diff --git a/examples/efficientnet.py b/examples/efficientnet.py
index a68089a488..8f0815285e 100644
--- a/examples/efficientnet.py
+++ b/examples/efficientnet.py
@@ -15,23 +15,33 @@ class BatchNorm2D:
     return x * self.weight + self.bias
 
 class MBConvBlock:
-  def __init__(self, d0, d1, d2, d3):
-    self._expand_conv = Tensor.zeros(d1, d0, 1, 1)
-    self._bn0 = BatchNorm2D(d1)
-    self._depthwise_conv = Tensor.zeros(d1, 1, 3, 3)
-    self._bn1 = BatchNorm2D(d1)
-    self._se_reduce = Tensor.zeros(d2, d1, 1, 1)
-    self._se_reduce_bias = Tensor.zeros(d2)
-    self._se_expand = Tensor.zeros(d1, d2, 1, 1)
-    self._se_expand_bias = Tensor.zeros(d1)
-    self._project_conv = Tensor.zeros(d3, d2, 1, 1)
-    self._bn2 = BatchNorm2D(d3)
+  def __init__(self, input_filters, expand_ratio, se_ratio, output_filters):
+    oup = expand_ratio * input_filters
+    if expand_ratio != 1:
+      self._expand_conv = Tensor.zeros(oup, input_filters, 1, 1)
+      self._bn0 = BatchNorm2D(oup)
+    self._depthwise_conv = Tensor.zeros(oup, 1, 3, 3)
+    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._project_conv = Tensor.zeros(output_filters, oup, 1, 1)
+    self._bn2 = BatchNorm2D(output_filters)
 
   def __call__(self, x):
-    x = self._bn0(x.conv2d(self._expand_conv))
-    x = self._bn1(x.conv2d(self._depthwise_conv))  # TODO: repeat on axis 1
-    x = x.conv2d(self._se_reduce) + self._se_reduce_bias
-    x = x.conv2d(self._se_expand) + self._se_expand_bias
+    x = self._bn0(x.conv2d(self._expand_conv)).swish()
+    x = self._bn1(x.conv2d(self._depthwise_conv)).swish()  # TODO: repeat on axis 1
+
+    # has_se
+    x_squeezed = x.avg_pool2d()
+    x_squeezed = (x_squeezed.conv2d(self._se_reduce) + self._se_reduce_bias).swish()
+    x_squeezed = x_squeezed.conv2d(self._se_expand) + self._se_expand_bias
+    x = x * x_squeezed.sigmoid()
+
     x = self._bn2(x.conv2d(self._project_conv))
     return x.swish()
 
@@ -51,7 +61,7 @@ class EfficientNet:
     for b in self._blocks:
       x = b(x)
     x = self._bn1(x.conv2d(self._conv_head))
-    x = x.avg_pool2d() # wrong
+    x = x.avg_pool2d() # wrong?
     x = x.dropout(0.2)
     return x.dot(self_fc).swish()
 
diff --git a/tinygrad/ops.py b/tinygrad/ops.py
index 1ef53358e3..2b7c0c57f0 100644
--- a/tinygrad/ops.py
+++ b/tinygrad/ops.py
@@ -68,6 +68,20 @@ class ReLU(Function):
     return grad_input
 register('relu', ReLU)
 
+class Sigmoid(Function):
+  @staticmethod
+  def forward(ctx, input):
+    ret = 1/(1 + np.exp(-input))
+    ctx.save_for_backward(ret)
+    return ret
+
+  @staticmethod
+  def backward(ctx, grad_output):
+    ret, = ctx.saved_tensors
+    grad_input = grad_output * (ret * (1 - ret))
+    return grad_input
+register('sigmoid', Sigmoid)
+
 class Reshape(Function):
   @staticmethod
   def forward(ctx, x, shape):