मैं के साथ एक समस्या है मेरी बहु इनपुट मॉडल. मैं का निर्माण किया है के साथ यह निम्नलिखित कोड टुकड़ा:
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
)
इनपुट परतों को स्वीकार करता है, निम्नलिखित आकार:
batch_size = 18
def get_img_input_shape(for_model=False):
if for_model:
return(299,299,3)
return (299, 299)
[![छवि आकार परत][1]][1]
मॉडल संरचना:
मैं बनाया एक कस्टम जनरेटर thats दो जनरेटर के साथ flowfromdataframe और आउटपुट दो इनपुट और एक लेबल.
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
)
जब मैं फोन मॉडल है । फिट हालांकि यह एक विशेषता त्रुटि:
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'
कर सकते हैं किसी को भी मदद या मुझसे बात करने के लिए, जहां समस्या है?
के dataframe समान हैं, को छोड़कर के लिए पथ.
अद्यतन: मैंने पाया है कि बाहर मीट्रिक['एसीसी] दौड़ रहा है इस समस्या का... बहुत कष्टप्रद... हालांकि मैं क्यों विफल रहता है, मैं नहीं मिला है अभी तक. [1]: https://i.stack.imgur.com/AU6HU.png
