version 1.2 du tp2

exos/tp2_decision_tree/pmc.py

@@ -5,6 +5,7 @@ from sklearn.metrics import accuracy_score
 from keras.models import Sequential
 from keras.layers import Dense, Input
 from keras.utils import to_categorical
+import tensorflow as tf
 
 def load_data(file_path):
     column_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']
@@ -24,13 +25,17 @@ def preprocess_data(X, y):
 def create_model():
     model = Sequential()
     model.add(Input(shape=(4,)))
-    model.add(Dense(4, activation='relu'))
-    model.add(Dense(4, activation='relu'))
+    model.add(Dense(8, activation='relu'))
+    model.add(Dense(8, activation='relu'))
     model.add(Dense(3, activation='softmax'))
     model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
     return model
 
-def cross_validate_model(X, y, n_splits=5, epochs=10, batch_size=20):
+@tf.function
+def train_model(model, X_train, y_train, epochs, batch_size):
+    model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, verbose=0)
+
+def cross_validate_model(X, y, n_splits=5, epochs=50, batch_size=10):
     kf = KFold(n_splits=n_splits, shuffle=True, random_state=42)
     accuracies = []
     for fold, (train_index, test_index) in enumerate(kf.split(X)):
@@ -38,7 +43,7 @@ def cross_validate_model(X, y, n_splits=5, epochs=10, batch_size=20):
         X_train, X_test = X[train_index], X[test_index]
         y_train, y_test = y[train_index], y[test_index]
         model = create_model()
-        model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, verbose=0)
+        train_model(model, X_train, y_train, epochs, batch_size)
         y_pred = model.predict(X_test)
         y_pred_classes = y_pred.argmax(axis=1)
         y_test_classes = y_test.argmax(axis=1)