Loss isn't decreasing in neural network - neural-network

Question

I am implementing a variant of the CNN described by this paper.
My problem is that the loss isn't decreasing and I don't understand why. Same have to be said concerning accuracy(stuck at 0.5 more or less).
This a problem of 2 classes classification. I am using the data from this website:
I suspected the optimizer so I changed it whithout any improvements. I am pretty sure the data I am using is ok because I used it on an LSTM and the classifier was fine.
Here is my code:
from keras.layers import Embedding
from keras.layers import Conv2D
from keras.models import Sequential
from keras.layers import MaxPooling2D
from keras.layers import Reshape
from keras.layers import Flatten
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Input
from keras import backend as K
from keras.models import Model
import tensorflow as tf
from sklearn import preprocessing
import keras
import numpy as np
import pdb
#using multiple filters
config = tf.ConfigProto()
# Don't pre-allocate memory; allocate as-needed
config.gpu_options.allow_growth = True
# Only allow a total of half the GPU memory to be allocated
config.gpu_options.per_process_gpu_memory_fraction = 0.7
# Create a session with the above options specified.
config.gpu_options.per_process_gpu_memory_fraction = 0.65
K.tensorflow_backend.set_session(tf.Session(config=config))
class Classifier():
def __init__(self,Vocab,maxlen=75):
self.model=Sequential()
self.EMBED_DIM=30
self.batch_size=30
self.MAX_SEQUENCE_LENGTH=maxlen
self.nb_labels=2
self.model = Sequential()
self.Vocab=Vocab
def fit(self, X,y):
#pdb.set_trace()
mainIn=Input(shape=(self.MAX_SEQUENCE_LENGTH,), dtype='int32', name='main_input')
x=Embedding(len(self.Vocab)+2,self.EMBED_DIM,input_length=self.MAX_SEQUENCE_LENGTH)(mainIn)
x=Reshape((1,self.MAX_SEQUENCE_LENGTH, self.EMBED_DIM))(x)
x1=Conv2D(128, strides=2,kernel_size=5 ,activation="relu", padding='same')(x)
x1=MaxPooling2D((self.MAX_SEQUENCE_LENGTH-5+1,1),padding='same')(x1)
x1=Flatten()(x1)
x2=Conv2D(128, strides=2, kernel_size=4, activation="sigmoid", padding='same')(x)
x2=MaxPooling2D((self.MAX_SEQUENCE_LENGTH-4+1,1),padding='same')(x2)
x2=Flatten()(x2)
x3=Conv2D(128, strides=2, kernel_size=3, activation="tanh", padding='same')(x)
x3=MaxPooling2D((self.MAX_SEQUENCE_LENGTH-3+1,1),padding='same')(x3)
x3=Flatten()(x3)
combinedX=keras.layers.concatenate([x1,x2,x3],axis=1)
combinedX=Dense(64, activation="relu")(combinedX)
combinedX=Dropout(0.2)(combinedX)
#output=Dense(self.nb_labels, activation="sigmoid")(combinedX)
#output=Dense(2, activation="softmax")(combinedX)
output=Dense(1, activation="sigmoid")(combinedX)
#encoder =preprocessing.LabelEncoder()
#encoder.fit(y)
#encoded_Y = encoder.transform(y)
#labels=keras.utils.to_categorical(encoded_Y, num_classes=2)
labels=y
pdb.set_trace()
inputs2=X
self.model = Model(inputs=mainIn, outputs=output)
# self.model.compile(loss='binary_crossentropy',
# optimizer='adam',
# metrics=['acc'])
self.model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['acc'])
self.model.fit(inputs2,labels,epochs=7, batch_size=self.batch_size)
def predict(self, X):
return self.model.predict(np.array(X))
def predict_proba(self, X):
return self.model.predict(np.array(X), self.batch_size)
Here is my code to preprocess the data:
#loading the file
file2=pd.read_csv("Sentiment_Analysis Dataset.csv",error_bad_lines=False)
#splitting into train et test set
from sklearn.model_selection import train_test_split
Text=list(file2.SentimentText)
file2.groupby('Sentiment').count()
train_data,test_data,train_label,test_label=train_test_split(Text, file2.Sentiment, test_size=0.4, random_state=42)
#Buidling the dictionary
vocabDic=dict()
for document in train_data:
document=document.split(" ")
for word in document:
if word not in vocabDic:
vocabDic[word]=len(vocabDic)+1
vocabDic['unk']=len(vocabDic)+1
#coding the documents
def codeDocuments(documents,dictionnary):
documentsArray=list()
for i,document in enumerate(documents):
tempList=list()
document=document.split(" ")
for word in document:
if word in vocabDic:
word=vocabDic[word]
else:
word=vocabDic['unk']
tempList.append(word)
documentsArray.append(tempList)
return np.array(documentsArray)
train_docs=codeDocuments(train_data,vocabDic)
test_docs=codeDocuments(test_data,vocabDic)
#padding the documents
from keras.preprocessing import sequence
maxlen=75
train_set = sequence.pad_sequences(train_docs, maxlen=maxlen)
test_set = sequence.pad_sequences(test_docs, maxlen=maxlen)
#calling the model
model=Classifier(vocabDic,maxlen)
model.fit(train_set[:50000],train_label[:50000])