ValueError: слой ожидает 2 входных сигнала, но получил 1 входной тензор при обучении CNN. ⇐ Python

[Anonymous]

Я пытаюсь построить сиамскую CNN, аналогичную тому, что сделано в этом руководстве.

Моя модель построена с использованием базовой модели, в которую затем дважды загружаются два разных изображения, которые проходят через одно и то же network.

Это код для построения сети:

Код: Выделить всё

class BaseModel(Model):

def __init__(self, base_network):
super(BaseModel, self).__init__()
self.network = base_network

def call(self, inputs):
print(inputs)
return self.network(inputs)

def get_base_model():
inputs = tf.keras.Input(shape=INPUT)

conv2d_1 = layers.Conv2D(name='seq_1', filters=64,
kernel_size=20,
activation='relu')(inputs)
maxpool_1 = layers.MaxPooling2D(pool_size=(2, 2))(conv2d_1)

conv2d_2 = layers.Conv2D(filters=128,
kernel_size=20,
activation='relu')(maxpool_1)
maxpool_2 = layers.MaxPooling2D(pool_size=(2, 2))(conv2d_2)

conv2d_3 = layers.Conv2D(filters=128,
kernel_size=20,
activation='relu')(maxpool_2)
maxpool_3 = layers.MaxPooling2D(pool_size=(2, 2))(conv2d_3)

conv2d_4 = layers.Conv2D(filters=256,
kernel_size=10,
activation='relu')(maxpool_3)

flatten_1 = layers.Flatten()(conv2d_4)
outputs = layers.Dense(units=4096,
activation='sigmoid')(flatten_1)

model = Model(inputs=inputs, outputs=outputs)

return model

Затем я строю сиамскую сеть, используя предыдущий метод:

Код: Выделить всё

INPUT = (250, 250, 3)

def get_siamese_model():
left_input = layers.Input(name='img1', shape=INPUT)
right_input = layers.Input(name='img2', shape=INPUT)

base_model = get_base_model()
base_model = BaseModel(base_model)

# bind the two input layers to the base network
left = base_model(left_input)
right = base_model(right_input)

# build distance measuring layer
l1_lambda = layers.Lambda(lambda tensors:abs(tensors[0] - tensors[1]))
l1_dist = l1_lambda([left, right])

pred = layers.Dense(1,activation='sigmoid')(l1_dist)

return Model(inputs=[left_input, right_input], outputs=pred)

class SiameseNetwork(Model):

def __init__(self, siamese_network):
super(SiameseNetwork, self).__init__()
self.siamese_network = siamese_network

def call(self, inputs):
print(inputs)
return self.siamese_network(inputs)

Затем я обучаю сеть, передавая ей tf.data.Dataset:

Код: Выделить всё

net.fit(x=train_dataset, epochs=10 ,verbose=True)

Код: Выделить всё

train_dataset

имеет тип:



Кажется, что форма ввода определена хорошо, но я все еще возникла ошибка:

Код: Выделить всё

ValueError                                Traceback (most recent call last)
 in ()
----> 1 net.fit(x=train_dataset, epochs=10 ,verbose=True)

9 frames
/usr/local/lib/python3.7/dist-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)
1098                 _r=1):
1099               callbacks.on_train_batch_begin(step)
-> 1100               tmp_logs = self.train_function(iterator)
1101               if data_handler.should_sync:
1102                 context.async_wait()

/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
826     tracing_count = self.experimental_get_tracing_count()
827     with trace.Trace(self._name) as tm:
--> 828       result = self._call(*args, **kwds)
829       compiler = "xla" if self._experimental_compile else "nonXla"
830       new_tracing_count = self.experimental_get_tracing_count()

/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
869       # This is the first call of __call__, so we have to initialize.
870       initializers = []
-->  871       self._initialize(args, kwds, add_initializers_to=initializers)
872     finally:
873       # At this point we know that the initialization is complete (or less

/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
724     self._concrete_stateful_fn = (
725         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
--> 726             *args, **kwds))
727
728     def invalid_creator_scope(*unused_args, **unused_kwds):

/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2967       args, kwargs = None, None
2968     with self._lock:
-> 2969       graph_function, _ = self._maybe_define_function(args, kwargs)
2970     return graph_function
2971

/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3359
3360           self._function_cache.missed.add(call_context_key)
-> 3361           graph_function = self._create_graph_function(args, kwargs)
3362           self._function_cache.primary[cache_key] = graph_function
3363

/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3204             arg_names=arg_names,
3205             override_flat_arg_shapes=override_flat_arg_shapes,
-> 3206             capture_by_value=self._capture_by_value),
3207         self._function_attributes,
3208         function_spec=self.function_spec,

/usr/local/lib/python3.7/dist-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)
988         _, original_func = tf_decorator.unwrap(python_func)
989
--> 990       func_outputs = python_func(*func_args, **func_kwargs)
991
992       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
632             xla_context.Exit()
633         else:
--> 634           out = weak_wrapped_fn().__wrapped__(*args, **kwds)
635         return out
636

/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
975           except Exception as e:  # pylint:disable=broad-except
976             if hasattr(e, "ag_error_metadata"):
--> 977               raise e.ag_error_metadata.to_exception(e)
978             else:
979               raise

ValueError: in user code:

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function  *
return step_function(self, iterator)
:9 call  *
return self.siamese_network(inputs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:998 __call__  **
input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_spec.py:207 assert_input_compatibility
' input tensors. Inputs received: ' + str(inputs))

ValueError: Layer model_16 expects 2 input(s), but it received 1 input tensors. Inputs received: []

Я понимаю, что model_16 — это базовая модель, однако не могу понять, что я здесь делаю не так.


Подробнее здесь: https://stackoverflow.com/questions/672 ... when-train