Merge branch 'Virgile'

code/fonctions_activations_classiques/Creation_donnee.py

@@ -0,0 +1,93 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Oct 26 17:53:54 2021
+
+@author: virgi
+"""
+import numpy as np
+import math as ma
+len_seq = 10
+def creation_sin_RNN(len_seq,tmin,tmax,n,w,a=1,b=0):
+    
+    
+    Datax, Datay = [], []
+    
+    t = np.linspace(tmin,tmax,n)
+    x =a* np.sin(2*ma.pi*w*t)+b
+    for i in range(len(x)-len_seq):
+        Datax.append([x[i:i+len_seq]])
+        Datay.append([x[i+1:i+len_seq+1]])    
+    Datax = np.array(Datax)
+    Datay = np.array(Datay)
+    
+    
+    return(Datax,Datay)
+
+
+
+
+def creation_x_sin_RNN(len_seq,tmin,tmax,n,w,a=1,b=1,c=0):
+    
+    
+    Datax, Datay = [], []
+    
+    t = np.linspace(tmin,tmax,n)
+    x=[]
+    for i in t:
+        x.append(a*i+b* np.sin(2*ma.pi*w*i)+c)
+    for i in range(len(x)-len_seq):
+        Datax.append([x[i:i+len_seq]])
+        Datay.append([x[i+1:i+len_seq+1]])    
+    Datax = np.array(Datax)
+    Datay = np.array(Datay)
+    
+    
+    return(Datax,Datay)
+
+def creation_x_sin2_RNN(len_seq,tmin,tmax,n,w,a=1,b=1,c=0):
+    
+    
+    Datax, Datay = [], []
+    
+    t = np.linspace(tmin,tmax,n)
+    x=[]
+    for i in t:
+        x.append(a*i+b*np.sin(2*ma.pi*w*i)*np.sin(2*ma.pi*w*i)+c)
+    for i in range(len(x)-len_seq):
+        Datax.append([x[i:i+len_seq]])
+        Datay.append([x[i+1:i+len_seq+1]])    
+    Datax = np.array(Datax)
+    Datay = np.array(Datay)
+    
+    
+    return(Datax,Datay)
+
+
+
+
+def creation_sin(tmin,tmax,n,w,a=1,c=0):
+    Lx=[]
+    t = np.linspace(tmin,tmax,n)
+    for i in t:
+        Lx.append(a*np.sin(w*i)+c)
+        
+    Lx=np.array(Lx)
+    return(t,Lx)
+
+def creation_x_sin(tmin,tmax,n,w,a=1,b=0,c=0):
+    Lx=[]
+    t = np.linspace(tmin,tmax,n)
+    for i in t:
+        Lx.append(a*i+b* np.sin(2*ma.pi*w*i)+c)
+    Lx=np.array(Lx)
+    return(t,Lx)
+
+def creation_x_sin2(tmin,tmax,n,w,a=1,b=1,c=0):
+    Lx=[]
+    t = np.linspace(tmin,tmax,n)
+    for i in t:
+        Lx.append(a*i+b*np.sin(2*ma.pi*w*i)*np.sin(2*ma.pi*w*i)+c)
+    Lx=np.array(Lx)
+    return(t,Lx)
+
+

code/fonctions_activations_classiques/fonction_activation.py

@@ -0,0 +1,22 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Nov 24 14:48:00 2021
+
+@author: virgi
+"""
+import tensorflow as tf
+
+
+
+def snake(x, alpha=1.0):
+    return (x + tf.sin(x)**2/alpha)
+
+
+def x_sin(x,alpha=1.0):
+    return (x + tf.sin(x)/alpha)
+
+def sin(x,alpha=1.0):
+    return(tf.sin(x)/alpha)
+
+
+

code/fonctions_activations_classiques/premier_reseau.py

@@ -0,0 +1,54 @@
+# -*- 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()

code/fonctions_activations_classiques/sin.py

@@ -0,0 +1,77 @@
+# -*- 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
+
+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_sin=tf.keras.models.Sequential()
+
+model_sin.add(tf.keras.Input(shape=(1,)))
+
+model_sin.add(tf.keras.layers.Dense(4, activation=sin))
+model_sin.add(tf.keras.layers.Dense(4, activation=sin))
+model_sin.add(tf.keras.layers.Dense(4, activation=sin))
+model_sin.add(tf.keras.layers.Dense(4, activation=sin))
+model_sin.add(tf.keras.layers.Dense(4, activation=sin))
+
+
+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=1, epochs=10, 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 donné avec sin comme activation')
+plt.plot(Xv,Y_predis_validation_sin,label='prediction sur la validation avec sin comme activation')
+plt.legend()
+plt.show()
+
+
+"""
+Created on Wed Nov 24 16:53:37 2021
+
+@author: virgi
+"""
+

code/fonctions_activations_classiques/snake_vs_ReLU.py

@@ -0,0 +1,108 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Nov 24 15:44:12 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_ReLU=tf.keras.models.Sequential()
+
+
+model_ReLU.add(tf.keras.Input(shape=(1,)))
+
+model_ReLU.add(tf.keras.layers.Dense(64, activation='relu'))
+model_ReLU.add(tf.keras.layers.Dense(64, activation='relu'))
+model_ReLU.add(tf.keras.layers.Dense(64, activation='relu'))
+model_ReLU.add(tf.keras.layers.Dense(64, activation='relu'))
+model_ReLU.add(tf.keras.layers.Dense(64, activation='relu'))
+
+
+model_ReLU.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_ReLU.compile(opti, loss='mse', metrics=['accuracy'])
+
+model_ReLU.summary()
+
+
+
+model_ReLU.fit(X, Y, batch_size=1, epochs=10, shuffle='True',validation_data=(Xv, Yv))
+
+
+
+model_snake=tf.keras.models.Sequential()
+
+model_snake.add(tf.keras.Input(shape=(1,)))
+
+model_snake.add(tf.keras.layers.Dense(64, activation=snake))
+model_snake.add(tf.keras.layers.Dense(64, activation=snake))
+model_snake.add(tf.keras.layers.Dense(64, activation=snake))
+model_snake.add(tf.keras.layers.Dense(64, activation=snake))
+model_snake.add(tf.keras.layers.Dense(64, activation=snake))
+
+
+model_snake.add(tf.keras.layers.Dense(1))
+
+opti=tf.keras.optimizers.Adam()
+
+model_snake.compile(opti, loss='mse', metrics=['accuracy'])
+
+
+model_snake.summary()
+
+model_snake.fit(X, Y, batch_size=1, epochs=10, shuffle='True',validation_data=(Xv, Yv))
+
+
+
+
+
+
+
+
+
+Y_predis_snake=model_snake.predict(X)
+Y_predis_validation_snake=model_snake.predict(Xv)
+
+Y_predis_ReLU=model_ReLU.predict(X)
+Y_predis_validation_ReLU=model_ReLU.predict(Xv)
+
+
+
+
+
+plt.figure()
+plt.plot(X,Y,'x',label='donnée')
+plt.plot(Xv,Yv,label="validation")
+plt.plot(X,Y_predis_ReLU,'o',label='prediction sur les donné')
+plt.plot(Xv,Y_predis_validation_ReLU,label='prediction sur la validation')
+plt.legend()
+plt.show()
+
+plt.figure()
+plt.plot(X,Y,'x',label='donnée')
+plt.plot(Xv,Yv,label="validation")
+plt.plot(X,Y_predis_snake,'o',label='prediction sur les donné avec snake comme activation')
+plt.plot(Xv,Y_predis_validation_snake,label='prediction sur la validation avec snake comme activation')
+plt.legend()
+plt.show()
+

code/fonctions_activations_classiques/swish.py

@@ -0,0 +1,75 @@
+# -*- 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
+"""
+

code/fonctions_activations_classiques/tanh_vs_ReLU.py

@@ -0,0 +1,110 @@
+# -*- 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_ReLU=tf.keras.models.Sequential()
+
+
+model_ReLU.add(tf.keras.Input(shape=(1,)))
+
+model_ReLU.add(tf.keras.layers.Dense(64, activation='relu'))
+model_ReLU.add(tf.keras.layers.Dense(64, activation='relu'))
+model_ReLU.add(tf.keras.layers.Dense(64, activation='relu'))
+model_ReLU.add(tf.keras.layers.Dense(64, activation='relu'))
+model_ReLU.add(tf.keras.layers.Dense(64, activation='relu'))
+
+
+model_ReLU.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_ReLU.compile(opti, loss='mse', metrics=['accuracy'])
+
+model_ReLU.summary()
+
+
+
+model_ReLU.fit(X, Y, batch_size=1, epochs=10, shuffle='True',validation_data=(Xv, Yv))
+
+
+
+model_tanh=tf.keras.models.Sequential()
+
+model_tanh.add(tf.keras.Input(shape=(1,)))
+
+model_tanh.add(tf.keras.layers.Dense(64, activation='tanh'))
+model_tanh.add(tf.keras.layers.Dense(64, activation='tanh'))
+model_tanh.add(tf.keras.layers.Dense(64, activation='tanh'))
+model_tanh.add(tf.keras.layers.Dense(64, activation='tanh'))
+model_tanh.add(tf.keras.layers.Dense(64, activation='tanh'))
+
+
+model_tanh.add(tf.keras.layers.Dense(1))
+
+opti=tf.keras.optimizers.Adam()
+
+model_tanh.compile(opti, loss='mse', metrics=['accuracy'])
+
+
+model_tanh.summary()
+
+model_tanh.fit(X, Y, batch_size=1, epochs=10, shuffle='True',validation_data=(Xv, Yv))
+
+
+
+
+
+
+
+
+
+Y_predis_tanh=model_tanh.predict(X)
+Y_predis_validation_tanh=model_tanh.predict(Xv)
+
+Y_predis_ReLU=model_ReLU.predict(X)
+Y_predis_validation_ReLU=model_ReLU.predict(Xv)
+
+
+
+
+
+plt.figure()
+plt.plot(X,Y,'x',label='donnée')
+plt.plot(Xv,Yv,label="validation")
+plt.plot(X,Y_predis_ReLU,'o',label='prediction sur les donné')
+plt.plot(Xv,Y_predis_validation_ReLU,label='prediction sur la validation')
+plt.legend()
+plt.show()
+
+plt.figure()
+plt.plot(X,Y,'x',label='donnée')
+plt.plot(Xv,Yv,label="validation")
+plt.plot(X,Y_predis_tanh,'o',label='prediction sur les donné avec tanh comme activation')
+plt.plot(Xv,Y_predis_validation_tanh,label='prediction sur la validation avec tanh comme activation')
+plt.legend()
+plt.show()
+

code/fonctions_activations_classiques/x_sin.py

@@ -0,0 +1,84 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Nov 24 17:02:16 2021
+
+@author: virgi
+"""
+
+# -*- 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
+
+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_xsin=tf.keras.models.Sequential()
+
+model_xsin.add(tf.keras.Input(shape=(1,)))
+
+model_xsin.add(tf.keras.layers.Dense(64, activation=x_sin))
+model_xsin.add(tf.keras.layers.Dense(64, activation=x_sin))
+model_xsin.add(tf.keras.layers.Dense(64, activation=x_sin))
+model_xsin.add(tf.keras.layers.Dense(64, activation=x_sin))
+model_xsin.add(tf.keras.layers.Dense(64, activation=x_sin))
+
+
+model_xsin.add(tf.keras.layers.Dense(1))
+
+opti=tf.keras.optimizers.Adam()
+
+model_xsin.compile(opti, loss='mse', metrics=['accuracy'])
+
+
+model_xsin.summary()
+
+model_xsin.fit(X, Y, batch_size=1, epochs=10, shuffle='True',validation_data=(Xv, Yv))
+
+
+
+
+Y_predis_xsin=model_xsin.predict(X)
+Y_predis_validation_xsin=model_xsin.predict(Xv)
+
+
+
+
+plt.figure()
+plt.plot(X,Y,'x',label='donnée')
+plt.plot(Xv,Yv,label="validation")
+plt.plot(X,Y_predis_xsin,'o',label='prediction sur les donné avec x+sin comme activation')
+plt.plot(Xv,Y_predis_validation_xsin,label='prediction sur la validation avec x+sin comme activation')
+plt.legend()
+plt.show()
+
+
+"""
+Created on Wed Nov 24 16:53:37 2021
+
+@author: virgi
+"""
+