[Python] ValueError: No gradients provided for any variable while doing regression for integer...

I have a problem where I need to predict some integers from an image. The problem is that this includes some negative integers too. I have done some research and came across Poisson which does count regression, however this does not work due to me also needing to predict some negative integers too, resulting in Poisson output nan as its loss. I was thinking of using Lambda to round the output of my model however this resulted in this error:

---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/var/folders/nc/c4mgwn897qbg8g52tp3mhbjr0000gp/T/ipykernel_8618/1788039059.py in <module>
----> 1 model.fit(x_train, y_train,callbacks=[callback], epochs = 999)

~/opt/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)
1181 _r=1):
1182 callbacks.on_train_batch_begin(step)
-> 1183 tmp_logs = self.train_function(iterator)
1184 if data_handler.should_sync:
1185 context.async_wait()

~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
887
888 with OptionalXlaContext(self._jit_compile):
--> 889 result = self._call(*args, **kwds)
890
891 new_tracing_count = self.experimental_get_tracing_count()

~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
931 # This is the first call of __call__, so we have to initialize.
932 initializers = []
--> 933 self._initialize(args, kwds, add_initializers_to=initializers)
934 finally:
935 # At this point we know that the initialization is complete (or less

~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
761 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
762 self._concrete_stateful_fn = (
--> 763 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
764 *args, **kwds))
765

~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
3048 args, kwargs = None, None
3049 with self._lock:
-> 3050 graph_function, _ = self._maybe_define_function(args, kwargs)
3051 return graph_function
3052

~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3442
3443 self._function_cache.missed.add(call_context_key)
-> 3444 graph_function = self._create_graph_function(args, kwargs)
3445 self._function_cache.primary[cache_key] = graph_function
3446

~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3277 arg_names = base_arg_names + missing_arg_names
3278 graph_function = ConcreteFunction(
-> 3279 func_graph_module.func_graph_from_py_func(
3280 self._name,
3281 self._python_function,

~/opt/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)
997 _, original_func = tf_decorator.unwrap(python_func)
998
--> 999 func_outputs = python_func(*func_args, **func_kwargs)
1000
1001 # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
670 # the function a weak reference to itself to avoid a reference cycle.
671 with OptionalXlaContext(compile_with_xla):
--> 672 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
673 return out
674

~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
984 except Exception as e: # pylint:disable=broad-except
985 if hasattr(e, "ag_error_metadata"):
--> 986 raise e.ag_error_metadata.to_exception(e)
987 else:
988 raise

ValueError: in user code:

/Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:855 train_function *
return step_function(self, iterator)
/Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:845 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:1285 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2833 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:3608 _call_for_each_replica
return fn(*args, **kwargs)
/Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:838 run_step **
outputs = model.train_step(data)
/Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:799 train_step
self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
/Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:530 minimize
return self.apply_gradients(grads_and_vars, name=name)
/Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:630 apply_gradients
grads_and_vars = optimizer_utils.filter_empty_gradients(grads_and_vars)
/Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/utils.py:75 filter_empty_gradients
raise ValueError("No gradients provided for any variable: %s." %

ValueError: No gradients provided for any variable: ['conv2d_2/kernel:0', 'conv2d_2/bias:0', 'conv2d_3/kernel:0', 'conv2d_3/bias:0', 'dense_3/kernel:0', 'dense_3/bias:0', 'dense_4/kernel:0', 'dense_4/bias:0', 'dense_5/kernel:0', 'dense_5/bias:0'].


Here is my implementation of the Lambda layer thing:

filter_size = (3,3)
filters = 32
pool = 2

input_layer = keras.Input(shape=(100,300,1))

conv_extractor = layers.Conv2D(filters,filter_size, activation='relu')(input_layer)
conv_extractor = layers.MaxPooling2D(pool_size=(pool, pool))(conv_extractor)
conv_extractor = layers.Conv2D(filters,filter_size, activation='relu')(conv_extractor)
conv_extractor = layers.MaxPooling2D(pool_size=(pool, pool))(conv_extractor)

#conv_extractor = layers.Reshape(target_shape=(100 // (pool ** 2), (100 // (pool ** 2)) * filters))(conv_extractor)
shape = ((100 // 4), (300 // 4) * 32)
#conv_extractor = layers.Dense(512, activation='relu')(conv_extractor)
conv_extractor = layers.Reshape(target_shape=(23,2336))(conv_extractor)

gru_1 = GRU(512, return_sequences=True)(conv_extractor)
gru_1b = GRU(512, return_sequences=True, go_backwards=True)(conv_extractor)
gru1_merged = add([gru_1, gru_1b])
gru_2 = GRU(512, return_sequences=True)(gru1_merged)
gru_2b = GRU(512, return_sequences=True, go_backwards=True)(gru1_merged)

x = layers.concatenate([gru_2, gru_2b]) # move concatenate layer aside
x = layers.Flatten()(x)
inner = layers.Dense(30, activation='LeakyReLU')(x)
inner = layers.Dense(10, activation='LeakyReLU')(inner)
inner = layers.Dense(3, activation='LeakyReLU')(inner)
inner layers.Lambda(keras.backend.round)(inner)


model = Model(input_layer,inner)
model.compile(loss = "MeanSquaredError", optimizer = optimizers.Adam(2e-4), metrics=['accuracy'])
model.fit(x_train, y_train, epochs = 999)


Why did I get this error? And how can I fix it? If it's not fixable, is there another way of solving my problem (e.g by modifying the poisson loss function)?

Continue reading...