diff --git a/examples/train_efficientnet.py b/examples/train_efficientnet.py
index 7a9ce6c5..1033b48a 100644
--- a/examples/train_efficientnet.py
+++ b/examples/train_efficientnet.py
@@ -41,7 +41,8 @@ if __name__ == "__main__":
   if TINY:
     model = TinyConvNet(classes)
   else:
-    model = EfficientNet(int(os.getenv("NUM", "0")), classes)
+    model = EfficientNet(int(os.getenv("NUM", "0")), classes, has_se=False)
+
   parameters = get_parameters(model)
   print("parameters", len(parameters))
   optimizer = optim.Adam(parameters, lr=0.001)
@@ -74,12 +75,19 @@ if __name__ == "__main__":
     optimizer.step()
     opt_time = (time.time()-st)*1000.0
 
+    st = time.time()
+    loss = loss.cpu().data
     cat = np.argmax(out.cpu().data, axis=1)
     accuracy = (cat == Y).mean()
+    finish_time = (time.time()-st)*1000.0
+
 
     # printing
-    t.set_description("loss %.2f accuracy %.2f -- %.2f %.2f %.2f -- %d" %
-      (loss.cpu().data, accuracy, fp_time, bp_time, opt_time, Tensor.allocated))
+    t.set_description("loss %.2f accuracy %.2f -- %.2f + %.2f + %.2f + %.2f = %.2f -- %d" %
+      (loss, accuracy,
+      fp_time, bp_time, opt_time, finish_time,
+      fp_time + bp_time + opt_time + finish_time,
+      Tensor.allocated))
 
     del out, y, loss
 
diff --git a/extra/efficientnet.py b/extra/efficientnet.py
index 36b7303e..46c8b514 100644
--- a/extra/efficientnet.py
+++ b/extra/efficientnet.py
@@ -70,7 +70,7 @@ def fake_torch_load(b0):
   return ret
 
 class MBConvBlock:
-  def __init__(self, kernel_size, strides, expand_ratio, input_filters, output_filters, se_ratio):
+  def __init__(self, kernel_size, strides, expand_ratio, input_filters, output_filters, se_ratio, has_se):
     oup = expand_ratio * input_filters
     if expand_ratio != 1:
       self._expand_conv = Tensor.zeros(oup, input_filters, 1, 1)
@@ -87,11 +87,13 @@ class MBConvBlock:
     self._depthwise_conv = Tensor.zeros(oup, 1, kernel_size, kernel_size)
     self._bn1 = BatchNorm2D(oup)
 
-    num_squeezed_channels = max(1, int(input_filters * se_ratio))
-    self._se_reduce = Tensor.zeros(num_squeezed_channels, oup, 1, 1)
-    self._se_reduce_bias = Tensor.zeros(num_squeezed_channels)
-    self._se_expand = Tensor.zeros(oup, num_squeezed_channels, 1, 1)
-    self._se_expand_bias = Tensor.zeros(oup)
+    self.has_se = has_se
+    if self.has_se:
+      num_squeezed_channels = max(1, int(input_filters * se_ratio))
+      self._se_reduce = Tensor.zeros(num_squeezed_channels, oup, 1, 1)
+      self._se_reduce_bias = Tensor.zeros(num_squeezed_channels)
+      self._se_expand = Tensor.zeros(oup, num_squeezed_channels, 1, 1)
+      self._se_expand_bias = Tensor.zeros(oup)
 
     self._project_conv = Tensor.zeros(output_filters, oup, 1, 1)
     self._bn2 = BatchNorm2D(output_filters)
@@ -105,10 +107,11 @@ class MBConvBlock:
     x = self._bn1(x).swish()
 
     # has_se
-    x_squeezed = x.avg_pool2d(kernel_size=x.shape[2:4])
-    x_squeezed = x_squeezed.conv2d(self._se_reduce).add(self._se_reduce_bias.reshape(shape=[1, -1, 1, 1])).swish()
-    x_squeezed = x_squeezed.conv2d(self._se_expand).add(self._se_expand_bias.reshape(shape=[1, -1, 1, 1]))
-    x = x.mul(x_squeezed.sigmoid())
+    if self.has_se:
+      x_squeezed = x.avg_pool2d(kernel_size=x.shape[2:4])
+      x_squeezed = x_squeezed.conv2d(self._se_reduce).add(self._se_reduce_bias.reshape(shape=[1, -1, 1, 1])).swish()
+      x_squeezed = x_squeezed.conv2d(self._se_expand).add(self._se_expand_bias.reshape(shape=[1, -1, 1, 1]))
+      x = x.mul(x_squeezed.sigmoid())
 
     x = self._bn2(x.conv2d(self._project_conv))
     if x.shape == inputs.shape:
@@ -116,7 +119,7 @@ class MBConvBlock:
     return x
 
 class EfficientNet:
-  def __init__(self, number=0, classes=1000):
+  def __init__(self, number=0, classes=1000, has_se=True):
     self.number = number
     global_params = [
       # width, depth
@@ -163,7 +166,7 @@ class EfficientNet:
       args[3] = round_filters(args[3])
       args[4] = round_filters(args[4])
       for n in range(round_repeats(b[0])):
-        self._blocks.append(MBConvBlock(*args))
+        self._blocks.append(MBConvBlock(*args, has_se=has_se))
         args[3] = args[4]
         args[1] = (1,1)