我在本地jupyter笔记本上重新制作了Tensorflow结构化数据教程。 这个教程可以在 https:/www.tensorflow.orgtutorialsstructured_datafeature_columns.
我添加了以下新数据的获取预测。
x = {
'age': np.array([1,1]),
'ca': np.array([1,1]),
'chol': np.array([1,1]),
'oldpeak': np.array([1,1]),
'slope': np.array([1,1]),
'thalach': np.array([1,1]),
'trestbps': np.array([1,1]),
'sex': np.array([1,1]),
'restecg': np.array([1,1]),
'cp': np.array([1,1]),
'exang': np.array([1,1]),
'fbs': np.array([1,1]),
'thal': np.array(['fixed','fixed'])
}
model.predict(x)
其中成功提供了2个预测。
array([[0.1833743],[0.1833743]], dtype=float32)
然而，我不能使用同样的dict从保存的模型进行预测。 我在保存模型时使用了
model.save('tf_tutorial_2020_05_15/test_1')
在一个新的笔记本中，我有以下代码。
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import feature_column
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
new_model = tf.keras.models.load_model('tf_tutorial_2020_05_15/test_1')
x = {
'age': np.array([1,1]),
'ca': np.array([1,1]),
'chol': np.array([1,1]),
'oldpeak': np.array([1,1]),
'slope': np.array([1,1]),
'thalach': np.array([1,1]),
'trestbps': np.array([1,1]),
'sex': np.array([1,1]),
'restecg': np.array([1,1]),
'cp': np.array([1,1]),
'exang': np.array([1,1]),
'fbs': np.array([1,1]),
'thal': np.array(['fixed','fixed'])
}
new_model.predict(x)
这引发了下面的错误。 我需要将什么结构传递给new_model.predict()才能得到预测结果呢，还是说我还做错了什么？
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-26ee037cb00c> in <module>
15 }
16
---> 17 new_model.predict(x)
~/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
1011 max_queue_size=max_queue_size,
1012 workers=workers,
-> 1013 use_multiprocessing=use_multiprocessing)
1014
1015 def reset_metrics(self):
~/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py in predict(self, model, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing, **kwargs)
496 model, ModeKeys.PREDICT, x=x, batch_size=batch_size, verbose=verbose,
497 steps=steps, callbacks=callbacks, max_queue_size=max_queue_size,
--> 498 workers=workers, use_multiprocessing=use_multiprocessing, **kwargs)
499
500
~/.local/lib/python3.6/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, max_queue_size, workers, use_multiprocessing, **kwargs)
424 max_queue_size=max_queue_size,
425 workers=workers,
--> 426 use_multiprocessing=use_multiprocessing)
427 total_samples = _get_total_number_of_samples(adapter)
428 use_sample = total_samples is not None
~/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py in _process_inputs(model, mode, x, y, batch_size, epochs, sample_weights, class_weights, shuffle, steps, distribution_strategy, max_queue_size, workers, use_multiprocessing)
644 standardize_function = None
645 x, y, sample_weights = standardize(
--> 646 x, y, sample_weight=sample_weights)
647 elif adapter_cls is data_adapter.ListsOfScalarsDataAdapter:
648 standardize_function = standardize
~/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split, shuffle, extract_tensors_from_dataset)
2344 # First, we build the model on the fly if necessary.
2345 if not self.inputs:
-> 2346 all_inputs, y_input, dict_inputs = self._build_model_with_inputs(x, y)
2347 is_build_called = True
2348 else:
~/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py in _build_model_with_inputs(self, inputs, targets)
2570 else:
2571 cast_inputs = inputs
-> 2572 self._set_inputs(cast_inputs)
2573 return processed_inputs, targets, is_dict_inputs
2574
~/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py in _set_inputs(self, inputs, outputs, training)
2657 kwargs['training'] = training
2658 try:
-> 2659 outputs = self(inputs, **kwargs)
2660 except NotImplementedError:
2661 # This Model or a submodel is dynamic and hasn't overridden
~/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
771 not base_layer_utils.is_in_eager_or_tf_function()):
772 with auto_control_deps.AutomaticControlDependencies() as acd:
--> 773 outputs = call_fn(cast_inputs, *args, **kwargs)
774 # Wrap Tensors in `outputs` in `tf.identity` to avoid
775 # circular dependencies.
~/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py in call(self, inputs, training, mask)
279 kwargs['training'] = training
280
--> 281 outputs = layer(inputs, **kwargs)
282
283 # `outputs` will be the inputs to the next layer.
~/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
771 not base_layer_utils.is_in_eager_or_tf_function()):
772 with auto_control_deps.AutomaticControlDependencies() as acd:
--> 773 outputs = call_fn(cast_inputs, *args, **kwargs)
774 # Wrap Tensors in `outputs` in `tf.identity` to avoid
775 # circular dependencies.
~/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/saving/saved_model/utils.py in return_outputs_and_add_losses(*args, **kwargs)
57 inputs = args[inputs_arg_index]
58 args = args[inputs_arg_index + 1:]
---> 59 outputs, losses = fn(inputs, *args, **kwargs)
60 layer.add_loss(losses, inputs)
61 return outputs
~/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py in __call__(self, *args, **kwds)
566 xla_context.Exit()
567 else:
--> 568 result = self._call(*args, **kwds)
569
570 if tracing_count == self._get_tracing_count():
~/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py in _call(self, *args, **kwds)
613 # This is the first call of __call__, so we have to initialize.
614 initializers = []
--> 615 self._initialize(args, kwds, add_initializers_to=initializers)
616 finally:
617 # At this point we know that the initialization is complete (or less
~/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
495 self._concrete_stateful_fn = (
496 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 497 *args, **kwds))
498
499 def invalid_creator_scope(*unused_args, **unused_kwds):
~/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2387 args, kwargs = None, None
2388 with self._lock:
-> 2389 graph_function, _, _ = self._maybe_define_function(args, kwargs)
2390 return graph_function
2391
~/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _maybe_define_function(self, args, kwargs)
2701
2702 self._function_cache.missed.add(call_context_key)
-> 2703 graph_function = self._create_graph_function(args, kwargs)
2704 self._function_cache.primary[cache_key] = graph_function
2705 return graph_function, args, kwargs
~/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2591 arg_names=arg_names,
2592 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2593 capture_by_value=self._capture_by_value),
2594 self._function_attributes,
2595 # Tell the ConcreteFunction to clean up its graph once it goes out of
~/.local/lib/python3.6/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)
976 converted_func)
977
--> 978 func_outputs = python_func(*func_args, **func_kwargs)
979
980 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
~/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py in wrapped_fn(*args, **kwds)
437 # __wrapped__ allows AutoGraph to swap in a converted function. We give
438 # the function a weak reference to itself to avoid a reference cycle.
--> 439 return weak_wrapped_fn().__wrapped__(*args, **kwds)
440 weak_wrapped_fn = weakref.ref(wrapped_fn)
441
~/.local/lib/python3.6/site-packages/tensorflow_core/python/saved_model/function_deserialization.py in restored_function_body(*args, **kwargs)
260 .format(_pretty_format_positional(args), kwargs,
261 len(saved_function.concrete_functions),
--> 262 "\n\n".join(signature_descriptions)))
263
264 concrete_function_objects = []
ValueError: Could not find matching function to call loaded from the SavedModel. Got:
Positional arguments (2 total):
* {'age': <tf.Tensor 'features:0' shape=(None, 1) dtype=int64>, 'ca': <tf.Tensor 'features_1:0' shape=(None, 1) dtype=int64>, 'chol': <tf.Tensor 'features_2:0' shape=(None, 1) dtype=int64>, 'cp': <tf.Tensor 'features_3:0' shape=(None, 1) dtype=int64>, 'exang': <tf.Tensor 'features_4:0' shape=(None, 1) dtype=int64>, 'fbs': <tf.Tensor 'features_5:0' shape=(None, 1) dtype=int64>, 'oldpeak': <tf.Tensor 'features_6:0' shape=(None, 1) dtype=int64>, 'restecg': <tf.Tensor 'features_7:0' shape=(None, 1) dtype=int64>, 'sex': <tf.Tensor 'features_8:0' shape=(None, 1) dtype=int64>, 'slope': <tf.Tensor 'features_9:0' shape=(None, 1) dtype=int64>, 'thal': <tf.Tensor 'features_10:0' shape=(None, 1) dtype=string>, 'thalach': <tf.Tensor 'features_11:0' shape=(None, 1) dtype=int64>, 'trestbps': <tf.Tensor 'features_12:0' shape=(None, 1) dtype=int64>}
* None
Keyword arguments: {}
Expected these arguments to match one of the following 1 option(s):
Option 1:
Positional arguments (2 total):
* {'ca': TensorSpec(shape=(None, 1), dtype=tf.int32, name='features/ca'), 'exang': TensorSpec(shape=(None, 1), dtype=tf.int32, name='features/exang'), 'fbs': TensorSpec(shape=(None, 1), dtype=tf.int32, name='features/fbs'), 'chol': TensorSpec(shape=(None, 1), dtype=tf.int32, name='features/chol'), 'thalach': TensorSpec(shape=(None, 1), dtype=tf.int32, name='features/thalach'), 'thal': TensorSpec(shape=(None, 1), dtype=tf.string, name='features/thal'), 'cp': TensorSpec(shape=(None, 1), dtype=tf.int32, name='features/cp'), 'age': TensorSpec(shape=(None, 1), dtype=tf.int32, name='features/age'), 'oldpeak': TensorSpec(shape=(None, 1), dtype=tf.float32, name='features/oldpeak'), 'restecg': TensorSpec(shape=(None, 1), dtype=tf.int32, name='features/restecg'), 'trestbps': TensorSpec(shape=(None, 1), dtype=tf.int32, name='features/trestbps'), 'slope': TensorSpec(shape=(None, 1), dtype=tf.int32, name='features/slope'), 'sex': TensorSpec(shape=(None, 1), dtype=tf.int32, name='features/sex')}
* None
Keyword arguments: {}
试试这个。
pip uninstall tensorflow
pip uninstall tensorboard
pip install -q tf-nightly
pip install --ignore-installed tf-nightly
应该可以解决这个问题。