Merge pull request #7 from oujago/develop

integrate auto test development tools

.coveragerc

@@ -0,0 +1,2 @@
+[run]
+omit = test/*

.gitignore

@@ -5,7 +5,7 @@ __pycache__/
 *.py[cod]
 *$py.class
 
-
+.pypirc
 .idea
 docs/_build/*
 

.travis.yml

@@ -0,0 +1,22 @@
+language: python
+sudo: false
+python:
+  - "3.3"
+  - "3.4"
+  - "3.5"
+  - "3.6"
+  - "pypy3"
+addons:
+  apt:
+    packages:
+    - libblas-dev
+    - liblapack-dev
+before_install:
+  - pip install -U pip
+install:
+  - travis_wait travis_retry pip install -r requirements.txt
+  - travis_retry pip install python-coveralls
+  - travis_retry python setup.py install
+script: py.test --runslow --cov-config=.coveragerc
+after_success:
+  - coveralls

CHANGES.rst

@@ -0,0 +1,7 @@
+Changelog
+---------
+
+0.1 (2017-04-11)
+~~~~~~~~~~~~~~~~
+
+First release.

MANIFEST.in

@@ -0,0 +1,4 @@
+# misc
+include LICENSE
+include requirements.txt
+include CHANGES.rst

README.rst

@@ -5,6 +5,14 @@
     :target: https://github.com/oujago/NumpyDL/blob/master/LICENSE
 
 
+.. image:: https://travis-ci.org/Lasagne/Lasagne.svg
+    :target: https://travis-ci.org/oujago/NumpyDL
+
+.. image:: https://img.shields.io/coveralls/Lasagne/Lasagne.svg
+    :target: https://coveralls.io/r/oujago/NumpyDL
+
+.. image:: https://zenodo.org/badge/16974/Lasagne/Lasagne.svg
+   :target: https://zenodo.org/badge/latestdoi/16974/oujago/NumpyDL
 
 
 
@@ -91,7 +99,7 @@ Install NumpyDL using pip:
 
 .. code-block:: bash
 
-    $> pip install numpydl
+    $> pip install npdl
 
 Install from source code:
 

docs/conf.py

@@ -193,7 +193,7 @@ def find_source():
     if domain != 'py' or not info['module']:
         return None
     try:
-        filename = 'numpydl/%s#L%d-L%d' % find_source()
+        filename = 'npdl/%s#L%d-L%d' % find_source()
     except Exception:
         filename = info['module'].replace('.', '/') + '.py'
     tag = 'master' if 'dev' in release else ('v' + release)

docs/index.rst

@@ -23,7 +23,7 @@ developer.
    :maxdepth: 2
    :caption: Contents:
 
-   user/installation
+   user_guide/installation
 
 
 API Reference
@@ -36,8 +36,20 @@ method, this part of the documentation is for you.
    :maxdepth: 2
    :caption: Contents:
 
-   modules/activation.rst
-   modules/initialization.rst
+   api_reference/activation
+   api_reference/initialization
+
+
+Examples
+--------
+
+This part provides examples for building deep neural networks.
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+
 
 
 Indices and tables

docs/user/installation.rst → docs/user_guide/installation.rst

@@ -71,7 +71,7 @@ on PyPI. To install a version that is known to work, run the following command:
 
 .. code-block:: bash
 
-  pip install numpydl
+  pip install npdl
 
 If you do not use ``virtualenv``, add ``--user`` to both commands to install
 into your home directory instead. To upgrade from an earlier installation, add

examples/01-mlp-digits.py

@@ -0,0 +1,35 @@
+# -*- coding: utf-8 -*-
+
+import numpy as np
+from sklearn.datasets import load_digits
+
+import npdl
+
+
+def main(max_iter):
+    # 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=max_iter, validation_split=0.1)
+
+
+if __name__ == '__main__':
+    main(150)
+
+

examples/01-mlp-mnist.py

@@ -0,0 +1,31 @@
+# -*- coding: utf-8 -*-
+
+import os
+import numpy as np
+from sklearn.datasets import fetch_mldata
+
+import npdl
+
+
+def main(max_iter):
+    # data
+    print("loading data ...")
+    mnist = fetch_mldata('MNIST original', data_home=os.path.join(os.path.dirname(__file__), './data'))
+    X_train = mnist.data / 255.0
+    y_train = mnist.target
+    n_classes = np.unique(y_train).size
+
+    # model
+    print("building model ...")
+    model = npdl.Model()
+    model.add(npdl.layers.Dense(n_out=500, n_in=784, 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=0.001))
+
+    # train
+    print("train model ... ")
+    model.fit(X_train, npdl.utils.data.one_hot(y_train), max_iter=max_iter, validation_split=0.1)
+
+
+if __name__ == '__main__':
+    main(150)

examples/02-rnn.py → examples/02-rnn-character-lm.py

@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 
-
+import os
 import csv
 import itertools
 
@@ -10,7 +10,7 @@
 import npdl
 
 
-def load_data(corpus_path='data/lm/reddit-comments-2015-08.csv',
+def load_data(corpus_path=os.path.join(os.path.dirname(__file__), 'data/lm/reddit-comments-2015-08.csv'),
               vocabulary_size=8000):
     sentence_start_token = "SENTENCE_START"
     sentence_end_token = "SENTENCE_END"
@@ -50,7 +50,7 @@ def load_data(corpus_path='data/lm/reddit-comments-2015-08.csv',
     return index_to_word, word_to_index, train_x, train_y
 
 
-def character_lm(corpus_path='data/lm/tiny_shakespeare.txt'):
+def main(max_iter, corpus_path=os.path.join(os.path.dirname(__file__), 'data/lm/tiny_shakespeare.txt')):
     raw_text = open(corpus_path, 'r').read()
     chars = list(set(raw_text))
     data_size, vocab_size = len(raw_text), len(chars)
@@ -77,8 +77,8 @@ def character_lm(corpus_path='data/lm/tiny_shakespeare.txt'):
     net.compile(loss=npdl.objectives.SCCE(), optimizer=npdl.optimizers.SGD(lr=0.00001, clip=5))
 
     print("Train model ...")
-    net.fit(batch_in, batch_out, max_iter=100, batch_size=batch_size)
+    net.fit(batch_in, batch_out, max_iter=max_iter, batch_size=batch_size)
 
 
 if __name__ == '__main__':
-    character_lm()
+    main(100)

examples/03-cnn-minist.py

@@ -0,0 +1,38 @@
+# -*- coding: utf-8 -*-
+
+
+import os
+import numpy as np
+from sklearn.datasets import fetch_mldata
+
+import npdl
+
+
+def main(max_iter):
+    seed = 100
+    nb_data = 1000
+
+    print("loading data ...")
+    mnist = fetch_mldata('MNIST original', data_home=os.path.join(os.path.dirname(__file__), './data'))
+    X_train = mnist.data.reshape((-1, 1, 28, 28)) / 255.0
+    np.random.seed(seed)
+    X_train = np.random.permutation(X_train)[:nb_data]
+    y_train = mnist.target
+    np.random.seed(seed)
+    y_train = np.random.permutation(y_train)[:nb_data]
+    n_classes = np.unique(y_train).size
+
+    print("building model ...")
+    net = npdl.Model()
+    net.add(npdl.layers.Convolution(1, (3, 3), input_shape=(None, 1, 28, 28)))
+    net.add(npdl.layers.MeanPooling((2, 2)))
+    net.add(npdl.layers.Flatten())
+    net.add(npdl.layers.Softmax(n_out=n_classes))
+    net.compile()
+
+    print("train model ... ")
+    net.fit(X_train, npdl.utils.data.one_hot(y_train), max_iter=max_iter, validation_split=0.1)
+
+
+if __name__ == '__main__':
+    main(10)

npdl/__init__.py

@@ -9,6 +9,5 @@
 from .model import Model
 
 
-
-__version__ = '0.1.0'
+__version__ = "0.1.0"