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Deep Learning Democrate_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)/

 


		                      

		                  
	                  
					

 
				

							
							
							
		
		
		

					

                 	  
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 OpenTTD Surveyor A tool for making map images from OpenTTD save games. This is not part of the main OpenTTD codebase, nor is it ever intended to be pa

		    
		  

		  
		 
		  

		       Aidan Randle-Conde
	            9  Feb 15, 2022
          	
     
     	 
     	 
 		       
 
             

		     
A neuroanatomy-based augmented reality experience powered by computer vision. Features 3D visuals of the Atlas Brain Map slices.

		    
 Brain Augmented Reality (AR) A neuroanatomy-based augmented reality experience powered by computer vision that features 3D visuals of the Atlas Brain 

		    
		  

		  
		 
		  

		       Yasmeen Brain
	            10  Oct 6, 2022
          	
     
     	 
     	 
 		       
 
               
                    A large dataset of 100k Google Satellite and matching Map images, resembling pix2pix's Google Maps dataset.
                
             

		     
A large dataset of 100k Google Satellite and matching Map images, resembling pix2pix's Google Maps dataset.

		    
 Larger Google Sat2Map dataset This dataset extends the aerial ⟷ Maps dataset used in pix2pix (Isola et al., CVPR17). The provide script download_sat2m

		    
		  

		  
		 
		  

		       null
	            34  Dec 28, 2022
          	
     
     	 
     	 
 		       
 
               
                    Codes for TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization.
                
             

		     
Codes for TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization.

		    
 TS-CAM: Token Semantic Coupled Attention Map for Weakly SupervisedObject Localization This is the official implementaion of paper TS-CAM: Token Semant

		    
		  

		  
		 
		  

		       vasgaowei
	            112  Jan 2, 2023
          	
     
     	 
     	 
 		       
 
               
                    Implementation of the Triangle Multiplicative module, used in Alphafold2 as an efficient way to mix rows or columns of a 2d feature map, as a standalone package for Pytorch
                
             

		     
Implementation of the Triangle Multiplicative module, used in Alphafold2 as an efficient way to mix rows or columns of a 2d feature map, as a standalone package for Pytorch

		    
 Triangle Multiplicative Module - Pytorch Implementation of the Triangle Multiplicative module, used in Alphafold2 as an efficient way to mix rows or c

		    
		  

		  
		 
		  

		       Phil Wang
	            22  Oct 28, 2022
          	
     
     	 
     	 
 		       
 
             

		     
Official implementation of "StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation" (SIGGRAPH 2021)

		    
 StyleCariGAN in PyTorch Official implementation of StyleCariGAN:Caricature Generation via StyleGAN Feature Map Modulation in PyTorch Requirements PyTo

		    
		  

		  
		 
		  

		       PeterZhouSZ
	            49  Oct 31, 2022
          	
     
     	 
     	 
 		       
	                    

	                

               
	
																															
				
                              

               

               

                  
                        

                        
	                     

		                     

		                        
Owner
		                        

		                     

                              

                                 

                                    

										Pamela Dekas
									

                                    

											Adepte de text mining, deep learning and data visualization
									

                                 

                                 	Pamela Dekas
                              

                              		                     
                              
       
    							 GitHub 
		                 	

		                    	                     
	                     

	                     
	                     	
		 						   

				                     

				                        
Heat transfer problemas solved using python

				                        
heat-transfer Heat transfer problems solved using python isolation-convection.py compares the temperature distribution on the problem as shown in the 

				                     

				                     

				                     	 null
				                          2  Nov 14, 2021
				                     

				                  

		 						   

				                     

				                        
Nest Protect integration for Home Assistant. This will allow you to integrate your smoke, heat, co and occupancy status real-time in HA.

				                        
Nest Protect integration for Home Assistant Custom component for Home Assistant to interact with Nest Protect devices via an undocumented and unoffici

				                     

				                     

				                     	 Mick Vleeshouwer
				                          175  Dec 29, 2022
				                     

				                  

		 						   

				                     

				                        
This YoloV5 based model is fit to detect people and different types of land vehicles, and displaying their density on a fitted map, according to their coordinates and detected labels.

				                        
This YoloV5 based model is fit to detect people and different types of land vehicles, and displaying their density on a fitted map, according to their

				                     

				                     

				                     	 Liron Bdolah
				                          8  May 22, 2022
				                     

				                  

		 						   

				                     

				                        
An end-to-end image translation model with weight-map for color constancy

				                        
CCUnet An end-to-end image translation model with weight-map for color constancy 1. Download the dataset (take Colorchecker_recommended dataset as an 

				                     

				                     

				                     	 Jianhui Qiu
				                          1  Dec 21, 2021
				                     

				                  

		 						   

				                     

				                        
Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation (CVPR 2022)

				                        
CCAM (Unsupervised) Code repository for our paper "CCAM: Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localizati

				                     

				                     

				                     	 Computer Vision Insitute, SZU
				                          113  Dec 27, 2022
				                     

				                  

		 						   

				                     

				                        
😇A pyTorch implementation of the DeepMoji model: state-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc

				                        
------ Update September 2018 ------ It's been a year since TorchMoji and DeepMoji were released. We're trying to understand how it's being used such t

				                     

				                     

				                     	 Hugging Face
				                          865  Dec 24, 2022
				                     

				                  

		 						   

				                     

				                        
A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.

				                        
chitra What is chitra? chitra (चित्र) is a multi-functional library for full-stack Deep Learning. It simplifies Model Building, API development, and M

				                     

				                     

				                     	 Aniket Maurya
				                          210  Dec 21, 2022
				                     

				                  

		 						   

				                     

				                        
deep-table implements various state-of-the-art deep learning and self-supervised learning algorithms for tabular data using PyTorch.

				                        
deep-table implements various state-of-the-art deep learning and self-supervised learning algorithms for tabular data using PyTorch.

				                     

				                     

				                     	 null
				                          63  Oct 17, 2022
				                     

				                  

		 						   

				                     

				                        
A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.

				                        
Object Pose Estimation Demo This tutorial will go through the steps necessary to perform pose estimation with a UR3 robotic arm in Unity. You’ll gain 

				                     

				                     

				                     	 Unity Technologies
				                          187  Dec 24, 2022
				                     

				                  

		 						   

				                     

				                        
 Predicting Semantic Map Representations from Images with Pyramid Occupancy Networks

				                        
This is the code associated with the paper Predicting Semantic Map Representations from Images with Pyramid Occupancy Networks, published at CVPR 2020.

				                     

				                     

				                     	 Thomas Roddick
				                          219  Dec 20, 2022