我有一组图像（以jpg表示的视频帧），并且想要创建一个CNN-LSTM模型来对每个图像进行分类（很多到很多LSTM）。如何创建Tensorflow 2数据集作为模型的输入？
一个视频包含约10,000至40,000帧，此序列对于LSTM而言仍太长而无法保留有关先前状态的信息。我想将视频切成100个帧的序列，并对其进行单独分类。
这是我的TF模型（我正在使用TF集线器进行预训练的图像嵌入）：
feature_extractor_url = "https://tfhub.dev/google/tf2-preview/inception_v3/feature_vector/4"
model = tf.keras.Sequential([
tf.keras.layers.TimeDistributed(hub.KerasLayer(feature_extractor_url, input_shape=(width, height, 3), output_shape=[2048],
trainable=True), batch_input_shape=(100, None, 299, 299, 3)),
tf.keras.layers.Bidirectional(tf.keras.layers.GRU(1024, return_sequences=True)),
tf.keras.layers.Bidirectional(tf.keras.layers.GRU(512, return_sequences=True)),
tf.keras.layers.Bidirectional(tf.keras.layers.GRU(256, return_sequences=True)),
tf.keras.layers.Dense(2, activation='softmax')
])
model.compile(
optimizer=tf.keras.optimizers.Adam(lr=learning_rate),
loss=tf.keras.losses.CategoricalCrossentropy(label_smoothing=label_smoothing),
metrics=['accuracy'])
这是我加载数据的方式：
def parse_image(file_path):
image = tf.io.read_file(file_path)
image = tf.image.decode_jpeg(image, channels=3)
image = tf.image.convert_image_dtype(image, tf.float32)
image = tf.image.resize(image, [constants.width, constants.height])
image /= 255.0
return image
def get_image_ds(dir_name: str, sequence_length: int):
data_root = constants.data_root
data_root = pathlib.Path(str(data_root) + dir_name)
list_ds = tf.data.Dataset.list_files(str(data_root / 'all/*'), shuffle=False)
image_data = list_ds.map(parse_image)
return image_data.batch(sequence_length, drop_remainder=True)\
def get_label_ds(filename: str, label_to_index: Dict[str, int]):
with open(filename, 'r', encoding='utf8') as file:
labels = [line.split(' ')[1].replace('\n', '') for line in file]
all_image_labels = [label_to_index[label_name] for label_name in labels]
label_ds = tf.data.Dataset.from_tensor_slices(all_image_labels)
return label_ds, len(all_image_labels)
def get_tf_dataset(sequence_length: int, batch_size: int):
label_names = ['selected', 'not_selected']
label_to_index = dict((name, index) for index, name in enumerate(label_names))
train_image_data = get_image_ds('/train', sequence_length)
train_labels, n_train_data = get_label_ds('data/train.txt', label_to_index)
train_ds = tf.data.Dataset.zip((train_image_data, train_labels))\
.batch(batch_size)\
.prefetch(buffer_size=AUTOTUNE)
test_image_data = get_image_ds('/test', sequence_length)
test_labels, _ = get_label_ds('data/test.txt', label_to_index)
test_ds = tf.data.Dataset.zip((test_image_data, test_labels))\
.batch(batch_size)\
.prefetch(buffer_size=AUTOTUNE)
val_image_data = get_image_ds('/val', sequence_length)
val_labels, _ = get_label_ds('data/val.txt', label_to_index)
val_ds = tf.data.Dataset.zip((val_image_data, val_labels))\
.batch(batch_size)\
.prefetch(buffer_size=AUTOTUNE)
在执行model.fit之后，Tensorflow现在引发NotImplementedError：
hist = model.fit(
train_image_data,
epochs=5,
steps_per_epoch=steps_per_epoch,
validation_data=val_image_data,
validation_steps=100,
).history
错误消息：
Train for 1266.0 steps, validate for 100 steps
Epoch 1/5
1/1266 [..............................] - ETA: 10:50
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in on_epoch(self, epoch, mode)
679 try:
--> 680 yield epoch_logs
681 finally:
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)
323 training_context=training_context,
--> 324 total_epochs=epochs)
325 cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN)
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)
122 try:
--> 123 batch_outs = execution_function(iterator)
124 except (StopIteration, errors.OutOfRangeError):
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in execution_function(input_fn)
85 return nest.map_structure(_non_none_constant_value,
---> 86 distributed_function(input_fn))
87
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\eager\def_function.py in __call__(self, *args, **kwds)
426 initializer_map = object_identity.ObjectIdentityDictionary()
--> 427 self._initialize(args, kwds, add_initializers_to=initializer_map)
428 if self._created_variables:
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to)
369 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 370 *args, **kwds))
371
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
1846 args, kwargs = None, None
-> 1847 graph_function, _, _ = self._maybe_define_function(args, kwargs)
1848 return graph_function
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\eager\function.py in _maybe_define_function(self, args, kwargs)
2146 if graph_function is None:
-> 2147 graph_function = self._create_graph_function(args, kwargs)
2148 self._function_cache.primary[cache_key] = graph_function
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2037 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2038 capture_by_value=self._capture_by_value),
2039 self._function_attributes,
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\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)
914
--> 915 func_outputs = python_func(*func_args, **func_kwargs)
916
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\eager\def_function.py in wrapped_fn(*args, **kwds)
319 # the function a weak reference to itself to avoid a reference cycle.
--> 320 return weak_wrapped_fn().__wrapped__(*args, **kwds)
321 weak_wrapped_fn = weakref.ref(wrapped_fn)
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in distributed_function(input_iterator)
72 outputs = strategy.experimental_run_v2(
---> 73 per_replica_function, args=(model, x, y, sample_weights))
74 # Out of PerReplica outputs reduce or pick values to return.
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\distribute\distribute_lib.py in experimental_run_v2(self, fn, args, kwargs)
759 convert_by_default=False)
--> 760 return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
761
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\distribute\distribute_lib.py in call_for_each_replica(self, fn, args, kwargs)
1786 with self._container_strategy().scope():
-> 1787 return self._call_for_each_replica(fn, args, kwargs)
1788
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\distribute\distribute_lib.py in _call_for_each_replica(self, fn, args, kwargs)
2131 replica_id_in_sync_group=constant_op.constant(0, dtypes.int32)):
-> 2132 return fn(*args, **kwargs)
2133
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\autograph\impl\api.py in wrapper(*args, **kwargs)
291 with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.DISABLED):
--> 292 return func(*args, **kwargs)
293
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in train_on_batch(model, x, y, sample_weight, class_weight, reset_metrics)
263 sample_weights=sample_weights,
--> 264 output_loss_metrics=model._output_loss_metrics)
265
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py in train_on_batch(model, inputs, targets, sample_weights, output_loss_metrics)
310 training=True,
--> 311 output_loss_metrics=output_loss_metrics))
312 if not isinstance(outs, list):
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py in _process_single_batch(model, inputs, targets, output_loss_metrics, sample_weights, training)
251 sample_weights=sample_weights,
--> 252 training=training))
253 if total_loss is None:
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py in _model_loss(model, inputs, targets, output_loss_metrics, sample_weights, training)
126
--> 127 outs = model(inputs, **kwargs)
128 outs = nest.flatten(outs)
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py in __call__(self, inputs, *args, **kwargs)
806 else:
--> 807 outputs = call_fn(cast_inputs, *args, **kwargs)
808
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\sequential.py in call(self, inputs, training, mask)
254 self._init_graph_network(self.inputs, self.outputs, name=self.name)
--> 255 return super(Sequential, self).call(inputs, training=training, mask=mask)
256
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\network.py in call(self, inputs, training, mask)
696
--> 697 return self._run_internal_graph(inputs, training=training, mask=mask)
698
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\network.py in _run_internal_graph(self, inputs, training, mask)
841 # Compute outputs.
--> 842 output_tensors = layer(computed_tensors, **kwargs)
843
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py in __call__(self, inputs, *args, **kwargs)
806 else:
--> 807 outputs = call_fn(cast_inputs, *args, **kwargs)
808
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\layers\wrappers.py in call(self, inputs, training, mask)
255 # Shape: (num_samples, timesteps, ...)
--> 256 output_shape = self.compute_output_shape(input_shape).as_list()
257 output_shape = self._get_shape_tuple(
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\layers\wrappers.py in compute_output_shape(self, input_shape)
209 input_shape[2:])
--> 210 child_output_shape = self.layer.compute_output_shape(child_input_shape)
211 if not isinstance(child_output_shape, tensor_shape.TensorShape):
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py in compute_output_shape(self, input_shape)
598 return nest.map_structure(lambda t: t.shape, outputs)
--> 599 raise NotImplementedError
600
NotImplementedError:
During handling of the above exception, another exception occurred:
NotImplementedError Traceback (most recent call last)
<ipython-input-8-6bf7fd9bb8ab> in <module>
5 callbacks=[tensorboard_callback, cm_callback],
6 validation_data=val_image_data,
----> 7 validation_steps=100,
8 ).history
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\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_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
732 max_queue_size=max_queue_size,
733 workers=workers,
--> 734 use_multiprocessing=use_multiprocessing)
735
736 def evaluate(self,
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)
370 total_epochs=1)
371 cbks.make_logs(model, epoch_logs, eval_result, ModeKeys.TEST,
--> 372 prefix='val_')
373
374 return model.history
~\AppData\Local\Programs\Python\Python36\Lib\contextlib.py in __exit__(self, type, value, traceback)
97 value = type()
98 try:
---> 99 self.gen.throw(type, value, traceback)
100 except StopIteration as exc:
101 # Suppress StopIteration *unless* it's the same exception that
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in on_epoch(self, epoch, mode)
682 if mode == ModeKeys.TRAIN:
683 # Epochs only apply to `fit`.
--> 684 self.callbacks.on_epoch_end(epoch, epoch_logs)
685 self.progbar.on_epoch_end(epoch, epoch_logs)
686
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\callbacks.py in on_epoch_end(self, epoch, logs)
295 logs = logs or {}
296 for callback in self.callbacks:
--> 297 callback.on_epoch_end(epoch, logs)
298
299 def on_train_batch_begin(self, batch, logs=None):
<ipython-input-6-ce1724da9329> in log_confusion_matrix(epoch, logs)
6 def log_confusion_matrix(epoch, logs):
7 # Use the model to predict the values from the validation dataset.
----> 8 test_pred_raw = model.predict(test_image_data)
9 test_pred = np.argmax(test_pred_raw, axis=1)
10
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
913 max_queue_size=max_queue_size,
914 workers=workers,
--> 915 use_multiprocessing=use_multiprocessing)
916
917 def reset_metrics(self):
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in predict(self, model, x, batch_size, verbose, steps, callbacks, **kwargs)
460 return self._model_iteration(
461 model, ModeKeys.PREDICT, x=x, batch_size=batch_size, verbose=verbose,
--> 462 steps=steps, callbacks=callbacks, **kwargs)
463
464
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in _model_iteration(self, model, mode, x, y, batch_size, verbose, sample_weight, steps, callbacks, **kwargs)
442 mode=mode,
443 training_context=training_context,
--> 444 total_epochs=1)
445 cbks.make_logs(model, epoch_logs, result, mode)
446
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)
121 step=step, mode=mode, size=current_batch_size) as batch_logs:
122 try:
--> 123 batch_outs = execution_function(iterator)
124 except (StopIteration, errors.OutOfRangeError):
125 # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in execution_function(input_fn)
84 # `numpy` translates Tensors to values in Eager mode.
85 return nest.map_structure(_non_none_constant_value,
---> 86 distributed_function(input_fn))
87
88 return execution_function
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\eager\def_function.py in __call__(self, *args, **kwds)
425 # This is the first call of __call__, so we have to initialize.
426 initializer_map = object_identity.ObjectIdentityDictionary()
--> 427 self._initialize(args, kwds, add_initializers_to=initializer_map)
428 if self._created_variables:
429 try:
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to)
368 self._concrete_stateful_fn = (
369 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 370 *args, **kwds))
371
372 def invalid_creator_scope(*unused_args, **unused_kwds):
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
1845 if self.input_signature:
1846 args, kwargs = None, None
-> 1847 graph_function, _, _ = self._maybe_define_function(args, kwargs)
1848 return graph_function
1849
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\eager\function.py in _maybe_define_function(self, args, kwargs)
2145 graph_function = self._function_cache.primary.get(cache_key, None)
2146 if graph_function is None:
-> 2147 graph_function = self._create_graph_function(args, kwargs)
2148 self._function_cache.primary[cache_key] = graph_function
2149 return graph_function, args, kwargs
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2036 arg_names=arg_names,
2037 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2038 capture_by_value=self._capture_by_value),
2039 self._function_attributes,
2040 # Tell the ConcreteFunction to clean up its graph once it goes out of
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\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)
913 converted_func)
914
--> 915 func_outputs = python_func(*func_args, **func_kwargs)
916
917 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\eager\def_function.py in wrapped_fn(*args, **kwds)
318 # __wrapped__ allows AutoGraph to swap in a converted function. We give
319 # the function a weak reference to itself to avoid a reference cycle.
--> 320 return weak_wrapped_fn().__wrapped__(*args, **kwds)
321 weak_wrapped_fn = weakref.ref(wrapped_fn)
322
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in distributed_function(input_iterator)
71 strategy = distribution_strategy_context.get_strategy()
72 outputs = strategy.experimental_run_v2(
---> 73 per_replica_function, args=(model, x, y, sample_weights))
74 # Out of PerReplica outputs reduce or pick values to return.
75 all_outputs = dist_utils.unwrap_output_dict(
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\distribute\distribute_lib.py in experimental_run_v2(self, fn, args, kwargs)
758 fn = autograph.tf_convert(fn, ag_ctx.control_status_ctx(),
759 convert_by_default=False)
--> 760 return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
761
762 def reduce(self, reduce_op, value, axis):
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\distribute\distribute_lib.py in call_for_each_replica(self, fn, args, kwargs)
1785 kwargs = {}
1786 with self._container_strategy().scope():
-> 1787 return self._call_for_each_replica(fn, args, kwargs)
1788
1789 def _call_for_each_replica(self, fn, args, kwargs):
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\distribute\distribute_lib.py in _call_for_each_replica(self, fn, args, kwargs)
2130 self._container_strategy(),
2131 replica_id_in_sync_group=constant_op.constant(0, dtypes.int32)):
-> 2132 return fn(*args, **kwargs)
2133
2134 def _reduce_to(self, reduce_op, value, destinations):
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\autograph\impl\api.py in wrapper(*args, **kwargs)
290 def wrapper(*args, **kwargs):
291 with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.DISABLED):
--> 292 return func(*args, **kwargs)
293
294 if inspect.isfunction(func) or inspect.ismethod(func):
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in _predict_on_batch(***failed resolving arguments***)
160 def _predict_on_batch(model, x, y=None, sample_weights=None):
161 del y, sample_weights
--> 162 return predict_on_batch(model, x)
163
164 func = _predict_on_batch
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in predict_on_batch(model, x)
368
369 with backend.eager_learning_phase_scope(0):
--> 370 return model(inputs) # pylint: disable=not-callable
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py in __call__(self, inputs, *args, **kwargs)
805 outputs = base_layer_utils.mark_as_return(outputs, acd)
806 else:
--> 807 outputs = call_fn(cast_inputs, *args, **kwargs)
808
809 except errors.OperatorNotAllowedInGraphError as e:
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\sequential.py in call(self, inputs, training, mask)
253 if not self.built:
254 self._init_graph_network(self.inputs, self.outputs, name=self.name)
--> 255 return super(Sequential, self).call(inputs, training=training, mask=mask)
256
257 outputs = inputs # handle the corner case where self.layers is empty
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\network.py in call(self, inputs, training, mask)
695 ' implement a `call` method.')
696
--> 697 return self._run_internal_graph(inputs, training=training, mask=mask)
698
699 def compute_output_shape(self, input_shape):
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\network.py in _run_internal_graph(self, inputs, training, mask)
840
841 # Compute outputs.
--> 842 output_tensors = layer(computed_tensors, **kwargs)
843
844 # Update tensor_dict.
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py in __call__(self, inputs, *args, **kwargs)
805 outputs = base_layer_utils.mark_as_return(outputs, acd)
806 else:
--> 807 outputs = call_fn(cast_inputs, *args, **kwargs)
808
809 except errors.OperatorNotAllowedInGraphError as e:
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\layers\wrappers.py in call(self, inputs, training, mask)
254 y = self.layer(inputs, **kwargs)
255 # Shape: (num_samples, timesteps, ...)
--> 256 output_shape = self.compute_output_shape(input_shape).as_list()
257 output_shape = self._get_shape_tuple(
258 (-1, input_length), y, 1, output_shape[2:])
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\layers\wrappers.py in compute_output_shape(self, input_shape)
208 child_input_shape = tensor_shape.TensorShape([input_shape[0]] +
209 input_shape[2:])
--> 210 child_output_shape = self.layer.compute_output_shape(child_input_shape)
211 if not isinstance(child_output_shape, tensor_shape.TensorShape):
212 child_output_shape = tensor_shape.TensorShape(child_output_shape)
c:\users\yanick\code\pycharmprojects\avisec\venv\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py in compute_output_shape(self, input_shape)
597 'layer (%s).' % self.__class__.__name__)
598 return nest.map_structure(lambda t: t.shape, outputs)
--> 599 raise NotImplementedError
600
601 @doc_controls.for_subclass_implementers
NotImplementedError:
```