diff --git a/main.py b/main.py
index 5a5754b..1fb0f42 100644
--- a/main.py
+++ b/main.py
@@ -84,18 +84,22 @@ def layers(vgg_layer3_out, vgg_layer4_out, vgg_layer7_out, is_training, num_clas
     vgg_layer4_out = tf.multiply(vgg_layer4_out, 0.01)
 
     new_layer7_1x1_out = tf.layers.conv2d(vgg_layer7_out, filters=num_classes, kernel_size=(1, 1), strides=(1, 1),
-                                      name='new_layer7_1x1_out', kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
+                                          name='new_layer7_1x1_out',
+                                          kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
+                                          activation='relu')
 
     new_layer7_1x1_upsampled = tf.layers.conv2d_transpose(new_layer7_1x1_out, filters=num_classes, kernel_size=(3, 3),
                                                           strides=(2, 2), name='new_layer7_1x1_out_upsampled', padding='same',
-                                                          kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
+                                                          kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
+                                                          activation='relu')
 
     new_layer7_1x1_upsampled_bn = tf.layers.batch_normalization(new_layer7_1x1_upsampled,
                                                              name="new_layer7_1x1_upsampled_bn",
                                                              training=is_training)
 
     new_layer4_1x1_out = tf.layers.conv2d(vgg_layer4_out, filters=num_classes, kernel_size=(1, 1), strides=(1, 1),
-                                      name="new_layer4_1x1_out", kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
+                                          name="new_layer4_1x1_out", activation='relu',
+                                          kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
 
     new_layer4_1x1_out_bn = tf.layers.batch_normalization(new_layer4_1x1_out,
                                                                 name="new_layer4_1x1_out_bn",
@@ -104,15 +108,17 @@ def layers(vgg_layer3_out, vgg_layer4_out, vgg_layer7_out, is_training, num_clas
     new_layer_4_7_combined = tf.add(new_layer7_1x1_upsampled_bn, new_layer4_1x1_out_bn, name="new_layer_4_7_combined")
 
     new_layer47_upsampled = tf.layers.conv2d_transpose(new_layer_4_7_combined, filters=num_classes, kernel_size=(3, 3),
-                                                          strides=(2, 2), name="new_layer47_upsampled", padding='same',
-                                                          kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
+                                                       strides=(2, 2), name="new_layer47_upsampled", padding='same',
+                                                       kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
+                                                       activation='relu')
 
     new_layer47_upsampled_bn = tf.layers.batch_normalization(new_layer47_upsampled,
                                                                 name="new_layer47_upsampled_bn",
                                                                 training = is_training)
 
     new_layer3_1x1_out = tf.layers.conv2d(vgg_layer3_out, filters=num_classes, kernel_size=(1, 1), strides=(1, 1),
-                                      name="new_layer3_1x1_out", kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
+                                      name="new_layer3_1x1_out", kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
+                                          activation='relu')
 
     new_layer3_1x1_out_bn = tf.layers.batch_normalization(new_layer3_1x1_out,
                                                           name="new_layer3_1x1_upsampled_bn", training = is_training)
@@ -121,7 +127,8 @@ def layers(vgg_layer3_out, vgg_layer4_out, vgg_layer7_out, is_training, num_clas
 
     new_final_layer_upsampled_4x = tf.layers.conv2d_transpose(out, filters=num_classes, kernel_size=(4, 4),
                                                               strides=(4, 4), name="new_final_layer_upsampled_4x",
-                                                              kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
+                                                              kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
+                                                              activation='relu')
 
     new_final_layer_upsampled_4x_bn = tf.layers.batch_normalization(new_final_layer_upsampled_4x,
                                                                     name="new_final_layer_upsampled_4x_bn",