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Deep LearningDemocrate_debat_usa
Overview

DEEP LEARNING ON USA DEMOCRATES DEBATE

By Pamela Dekas

import sys
import csv
import re 
import nltk
import string
import unicodedata
from textblob import TextBlob
from collections import Counter
import pandas as pd
import numpy as np
from wordcloud import WordCloud
from nltk.classify import * 
from nltk.corpus import stopwords
from sklearn.metrics import f1_score, roc_auc_score
from sklearn.feature_extraction.text import CountVectorizer
from nltk.tokenize import word_tokenize
import nltk.classify.util
import matplotlib.pyplot as plt
from string import punctuation 
from nltk.corpus import stopwords
from wordcloud import STOPWORDS
import os
from sklearn.model_selection import train_test_split
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence, text
from keras.callbacks import EarlyStopping
Using TensorFlow backend.



---------------------------------------------------------------------------

AttributeError                            Traceback (most recent call last)


   
     in 
    
     ()
     22 import os
     23 from sklearn.model_selection import train_test_split
---> 24 from keras.datasets import imdb
     25 from keras.models import Sequential
     26 from keras.layers import Dense


~\Anaconda3\lib\site-packages\keras\__init__.py in 
     
      ()
      1 from __future__ import absolute_import
      2 
----> 3 from . import utils
      4 from . import activations
      5 from . import applications


~\Anaconda3\lib\site-packages\keras\utils\__init__.py in 
      
       ()
      4 from . import data_utils
      5 from . import io_utils
----> 6 from . import conv_utils
      7 from . import losses_utils
      8 from . import metrics_utils


~\Anaconda3\lib\site-packages\keras\utils\conv_utils.py in 
       
        () 7 from six.moves import range 8 import numpy as np ----> 9 from .. import backend as K 10 11 ~\Anaconda3\lib\site-packages\keras\backend\__init__.py in 
        
         () ----> 1 from .load_backend import epsilon 2 from .load_backend import set_epsilon 3 from .load_backend import floatx 4 from .load_backend import set_floatx 5 from .load_backend import cast_to_floatx ~\Anaconda3\lib\site-packages\keras\backend\load_backend.py in 
         
          () 88 elif _BACKEND == 'tensorflow': 89 sys.stderr.write('Using TensorFlow backend.\n') ---> 90 from .tensorflow_backend import * 91 else: 92 # Try and load external backend. ~\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py in 
          
           () 52 53 # Private TF Keras utils ---> 54 get_graph = tf_keras_backend.get_graph 55 # learning_phase_scope = tf_keras_backend.learning_phase_scope # TODO 56 name_scope = tf.name_scope AttributeError: module 'tensorflow.python.keras.backend' has no attribute 'get_graph' 
          
         
        
       
      
     
    
   
speech = pd.read_csv('debate_transcripts_v3_2020-02-26.csv',encoding= 'unicode_escape')
df= pd.DataFrame(speech)
dem_speakers = df["speaker"]
number_of_speakers = len(set(dem_speakers))
print("Nombre de speakers:",number_of_speakers, "speakers")

# Mean duration of speech.
print("temps moyen de parole:",np.mean(df["speaking_time_seconds"]), "seconds")
print("Dataset size:", len(df))
Nombre de speakers: 106 speakers
temps moyen de parole: 16.49230769230769 seconds
Dataset size: 5911

df.info()

   
    
RangeIndex: 5911 entries, 0 to 5910
Data columns (total 6 columns):
date                     5911 non-null object
debate_name              5911 non-null object
debate_section           5911 non-null object
speaker                  5911 non-null object
speech                   5911 non-null object
speaking_time_seconds    5395 non-null float64
dtypes: float64(1), object(5)
memory usage: 277.2+ KB

   
df.groupby('speaker')['speaking_time_seconds'].sum(level=0).nlargest(10).plot.bar()
plt.title('Repartition par temps de parole')
plt.show()

png

debate_time = df.groupby(by=['speaker', 'date']).speaking_time_seconds.sum().nlargest(15)
debate_time.plot()

png

suppresion des colonnes qui ne seront pas utilisé dans la suite du projet et creation du dataset final###

df=df.drop(['date','debate_name','debate_section','speaking_time_seconds'],1)
df.head(5)
speaker speech
0 Norah O�Donnell Good evening and welcome, the Democratic presi...
1 Gayle King And Super Tuesday is just a week away and this...
2 Norah O�Donnell And CBS News is proud to bring you this debate...
3 Gayle King And we are partnering tonight also with Twitte...
4 Norah O�Donnell Now, here are the rules for the next two hours...

PREPROCESSING 

import nltk 
nltk.download('punkt')
stopwords = nltk.corpus.stopwords.words('english')
Tailored_stopwords=('im','ive','mr','weve','dont','well','will','make','us','we',
                      'I','make','got','need','want','think',
                      'going','go','one','thank','going',
                      'way','say','every','re','us','first',
                     'now','said','know','look','done','take',
                     'number','two','three','s','m',"t",
                      'let','don','tell','ve','im','mr','put','maybe','whether','many', 'll','around','thing','Secondly','doesn','lot')
#stopwords = nltk.corpus.stopwords.words('english')
stopwords = set(STOPWORDS)
stopwords= stopwords.union(Tailored_stopwords)
[nltk_data] Downloading package punkt to C:\Users\pamel.DESKTOP-O19M7N
[nltk_data]     F\AppData\Roaming\nltk_data...
[nltk_data]   Package punkt is already up-to-date!

def Text_cleansing(speech):
    speech = re.sub('@[A-Za-z0–9]+', '', str(speech))
    speech = re.sub('#', '', speech) # Enlever les '#' hash tag
    speech = re.sub('rt', '', speech)
    speech=re.sub(',',' ', speech)
    speech=re.sub('!',' ',speech)
    speech=re.sub(':',' ',speech)
    speech=re.sub("'","",speech)
    speech=re.sub('"','',speech)
    speech=speech.lower()
    speech = word_tokenize(speech)
    return speech
def remove_stopwords(speech):
    speech_clean = [word for word in speech if word not in stopwords]
    return speech_clean
                         
df['speech_tokens']= df['speech'].apply(Text_cleansing)
df.head(5)
speaker speech speech_tokens
0 Norah O�Donnell Good evening and welcome, the Democratic presi... [good, evening, and, welcome, the, democratic,...
1 Gayle King And Super Tuesday is just a week away and this... [and, super, tuesday, is, just, a, week, away,...
2 Norah O�Donnell And CBS News is proud to bring you this debate... [and, cbs, news, is, proud, to, bring, you, th...
3 Gayle King And we are partnering tonight also with Twitte... [and, we, are, panering, tonight, also, with, ...
4 Norah O�Donnell Now, here are the rules for the next two hours... [now, here, are, the, rules, for, the, next, t... 
df['speech_clean']=df['speech_tokens'].apply(remove_stopwords)
df.head(5)
speaker speech speech_tokens speech_clean
0 Norah O�Donnell Good evening and welcome, the Democratic presi... [good, evening, and, welcome, the, democratic,... [good, evening, welcome, democratic, president...
1 Gayle King And Super Tuesday is just a week away and this... [and, super, tuesday, is, just, a, week, away,... [super, tuesday, week, away, biggest, primary,...
2 Norah O�Donnell And CBS News is proud to bring you this debate... [and, cbs, news, is, proud, to, bring, you, th... [cbs, news, proud, bring, debate, along, co-sp...
3 Gayle King And we are partnering tonight also with Twitte... [and, we, are, panering, tonight, also, with, ... [panering, tonight, twitter, ., home, paicipat...
4 Norah O�Donnell Now, here are the rules for the next two hours... [now, here, are, the, rules, for, the, next, t... [rules, next, hours, ., asked, question, minut... 
def wordcloud(dataframe):
    Aw= df['speech_clean']
    wordCloud = WordCloud(width=500, height=300,background_color='white', max_font_size=110).generate(str(Aw))
    plt.imshow(wordCloud, interpolation="bilinear")
    plt.axis("off")
    plt.title("speech wordcloud")

wordcloud(df['speech_clean'])

png

Pour la suite du projet on reduira la liste des speakers aux candidats les plus notoires (top 7 speakers)###

df = df.loc[df.speaker.isin({'Joe Biden', 'Bernie Sanders', 'Elizabeth Warren', 'Michael Bloomberg', 'Pete Buttigieg', 'Amy Klobuchar',  'Tulsi Gabbard'})]
df.head()
df.shape
(2245, 4)

CountVectorizer et creation du dict des mots par candidat a utiliser sur les modeles ML qui seront en back-up###

Analyse Lexicale

cv = CountVectorizer(stop_words=stopwords)
df_cv = cv.fit_transform(df.speech)
df_words = pd.DataFrame(df_cv.toarray(), columns=cv.get_feature_names())
df_words.index = df.speaker
df_words = df_words.transpose()
df_words
speaker Bernie Sanders Michael Bloomberg Michael Bloomberg Bernie Sanders Pete Buttigieg Elizabeth Warren Elizabeth Warren Pete Buttigieg Joe Biden Bernie Sanders ... Amy Klobuchar Elizabeth Warren Amy Klobuchar Tulsi Gabbard Tulsi Gabbard Amy Klobuchar Amy Klobuchar Amy Klobuchar Elizabeth Warren Elizabeth Warren
00 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
000 2 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
001st 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
01 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
02 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
03 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
04 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
05 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
06 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
07 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
08 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
09 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
100 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
10000 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
100s 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
10th 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
11 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
12 0 0 0 0 0 0 0 0 1 0 ... 0 0 0 0 0 0 0 0 0 0
120 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
125 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
12th 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
13 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
130 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
135 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
137 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
13th 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
14 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
140 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
149 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
xinjiang 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
yachts 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
yale 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
yang 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
yanked 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
ye 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
yeah 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 1 0 0
year 1 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
yearly 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
years 2 0 0 0 0 0 0 1 0 0 ... 0 0 0 0 0 0 0 0 1 0
yellow 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
yemen 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
yemin 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
yep 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
yes 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
yesterday 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
yet 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
yo 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
york 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
yorker 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
young 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
younger 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
youngest 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
youth 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
youtube 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
zealand 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
zero 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
zeroed 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
zip 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
zone 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0

6385 rows × 2245 columns

top_dict = {}
for c in df_words.columns:
    top = df_words[c].sort_values(ascending=False).head(30)
    top_dict[c]= list(zip(top.index, top.values))
for speaker, top_words in top_dict.items():
    print(speaker)
    print(', '.join([word for word, count in top_words[0:9]]))
    print('---')
---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)


   
     in 
    
     ()
      1 top_dict = {}
      2 for c in df_words.columns:
----> 3     top = df_words[c].sort_values(ascending=False).head(30)
      4     top_dict[c]= list(zip(top.index, top.values))
      5 for speaker, top_words in top_dict.items():


TypeError: sort_values() missing 1 required positional argument: 'by'

    
   
df2=pd.DataFrame(top_dict)
df2.head(15)
 
from collections import Counter
words = []
for speaker in df_words.columns:
    top = [word for (word, count) in top_dict[speaker]]
    for t in top:
        words.append(t)
Counter(words).most_common(15)
---------------------------------------------------------------------------

KeyError                                  Traceback (most recent call last)


   
     in 
    
     ()
      2 words = []
      3 for speaker in df_words.columns:
----> 4     top = [word for (word, count) in top_dict[speaker]]
      5     for t in top:
      6         words.append(t)


KeyError: 'Bernie Sanders'

Implemantation du modèle###

print(df.columns)
print(df.shape)
df['speaker'] = df['speaker'].astype(str)
Index(['speaker', 'speech', 'speech_tokens', 'speech_clean'], dtype='object')
(2245, 4)

Embedding

import gensim
RANDOM_STATE = 50
EPOCHS = 5
BATCH_SIZE = 256
EMB_DIM = 100
SAVE_MODEL = True

X = df['speech_clean']
print(X.head())
X.shape
5     [well, you�re, right, economy, really, great, ...
6                                            [senator-]
8     [think, donald, trump, thinks, would, better, ...
9     [oh, mr., bloomberg, ., let, tell, mr., putin,...
11     [know, president, russia, wants, it�s, chaos, .]
Name: speech_clean, dtype: object





(2245,)

emb_model = gensim.models.Word2Vec(sentences = X, size = EMB_DIM, window = 5, workers = 4, min_count = 1)
print('La taille du vocabulaire appris est de ',len(list(emb_model.wv.vocab)))
La taille du vocabulaire appris est de  7139

from keras.preprocessing.text import Tokenizer
import tokenize
max_length = max([len(s) for s in X])

tokenizer_new = Tokenizer()
tokenizer_new.fit_on_texts(X)

X_seq = tokenizer_new.texts_to_sequences(X)
X_fin = sequence.pad_sequences(X_seq, maxlen = max_length)
print(X_fin.shape)
(2245, 140)

emb_vec = emb_model.wv
MAX_NB_WORDS = len(list(emb_vec.vocab))
tokenizer_word_index = tokenizer_new.word_index
vocab_size = len(tokenizer_new.word_index) + 1
embedded_matrix = np.zeros((vocab_size, EMB_DIM))


for word, i in tokenizer_word_index.items():
    if i>= MAX_NB_WORDS:
        continue
    try:
        embedding_vector = emb_vec[word]
        wv_matrix[i] = embedding_vector
    except:
        pass      
embedded_matrix.shape
print(embedded_matrix)
[[0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]]

Préparation des variables

from keras.utils import to_categorical
from sklearn.preprocessing import LabelEncoder
y = df.speaker
print(y.head(10))
y.shape
5     1
6     4
8     4
9     1
11    5
12    2
13    2
15    5
21    3
23    1
Name: speaker, dtype: int32





(2245,)

Counter(y)
Counter({'Bernie Sanders': 430,
         'Michael Bloomberg': 97,
         'Pete Buttigieg': 392,
         'Elizabeth Warren': 440,
         'Joe Biden': 456,
         'Amy Klobuchar': 353,
         'Tulsi Gabbard': 77})

le=LabelEncoder()
df['speaker'] = le.fit_transform(df['speaker'])
df.head()

y = df.speaker
y.head()
print(y.shape)
print(X_fin.shape)
(2245,)
(2245, 140)

X_train, X_test, y_train, y_test = train_test_split(X_fin , y, test_size = 0.2, random_state = 42)


print(X_train.shape)
print(y_train.shape)
(1796, 140)
(1796,)

Construction des NN

model_pre_trained = Sequential()

model_pre_trained.add(Embedding(vocab_size, EMB_DIM, weights = [embedded_matrix], 
                    input_length = max_length, trainable = False))
model_pre_trained.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))
model_pre_trained.add(Dense(1, activation='softmax'))

model_pre_trained.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

model_pre_trained.summary()
Model: "sequential_11"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_11 (Embedding)     (None, 140, 100)          714000    
_________________________________________________________________
lstm_13 (LSTM)               (None, 128)               117248    
_________________________________________________________________
dense_9 (Dense)              (None, 1)                 129       
=================================================================
Total params: 831,377
Trainable params: 117,377
Non-trainable params: 714,000
_________________________________________________________________

Fitting

history_pre_trained = model_pre_trained.fit(X_fin, y, batch_size = BATCH_SIZE, epochs =20, verbose =1, validation_split = 0.2)
Train on 1796 samples, validate on 449 samples
Epoch 1/20
1796/1796 [==============================] - 4s 2ms/step - loss: 0.5429 - accuracy: 0.1754 - val_loss: -0.4417 - val_accuracy: 0.2472
Epoch 2/20
1796/1796 [==============================] - 3s 2ms/step - loss: -6.7429 - accuracy: 0.1776 - val_loss: -14.1017 - val_accuracy: 0.2472
Epoch 3/20
1796/1796 [==============================] - 3s 2ms/step - loss: -15.8550 - accuracy: 0.1776 - val_loss: -19.5441 - val_accuracy: 0.2472
Epoch 4/20
1796/1796 [==============================] - 3s 2ms/step - loss: -20.7949 - accuracy: 0.1776 - val_loss: -23.4335 - val_accuracy: 0.2472
Epoch 5/20
1796/1796 [==============================] - 3s 2ms/step - loss: -24.1430 - accuracy: 0.1776 - val_loss: -25.9735 - val_accuracy: 0.2472
Epoch 6/20
1796/1796 [==============================] - 3s 2ms/step - loss: -26.4535 - accuracy: 0.1776 - val_loss: -28.0725 - val_accuracy: 0.2472
Epoch 7/20
1796/1796 [==============================] - 3s 2ms/step - loss: -28.4266 - accuracy: 0.1776 - val_loss: -29.9313 - val_accuracy: 0.2472
Epoch 8/20
1796/1796 [==============================] - 3s 2ms/step - loss: -30.1754 - accuracy: 0.1776 - val_loss: -31.6261 - val_accuracy: 0.2472
Epoch 9/20
1796/1796 [==============================] - 3s 2ms/step - loss: -31.8791 - accuracy: 0.1776 - val_loss: -33.3337 - val_accuracy: 0.2472
Epoch 10/20
1796/1796 [==============================] - 4s 2ms/step - loss: -33.5166 - accuracy: 0.1776 - val_loss: -34.9834 - val_accuracy: 0.2472
Epoch 11/20
1796/1796 [==============================] - 3s 2ms/step - loss: -35.1544 - accuracy: 0.1776 - val_loss: -36.5973 - val_accuracy: 0.2472
Epoch 12/20
1796/1796 [==============================] - 3s 2ms/step - loss: -36.7253 - accuracy: 0.1776 - val_loss: -38.2070 - val_accuracy: 0.2472
Epoch 13/20
1796/1796 [==============================] - 3s 2ms/step - loss: -38.3344 - accuracy: 0.1776 - val_loss: -39.8655 - val_accuracy: 0.2472
Epoch 14/20
1796/1796 [==============================] - 3s 2ms/step - loss: -39.9810 - accuracy: 0.1776 - val_loss: -41.5162 - val_accuracy: 0.2472
Epoch 15/20
1796/1796 [==============================] - 3s 1ms/step - loss: -41.6567 - accuracy: 0.1776 - val_loss: -43.2049 - val_accuracy: 0.2472
Epoch 16/20
1796/1796 [==============================] - 3s 1ms/step - loss: -43.2579 - accuracy: 0.1776 - val_loss: -44.8235 - val_accuracy: 0.2472
Epoch 17/20
1796/1796 [==============================] - 3s 1ms/step - loss: -44.9030 - accuracy: 0.1776 - val_loss: -46.4982 - val_accuracy: 0.2472
Epoch 18/20
1796/1796 [==============================] - 2s 1ms/step - loss: -46.5038 - accuracy: 0.1776 - val_loss: -48.0627 - val_accuracy: 0.2472
Epoch 19/20
1796/1796 [==============================] - 3s 1ms/step - loss: -48.0124 - accuracy: 0.1776 - val_loss: -49.5424 - val_accuracy: 0.2472
Epoch 20/20
1796/1796 [==============================] - 2s 1ms/step - loss: -49.5209 - accuracy: 0.1776 - val_loss: -51.1489 - val_accuracy: 0.2472

Evaluation du modèle

score = model_pre_trained.evaluate(X_test, y_test, verbose = 0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
Test loss: -51.148848297866785
Test accuracy: 0.18930958211421967

ptoblèmes: npmbre important de stopwords à rajouter au dictionnaire, doutes sur la fonction dactivation, stemming/lemmatization qui semble peu efficace; axes d'amélioration: explorer les N grammes pouir contextualiser les mots et creer u_n dictionnaire de stopwords customisé pour les deabts ( association d'idées)/






























Owner









Pamela Dekas



Adepte de text mining, deep learning and data visualization



Pamela Dekas




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Gabriele Corso
 56  Dec 23, 2022









Realtime micro-expression recognition using OpenCV and PyTorch


 Micro-expression Recognition Realtime micro-expression recognition from scratch using OpenCV and PyTorch Try it out with a webcam or video using the e





Irfan
 35  Dec 05, 2022









Official code for "End-to-End Optimization of Scene Layout" -- including VAE, Diff Render, SPADE for colorization (CVPR 2020 Oral)


 End-to-End Optimization of Scene Layout Code release for: End-to-End Optimization of Scene Layout CVPR 2020 (Oral) Project site, Bibtex For help conta





Andrew Luo
 41  Dec 09, 2022









Implementation for "Exploiting Aliasing for Manga Restoration" (CVPR 2021)


 [CVPR Paper](To appear) | [Project Website](To appear) | BibTex Introduction As a popular entertainment art form, manga enriches the line drawings det






 133  Dec 15, 2022









Code for the paper: Hierarchical Reinforcement Learning With Timed Subgoals, published at NeurIPS 2021


 Hierarchical reinforcement learning with Timed Subgoals (HiTS) This repository contains code for reproducing experiments from our paper "Hierarchical 





Autonomous Learning Group
 21  Dec 03, 2022









Neuralnetwork - Basic Multilayer Perceptron Neural Network for deep learning


 Neural Network Just a basic Neural Network module Usage Example Importing Module





andreecy
 0  Nov 01, 2022









PyTorch implementation of D2C: Diffuison-Decoding Models for Few-shot Conditional Generation.


 D2C: Diffuison-Decoding Models for Few-shot Conditional Generation Project | Paper PyTorch implementation of D2C: Diffuison-Decoding Models for Few-sh





Jiaming Song
 90  Dec 27, 2022









This is an official implementation of "Polarized Self-Attention: Towards High-quality Pixel-wise Regression"


 Polarized Self-Attention: Towards High-quality Pixel-wise Regression This is an official implementation of: Huajun Liu, Fuqiang Liu, Xinyi Fan and Don





DeLightCMU
 212  Jan 08, 2023









A deep learning network built with TensorFlow and Keras to classify gender and estimate age.


 Convolutional Neural Network (CNN). This repository contains a source code of a deep learning network built with TensorFlow and Keras to classify gend





Pawel Dziemiach
 1  Dec 19, 2021









Distributed Evolutionary Algorithms in Python


 DEAP DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data stru





Distributed Evolutionary Algorithms in Python
 4.9k  Jan 05, 2023









A very short and easy implementation of Quantile Regression DQN


 Quantile Regression DQN Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression (https://arx





Arsenii Senya Ashukha
 80  Sep 17, 2022









AFL binary instrumentation


 E9AFL --- Binary AFL E9AFL inserts American Fuzzy Lop (AFL) instrumentation into x86_64 Linux binaries. This allows binaries to be fuzzed without the 






 242  Dec 12, 2022









Official Implementation for "StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery" (ICCV 2021 Oral)


 StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery (ICCV 2021 Oral) Run this model on Replicate Optimization: Global directions: Mapper: Check ou






 3.3k  Jan 05, 2023









Algorithmic Trading using RNN


 Deep-Trading This an implementation adapted from Rachnog Neural networks for algorithmic trading. Part One — Simple time series forecasting and this c





Hazem Nomer
 29  Sep 04, 2022









simple_pytorch_example project is a toy example of a python script that instantiates and trains a PyTorch neural network on the FashionMNIST dataset


 simple_pytorch_example project is a toy example of a python script that instantiates and trains a PyTorch neural network on the FashionMNIST dataset





Ramón Casero
 1  Jan 07, 2022