MLA_Learn_Periodic_Functions/code/fonctions_activations_classiques/swish.py

# -*- coding: utf-8 -*-
"""
Created on Wed Nov 24 16:48:52 2021

@author: virgi
"""



import tensorflow as tf
import matplotlib.pyplot as plt
from fonction_activation import *

from Creation_donnee import *
import numpy as np

n=20
#création de la base de donnéé
X,Y=creation_sin(-15,-8,n,1,)
X2,Y2=creation_sin(10,18,n,1,)
X=np.concatenate([X,X2])
Y=np.concatenate([Y,Y2])

n=10000
Xv,Yv=creation_sin(-20,20,n,1)




model_swish=tf.keras.models.Sequential()

model_swish.add(tf.keras.Input(shape=(1,)))

model_swish.add(tf.keras.layers.Dense(64, activation='swish'))
model_swish.add(tf.keras.layers.Dense(64, activation='swish'))
model_swish.add(tf.keras.layers.Dense(64, activation='swish'))
model_swish.add(tf.keras.layers.Dense(64, activation='swish'))
model_swish.add(tf.keras.layers.Dense(64, activation='swish'))


model_swish.add(tf.keras.layers.Dense(1))

opti=tf.keras.optimizers.Adam()

model_swish.compile(opti, loss='mse', metrics=['accuracy'])


model_swish.summary()

model_swish.fit(X, Y, batch_size=1, epochs=10, shuffle='True',validation_data=(Xv, Yv))




Y_predis_swish=model_swish.predict(X)
Y_predis_validation_swish=model_swish.predict(Xv)




plt.figure()
plt.plot(X,Y,'x',label='donnée')
plt.plot(Xv,Yv,label="validation")
plt.plot(X,Y_predis_swish,'o',label='prediction sur les donné avec swish comme activation')
plt.plot(Xv,Y_predis_validation_swish,label='prediction sur la validation avec swish comme activation')
plt.legend()
plt.show()


"""
Created on Wed Nov 24 16:53:37 2021

@author: virgi
"""