MLA_Learn_Periodic_Functions/code/fonctions_activations_classiques/premier_reseau.py

# -*- coding: utf-8 -*-
"""
Created on Wed Nov 10 10:30:04 2021

@author: virgi
"""
from Creation_donnee import *
import numpy as np
import math as ma
import matplotlib.pyplot as plt
import tensorflow as tf
tmin=-20
tmax=5
n=100000
X,Y=creation_sin(tmin,tmax,n,1,a=1,c=0)
tmin=5
tmax=20
Xv,Yv=creation_sin(tmin,tmax,n,1,a=1,c=0)

model = tf.keras.models.Sequential()

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

model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(128, activation='relu'))


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


# Choix de la méthode d'optimisation
opti=tf.keras.optimizers.Adam()
# Compilation du graphe et choix de la fonction de coût
model.compile(opti, loss='mse', metrics=['accuracy'])


model.summary()


model.fit(X, Y, batch_size=32, epochs=2, shuffle='True',validation_data=(Xv, Yv))

Y_predis=model.predict(X)
Y_predis_validation=model.predict(Xv)

plt.figure()
plt.plot(X,Y,label='donnée')
plt.plot(Xv,Yv,label="validation")
plt.plot(X,Y_predis,label='prediction sur les donné')
plt.plot(Xv,Y_predis_validation,label='prediction sur la validation')
plt.legend()
plt.show()