Modele_noms.py

#@title Le modele pour nom et son entrainement



        
# Création du modèle
import keras
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten, Dropout



model = Sequential()
# input: images de 128x36x1
# On applique des conv2D
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(128, 36, 1)))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.SGD(lr=0.01), metrics=['accuracy'])


#Séparer nos données entre "train" et "test"
X_train, Y_train, X_test, Y_test=(gerer_data(0.5,1,1))

print(np.asarray(Y_train).shape)

#Entrainement du modèle
model.fit(np.asarray(X_train), np.asarray(Y_train), epochs=500, batch_size=10)


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#@title générer X et Y des noms, faire prediction, adapter resultats, afficher matrice de sklearn.metrics



#Calculer les resultats (Y_prediction) avec notre modèle entrainé
Y_pred = model.predict(np.asarray(X_test))

#Transformer les résultats : [0.001, 0.001, 0.9999,...] -> [0, 0, 1,...]
Y_pred = remettre_0_ou_1_dans_Y_pred(Y_pred)

#Transformer [ [0,1,0,0] , [0,0,1,0]... ] en [1,2,3,1,0...]
Y_test_mat = adapter(Y_test)
Y_pred_mat = adapter(Y_pred)

#Afficher matrice
from sklearn.metrics import confusion_matrix
Matrice = confusion_matrix(Y_test_mat, Y_pred_mat)

print(Matrice)

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#@title Afficher matrice noms avec la librairie
plot_confusion_matrix_from_data(Y_test_mat, Y_pred_mat, columns=["jackson","nicolas","theo","yweweler"], annot=True, cmap="Blue",
      fmt='.2f', fz=11, lw=0.5, cbar=False, figsize=[8,8], show_null_values=0, pred_val_axis='lin')