MLA_Learn_Periodic_Functions/code/fonctions_activations_classiques/snake.py

# -*- coding: utf-8 -*-
"""
Created on Wed Nov 24 16:58:44 2021

@author: virgi
"""





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

from Creation_donnee import *
import numpy as np

w=10
n=2000
#création de la base de donnéé
X,Y=creation_sin(-2.5,-1,n,w)
X2,Y2=creation_sin(3,3.5,n,w)
X=np.concatenate([X,X2])
Y=np.concatenate([Y,Y2])

n=10000
Xv,Yv=creation_sin(-3,3,n,w)





model_sin=tf.keras.models.Sequential()

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

# model_sin.add(tf.keras.layers.Dense(64, activation=sin))
# model_sin.add(tf.keras.layers.Dense(64, activation=sin))
# model_sin.add(tf.keras.layers.Dense(64, activation=sin))
# model_sin.add(tf.keras.layers.Dense(64, activation=sin))
# model_sin.add(tf.keras.layers.Dense(512, activation=sin))
# model_sin.add(tf.keras.layers.Dense(64, activation=sin))
model_sin.add(tf.keras.layers.Dense(512, activation=snake))
model_sin.add(tf.keras.layers.Dense(1))

opti=tf.keras.optimizers.Adam()

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


model_sin.summary()

model_sin.fit(X, Y, batch_size=16, epochs=100, shuffle='True',validation_data=(Xv, Yv))




Y_predis_sin=model_sin.predict(X)
Y_predis_validation_sin=model_sin.predict(Xv)




plt.figure()
plt.plot(X,Y,'x',label='donnée')
plt.plot(Xv,Yv,label="validation")
plt.plot(X,Y_predis_sin,'o',label='prediction sur les données avec snake')
plt.plot(Xv,Y_predis_validation_sin,label='prediction sur la validation avec snake')
plt.legend()
plt.show()