TypeError: Input 'y' of 'Mul' Op has type float32 that does not match type uint8 of argument 'x'.

[josegpl]

In [9]: model_seg()

Epoch 1/10

TypeError Traceback (most recent call last)
in
----> 1 model_seg()

in model_seg()
83 model.compile(optimizer= Adam(lr = 0.003), loss= [jaccard_distance], metrics=[iou])
84
---> 85 hist = model.fit(x_train, y_train, epochs= 10, batch_size= 16,validation_data=(x_test, y_test), verbose=1)
86
87 model.save("model.h5")

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
106 def _method_wrapper(self, *args, **kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
--> 108 return method(self, *args, **kwargs)
109
110 # Running inside run_distribute_coordinator already.

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1096 batch_size=batch_size):
1097 callbacks.on_train_batch_begin(step)
-> 1098 tmp_logs = train_function(iterator)
1099 if data_handler.should_sync:
1100 context.async_wait()

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in call(self, *args, **kwds)
778 else:
779 compiler = "nonXla"
--> 780 result = self._call(*args, **kwds)
781
782 new_tracing_count = self._get_tracing_count()

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821 # This is the first call of call, so we have to initialize.
822 initializers = []
--> 823 self._initialize(args, kwds, add_initializers_to=initializers)
824 finally:
825 # At this point we know that the initialization is complete (or less

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
694 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695 self._concrete_stateful_fn = (
--> 696 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
697 *args, **kwds))
698

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853 args, kwargs = None, None
2854 with self._lock:
-> 2855 graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856 return graph_function
2857

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212 self._function_cache.missed.add(call_context_key)
-> 3213 graph_function = self._create_graph_function(args, kwargs)
3214 self._function_cache.primary[cache_key] = graph_function
3215 return graph_function, args, kwargs

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063 arg_names = base_arg_names + missing_arg_names
3064 graph_function = ConcreteFunction(
-> 3065 func_graph_module.func_graph_from_py_func(
3066 self._name,
3067 self._python_function,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984 _, original_func = tf_decorator.unwrap(python_func)
985
--> 986 func_outputs = python_func(*func_args, **func_kwargs)
987
988 # invariant: func_outputs contains only Tensors, CompositeTensors,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598 # wrapped allows AutoGraph to swap in a converted function. We give
599 # the function a weak reference to itself to avoid a reference cycle.
--> 600 return weak_wrapped_fn().wrapped(*args, **kwds)
601 weak_wrapped_fn = weakref.ref(wrapped_fn)
602

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971 except Exception as e: # pylint:disable=broad-except
972 if hasattr(e, "ag_error_metadata"):
--> 973 raise e.ag_error_metadata.to_exception(e)
974 else:
975 raise

TypeError: in user code:

/home/gabriel/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
    return step_function(self, iterator)
<ipython-input-2-1db05eda7c87>:2 jaccard_distance  *
    intersection = K.sum(K.abs(y_true * y_pred), axis=-1)
/home/gabriel/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/math_ops.py:1140 binary_op_wrapper
    raise e
/home/gabriel/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/math_ops.py:1124 binary_op_wrapper
    return func(x, y, name=name)
/home/gabriel/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/math_ops.py:1456 _mul_dispatch
    return multiply(x, y, name=name)
/home/gabriel/anaconda3/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:201 wrapper
    return target(*args, **kwargs)
/home/gabriel/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/math_ops.py:508 multiply
    return gen_math_ops.mul(x, y, name)
/home/gabriel/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/gen_math_ops.py:6175 mul
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
/home/gabriel/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/op_def_library.py:503 _apply_op_helper
    raise TypeError(

TypeError: Input 'y' of 'Mul' Op has type float32 that does not match type uint8 of argument 'x'.

[josegpl]

Solution is converting y input to float32 type in loss function

import keras.backend as K

def jaccard_distance(y_true, y_pred, smooth=100):
y_true = K.cast(y_true, 'float32')
intersection = K.sum(K.abs(y_true * y_pred), axis=-1)
sum_ = K.sum(K.square(y_true), axis = -1) + K.sum(K.square(y_pred), axis=-1)
jac = (intersection + smooth) / (sum_ - intersection + smooth)
return (1 - jac)