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diff --git a/Codes/flownet2/src/flownet_cs/flownet_cs.py b/Codes/flownet2/src/flownet_cs/flownet_cs.py
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+++ b/Codes/flownet2/src/flownet_cs/flownet_cs.py
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+from ..net import Net, Mode
+from ..flownet_c.flownet_c import FlowNetC
+from ..flownet_s.flownet_s import FlowNetS
+from ..flow_warp import flow_warp
+import tensorflow as tf
+
+
+class FlowNetCS(Net):
+
+    def __init__(self, mode=Mode.TRAIN, debug=False):
+        self.net_c = FlowNetC(mode, debug)
+        self.net_s = FlowNetS(mode, debug)
+        super(FlowNetCS, self).__init__(mode=mode, debug=debug)
+
+    def model(self, inputs, training_schedule, trainable=True):
+        with tf.variable_scope('FlowNetCS'):
+            # Forward pass through FlowNetC with weights frozen
+            net_c_predictions = self.net_c.model(inputs, training_schedule, trainable=True)
+
+            # Perform flow warping (to move image B closer to image A based on flow prediction)
+            warped = flow_warp(inputs['input_b'], net_c_predictions['flow'])
+
+            # Compute brightness error: sqrt(sum (input_a - warped)^2 over channels)
+            brightness_error = inputs['input_a'] - warped
+            brightness_error = tf.square(brightness_error)
+            brightness_error = tf.reduce_sum(brightness_error, keep_dims=True, axis=3)
+            brightness_error = tf.sqrt(brightness_error)
+
+            # Gather all inputs to FlowNetS
+            inputs_to_s = {
+                'input_a': inputs['input_a'],
+                'input_b': inputs['input_b'],
+                'warped': warped,
+                'flow': net_c_predictions['flow'] * 0.05,
+                'brightness_error': brightness_error,
+            }
+
+            return self.net_s.model(inputs_to_s, training_schedule, trainable=trainable)
+
+    def loss(self, flow, predictions):
+        return self.net_s.loss(flow, predictions)