I am getting TypeError when I run the dice_coef_loss in model.fit:

TypeError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_15776/2786157346.py in
----> 1 results = model.fit(X_train, Y_train, validation_split=0.1, batch_size=4, callbacks = model_checkpoint_callback, epochs=100)

C:\ProgramData\Anaconda3\envs\env1\lib\site-packages\keras\utils\traceback_utils.py in error_handler(*args, **kwargs)
65 except Exception as e: # pylint: disable=broad-except
66 filtered_tb = _process_traceback_frames(e.traceback)
---> 67 raise e.with_traceback(filtered_tb) from None
68 finally:
69 del filtered_tb

C:\ProgramData\Anaconda3\envs\env1\lib\site-packages\tensorflow\python\framework\func_graph.py in autograph_handler(*args, **kwargs)
1145 except Exception as e: # pylint:disable=broad-except
1146 if hasattr(e, "ag_error_metadata"):
-> 1147 raise e.ag_error_metadata.to_exception(e)
1148 else:
1149 raise

TypeError: in user code:

File "C:\ProgramData\Anaconda3\envs\env1\lib\site-packages\keras\engine\training.py", line 1021, in train_function  *
    return step_function(self, iterator)
File "C:\../ipykernel_15776/709000421.py", line 11, in dice_coef_loss  *
    return float(1.0 - dice_coef(y_true, y_pred))
File "C:\..\Local\Temp/ipykernel_15776/2098934712.py", line 6, in dice_coef  *
    intersection = tf.reduce_sum(y_true_f * y_pred_f)

TypeError: Value passed to parameter 'x' has DataType bool not in list of allowed values: b

what does it do

Thank you for the implementation and explanation of ResUNet! That's really interesting.

It seems to me that you should have a different "bridge" for ResUNet. According to the article, the "bridge" is the same as the "residual block" of ResUNet.
The "bridge" and "residual block" are the same, except that dashed arrow from the entrance of "residual block" to the "Addition" block. But I think this is an error in the article :-) There must be a dashed arrow otherwise we don't need "Addition" block in the "bridge".
So I've changed your code to this:

def ResUNet():
f = [16, 32, 64, 128, 256]
inputs = keras.layers.Input((image_size, image_size, 3))

## Encoder
e0 = inputs
e1 = stem(e0, f[0])
e2 = residual_block(e1, f[1], strides=2)
e3 = residual_block(e2, f[2], strides=2)
e4 = residual_block(e3, f[3], strides=2)

## Bridge
b0 = residual_block(e4, f[4], strides=2)

## Decoder
u1 = upsample_concat_block(b0, e4)
d1 = residual_block(u1, f[4])

u2 = upsample_concat_block(d1, e3)
d2 = residual_block(u2, f[3])

u3 = upsample_concat_block(d2, e2)
d3 = residual_block(u3, f[2])

u4 = upsample_concat_block(d3, e1)
d4 = residual_block(u4, f[1])

outputs = keras.layers.Conv2D(1, (1, 1), padding="same", activation="sigmoid")(d4)
model = keras.models.Model(inputs, outputs)
return model

As you can see I just put "e5" to the "bridge" and that's all :-)
Also I've set 120 epochs and obtained dice coefficient equal to 0.9825
237/237 [==============================] - 11s 48ms/step - loss: 0.0175 - dice_coef: 0.9825 - val_loss: 0.1533 - val_dice_coef: 0.8467

The result is on 8.6% higher than the 1st place on Kaggle, team name "b.e.s. & phalanx". It means that YOU can be a Kaggle winner next time.
Keep doing don't stop :-)