model.fit AttributeError：“元组”对象没有属性“形状”
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【中文标题】model.fit AttributeError：“元组”对象没有属性“形状”【英文标题】：model.fit AttributeError: 'tuple' object has no attribute 'shape' 【发布时间】：2021-12-31 23:49:27 【问题描述】：我在训练多输入模型时遇到问题。我使用以下代码构建了它：
def create_covn_layers(input_layer):
input = layers.Conv2D(32, (3,3), input_shape=get_img_input_shape(True))(input_layer)
covn01 = layers.Conv2D(32, (3, 3))(input)
acti01 = layers.Activation('relu')(covn01)
pool01 = layers.MaxPooling2D((2, 2))(acti01)
covn02 = layers.Conv2D(64, (3, 3))(pool01)
acti02 = layers.Activation('relu')(covn02)
pool02 = layers.MaxPooling2D(2, 2)(acti02)
covn03 = layers.Conv2D(128, (3, 3))(pool02)
acti02 = layers.Activation('relu')(covn03)
pool02 = layers.MaxPooling2D(pool_size=(2,2), padding='same')(acti02)
covn_base = layers.Dropout(0.2)(pool02)
return covn_base
#flat = layers.Flatten()(pool03)
model_one_input = layers.Input(shape=get_img_input_shape(True))
model_one = create_covn_layers(model_one_input)
model_two_input = layers.Input(shape=get_img_input_shape(True))
model_two = create_covn_layers(model_two_input)
concat_feature_layer = layers.concatenate([model_one, model_two])
flatten_layer = layers.Flatten()(concat_feature_layer)
fully_connected_dense_big = layers.Dense(256, activation='relu')(flatten_layer)
dropout_one = layers.Dropout(0.3)(fully_connected_dense_big)
fully_connected_dense_small = layers.Dense(128, activation='relu')(dropout_one)
dropout_two = layers.Dropout(0.3)(fully_connected_dense_small)
output = layers.Dense(3, activation='softmax')(dropout_two)
model = Model(
inputs=[model_one_input, model_two_input],
outputs=output
)
输入层接受以下形状：
batch_size = 18
def get_img_input_shape(for_model=False):
if for_model:
return(299,299,3)
return (299, 299)
[![图像形状层][1]][1]
模型结构：
https://imgur.com/eNtPnjA
我已经构建了一个自定义生成器，它需要两个带有 flowfromdataframe 的生成器并输出两个输入和一个标签。
train_generator_one = ImageDataGenerator(
rescale = 1./255,
validation_split=0.2
)
train_generator_two = ImageDataGenerator(
rescale = 1./255,
validation_split=0.2
)
input_1_train_gen = train_generator_one.flow_from_dataframe(
balanced_eeg_data,
batch_size=batch_size,
target_size=get_img_input_shape(),
shuffle=False,
color_mode="rgb",
class_mode="categorical",
subset="training")
input_2_train_gen = train_generator_two.flow_from_dataframe(
balanced_ecg_data,
batch_size=batch_size,
target_size=get_img_input_shape(),
shuffle=False,
color_mode="rgb",
class_mode="categorical",
subset="training")
input_1_validation_gen = train_generator_one.flow_from_dataframe(
balanced_eeg_data,
batch_size=batch_size,
target_size=get_img_input_shape(),
shuffle=False,
color_mode="rgb",
class_mode="categorical",
subset="validation")
input_2_validation_gen = train_generator_two.flow_from_dataframe(
balanced_ecg_data,
batch_size=batch_size,
target_size=get_img_input_shape(),
shuffle=False,
color_mode="rgb",
class_mode="categorical",
subset="validation")
def create_data_generator(data_gen_one, data_gen_two):
while(True):
_gen1, _gen1_l = next(data_gen_one)
_gen2, _gen2_l = next(data_gen_two)
yield [_gen1, _gen2], [_gen1_l]
multi_train_generator = create_data_generator(
input_1_train_gen,
input_2_train_gen
)
multi_validation_generator = create_data_generator(
input_1_validation_gen,
input_2_validation_gen
)
当我调用 model.fit 但是它给出了一个属性错误：
history = model.fit(
multi_train_generator,
epochs=2,
steps_per_epoch = input_1_train_gen.samples//batch_size,
validation_data=multi_validation_generator,
validation_steps = input_1_validation_gen.samples//batch_size,
)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/var/folders/0v/m6wt8rqj7s1dcljdyjrdfxmw0000gn/T/ipykernel_84306/4129641024.py in <module>
----> 1 history = model.fit(
2 multi_train_generator,
3 epochs=2,
4 steps_per_epoch = input_1_train_gen.samples//batch_size,
5 validation_data=multi_validation_generator,
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/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()
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/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()
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/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
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/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
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/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
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/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
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/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,
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/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,
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/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
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/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
AttributeError: in user code:
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/keras/engine/training.py:855 train_function *
return step_function(self, iterator)
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/keras/engine/training.py:845 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/distribute/distribute_lib.py:1285 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/distribute/distribute_lib.py:2833 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/distribute/distribute_lib.py:3608 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/keras/engine/training.py:838 run_step **
outputs = model.train_step(data)
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/keras/engine/training.py:800 train_step
self.compiled_metrics.update_state(y, y_pred, sample_weight)
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/keras/engine/compile_utils.py:439 update_state
self.build(y_pred, y_true)
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/keras/engine/compile_utils.py:361 build
self._metrics = nest.map_structure_up_to(y_pred, self._get_metric_objects,
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/util/nest.py:1374 map_structure_up_to
return map_structure_with_tuple_paths_up_to(
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/util/nest.py:1472 map_structure_with_tuple_paths_up_to
results = [
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/util/nest.py:1473 <listcomp>
func(*args, **kwargs) for args in zip(flat_path_gen, *flat_value_gen)
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/util/nest.py:1376 <lambda>
lambda _, *values: func(*values), # Discards the path arg.
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/keras/engine/compile_utils.py:485 _get_metric_objects
return [self._get_metric_object(m, y_t, y_p) for m in metrics]
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/keras/engine/compile_utils.py:485 <listcomp>
return [self._get_metric_object(m, y_t, y_p) for m in metrics]
/usr/local/Caskroom/miniforge/base/envs/speciale_01_01/lib/python3.9/site-packages/tensorflow/python/keras/engine/compile_utils.py:506 _get_metric_object
y_t_rank = len(y_t.shape.as_list())
AttributeError: 'tuple' object has no attribute 'shape'
谁能帮助或指出问题出在哪里？
除了路径之外，数据框是相同的。
更新： 我发现 metrics['acc] 正在解决这个问题......非常烦人...... 但是为什么我失败了我还没有发现。 [1]：https://i.stack.imgur.com/AU6HU.png
【问题讨论】：
【参考方案1】：因此，对于遇到此问题的其他任何人，我都发现了问题。
OBS：使用顺序模型时不会出现此问题...不知道为什么。
但是，当您像我一样对标签进行热编码时：
并使用带有生成器的多输入模型，例如：
那就不要使用metrics=['acc'] 这不起作用，你会得到属性错误。
请参阅以下内容：https://www.tensorflow.org/api_docs/python/tf/keras/metrics/CategoricalAccuracy
使用 tf.keras.metrics.CategoricalAccuracy 这适用于热编码标签。
【讨论】：
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