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










Pytorch implementation for the paper: Contrastive Learning for Cold-start Recommendation


 Contrastive Learning for Cold-start Recommendation This is our Pytorch implementation for the paper: Yinwei Wei, Xiang Wang, Qi Li, Liqiang Nie, Yan L






 45  Dec 13, 2022









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


 StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation This repository contains the official PyTorch implementation of the following 





Wonjong Jang
 270  Dec 30, 2022









This was initially the repo for the project of [email protected] of Asaf Mazar, Millad Kassaie and Georgios Chochlakis named "Powered by the Will? Exploring Lay Theories of Behavior Change through Social Media"


 Subreddit Analysis This repo includes tools for Subreddit analysis, originally developed for our class project of PSYC 626 in USC, titled "Powered by 





Georgios Chochlakis
 1  Dec 17, 2021









A full pipeline AutoML tool for tabular data


 HyperGBM Doc | 中文 We Are Hiring！ Dear folks,we are offering challenging opportunities located in Beijing for both professionals and students who are k





DataCanvas
 240  Jan 03, 2023









A collection of educational notebooks on multi-view geometry and computer vision.


 Multiview notebooks This is a collection of educational notebooks on multi-view geometry and computer vision. Subjects covered in these notebooks incl





Max
 65  Dec 09, 2022









Tooling for converting STAC metadata to ODC data model


 手语识别 0、使用到的模型 (1). openpose，作者：CMU-Perceptual-Computing-Lab https://github.com/CMU-Perceptual-Computing-Lab/openpose
(2). 图像分类classification，作者：Bubbl





Open Data Cube
 65  Dec 20, 2022









Gradient Step Denoiser for convergent Plug-and-Play


 Source code for the paper "Gradient Step Denoiser for convergent Plug-and-Play"





Samuel Hurault
 11  Sep 17, 2022









Selfplay In MultiPlayer Environments


 This project allows you to train AI agents on custom-built multiplayer environments, through self-play reinforcement learning.






 200  Jan 08, 2023









YKKDetector For Python


 YKKDetector OpenCVを利用した機械学習データをもとに、VRChatのスクリーンショットなどからＹＫＫさん（もとい「幽狐族のお姉様」）を検出できるソフトウェアです。 マニュアル こちらから実行環境のセットアップから解説する詳細なマニュアルをご覧いただけます。 ライセンス 本ソフトウェア





あんふぃとらいと
 5  Dec 07, 2021









This solves the autonomous driving issue which is supported by deep learning technology. Given a video, it splits into images and predicts the angle of turning for each frame.


 Self Driving Car An autonomous car (also known as a driverless car, self-driving car, and robotic car) is a vehicle that is capable of sensing its env





Sagor Saha
 4  Sep 04, 2021









 Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation


 Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation Introduction This is a PyTorch





XMed-Lab
 30  Sep 23, 2022









Exploring Relational Context for Multi-Task Dense Prediction [ICCV 2021]


 Adaptive Task-Relational Context (ATRC) This repository provides source code for the ICCV 2021 paper Exploring Relational Context for Multi-Task Dense





David Brüggemann
 35  Dec 05, 2022









This repository provides data for the VAW dataset as described in the CVPR 2021 paper titled "Learning to Predict Visual Attributes in the Wild"


 Visual Attributes in the Wild (VAW) This repository provides data for the VAW dataset as described in the CVPR 2021 Paper: Learning to Predict Visual 





Adobe Research
 36  Dec 30, 2022









Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.


 NuPIC Numenta Platform for Intelligent Computing The Numenta Platform for Intelligent Computing (NuPIC) is a machine intelligence platform that implem





Numenta
 6.3k  Dec 30, 2022









Music Generation using Neural Networks Streamlit App


 Music_Gen_Streamlit "Music Generation using Neural Networks" Streamlit App TO DO: Make a run_app.sh Introduction [~5 min] (Sohaib) Team Member names/i





Muhammad Sohaib Arshid
 6  Aug 09, 2022









MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.


 MMdnn MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model manage





Microsoft
 5.7k  Jan 09, 2023









code and models for "Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation"


 Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation This repository contains code and models for the method described in: Golnaz






 55  Jun 18, 2022









This repository contains datasets and baselines for benchmarking Chinese text recognition.


 Benchmarking-Chinese-Text-Recognition This repository contains datasets and baselines for benchmarking Chinese text recognition. Please see the corres





FudanVI Lab
 254  Dec 30, 2022









HybVIO visual-inertial odometry and SLAM system


 HybVIO A visual-inertial odometry system with an optional SLAM module. This is a research-oriented codebase, which has been published for the purposes





Spectacular AI
 320  Jan 03, 2023









PyExplainer: A Local Rule-Based Model-Agnostic Technique (Explainable AI)


 PyExplainer PyExplainer is a local rule-based model-agnostic technique for generating explanations (i.e., why a commit is predicted as defective) of J





AI Wizards for Software Management (AWSM) Research Group
 14  Nov 13, 2022