maj des fonctions d'activations classique pour nouveau tests

code/fonctions_activations_classiques_bis/trian_sin.py

 import numpy as np
 import optuna
 
-densite = 10
+densite = 100
 
-start1 = 5
-stop1 = 15
-start2 = 35
-stop2 = 45
+start1 = 15
+stop1 = 35
+start2 = 55
+stop2 = 75
 
-start = -50
-stop = 150
+start = 0
+stop = 100
 
 training = [np.concatenate((np.linspace(start1, stop1, (stop1-start1)*densite),
 							np.linspace(start2, stop2, (stop2-start2)*densite)))]
 
 
 def objectif(trial) :
-	HIDDEN = trial.suggest_int('hidden',64,512)
-	SIZE = trial.suggest_int('size',0,2)
+	HIDDEN = trial.suggest_int('hidden',32,128)
+	SIZE = trial.suggest_int('size',0,4)
 	DROPOUT = trial.suggest_float('dropout', 0,0.3)
 	model = tf.keras.Sequential()
 	model.add(tf.keras.layers.Dense(HIDDEN, input_shape=(1,), activation=tfa.activations.snake))
 				  metrics=['mse'])
 
 	history = model.fit(x=training[0], y=training[1], batch_size=4,
-		                epochs=3000, shuffle=True,
+		                epochs=100, shuffle=True,
 		                #validation_data=(validation[0], validation[1]),
 		                verbose='auto')