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Referenced paper : RMDL: Random Multimodel Deep Learning for Classification <https://arxiv.org/abs/1805.01890>
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Referenced paper : An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL) <https://arxiv.org/abs/1808.08121>
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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 as input a variety data to include text, video, images, and symbolic.
|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).
There are pip and git for RMDL installation:
.. code:: python
pip install RMDL
.. 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
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.
IMDB Dataset <http://ai.stanford.edu/~amaas/data/sentiment/>
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Reters-21578 Dataset <https://keras.io/datasets/>
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20Newsgroups Dataset <https://archive.ics.uci.edu/ml/datasets/Twenty+Newsgroups>
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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>
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Web of Science Dataset
WOS-46985 <http://dx.doi.org/10.17632/9rw3vkcfy4.2>
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Web of Science Dataset
WOS-5736 <http://dx.doi.org/10.17632/9rw3vkcfy4.2>
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MNIST Dataset <https://en.wikipedia.org/wiki/MNIST_database>
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CIFAR-10 Dataset <https://www.cs.toronto.edu/~kriz/cifar.html>
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The Database of Faces (The Olivetti Faces Dataset) <http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html>
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Python 3.5 or later see Instruction Documents <https://www.python.org/>
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TensorFlow see Instruction Documents <https://www.tensorflow.org/install/install_linux>
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scikit-learn see Instruction Documents <http://scikit-learn.org/stable/install.html>
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Keras see Instruction Documents <https://keras.io/>
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scipy see Instruction Documents <https://www.scipy.org/install.html>
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CUDA® Toolkit 8.0. For details, see NVIDIA’s documentation <https://developer.nvidia.com/cuda-toolkit>
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The NVIDIA drivers associated with CUDA Toolkit 8.0 <http://www.nvidia.com/Download/index.aspx>
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cuDNN v6. For details, see NVIDIA’s documentation <https://developer.nvidia.com/cudnn>
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GPU card with CUDA Compute Capability 3.0 or higher.
The libcupti-dev library,
Download GloVe: Global Vectors for Word Representation Instruction Documents <https://nlp.stanford.edu/projects/glove/>
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Set data directory into
Global.py <https://github.com/kk7nc/RMDL/blob/master/src/Global.py>
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if you are not setting GloVe directory, GloVe will be downloaded
.. 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
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_NB_WORDS
- MAX_NB_WORDS: Integer. Maximum number of unique words in datasets, it will default to 75000.
GloVe_dir
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
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
plot
- plot: bool. True: shows confusion matrix and accuracy and loss
min_hidden_layer_dnn
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
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
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
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
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
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)
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
.. 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
batch_size
- batch_size: Integer. Number of samples per gradient update. If unspecified, it will default to 128.
sparse_categorical
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
plot
- plot: bool. True: shows confusion matrix and accuracy and loss
min_hidden_layer_dnn
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
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
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
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
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)
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
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,shape,
batch_size=batch_size,
sparse_categorical=True,
random_deep=Random_Deep,
epochs=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=batch_size,
sparse_categorical=sparse_categorical,
random_deep=Random_Deep,
epochs=n_epochs)
Linke of dataset: |Data|
Web of Science Dataset
WOS-11967 <http://dx.doi.org/10.17632/9rw3vkcfy4.2>
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Web of Science Dataset
WOS-46985 <http://dx.doi.org/10.17632/9rw3vkcfy4.2>
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Web of Science Dataset
WOS-5736 <http://dx.doi.org/10.17632/9rw3vkcfy4.2>
__
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=batch_size,
sparse_categorical=True,
random_deep=Random_Deep,
epochs=n_epochs,no_of_classes=12)
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=batch_size,
sparse_categorical=True,
random_deep=Random_Deep,
epochs=n_epochs)
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,
shape,
random_optimizor=False,
batch_size=batch_size,
random_deep=Random_Deep,
epochs=n_epochs)
More Example
`link <https://github.com/kk7nc/RMDL/tree/master/Examples>`__
|Results|
Error and Comments:
----------------------
Send an email to kk7nc@virginia.edu
Citations
---------
.. code::
@inproceedings{Kowsari2018RMDL,
author = {Kowsari, Kamran and Heidarysafa, Mojtaba and Brown, Donald E. and Meimandi, Kiana Jafari and Barnes, Laura E.},
title = {RMDL: Random Multimodel Deep Learning for Classification},
booktitle = {Proceedings of the 2Nd International Conference on Information System and Data Mining},
series = {ICISDM '18},
year = {2018},
isbn = {978-1-4503-6354-9},
location = {Lakeland, FL, USA},
pages = {19--28},
numpages = {10},
url = {http://doi.acm.org/10.1145/3206098.3206111},
doi = {10.1145/3206098.3206111},
acmid = {3206111},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {Data Mining, Deep Learning, Deep Neural Networks, Image Classification, Supervised Learning, Text Classification},
}
and
.. code::
@article{Heidarysafa2018RMDL,
title={An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)},
author={Heidarysafa, Mojtaba and Kowsari, Kamran and Brown, Donald E. and Jafari Meimandi, Kiana and Barnes, Laura E.},
booktitle={International Journal of Machine Learning and Computing (IJMLC)},
year={2018},
Volume={8},
Number={4},
pages={298--310},
DOI={https://doi.org/10.18178/ijmlc.2018.8.4.703}
}
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