Deep learning model, heat map, data prepo

Related tags

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




 GitHub Repository










Deep Learning Visuals contains 215 unique images divided in 23 categories 


 Deep Learning Visuals contains 215 unique images divided in 23 categories (some images may appear in more than one category). All the images were originally published in my book "Deep Learning with P





Daniel Voigt Godoy
 1.3k  Dec 28, 2022









 NEO: Non Equilibrium Sampling on the orbit of a deterministic transform


 NEO: Non Equilibrium Sampling on the orbit of a deterministic transform Description of the code This repo describes the NEO estimator described in the






 0  Dec 01, 2021









 Fake News Detection Using Machine Learning Methods


 Fake-News-Detection-Using-Machine-Learning-Methods Fake news is always a real and dangerous issue. However, with the presence and abundance of various





Achraf Safsafi
 1  Jan 11, 2022









Ranger deep learning optimizer rewrite to use newest components 


 Ranger21 - integrating the latest deep learning components into a single optimizer Ranger deep learning optimizer rewrite to use newest components Ran





Less Wright
 266  Dec 28, 2022









Pytorch implementation of MaskGIT: Masked Generative Image Transformer


 Pytorch implementation of MaskGIT: Masked Generative Image Transformer





Dominic Rampas
 247  Dec 16, 2022









Official PyTorch implementation of "BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation" (NeurIPS 2021)


 BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation Official PyTorch implementation of the NeurIPS 2021 paper Mingcong Liu, Qiang





onion
 462  Dec 29, 2022









[ACM MM 2021] Diverse Image Inpainting with Bidirectional and Autoregressive Transformers


 Diverse Image Inpainting with Bidirectional and Autoregressive Transformers Installation pip install -r requirements.txt Dataset Preparation Given the





Yingchen Yu
 25  Nov 09, 2022









FinGAT: A Financial Graph Attention Networkto Recommend Top-K Profitable Stocks


 FinGAT: A Financial Graph Attention Networkto Recommend Top-K Profitable Stocks This is our implementation for the paper: FinGAT: A Financial Graph At





Yu-Che Tsai
 64  Dec 13, 2022









A highly efficient, fast, powerful and light-weight anime downloader and streamer for your favorite anime.


 AnimDL - Download & Stream Your Favorite Anime AnimDL is an incredibly powerful tool for downloading and streaming anime. Core features Abuses the dev





KR
 759  Jan 08, 2023









State of the Art Neural Networks for Deep Learning


 pyradox This python library helps you with implementing various state of the art neural networks in a totally customizable fashion using Tensorflow 2 





Ritvik Rastogi
 60  May 29, 2022









This repository allows you to anonymize sensitive information in images/videos. The solution is fully compatible with the DL-based training/inference solutions that we already published/will publish for Object Detection and Semantic Segmentation.


 BMW-Anonymization-Api Data privacy and individuals’ anonymity are and always have been a major concern for data-driven companies. Therefore, we design





BMW TechOffice MUNICH
 148  Dec 21, 2022









Probabilistic Tensor Decomposition of Neural Population Spiking Activity


 Probabilistic Tensor Decomposition of Neural Population Spiking Activity Matlab (recommended) and Python (in developement) implementations of Soulat e





Hugo Soulat
 6  Nov 30, 2022









Apache Flink


 Apache Flink Apache Flink is an open source stream processing framework with powerful stream- and batch-processing capabilities. Learn more about Flin





The Apache Software Foundation
 20.4k  Dec 30, 2022









Implementation of Transformer in Transformer, pixel level attention paired with patch level attention for image classification, in Pytorch


 Transformer in Transformer Implementation of Transformer in Transformer, pixel level attention paired with patch level attention for image c





Phil Wang
 272  Dec 23, 2022









Very large and sparse networks appear often in the wild and present unique algorithmic opportunities and challenges for the practitioner


 Sparse network learning with snlpy Very large and sparse networks appear often in the wild and present unique algorithmic opportunities and challenges





Andrew Stolman
 1  Apr 30, 2021









Python library to receive live stream events like comments and gifts in realtime from TikTok LIVE.


 TikTokLive A python library to connect to and read events from TikTok's LIVE service A python library to receive and decode livestream events such as 





Isaac Kogan
 277  Dec 23, 2022









Making Structure-from-Motion (COLMAP) more robust to symmetries and duplicated structures


 SfM disambiguation with COLMAP About Structure-from-Motion generally fails when the scene exhibits symmetries and duplicated structures. In this repos





Computer Vision and Geometry Lab
 193  Dec 26, 2022









This is the official repository of Music Playlist Title Generation: A Machine-Translation Approach.


 PlyTitle_Generation This is the official repository of Music Playlist Title Generation: A Machine-Translation Approach. The paper has been accepted by





SeungHeonDoh
 6  Jan 03, 2022









Unofficial Implementation of MLP-Mixer, Image Classification Model


 MLP-Mixer Unoffical Implementation of MLP-Mixer, easy to use with terminal. Train and test easly. https://arxiv.org/abs/2105.01601 MLP-Mixer is an arc





Oğuzhan Ercan
 6  Dec 05, 2022









UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language


 UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language This repository contains UA-GEC data and an accompanying Python lib





Grammarly
 226  Dec 29, 2022