معرفی شرکت ها
RMDL-1.0.8
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
RMDL: Random Multimodel Deep Learning for Classification
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | RMDL-1.0.8 |
| نام | RMDL |
| نسخه کتابخانه | 1.0.8 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Kamran Kowsari |
| ایمیل نویسنده | kk7nc@virginia.edu |
| آدرس صفحه اصلی | https://github.com/kk7nc/RMDL |
| آدرس اینترنتی | https://pypi.org/project/RMDL/ |
| مجوز | - |
|DOI| |Pypi| |arxiv| |werckerstatus| |appveyor| |BuildStatus| |PowerPoint| |researchgate| |Binder| |pdf| |GitHublicense| |twitter|
Referenced paper : `RMDL: Random Multimodel Deep Learning for
Classification <https://www.researchgate.net/publication/324922651_RMDL_Random_Multimodel_Deep_Learning_for_Classification>`__
Random Multimodel Deep Learning (RMDL):
=======================================
A new ensemble, deep learning approach for classification. Deep
learning models have achieved state-of-the-art results across many domains.
RMDL solves the problem of finding the best deep learning structure
and architecture while simultaneously improving robustness and accuracy
through ensembles of deep learning architectures. RDML can accept
asinput a variety data to include text, video, images, and symbolic.
|RDL|
Overview of RDML: Random Multimodel Deep Learning for classification. The
RMDL includesnRandom modelswhich aredrandom model of DNN classifiers,
cmodels of CNN classifiers, andrRNN classifiers wherer+c+d=n.
|RMDL|
Random Multimodel Deep Learning (RDML) architecture for classification.
RMDL includes 3 Random models, oneDNN classifier at left, one Deep CNN
classifier at middle, and one Deep RNN classifier at right (each unit could be LSTMor GRU).
Installation
=============
There are git RMDL in this repository; to clone all the needed files,
please use:
Using pip
----------
.. code:: python
pip install RMDL
Using git
---------
.. code:: bash
git clone --recursive https://github.com/kk7nc/RMDL.git
The primary requirements for this package are Python 3 with Tensorflow. The requirements.txt file
contains a listing of the required Python packages; to install all requirements, run the following:
.. code:: bash
pip -r install requirements.txt
Or
.. code:: bash
pip3 install -r requirements.txt
Or:
.. code:: bash
conda install --file requirements.txt
Documentation:
==============
The exponential growth in the number of complex datasets every year requires more enhancement in
machine learning methods to provide robust and accurate data classification. Lately, deep learning
approaches have been achieved surpassing results in comparison to previous machine learning algorithms
on tasks such as image classification, natural language processing, face recognition, and etc. The
success of these deep learning algorithms relys on their capacity to model complex and non-linear
relationships within data. However, finding the suitable structure for these models has been a challenge
for researchers. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning
approach for classification. RMDL solves the problem of finding the best deep learning structure and
architecture while simultaneously improving robustness and accuracy through ensembles of deep
learning architectures. In short, RMDL trains multiple models of Deep Neural Network (DNN),
Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in parallel and combines
their results to produce better result of any of those models individually. To create these models,
each deep learning model has been constructed in a random fashion regarding the number of layers and
nodes in their neural network structure. The resulting RDML model can be used for various domains such
as text, video, images, and symbolic. In this Project, we describe RMDL model in depth and show the results
for image and text classification as well as face recognition. For image classification, we compared our
model with some of the available baselines using MNIST and CIFAR-10 datasets. Similarly, we used four
datasets namely, WOS, Reuters, IMDB, and 20newsgroup and compared our results with available baselines.
Web of Science (WOS) has been collected by authors and consists of three sets~(small, medium and large set).
Lastly, we used ORL dataset to compare the performance of our approach with other face recognition methods.
These test results show that RDML model consistently outperform standard methods over a broad range of
data types and classification problems.
Datasets for RMDL:
==================
Text Datasets:
--------------
- `IMDB Dataset <http://ai.stanford.edu/~amaas/data/sentiment/>`__
* This dataset contains 50,000 documents with 2 categories.
- `Reters-21578 Dataset <https://keras.io/datasets/>`__
* This dataset contains 21,578 documents with 90 categories.
- `20Newsgroups Dataset <https://archive.ics.uci.edu/ml/datasets/Twenty+Newsgroups>`__
* This dataset contains 20,000 documents with 20 categories.
- Web of Science Dataset (DOI:
`10.17632/9rw3vkcfy4.2 <http://dx.doi.org/10.17632/9rw3vkcfy4.2>`__)
- Web of Science Dataset
`WOS-11967 <http://dx.doi.org/10.17632/9rw3vkcfy4.2>`__
- This dataset contains 11,967 documents with 35 categories which
include 7 parents categories.
- Web of Science Dataset
`WOS-46985 <http://dx.doi.org/10.17632/9rw3vkcfy4.2>`__
- This dataset contains 46,985 documents with 134 categories
which include 7 parents categories.
- Web of Science Dataset
`WOS-5736 <http://dx.doi.org/10.17632/9rw3vkcfy4.2>`__
- This dataset contains 5,736 documents with 11 categories which
include 3 parents categories.
Image datasets:
---------------
- `MNIST Dataset <https://en.wikipedia.org/wiki/MNIST_database>`__
- The MNIST database contains 60,000 training images and 10,000
testing images.
- `CIFAR-10 Dataset <https://www.cs.toronto.edu/~kriz/cifar.html>`__
- The CIFAR-10 dataset consists of 60000 32x32 colour images in 10
classes, with 6000 images per class. There are 50000 training
images and 10000 test images.
Face Recognition
----------------
`The Database of Faces (The Olivetti Faces
Dataset) <http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html>`__
- The The Database of Faces dataset consists of 400 92x112 colour
images and grayscale in 40 person
Requirements for RMDL :
=======================
General:
----------
- Python 3.5 or later see `Instruction
Documents <https://www.python.org/>`__
- TensorFlow see `Instruction
Documents <https://www.tensorflow.org/install/install_linux>`__.
- scikit-learn see `Instruction
Documents <http://scikit-learn.org/stable/install.html>`__
- Keras see `Instruction Documents <https://keras.io/>`__
- scipy see `Instruction
Documents <https://www.scipy.org/install.html>`__
GPU (if you want to run on GPU):
--------------------------------
- CUDA® Toolkit 8.0. For details, see `NVIDIA’s
documentation <https://developer.nvidia.com/cuda-toolkit>`__.
- The `NVIDIA drivers associated with CUDA Toolkit
8.0 <http://www.nvidia.com/Download/index.aspx>`__.
- cuDNN v6. For details, see `NVIDIA’s
documentation <https://developer.nvidia.com/cudnn>`__.
- GPU card with CUDA Compute Capability 3.0 or higher.
- The libcupti-dev library,
Text and Document Classification
=================================
- Download GloVe: Global Vectors for Word Representation `Instruction
Documents <https://nlp.stanford.edu/projects/glove/>`__
- Set data directory into
`Global.py <https://github.com/kk7nc/RMDL/blob/master/src/Global.py>`__
- if you are not setting GloVe directory, GloVe will be downloaded
Parameters:
===========
Text_Classification
-------------------
.. code:: python
from RMDL import RMDL_Text
.. code:: python
Text_Classification(x_train, y_train, x_test, y_test, batch_size=128,
EMBEDDING_DIM=50,MAX_SEQUENCE_LENGTH = 500, MAX_NB_WORDS = 75000,
GloVe_dir="", GloVe_file = "glove.6B.50d.txt",
sparse_categorical=True, random_deep=[3, 3, 3], epochs=[500, 500, 500], plot=True,
min_hidden_layer_dnn=1, max_hidden_layer_dnn=8, min_nodes_dnn=128, max_nodes_dnn=1024,
min_hidden_layer_rnn=1, max_hidden_layer_rnn=5, min_nodes_rnn=32, max_nodes_rnn=128,
min_hidden_layer_cnn=3, max_hidden_layer_cnn=10, min_nodes_cnn=128, max_nodes_cnn=512,
random_state=42, random_optimizor=True, dropout=0.05):
Input
~~~~~
- x_train
- y_train
- x_test
- y_test
batch_size
~~~~~~~~~~
- batch_size: Integer. Number of samples per gradient update. If unspecified, it will default to 128.
EMBEDDING_DIM
~~~~~~~~~~~~~~
- batch_size: Integer. Shape of word embedding (this number should be same with GloVe or other pre-trained embedding techniques that be used), it will default to 50 that used with pain of glove.6B.50d.txt file.
MAX_SEQUENCE_LENGTH
~~~~~~~~~~~~~~~~~~~
- MAX_SEQUENCE_LENGTH: Integer. Maximum length of sequence or document in datasets, it will default to 500.
MAX_NB_WORDS
~~~~~~~~~~~~~~~~~~~~~~~
- MAX_NB_WORDS: Integer. Maximum number of unique words in datasets, it will default to 75000.
GloVe_dir
~~~~~~~~~~~~~~~~~~~~~~~
- GloVe_dir: String. Address of GloVe or any pre-trained directory, it will default to null which glove.6B.zip will be download.
GloVe_file
~~~~~~~~~~~~~~~~~~~~~~~
- GloVe_dir: String. Which version of GloVe or pre-trained word emending will be used, it will default to glove.6B.50d.txt.
- NOTE: if you use other version of GloVe EMBEDDING_DIM must be same dimensions.
sparse_categorical
~~~~~~~~~~~~~~~~~~~~~~~
- sparse_categorical: bool. When target's dataset is (n,1) should be True, it will default to True.
random_deep
~~~~~~~~~~~~~~~~~~~~~~~
- random_deep: Integer [3]. Number of ensembled model used in RMDL random_deep[0] is number of DNN, random_deep[1] is number of RNN, random_deep[0] is number of CNN, it will default to [3, 3, 3].
epochs
~~~~~~~~~~~~~~~~~~~~~~~
- epochs: Integer [3]. Number of epochs in each ensembled model used in RMDL epochs[0] is number of epochs used in DNN, epochs[1] is number of epochs used in RNN, epochs[0] is number of epochs used in CNN, it will default to [500, 500, 500].
plot
~~~~~~~~~~~~~~~~~~~~~~~
- plot: bool. True: shows confusion matrix and accuracy and loss
min_hidden_layer_dnn
~~~~~~~~~~~~~~~~~~~~~~~
- min_hidden_layer_dnn: Integer. Lower Bounds of hidden layers of DNN used in RMDL, it will default to 1.
max_hidden_layer_dnn
~~~~~~~~~~~~~~~~~~~~~~~
- max_hidden_layer_dnn: Integer. Upper bounds of hidden layers of DNN used in RMDL, it will default to 8.
min_nodes_dnn
~~~~~~~~~~~~~~~~~~~~~~~
- min_nodes_dnn: Integer. Lower bounds of nodes in each layer of DNN used in RMDL, it will default to 128.
max_nodes_dnn
~~~~~~~~~~~~~~~~~~~~~~~
- max_nodes_dnn: Integer. Upper bounds of nodes in each layer of DNN used in RMDL, it will default to 1024.
min_hidden_layer_rnn
~~~~~~~~~~~~~~~~~~~~~~~
- min_hidden_layer_rnn: Integer. Lower Bounds of hidden layers of RNN used in RMDL, it will default to 1.
max_hidden_layer_rnn
~~~~~~~~~~~~~~~~~~~~~~~
- man_hidden_layer_rnn: Integer. Upper Bounds of hidden layers of RNN used in RMDL, it will default to 5.
min_nodes_rnn
~~~~~~~~~~~~~~~~~~~~~~~
- min_nodes_rnn: Integer. Lower bounds of nodes (LSTM or GRU) in each layer of RNN used in RMDL, it will default to 32.
max_nodes_rnn
~~~~~~~~~~~~~~~~~~~~~~~
- max_nodes_rnn: Integer. Upper bounds of nodes (LSTM or GRU) in each layer of RNN used in RMDL, it will default to 128.
min_hidden_layer_cnn
~~~~~~~~~~~~~~~~~~~~~~~
- min_hidden_layer_cnn: Integer. Lower Bounds of hidden layers of CNN used in RMDL, it will default to 3.
max_hidden_layer_cnn
~~~~~~~~~~~~~~~~~~~~~~~
- max_hidden_layer_cnn: Integer. Upper Bounds of hidden layers of CNN used in RMDL, it will default to 10.
min_nodes_cnn
~~~~~~~~~~~~~~~~~~~~~~~
- min_nodes_cnn: Integer. Lower bounds of nodes (2D convolution layer) in each layer of CNN used in RMDL, it will default to 128.
max_nodes_cnn
~~~~~~~~~~~~~~~~~~~~~~~
- min_nodes_cnn: Integer. Upper bounds of nodes (2D convolution layer) in each layer of CNN used in RMDL, it will default to 512.
random_state
~~~~~~~~~~~~~~~~~~~~~~~
- random_state : Integer, RandomState instance or None, optional (default=None)
* If Integer, random_state is the seed used by the random number generator;
random_optimizor
~~~~~~~~~~~~~~~~~~~~~~~
- random_optimizor : bool, If False, all models use adam optimizer. If True, all models use random optimizers. it will default to True
dropout
~~~~~~~~~~~~~~~~~~~~~~~
- dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
Image_Classification
---------------------
.. code:: python
from RMDL import RMDL_Image
.. code:: python
Image_Classification(x_train, y_train, x_test, y_test, shape, batch_size=128,
sparse_categorical=True, random_deep=[3, 3, 3], epochs=[500, 500, 500], plot=True,
min_hidden_layer_dnn=1, max_hidden_layer_dnn=8, min_nodes_dnn=128, max_nodes_dnn=1024,
min_hidden_layer_rnn=1, max_hidden_layer_rnn=5, min_nodes_rnn=32, max_nodes_rnn=128,
min_hidden_layer_cnn=3, max_hidden_layer_cnn=10, min_nodes_cnn=128, max_nodes_cnn=512,
random_state=42, random_optimizor=True, dropout=0.05)
Input
~~~~~
- x_train
- y_train
- x_test
- y_test
shape
~~~~~
- shape: np.shape . shape of image. The most common situation would be a 2D input with shape (batch_size, input_dim).
batch_size
~~~~~~~~~~
- batch_size: Integer. Number of samples per gradient update. If unspecified, it will default to 128.
sparse_categorical
~~~~~~~~~~~~~~~~~~~~~~~
- sparse_categorical: bool. When target's dataset is (n,1) should be True, it will default to True.
random_deep
~~~~~~~~~~~~~~~~~~~~~~~
- random_deep: Integer [3]. Number of ensembled model used in RMDL random_deep[0] is number of DNN, random_deep[1] is number of RNN, random_deep[0] is number of CNN, it will default to [3, 3, 3].
epochs
~~~~~~~~~~~~~~~~~~~~~~~
- epochs: Integer [3]. Number of epochs in each ensembled model used in RMDL epochs[0] is number of epochs used in DNN, epochs[1] is number of epochs used in RNN, epochs[0] is number of epochs used in CNN, it will default to [500, 500, 500].
plot
~~~~~~~~~~~~~~~~~~~~~~~
- plot: bool. True: shows confusion matrix and accuracy and loss
min_hidden_layer_dnn
~~~~~~~~~~~~~~~~~~~~~~~
- min_hidden_layer_dnn: Integer. Lower Bounds of hidden layers of DNN used in RMDL, it will default to 1.
max_hidden_layer_dnn
~~~~~~~~~~~~~~~~~~~~~~~
- max_hidden_layer_dnn: Integer. Upper bounds of hidden layers of DNN used in RMDL, it will default to 8.
min_nodes_dnn
~~~~~~~~~~~~~~~~~~~~~~~
- min_nodes_dnn: Integer. Lower bounds of nodes in each layer of DNN used in RMDL, it will default to 128.
max_nodes_dnn
~~~~~~~~~~~~~~~~~~~~~~~
- max_nodes_dnn: Integer. Upper bounds of nodes in each layer of DNN used in RMDL, it will default to 1024.
min_nodes_rnn
~~~~~~~~~~~~~~~~~~~~~~~
- min_nodes_rnn: Integer. Lower bounds of nodes (LSTM or GRU) in each layer of RNN used in RMDL, it will default to 32.
max_nodes_rnn
~~~~~~~~~~~~~~~~~~~~~~~
- maz_nodes_rnn: Integer. Upper bounds of nodes (LSTM or GRU) in each layer of RNN used in RMDL, it will default to 128.
min_hidden_layer_cnn
~~~~~~~~~~~~~~~~~~~~~~~
- min_hidden_layer_cnn: Integer. Lower Bounds of hidden layers of CNN used in RMDL, it will default to 3.
max_hidden_layer_cnn
~~~~~~~~~~~~~~~~~~~~~~~
- max_hidden_layer_cnn: Integer. Upper Bounds of hidden layers of CNN used in RMDL, it will default to 10.
min_nodes_cnn
~~~~~~~~~~~~~~~~~~~~~~~
- min_nodes_cnn: Integer. Lower bounds of nodes (2D convolution layer) in each layer of CNN used in RMDL, it will default to 128.
max_nodes_cnn
~~~~~~~~~~~~~~~~~~~~~~~
- min_nodes_cnn: Integer. Upper bounds of nodes (2D convolution layer) in each layer of CNN used in RMDL, it will default to 512.
random_state
~~~~~~~~~~~~~~~~~~~~~~~
- random_state : Integer, RandomState instance or None, optional (default=None)
* If Integer, random_state is the seed used by the random number generator;
random_optimizor
~~~~~~~~~~~~~~~~~~~~~~~
- random_optimizor : bool, If False, all models use adam optimizer. If True, all models use random optimizers. it will default to True
dropout
~~~~~~~~~~~~~~~~~~~~~~~
- dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
Example
========
MNIST
-----
- The MNIST database contains 60,000 training images and 10,000 testing images.
Import Packages
~~~~~~~~~~~~~~~
.. code:: python
from keras.datasets import mnist
import numpy as np
from RMDL import RMDL_Image as RMDL
Load Data
~~~~~~~~~
.. code:: python
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train_D = X_train.reshape(X_train.shape[0], 28, 28, 1).astype('float32')
X_test_D = X_test.reshape(X_test.shape[0], 28, 28, 1).astype('float32')
X_train = X_train_D / 255.0
X_test = X_test_D / 255.0
number_of_classes = np.unique(y_train).shape[0]
shape = (28, 28, 1)
Using RMDL
~~~~~~~~~~~
.. code:: python
batch_size = 128
sparse_categorical = 0
n_epochs = [100, 100, 100] ## DNN-RNN-CNN
Random_Deep = [3, 3, 3] ## DNN-RNN-CNN
RMDL.Image_Classification(X_train, y_train, X_test, y_test, batch_size, shape, sparse_categorical, Random_Deep,
n_epochs)
IMDB
-----
- This dataset contains 50,000 documents with 2 categories.
Import Packages
~~~~~~~~~~~~~~~
.. code:: python
import sys
import os
from RMDL import text_feature_extraction as txt
from keras.datasets import imdb
import numpy as np
from RMDL import RMDL_Text as RMDL
Load Data
~~~~~~~~~
.. code:: python
print("Load IMDB dataset....")
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=MAX_NB_WORDS)
print(len(X_train))
print(y_test)
word_index = imdb.get_word_index()
index_word = {v: k for k, v in word_index.items()}
X_train = [txt.text_cleaner(' '.join(index_word.get(w) for w in x)) for x in X_train]
X_test = [txt.text_cleaner(' '.join(index_word.get(w) for w in x)) for x in X_test]
X_train = np.array(X_train)
X_train = np.array(X_train).ravel()
print(X_train.shape)
X_test = np.array(X_test)
X_test = np.array(X_test).ravel()
Using RMDL
~~~~~~~~~~~
.. code:: python
batch_size = 100
sparse_categorical = 0
n_epochs = [100, 100, 100] ## DNN--RNN-CNN
Random_Deep = [3, 3, 3] ## DNN--RNN-CNN
RMDL.Text_Classification(X_train, y_train, X_test, y_test, batch_size, sparse_categorical, Random_Deep,
n_epochs)
Web Of Science
--------------
- Linke of dataset: |Data|
- Web of Science Dataset
`WOS-11967 <http://dx.doi.org/10.17632/9rw3vkcfy4.2>`__
- This dataset contains 11,967 documents with 35 categories which
include 7 parents categories.
- Web of Science Dataset
`WOS-46985 <http://dx.doi.org/10.17632/9rw3vkcfy4.2>`__
- This dataset contains 46,985 documents with 134 categories
which include 7 parents categories.
- Web of Science Dataset
`WOS-5736 <http://dx.doi.org/10.17632/9rw3vkcfy4.2>`__
- This dataset contains 5,736 documents with 11 categories which
include 3 parents categories.
Import Packages
~~~~~~~~~~~~~~~
.. code:: python
from RMDL import text_feature_extraction as txt
from sklearn.model_selection import train_test_split
from RMDL.Download import Download_WOS as WOS
import numpy as np
from RMDL import RMDL_Text as RMDL
Load Data
~~~~~~~~~
.. code:: python
path_WOS = WOS.download_and_extract()
fname = os.path.join(path_WOS,"WebOfScience/WOS11967/X.txt")
fnamek = os.path.join(path_WOS,"WebOfScience/WOS11967/Y.txt")
with open(fname, encoding="utf-8") as f:
content = f.readlines()
content = [txt.text_cleaner(x) for x in content]
with open(fnamek) as fk:
contentk = fk.readlines()
contentk = [x.strip() for x in contentk]
Label = np.matrix(contentk, dtype=int)
Label = np.transpose(Label)
np.random.seed(7)
print(Label.shape)
X_train, X_test, y_train, y_test = train_test_split(content, Label, test_size=0.2, random_state=4)
Using RMDL
~~~~~~~~~~~
.. code:: python
batch_size = 100
sparse_categorical = 0
n_epochs = [5000, 500, 500] ## DNN--RNN-CNN
Random_Deep = [3, 3, 3] ## DNN--RNN-CNN
RMDL.Text_Classification(X_train, y_train, X_test, y_test, batch_size, sparse_categorical, Random_Deep,
n_epochs)
Reuters-21578
-------------
- This dataset contains 21,578 documents with 90 categories.
Import Packages
~~~~~~~~~~~~~~~
.. code:: python
import sys
import os
import nltk
nltk.download("reuters")
from nltk.corpus import reuters
from sklearn.preprocessing import MultiLabelBinarizer
import numpy as np
from RMDL import RMDL_Text as RMDL
Load Data
~~~~~~~~~
.. code:: python
documents = reuters.fileids()
train_docs_id = list(filter(lambda doc: doc.startswith("train"),
documents))
test_docs_id = list(filter(lambda doc: doc.startswith("test"),
documents))
X_train = [(reuters.raw(doc_id)) for doc_id in train_docs_id]
X_test = [(reuters.raw(doc_id)) for doc_id in test_docs_id]
mlb = MultiLabelBinarizer()
y_train = mlb.fit_transform([reuters.categories(doc_id)
for doc_id in train_docs_id])
y_test = mlb.transform([reuters.categories(doc_id)
for doc_id in test_docs_id])
y_train = np.argmax(y_train, axis=1)
y_test = np.argmax(y_test, axis=1)
Using RMDL
~~~~~~~~~~~
.. code:: python
batch_size = 100
sparse_categorical = 0
n_epochs = [20, 500, 50] ## DNN--RNN-CNN
Random_Deep = [3, 0, 0] ## DNN--RNN-CNN
RMDL.Text_Classification(X_train, y_train, X_test, y_test, batch_size, sparse_categorical, Random_Deep,
n_epochs)
Olivetti Faces
--------------
- There are ten different images of each of 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no glasses). All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position (with tolerance for some side movement).
Import Packages
~~~~~~~~~~~~~~~
.. code:: python
from sklearn.datasets import fetch_olivetti_faces
from sklearn.model_selection import train_test_split
from RMDL import RMDL_Image as RMDL
Load Data
~~~~~~~~~
.. code:: python
number_of_classes = 40
shape = (64, 64, 1)
data = fetch_olivetti_faces()
X_train, X_test, y_train, y_test = train_test_split(data.data,
data.target, stratify=data.target, test_size=40)
X_train = X_train.reshape(X_train.shape[0], 64, 64, 1).astype('float32')
X_test = X_test.reshape(X_test.shape[0], 64, 64, 1).astype('float32')
Using RMDL
~~~~~~~~~~~
.. code:: python
batch_size = 100
sparse_categorical = 0
n_epochs = [500, 500, 50] ## DNN--RNN-CNN
Random_Deep = [0, 0, 1] ## DNN--RNN-CNN
RMDL.Image_Classification(X_train, y_train, X_test, y_test, batch_size, shape, sparse_categorical, Random_Deep,
n_epochs)
More Exanmple
`link <https://github.com/kk7nc/RMDL/tree/master/Examples>`__
|Results|
Error and Comments:
----------------------
Send an email to kk7nc@virginia.edu
Citations
---------
.. code::
@inproceedings{Kowsari2018RMDL,
title={RMDL: Random Multimodel Deep Learning for Classification},
author={Kowsari, Kamran and Heidarysafa, Mojtaba and Brown, Donald E. and Jafari Meimandi, Kiana and Barnes, Laura E.},
booktitle={Proceedings of the 2018 International Conference on Information System and Data Mining},
year={2018},
DOI={https://doi.org/10.1145/3206098.3206111},
organization={ACM}
}
.. |werckerstatus| image:: https://app.wercker.com/status/3a564158e809971e2f7416beba5d05af/s/master
:target: https://app.wercker.com/project/byKey/3a564158e809971e2f7416beba5d05af
.. |BuildStatus| image:: https://travis-ci.org/kk7nc/RMDL.svg?branch=master
:target: https://travis-ci.org/kk7nc/RMDL
.. |PowerPoint| image:: https://img.shields.io/badge/Presentation-download-red.svg?style=flat
:target: https://github.com/kk7nc/RMDL/blob/master/docs/RMDL.pdf
.. |researchgate| image:: https://img.shields.io/badge/ResearchGate-RMDL-blue.svg?style=flat
:target: https://www.researchgate.net/publication/324922651_RMDL_Random_Multimodel_Deep_Learning_for_Classification
.. |Binder| image:: https://mybinder.org/badge.svg
:target: https://mybinder.org/v2/gh/kk7nc/RMDL/master
.. |pdf| image:: https://img.shields.io/badge/pdf-download-red.svg?style=flat
:target: https://github.com/kk7nc/RMDL/blob/master/docs/ACM-RMDL.pdf
.. |GitHublicense| image:: https://img.shields.io/badge/licence-GPL-blue.svg
:target: ./LICENSE
.. |RDL| image:: http://kowsari.net/onewebmedia/RDL.jpg
.. |RMDL| image:: http://kowsari.net/onewebmedia/RMDL.jpg
.. |Results| image:: http://kowsari.net/onewebmedia/RMDL_Results.png
.. |Data| image:: https://img.shields.io/badge/DOI-10.17632/9rw3vkcfy4.6-blue.svg?style=flat
:target: http://dx.doi.org/10.17632/9rw3vkcfy4.6
.. |Pypi| image:: https://img.shields.io/badge/Pypi-RMDL%201.0.5-blue.svg
:target: https://pypi.org/project/RMDL/
.. |DOI| image:: https://img.shields.io/badge/DOI-10.1145/3206098.3206111-blue.svg?style=flat
:target: https://doi.org/10.1145/3206098.3206111
.. |appveyor| image:: https://ci.appveyor.com/api/projects/status/github/kk7nc/RMDL?branch=master&svg=true
:target: https://ci.appveyor.com/project/kk7nc/RMDL
.. |arxiv| image:: https://img.shields.io/badge/arXiv-1805.01890-red.svg
:target: https://arxiv.org/abs/1805.01890
.. |twitter| image:: https://img.shields.io/twitter/url/http/shields.io.svg?style=social
:target: https://twitter.com/intent/tweet?text=RMDL:%20Random%20Multimodel%20Deep%20Learning%20for%20Classification%0aGitHub:&url=https://github.com/kk7nc/RMDL&hashtags=DeepLearning,classification,MachineLearning,deep_neural_networks,Image_Classification,Text_Classification,EnsembleLearning
نیازمندی
| مقدار | نام |
|---|---|
| >=2.1.2 | matplotlib |
| >=1.12.1 | numpy |
| >=0.22.0 | pandas |
| - | scipy |
| - | tensorflow |
| >=2.0.9 | keras |
| >=0.19.0 | scikit-learn |
| >=3.2.4 | nltk |
نحوه نصب
نصب پکیج whl RMDL-1.0.8:
pip install RMDL-1.0.8.whl
نصب پکیج tar.gz RMDL-1.0.8:
pip install RMDL-1.0.8.tar.gz