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All Projects → oujago → Numpydl

oujago / Numpydl

Licence: other
Deep Learning Library. For education. Based on pure Numpy. Support CNN, RNN, LSTM, GRU etc.

Programming Languages

python
139335 projects - #7 most used programming language

Labels

deep-learningdeep-neural-networksdeep-learning-tutorialdeep-learning-algorithms

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NumpyDL: Numpy Deep Learning Library

Descriptions

NumpyDL is:

  1. Based on Pure Numpy/Python
  2. For DL Education

Features

Its main features are:

  1. Pure in Numpy
  2. Native to Python
  3. Automatic differentiations are basically supported
  4. Commonly used models are provided: MLP, RNNs, LSTMs and CNNs
  5. Examples for several AI tasks
  6. Application for a toy chatbot

Documentation

Available online documents:

  1. latest docs <http://numpydl.readthedocs.io/en/latest>_
  2. development docs <http://numpydl.readthedocs.io/en/develop/>_
  3. stable docs <http://numpydl.readthedocs.io/en/stable/>_

Available offline PDF:

  1. latest PDF <http://readthedocs.org/projects/numpydl/downloads/pdf/latest>_

Installation

Install NumpyDL using pip:

.. code-block:: bash

$> pip install npdl

Install from source code:

.. code-block:: bash

$> python setup.py install

Examples

NumpyDL provides several examples of AI tasks:

  • sentence classification
    • LSTM in examples/lstm_sentence_classification.py
    • CNN in examples/cnn_sentence_classification.py
  • mnist handwritten recognition
    • MLP in examples/mlp-mnist.py
    • MLP in examples/mlp-digits.py
    • CNN in examples/cnn-minist.py
  • language modeling
    • RNN in examples/rnn-character-lm.py
    • LSTM in examples/lstm-character-lm.py

One concrete code example in examples/mlp-digits.py:

.. code-block:: python

import numpy as np
from sklearn.datasets import load_digits
import npdl

# prepare
npdl.utils.random.set_seed(1234)

# data
digits = load_digits()
X_train = digits.data
X_train /= np.max(X_train)
Y_train = digits.target
n_classes = np.unique(Y_train).size

# model
model = npdl.model.Model()
model.add(npdl.layers.Dense(n_out=500, n_in=64, activation=npdl.activation.ReLU()))
model.add(npdl.layers.Dense(n_out=n_classes, activation=npdl.activation.Softmax()))
model.compile(loss=npdl.objectives.SCCE(), optimizer=npdl.optimizers.SGD(lr=0.005))

# train
model.fit(X_train, npdl.utils.data.one_hot(Y_train), max_iter=150, validation_split=0.1)

Applications

NumpyDL provides one toy application:

  • Chatbot
    • seq2seq in applications/chatbot/model.py

And its final result:

.. figure:: applications/chatbot/pics/chatbot.png :width: 80%

Supports

NumpyDL supports following deep learning techniques:

  • Layers
    • Linear
    • Dense
    • Softmax
    • Dropout
    • Convolution
    • Embedding
    • BatchNormal
    • MeanPooling
    • MaxPooling
    • SimpleRNN
    • GRU
    • LSTM
    • Flatten
    • DimShuffle
  • Optimizers
    • SGD
    • Momentum
    • NesterovMomentum
    • Adagrad
    • RMSprop
    • Adadelta
    • Adam
    • Adamax
  • Objectives
    • MeanSquaredError
    • HellingerDistance
    • BinaryCrossEntropy
    • SoftmaxCategoricalCrossEntropy
  • Initializations
    • Zero
    • One
    • Uniform
    • Normal
    • LecunUniform
    • GlorotUniform
    • GlorotNormal
    • HeNormal
    • HeUniform
    • Orthogonal
  • Activations
    • Sigmoid
    • Tanh
    • ReLU
    • Linear
    • Softmax
    • Elliot
    • SymmetricElliot
    • SoftPlus
    • SoftSign
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].