Text Classification for 20 Newsgroups Dataset using Convolutional Neural Network

This is source code for Text Classification for Different Datasets CNN based on the code from
Text Classification, Part I – Convolutional Networks

import numpy as np
import os

os.environ['KERAS_BACKEND']='tensorflow'



from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils.np_utils import to_categorical
from keras.layers import Dense, Input, Flatten
from keras.layers import Conv1D, MaxPooling1D, Embedding, Merge, Dropout
from keras.models import Model



MAX_SEQUENCE_LENGTH = 1000
MAX_NB_WORDS = 20000
EMBEDDING_DIM = 100
VALIDATION_SPLIT = 0.2



categories = ['alt.atheism', 'soc.religion.christian'] 

print ("START")
#Loading the data set - training data.
from sklearn.datasets import fetch_20newsgroups


newsgroups_train = fetch_20newsgroups(subset='train', shuffle=True, categories=categories,)

# You can check the target names (categories) and some data files by following commands.
print (newsgroups_train.target_names) #prints all the categories

print("\n".join(newsgroups_train.data[0].split("\n")[:3])) #prints first line of the first data file
print (newsgroups_train.target_names)
print (len(newsgroups_train.data))

texts = []

labels=newsgroups_train.target
texts = newsgroups_train.data

tokenizer = Tokenizer(nb_words=MAX_NB_WORDS)
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)

word_index = tokenizer.word_index

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

data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)



labels = to_categorical(np.asarray(labels))

print('Shape of data tensor:', data.shape)

print('Shape of label tensor:', labels.shape)



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

np.random.shuffle(indices)

data = data[indices]

labels = labels[indices]

nb_validation_samples = int(VALIDATION_SPLIT * data.shape[0])



x_train = data[:-nb_validation_samples]

y_train = labels[:-nb_validation_samples]

x_val = data[-nb_validation_samples:]

y_val = labels[-nb_validation_samples:]



print('Number of positive and negative reviews in traing and validation set ')

print (y_train.sum(axis=0))
print (y_val.sum(axis=0))




GLOVE_DIR = "C:\\Users\\pythonrunfiles"

embeddings_index = {}

f = open(os.path.join(GLOVE_DIR, 'glove.6B.100d.txt'), encoding="utf-8")

for line in f:
  
    values = line.split()

    word = values[0]

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

    embeddings_index[word] = coefs

f.close()



print('Total %s word vectors in Glove 6B 100d.' % len(embeddings_index))



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.

        embedding_matrix[i] = embedding_vector

        

embedding_layer = Embedding(len(word_index) + 1,

                            EMBEDDING_DIM,

                            weights=[embedding_matrix],

                            input_length=MAX_SEQUENCE_LENGTH,

                            trainable=True)



sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')

embedded_sequences = embedding_layer(sequence_input)

l_cov1= Conv1D(128, 5, activation='relu')(embedded_sequences)

l_pool1 = MaxPooling1D(5)(l_cov1)

l_cov2 = Conv1D(128, 5, activation='relu')(l_pool1)

l_pool2 = MaxPooling1D(5)(l_cov2)

l_cov3 = Conv1D(128, 5, activation='relu')(l_pool2)

l_pool3 = MaxPooling1D(35)(l_cov3)  # global max pooling

l_flat = Flatten()(l_pool3)

l_dense = Dense(128, activation='relu')(l_flat)

preds = Dense(2, activation='softmax')(l_dense)



model = Model(sequence_input, preds)

model.compile(loss='categorical_crossentropy',

              optimizer='rmsprop',

              metrics=['acc'])



print("model fitting - simplified convolutional neural network")

model.summary()

print (x_train)
print (y_train)

model.fit(x_train, y_train, validation_data=(x_val, y_val),

          nb_epoch=1, batch_size=128)   



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.

        embedding_matrix[i] = embedding_vector

        

embedding_layer = Embedding(len(word_index) + 1,

                            EMBEDDING_DIM,

                            weights=[embedding_matrix],

                            input_length=MAX_SEQUENCE_LENGTH,

                            trainable=True)



# applying a more complex convolutional approach

convs = []

filter_sizes = [3,4,5]



sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')

embedded_sequences = embedding_layer(sequence_input)



for fsz in filter_sizes:

    l_conv = Conv1D(nb_filter=128,filter_length=fsz,activation='relu')(embedded_sequences)

    l_pool = MaxPooling1D(5)(l_conv)

    convs.append(l_pool)

    

l_merge = Merge(mode='concat', concat_axis=1)(convs)

l_cov1= Conv1D(128, 5, activation='relu')(l_merge)

l_pool1 = MaxPooling1D(5)(l_cov1)

l_cov2 = Conv1D(128, 5, activation='relu')(l_pool1)

l_pool2 = MaxPooling1D(30)(l_cov2)

l_flat = Flatten()(l_pool2)

l_dense = Dense(128, activation='relu')(l_flat)

preds = Dense(2, activation='softmax')(l_dense)



model = Model(sequence_input, preds)

model.compile(loss='categorical_crossentropy',

              optimizer='rmsprop',

              metrics=['acc'])



print("model fitting - more complex convolutional neural network")

model.summary()

model.fit(x_train, y_train, validation_data=(x_val, y_val),

          nb_epoch=2, batch_size=50)