diff --git a/PyPI/setup.cfg b/PyPI/setup.cfg
index c9827c7..fa4fa14 100644
--- a/PyPI/setup.cfg
+++ b/PyPI/setup.cfg
@@ -1,7 +1,7 @@
 [metadata]
 # replace with your username:
 name = guan
-version = 0.1.98
+version = 0.1.99
 author = guanjihuan
 author_email = guanjihuan@163.com
 description = An open source python package
diff --git a/PyPI/src/guan.egg-info/PKG-INFO b/PyPI/src/guan.egg-info/PKG-INFO
index 05c6917..0b46252 100644
--- a/PyPI/src/guan.egg-info/PKG-INFO
+++ b/PyPI/src/guan.egg-info/PKG-INFO
@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: guan
-Version: 0.1.98
+Version: 0.1.99
 Summary: An open source python package
 Home-page: https://py.guanjihuan.com
 Author: guanjihuan
diff --git a/PyPI/src/guan/machine_learning.py b/PyPI/src/guan/machine_learning.py
index 4fbabad..ac698d0 100644
--- a/PyPI/src/guan/machine_learning.py
+++ b/PyPI/src/guan/machine_learning.py
@@ -18,6 +18,10 @@ def fully_connected_neural_network_with_one_hidden_layer(input_size=1, hidden_si
                 hidden_output = torch.nn.functional.sigmoid(self.hidden_layer(x))
             elif activation == 'tanh':
                 hidden_output = torch.nn.functional.tanh(self.hidden_layer(x))
+            elif activation == 'gelu':
+                hidden_output = torch.nn.functional.gelu(self.hidden_layer(x))
+            elif activation == 'silu':
+                hidden_output = torch.nn.functional.silu(self.hidden_layer(x))
             else:
                 hidden_output = self.hidden_layer(x)
             output = self.output_layer(hidden_output)
@@ -44,6 +48,10 @@ def fully_connected_neural_network_with_two_hidden_layers(input_size=1, hidden_s
                 hidden_output_1 = torch.nn.functional.sigmoid(self.hidden_layer_1(x))
             elif activation_1 == 'tanh':
                 hidden_output_1 = torch.nn.functional.tanh(self.hidden_layer_1(x))
+            elif activation_1 == 'gelu':
+                hidden_output_1 = torch.nn.functional.gelu(self.hidden_layer_1(x))
+            elif activation_1 == 'silu':
+                hidden_output_1 = torch.nn.functional.silu(self.hidden_layer_1(x))
             else:
                 hidden_output_1 = self.hidden_layer_1(x)
             
@@ -55,6 +63,10 @@ def fully_connected_neural_network_with_two_hidden_layers(input_size=1, hidden_s
                 hidden_output_2 = torch.nn.functional.sigmoid(self.hidden_layer_2(hidden_output_1))
             elif activation_2 == 'tanh':
                 hidden_output_2 = torch.nn.functional.tanh(self.hidden_layer_2(hidden_output_1))
+            elif activation_2 == 'gelu':
+                hidden_output_2 = torch.nn.functional.gelu(self.hidden_layer_2(hidden_output_1))
+            elif activation_2 == 'silu':
+                hidden_output_2 = torch.nn.functional.silu(self.hidden_layer_2(hidden_output_1))
             else:
                 hidden_output_2 = self.hidden_layer_2(hidden_output_1)
             
@@ -83,6 +95,10 @@ def fully_connected_neural_network_with_three_hidden_layers(input_size=1, hidden
                 hidden_output_1 = torch.nn.functional.sigmoid(self.hidden_layer_1(x))
             elif activation_1 == 'tanh':
                 hidden_output_1 = torch.nn.functional.tanh(self.hidden_layer_1(x))
+            elif activation_1 == 'gelu':
+                hidden_output_1 = torch.nn.functional.gelu(self.hidden_layer_1(x))
+            elif activation_1 == 'silu':
+                hidden_output_1 = torch.nn.functional.silu(self.hidden_layer_1(x))
             else:
                 hidden_output_1 = self.hidden_layer_1(x)
             
@@ -94,6 +110,10 @@ def fully_connected_neural_network_with_three_hidden_layers(input_size=1, hidden
                 hidden_output_2 = torch.nn.functional.sigmoid(self.hidden_layer_2(hidden_output_1))
             elif activation_2 == 'tanh':
                 hidden_output_2 = torch.nn.functional.tanh(self.hidden_layer_2(hidden_output_1))
+            elif activation_2 == 'gelu':
+                hidden_output_2 = torch.nn.functional.gelu(self.hidden_layer_2(hidden_output_1))
+            elif activation_2 == 'silu':
+                hidden_output_2 = torch.nn.functional.silu(self.hidden_layer_2(hidden_output_1))
             else:
                 hidden_output_2 = self.hidden_layer_2(hidden_output_1)
 
@@ -105,6 +125,10 @@ def fully_connected_neural_network_with_three_hidden_layers(input_size=1, hidden
                 hidden_output_3 = torch.nn.functional.sigmoid(self.hidden_layer_3(hidden_output_2))
             elif activation_3 == 'tanh':
                 hidden_output_3 = torch.nn.functional.tanh(self.hidden_layer_3(hidden_output_2))
+            elif activation_3 == 'gelu':
+                hidden_output_3 = torch.nn.functional.gelu(self.hidden_layer_3(hidden_output_2))
+            elif activation_3 == 'silu':
+                hidden_output_3 = torch.nn.functional.silu(self.hidden_layer_3(hidden_output_2))
             else:
                 hidden_output_3 = self.hidden_layer_3(hidden_output_2)