add sanke function to resnet

code/resnet18/resnet18.py

 
         x = self.conv_1(inputs)
         x = self.bn_1(x)
-        x = tf.nn.relu(x)
+        x = x + tf.sin(x)**2 #tf.nn.relu(x)
         x = self.conv_2(x)
         x = self.bn_2(x)
 
 
         # if not perform down sample, then add a shortcut directly
         x = self.merge([x, res])
-        out = tf.nn.relu(x)
+        out = x + tf.sin(x)**2 #tf.nn.relu(x)
         return out
 
 
     def call(self, inputs):
         out = self.conv_1(inputs)
         out = self.init_bn(out)
-        out = tf.nn.relu(out)
+        out = x + tf.sin(x)**2 #tf.nn.relu(out)
         out = self.pool_2(out)
         for res_block in [self.res_1_1, self.res_1_2, self.res_2_1, self.res_2_2, self.res_3_1, self.res_3_2, self.res_4_1, self.res_4_2]:
             out = res_block(out)

code/resnet18/resnet18_snake.py

 
 model = ResNet18(10)
 model.build(input_shape = (None,32,32,3))
-filter_size_conv1 = (5,5)
 
 '''
+filter_size_conv1 = (5,5)
 ## Définition de l'architecture du modèle
 model = tf.keras.models.Sequential()
 # Expliquez à quoi correspondent les valeurs numériques qui définissent les couches du réseau