diff --git a/src/cnn/models.c b/src/cnn/models.c
index 00613b0..8061a0e 100644
--- a/src/cnn/models.c
+++ b/src/cnn/models.c
@@ -36,33 +36,32 @@ Network* create_network_alexnet(float learning_rate, int dropout, int activation
 }
 
 Network* create_network_VGG16(float learning_rate, int dropout, int activation, int initialisation, int size_output) {
-    Network* network = create_network(23, learning_rate, dropout, initialisation, 256, 3);
-    add_convolution(network, 3, 64, 1, 0, activation); // Conv3-64
-    add_convolution(network, 3, 64, 1, 0, activation); // Conv3-64
+    Network* network = create_network(22, learning_rate, dropout, initialisation, 256, 3);
+    add_convolution(network, 3, 64, 1, 1, activation); // Conv3-64
+    add_convolution(network, 3, 64, 1, 1, activation); // Conv3-64
     add_average_pooling(network, 2, 2, 0); // Max Pool
 
-    add_convolution(network, 3, 128, 1, 0, activation); // Conv3-128
+    add_convolution(network, 3, 128, 1, 1, activation); // Conv3-128
     add_convolution(network, 1, 128, 1, 0, activation); // Conv1-128
     add_average_pooling(network, 2, 2, 0); // Max Pool
 
-    add_convolution(network, 3, 256, 1, 0, activation); // Conv3-256
-    add_convolution(network, 3, 256, 1, 0, activation); // Conv3-256
+    add_convolution(network, 3, 256, 1, 1, activation); // Conv3-256
+    add_convolution(network, 3, 256, 1, 1, activation); // Conv3-256
     add_convolution(network, 1, 256, 1, 0, activation); // Conv1-256
     add_average_pooling(network, 2, 2, 0); // Max Pool
 
-    add_convolution(network, 3, 512, 1, 0, activation); // Conv3-512
-    add_convolution(network, 3, 512, 1, 0, activation); // Conv3-512
+    add_convolution(network, 3, 512, 1, 1, activation); // Conv3-512
+    add_convolution(network, 3, 512, 1, 1, activation); // Conv3-512
     add_convolution(network, 1, 512, 1, 0, activation); // Conv1-512
     add_average_pooling(network, 2, 2, 0); // Max Pool
 
-    add_convolution(network, 3, 512, 1, 0, activation); // Conv3-512
-    add_convolution(network, 3, 512, 1, 0, activation); // Conv3-512
+    add_convolution(network, 3, 512, 1, 1, activation); // Conv3-512
+    add_convolution(network, 3, 512, 1, 1, activation); // Conv3-512
     add_convolution(network, 1, 512, 1, 0, activation); // Conv1-512
     add_average_pooling(network, 2, 2, 0); // Max Pool
 
-    add_dense_linearisation(network, 2048, activation);
-    add_dense(network, 2048, activation);
-    add_dense(network, 256, activation);
+    add_dense_linearisation(network, 4096, activation);
+    add_dense(network, 4096, activation);
     add_dense(network, size_output, SOFTMAX);
     return network;
 }