CNN.py

from keras.layers import Dropout, Dense,Input,Embedding,Flatten, AveragePooling2D, Conv2D,Reshape
from keras.models import Sequential,Model
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
from keras.layers.merge import Concatenate


def loadData_Tokenizer(X_train, X_test,MAX_NB_WORDS=75000,MAX_SEQUENCE_LENGTH=1000):
    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(".\glove.6B.100d.txt", encoding="utf8") ## GloVe file which could be download https://nlp.stanford.edu/projects/glove/
    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_CNN_Text(word_index, embeddings_index, nclasses, MAX_SEQUENCE_LENGTH=500, EMBEDDING_DIM=100, dropout=0.5):

    """
        def buildModel_CNN(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,
        EMBEDDING_DIM is an int value for dimention of word embedding look at data_helper.py
    """

    model = Sequential()
    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

    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 = []
    layer = 5
    print("Filter  ",layer)
    for fl in range(0,layer):
        filter_sizes.append((fl+2,fl+2))

    node = 128
    sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')
    embedded_sequences = embedding_layer(sequence_input)
    emb = Reshape((500,10, 10), input_shape=(500,100))(embedded_sequences)

    for fsz in filter_sizes:
        l_conv = Conv2D(node, padding="same", kernel_size=fsz, activation='relu')(emb)
        l_pool = AveragePooling2D(pool_size=(5,1), padding="same")(l_conv)
        #l_pool = Dropout(0.25)(l_pool)
        convs.append(l_pool)

    l_merge = Concatenate(axis=1)(convs)
    l_cov1 = Conv2D(node, (5,5), padding="same", activation='relu')(l_merge)
    l_cov1 = AveragePooling2D(pool_size=(5,2), padding="same")(l_cov1)
    l_cov2 = Conv2D(node, (5,5), padding="same", activation='relu')(l_cov1)
    l_pool2 = AveragePooling2D(pool_size=(5,2), padding="same")(l_cov2)
    l_cov2 = Dropout(dropout)(l_pool2)
    l_flat = Flatten()(l_cov2)
    l_dense = Dense(128, activation='relu')(l_flat)
    l_dense = Dropout(dropout)(l_dense)

    preds = Dense(nclasses, activation='softmax')(l_dense)
    model = Model(sequence_input, preds)

    model.compile(loss='sparse_categorical_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])



    return model



from sklearn.datasets import fetch_20newsgroups
from RMDL import text_feature_extraction as txt

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_CNN = Build_Model_CNN_Text(word_index,embeddings_index, 20)


model_CNN.summary()

model_CNN.fit(X_train_Glove, y_train,
                              validation_data=(X_test_Glove, y_test),
                              epochs=1000,
                              batch_size=128,
                              verbose=2)

predicted = model_CNN.predict(X_test_Glove)

predicted = np.argmax(predicted, axis=1)


print(metrics.classification_report(y_test, predicted))