from keras.layers import Dropout, Dense, GRU, Embedding

from keras.models import Sequential

from sklearn.feature_extraction.text import TfidfVectorizer

import numpy as np

from sklearn import metrics

from keras.preprocessing.text import Tokenizer

from keras.preprocessing.sequence import pad_sequences

from sklearn.datasets import fetch_20newsgroups

def loadData_Tokenizer(X_train, X_test,MAX_NB_WORDS=75000,MAX_SEQUENCE_LENGTH=500):

np.random.seed(7)

text = np.concatenate((X_train, X_test), axis=0)

text = np.array(text)

tokenizer = Tokenizer(num_words=MAX_NB_WORDS)

tokenizer.fit_on_texts(text)

sequences = tokenizer.texts_to_sequences(text)

word_index = tokenizer.word_index

text = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)

print('Found %s unique tokens.' % len(word_index))

indices = np.arange(text.shape[0])

# np.random.shuffle(indices)

text = text[indices]

print(text.shape)

X_train = text[0:len(X_train), ]

X_test = text[len(X_train):, ]

embeddings_index = {}

f = open("C:\\Users\\kamran\\Documents\\GitHub\\RMDL\\Examples\\Glove\\glove.6B.50d.txt", encoding="utf8")

for line in f:

values = line.split()

word = values[0]

try:

coefs = np.asarray(values[1:], dtype='float32')

except:

pass

embeddings_index[word] = coefs

f.close()

print('Total %s word vectors.' % len(embeddings_index))

return (X_train, X_test, word_index,embeddings_index)

def Build_Model_RNN_Text(word_index, embeddings_index, nclasses, MAX_SEQUENCE_LENGTH=500, EMBEDDING_DIM=50, dropout=0.5):

"""

def buildModel_RNN(word_index, embeddings_index, nclasses, MAX_SEQUENCE_LENGTH=500, EMBEDDING_DIM=50, dropout=0.5):

word_index in word index ,

embeddings_index is embeddings index, look at data_helper.py

nClasses is number of classes,

MAX_SEQUENCE_LENGTH is maximum lenght of text sequences

"""

model = Sequential()

hidden_layer = 3

gru_node = 256

embedding_matrix = np.random.random((len(word_index) + 1, EMBEDDING_DIM))

for word, i in word_index.items():

embedding_vector = embeddings_index.get(word)

if embedding_vector is not None:

# words not found in embedding index will be all-zeros.

if len(embedding_matrix[i]) != len(embedding_vector):

print("could not broadcast input array from shape", str(len(embedding_matrix[i])),

"into shape", str(len(embedding_vector)), " Please make sure your"

" EMBEDDING_DIM is equal to embedding_vector file ,GloVe,")

exit(1)

embedding_matrix[i] = embedding_vector

model.add(Embedding(len(word_index) + 1,

EMBEDDING_DIM,

weights=[embedding_matrix],

input_length=MAX_SEQUENCE_LENGTH,

trainable=True))

print(gru_node)

for i in range(0,hidden_layer):

model.add(GRU(gru_node,return_sequences=True, recurrent_dropout=0.2))

model.add(Dropout(dropout))

model.add(GRU(gru_node, recurrent_dropout=0.2))

#model.add(Dense(, activation='relu'))

model.add(Dense(nclasses, activation='softmax'))

model.compile(loss='sparse_categorical_crossentropy',

optimizer='adam',

metrics=['accuracy'])

return model

newsgroups_train = fetch_20newsgroups(subset='train')

newsgroups_test = fetch_20newsgroups(subset='test')

X_train = newsgroups_train.data

X_test = newsgroups_test.data

y_train = newsgroups_train.target

y_test = newsgroups_test.target

X_train_Glove,X_test_Glove, word_index,embeddings_index = loadData_Tokenizer(X_train,X_test)

model_RNN = Build_Model_RNN_Text(word_index,embeddings_index, 20)

model_RNN.summary()

model_RNN.fit(X_train_Glove, y_train,

validation_data=(X_test_Glove, y_test),

epochs=20,

batch_size=128,

verbose=2)

predicted = model_RNN.predict_classes(X_test_Glove)

print(metrics.classification_report(y_test, predicted))