Я пытаюсь поэкспериментировать со слоем Conv1D. Я хочу запустить код из книги ml2 с моими собственными данными.
Код выглядит следующим образом:
model = keras.models.Sequential([
keras.layers.Conv1D(filters=20, kernel_size=4, strides=2, padding="valid",
input_shape=[None,1]),
keras.layers.GRU(20, return_sequences=True),
keras.layers.GRU(20, return_sequences=True),
keras.layers.TimeDistributed(keras.layers.Dense(10))
])
model.compile(loss="mse", optimizer="adam")
history = model.fit(bx, y, epochs=20)
Форма bx: (1256247, 120, 1)
Форма y: (1256247, 10)
То, что я хочу сделать, - это модель прогнозирования поезда, которая принимает в качестве входных данных 120 значений и выдает 10, и есть1256247 записей в наборе данных.
При запуске выполнения происходит сбой со следующей ошибкой Incompatible shapes: [32,59,10] vs. [32,10]:
InvalidArgumentError Traceback (most recent call last)
<ipython-input-14-02fdddb39c43> in <module>()
9 model.compile(loss="mse", optimizer="adam")
10
---> 11 history = model.fit(bx, y, epochs=20)
~/anaconda3/envs/tensorflow_p36/lib/python3.6/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)
726 max_queue_size=max_queue_size,
727 workers=workers,
--> 728 use_multiprocessing=use_multiprocessing)
729
730 def evaluate(self,
~/anaconda3/envs/tensorflow_p36/lib/python3.6/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)
322 mode=ModeKeys.TRAIN,
323 training_context=training_context,
--> 324 total_epochs=epochs)
325 cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN)
326
~/anaconda3/envs/tensorflow_p36/lib/python3.6/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?
~/anaconda3/envs/tensorflow_p36/lib/python3.6/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
~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py in __call__(self, *args, **kwds)
455
456 tracing_count = self._get_tracing_count()
--> 457 result = self._call(*args, **kwds)
458 if tracing_count == self._get_tracing_count():
459 self._call_counter.called_without_tracing()
~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py in _call(self, *args, **kwds)
518 # Lifting succeeded, so variables are initialized and we can run the
519 # stateless function.
--> 520 return self._stateless_fn(*args, **kwds)
521 else:
522 canon_args, canon_kwds = \
~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in __call__(self, *args, **kwargs)
1821 """Calls a graph function specialized to the inputs."""
1822 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 1823 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
1824
1825 @property
~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _filtered_call(self, args, kwargs)
1139 if isinstance(t, (ops.Tensor,
1140 resource_variable_ops.BaseResourceVariable))),
-> 1141 self.captured_inputs)
1142
1143 def _call_flat(self, args, captured_inputs, cancellation_manager=None):
~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1222 if executing_eagerly:
1223 flat_outputs = forward_function.call(
-> 1224 ctx, args, cancellation_manager=cancellation_manager)
1225 else:
1226 gradient_name = self._delayed_rewrite_functions.register()
~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in call(self, ctx, args, cancellation_manager)
509 inputs=args,
510 attrs=("executor_type", executor_type, "config_proto", config),
--> 511 ctx=ctx)
512 else:
513 outputs = execute.execute_with_cancellation(
~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
65 else:
66 message = e.message
---> 67 six.raise_from(core._status_to_exception(e.code, message), None)
68 except TypeError as e:
69 keras_symbolic_tensors = [
~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/six.py in raise_from(value, from_value)
InvalidArgumentError: Incompatible shapes: [32,59,10] vs. [32,10]
[[node BroadcastGradientArgs_2 (defined at /home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1751) ]] [Op:__inference_distributed_function_12526]
Function call stack:
distributed_function
Как изменить код для приема данных в формате I 'м обеспечение?