352_ws
   $ pyenv virtualenv 3.5.2 <name of this environment>

can be like py352_env, python3_env, or anything you like.
Here, it's assumed that you named the environment as "py352_env".

   $ cd ~/Work/py352_ws
   $ pyenv local py352_env
   $ pip install --upgrade pip

Example

MNIST

Run neural network for mnist.

cd <path-to-dnnet>/examples/mnist
python mnist.py

If you get an error "ImportError: Python is not installed as a framework.", it might be because of matplotlib issue.(This happened to me when working with MacOS.)

In the case, please try the follow.

cd ~/.matplotlib
echo "backend: TkAgg" >> matplotlibrc

Usage in your project

If you pip installed dnnet

from dnnet.neuralnet import NeuralNetwork

If you git cloned dnnet

import sys
sys.path.append('<path-to-dnnet-root-dir>')

from dnnet.neuralnet import NeuralNetwork

For example, if dnnet directory is in ~/Work/dnnet, do like below.

import os
import sys
sys.path.append(os.path.join(os.getenv('HOME'), 'Work/dnnet'))

from dnnet.neuralnet import NeuralNetwork

daichi-yoshikawa/dnnet