ValueError: Layer model expects 1 input(s), but it received 2 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, None, None, None) dtype=uint8>, <tf.Tensor 'IteratorGetNext:1' shape=(None, None, None) dtype=float32>]

Tensorflow V2 (latest) Keras (latest) ssd300_training.ipynb

I have managed to convert most of the V1 code to V2 and successfully run it. I have made changes to all the python files as necessary too. However, this issue occurs on the line

history = model.fit_generator(generator=train_generator, steps_per_epoch=steps_per_epoch, epochs=final_epoch, callbacks=callbacks, validation_data=val_generator, validation_steps=ceil(val_dataset_size/batch_size), initial_epoch=initial_epoch)

Entire error:

---

Epoch 1/120

Epoch 00001: LearningRateScheduler reducing learning rate to 0.001.

ValueError Traceback (most recent call last) <ipython-input-8-9e051c003cc7> in <module> 4 steps_per_epoch = 1000 5 ----> 6 history = model.fit_generator(generator=train_generator, 7 steps_per_epoch=steps_per_epoch, 8 epochs=final_epoch,

c:\users\dolphin48.conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\keras\engine\training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch) 1844 'will be removed in a future version. ’ 1845 ‘Please use Model.fit, which supports generators.’) -> 1846 return self.fit( 1847 generator, 1848 steps_per_epoch=steps_per_epoch,

c:\users\dolphin48.conda\envs\tensorflow-gpu\lib\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) 1097 _r=1): 1098 callbacks.on_train_batch_begin(step) -> 1099 tmp_logs = self.train_function(iterator) 1100 if data_handler.should_sync: 1101 context.async_wait()

c:\users\dolphin48.conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\eager\def_function.py in call(self, *args, **kwds) 782 tracing_count = self.experimental_get_tracing_count() 783 with trace.Trace(self._name) as tm: –> 784 result = self._call(*args, **kwds) 785 compiler = “xla” if self._experimental_compile else “nonXla” 786 new_tracing_count = self.experimental_get_tracing_count()

c:\users\dolphin48.conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds) 825 # This is the first call of call, so we have to initialize. 826 initializers = [] –> 827 self._initialize(args, kwds, add_initializers_to=initializers) 828 finally: 829 # At this point we know that the initialization is complete (or less

c:\users\dolphin48.conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to) 679 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph) 680 self._concrete_stateful_fn = ( –> 681 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access 682 *args, **kwds)) 683

c:\users\dolphin48.conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs) 2995 args, kwargs = None, None 2996 with self._lock: -> 2997 graph_function, _ = self._maybe_define_function(args, kwargs) 2998 return graph_function 2999

c:\users\dolphin48.conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self, args, kwargs) 3387 3388 self._function_cache.missed.add(call_context_key) -> 3389 graph_function = self._create_graph_function(args, kwargs) 3390 self._function_cache.primary[cache_key] = graph_function 3391

c:\users\dolphin48.conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes) 3222 arg_names = base_arg_names + missing_arg_names 3223 graph_function = ConcreteFunction( -> 3224 func_graph_module.func_graph_from_py_func( 3225 self._name, 3226 self._python_function,

c:\users\dolphin48.conda\envs\tensorflow-gpu\lib\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) 994 _, original_func = tf_decorator.unwrap(python_func) 995 –> 996 func_outputs = python_func(*func_args, **func_kwargs) 997 998 # invariant: func_outputs contains only Tensors, CompositeTensors,

c:\users\dolphin48.conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args, **kwds) 588 xla_context.Exit() 589 else: –> 590 out = weak_wrapped_fn().wrapped(*args, **kwds) 591 return out 592

c:\users\dolphin48.conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\framework\func_graph.py in wrapper(*args, **kwargs) 981 except Exception as e: # pylint:disable=broad-except 982 if hasattr(e, “ag_error_metadata”): –> 983 raise e.ag_error_metadata.to_exception(e) 984 else: 985 raise

ValueError: in user code:

c:\users\dolphin48\.conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\keras\engine\training.py:804 train_function  *
    return step_function(self, iterator)
c:\users\dolphin48\.conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\keras\engine\training.py:794 step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
c:\users\dolphin48\.conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1259 run
    return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
c:\users\dolphin48\.conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2730 call_for_each_replica
    return self._call_for_each_replica(fn, args, kwargs)
c:\users\dolphin48\.conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3417 _call_for_each_replica
    return fn(*args, **kwargs)
c:\users\dolphin48\.conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\keras\engine\training.py:787 run_step  **
    outputs = model.train_step(data)
c:\users\dolphin48\.conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\keras\engine\training.py:753 train_step
    y_pred = self(x, training=True)
c:\users\dolphin48\.conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:1000 __call__
    input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
c:\users\dolphin48\.conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\keras\engine\input_spec.py:204 assert_input_compatibility
    raise ValueError('Layer ' + layer_name + ' expects ' +

ValueError: Layer model expects 1 input(s), but it received 2 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, None, None, None) dtype=uint8>, <tf.Tensor 'IteratorGetNext:1' shape=(None, None, None) dtype=float32>]

---

This Stackoverflow post (https://stackoverflow.com/questions/61586981/valueerror-layer-sequential-20-expects-1-inputs-but-it-received-2-input-tensor#) suggests it has something to do with fit() parameter validation_data. It points to a change in structural requirements, which has been changed from lists to tuples across tfv1.x and tfv2.x. However, we are not using a structure at all, but a generator to accomplish our task. I don’t understand what is going wrong.

[ukowa]

@bfhaha thanks for your fix. I’ve tried it as well. Same here: memory error (32GB RAM Predator, GForce 1070). I gave it a second try with a reduced data set of just 8 images, but same result. I know it doesn’t really help, but I just wanted to share the information…

[bfhaha]

@daviddanialy Thanks. It doesn’t work for me.