When saving a Keras model defined like this:
# define model
model = Sequential()
model.add(LSTM(200, activation='relu', input_shape=(n_steps_in, n_features)))
model.add(RepeatVector(n_steps_out))
model.add(LSTM(200, activation='relu', return_sequences=True))
model.add(TimeDistributed(Dense(n_features)))
model.compile(optimizer='adam', loss='mse')
model.save(path)
I got this following message:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~/anaconda3/envs/topic_forecaster/lib/python3.7/inspect.py in getfullargspec(func)
1125 skip_bound_arg=False,
-> 1126 sigcls=Signature)
1127 except Exception as ex:
~/anaconda3/envs/topic_forecaster/lib/python3.7/inspect.py in _signature_from_callable(obj, follow_wrapper_chains, skip_bound_arg, sigcls)
2287 return _signature_from_builtin(sigcls, obj,
-> 2288 skip_bound_arg=skip_bound_arg)
2289
~/anaconda3/envs/topic_forecaster/lib/python3.7/inspect.py in _signature_from_builtin(cls, func, skip_bound_arg)
2111 if not s:
-> 2112 raise ValueError("no signature found for builtin {!r}".format(func))
2113
ValueError: no signature found for builtin <tensorflow.python.keras.saving.saved_model.save_impl.LayerCall object at 0x7f8c1f357190>
The above exception was the direct cause of the following exception:
TypeError Traceback (most recent call last)
<ipython-input-9-3fe80778ab16> in <module>
3 path = Path.cwd().parent / 'models' / 'tpi'
4 Path(path).mkdir(parents=True, exist_ok=True)
----> 5 model.save(str(path))
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/network.py in save(self, filepath, overwrite, include_optimizer, save_format, signatures, options)
973 """
974 saving.save_model(self, filepath, overwrite, include_optimizer, save_format,
--> 975 signatures, options)
976
977 def save_weights(self, filepath, overwrite=True, save_format=None):
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/save.py in save_model(model, filepath, overwrite, include_optimizer, save_format, signatures, options)
113 else:
114 saved_model_save.save(model, filepath, overwrite, include_optimizer,
--> 115 signatures, options)
116
117
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save.py in save(model, filepath, overwrite, include_optimizer, signatures, options)
72 # default learning phase placeholder.
73 with K.learning_phase_scope(0):
---> 74 save_lib.save(model, filepath, signatures, options)
75
76 if not include_optimizer:
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/saved_model/save.py in save(obj, export_dir, signatures, options)
868 if signatures is None:
869 signatures = signature_serialization.find_function_to_export(
--> 870 checkpoint_graph_view)
871
872 signatures = signature_serialization.canonicalize_signatures(signatures)
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/saved_model/signature_serialization.py in find_function_to_export(saveable_view)
62 # If the user did not specify signatures, check the root object for a function
63 # that can be made into a signature.
---> 64 functions = saveable_view.list_functions(saveable_view.root)
65 signature = functions.get(DEFAULT_SIGNATURE_ATTR, None)
66 if signature is not None:
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/saved_model/save.py in list_functions(self, obj)
139 if obj_functions is None:
140 obj_functions = obj._list_functions_for_serialization( # pylint: disable=protected-access
--> 141 self._serialization_cache)
142 self._functions[obj] = obj_functions
143 return obj_functions
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py in _list_functions_for_serialization(self, serialization_cache)
2420 def _list_functions_for_serialization(self, serialization_cache):
2421 return (self._trackable_saved_model_saver
-> 2422 .list_functions_for_serialization(serialization_cache))
2423
2424
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/base_serialization.py in list_functions_for_serialization(self, serialization_cache)
89 `ConcreteFunction`.
90 """
---> 91 fns = self.functions_to_serialize(serialization_cache)
92
93 # The parent AutoTrackable class saves all user-defined tf.functions, and
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/layer_serialization.py in functions_to_serialize(self, serialization_cache)
77 def functions_to_serialize(self, serialization_cache):
78 return (self._get_serialized_attributes(
---> 79 serialization_cache).functions_to_serialize)
80
81 def _get_serialized_attributes(self, serialization_cache):
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/layer_serialization.py in _get_serialized_attributes(self, serialization_cache)
92
93 object_dict, function_dict = self._get_serialized_attributes_internal(
---> 94 serialization_cache)
95
96 serialized_attr.set_and_validate_objects(object_dict)
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/model_serialization.py in _get_serialized_attributes_internal(self, serialization_cache)
51 objects, functions = (
52 super(ModelSavedModelSaver, self)._get_serialized_attributes_internal(
---> 53 serialization_cache))
54 functions['_default_save_signature'] = default_signature
55 return objects, functions
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/layer_serialization.py in _get_serialized_attributes_internal(self, serialization_cache)
101 """Returns dictionary of serialized attributes."""
102 objects = save_impl.wrap_layer_objects(self.obj, serialization_cache)
--> 103 functions = save_impl.wrap_layer_functions(self.obj, serialization_cache)
104 # Attribute validator requires that the default save signature is added to
105 # function dict, even if the value is None.
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save_impl.py in wrap_layer_functions(layer, serialization_cache)
154 # Reset the losses of the layer and its children. The call function in each
155 # child layer is replaced with tf.functions.
--> 156 original_fns = _replace_child_layer_functions(layer, serialization_cache)
157 original_losses = _reset_layer_losses(layer)
158
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save_impl.py in _replace_child_layer_functions(layer, serialization_cache)
246 layer_fns = (
247 child_layer._trackable_saved_model_saver._get_serialized_attributes(
--> 248 serialization_cache).functions)
249 else:
250 layer_fns = (
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/layer_serialization.py in _get_serialized_attributes(self, serialization_cache)
92
93 object_dict, function_dict = self._get_serialized_attributes_internal(
---> 94 serialization_cache)
95
96 serialized_attr.set_and_validate_objects(object_dict)
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/layer_serialization.py in _get_serialized_attributes_internal(self, serialization_cache)
101 """Returns dictionary of serialized attributes."""
102 objects = save_impl.wrap_layer_objects(self.obj, serialization_cache)
--> 103 functions = save_impl.wrap_layer_functions(self.obj, serialization_cache)
104 # Attribute validator requires that the default save signature is added to
105 # function dict, even if the value is None.
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save_impl.py in wrap_layer_functions(layer, serialization_cache)
164 call_fn_with_losses = call_collection.add_function(
165 _wrap_call_and_conditional_losses(layer),
--> 166 '{}_layer_call_and_return_conditional_losses'.format(layer.name))
167 call_fn = call_collection.add_function(
168 _extract_outputs_from_fn(layer, call_fn_with_losses),
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save_impl.py in add_function(self, call_fn, name)
492 # Manually add traces for layers that have keyword arguments and have
493 # a fully defined input signature.
--> 494 self.add_trace(*self._input_signature)
495 return fn
496
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save_impl.py in add_trace(self, *args, **kwargs)
411 fn.get_concrete_function(*args, **kwargs)
412
--> 413 trace_with_training(True)
414 trace_with_training(False)
415 else:
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save_impl.py in trace_with_training(value, fn)
409 utils.set_training_arg(value, self._training_arg_index, args, kwargs)
410 with K.learning_phase_scope(value):
--> 411 fn.get_concrete_function(*args, **kwargs)
412
413 trace_with_training(True)
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save_impl.py in get_concrete_function(self, *args, **kwargs)
536 if not self.call_collection.tracing:
537 self.call_collection.add_trace(*args, **kwargs)
--> 538 return super(LayerCall, self).get_concrete_function(*args, **kwargs)
539
540
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in get_concrete_function(self, *args, **kwargs)
774 if self._stateful_fn is None:
775 initializer_map = object_identity.ObjectIdentityDictionary()
--> 776 self._initialize(args, kwargs, add_initializers_to=initializer_map)
777 self._initialize_uninitialized_variables(initializer_map)
778
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
406 self._concrete_stateful_fn = (
407 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 408 *args, **kwds))
409
410 def invalid_creator_scope(*unused_args, **unused_kwds):
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
1846 if self.input_signature:
1847 args, kwargs = None, None
-> 1848 graph_function, _, _ = self._maybe_define_function(args, kwargs)
1849 return graph_function
1850
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _maybe_define_function(self, args, kwargs)
2148 graph_function = self._function_cache.primary.get(cache_key, None)
2149 if graph_function is None:
-> 2150 graph_function = self._create_graph_function(args, kwargs)
2151 self._function_cache.primary[cache_key] = graph_function
2152 return graph_function, args, kwargs
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2039 arg_names=arg_names,
2040 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2041 capture_by_value=self._capture_by_value),
2042 self._function_attributes,
2043 # Tell the ConcreteFunction to clean up its graph once it goes out of
~/anaconda3/envs/topic_forecaster/lib/python3.7/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,
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in wrapped_fn(*args, **kwds)
356 # __wrapped__ allows AutoGraph to swap in a converted function. We give
357 # the function a weak reference to itself to avoid a reference cycle.
--> 358 return weak_wrapped_fn().__wrapped__(*args, **kwds)
359 weak_wrapped_fn = weakref.ref(wrapped_fn)
360
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save_impl.py in wrapper(*args, **kwargs)
513 layer, inputs=inputs, build_graph=False, training=training,
514 saving=True):
--> 515 ret = method(*args, **kwargs)
516 _restore_layer_losses(original_losses)
517 return ret
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/utils.py in wrap_with_training_arg(*args, **kwargs)
109 training,
110 lambda: replace_training_and_call(True),
--> 111 lambda: replace_training_and_call(False))
112
113 # Create arg spec for decorated function. If 'training' is not defined in the
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/utils/tf_utils.py in smart_cond(pred, true_fn, false_fn, name)
57 pred, true_fn=true_fn, false_fn=false_fn, name=name)
58 return smart_module.smart_cond(
---> 59 pred, true_fn=true_fn, false_fn=false_fn, name=name)
60
61
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/framework/smart_cond.py in smart_cond(pred, true_fn, false_fn, name)
52 if pred_value is not None:
53 if pred_value:
---> 54 return true_fn()
55 else:
56 return false_fn()
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/utils.py in <lambda>()
108 return tf_utils.smart_cond(
109 training,
--> 110 lambda: replace_training_and_call(True),
111 lambda: replace_training_and_call(False))
112
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/utils.py in replace_training_and_call(training)
104 def replace_training_and_call(training):
105 set_training_arg(training, training_arg_index, args, kwargs)
--> 106 return wrapped_call(*args, **kwargs)
107
108 return tf_utils.smart_cond(
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save_impl.py in call_and_return_conditional_losses(inputs, *args, **kwargs)
555 layer_call = _get_layer_call_method(layer)
556 def call_and_return_conditional_losses(inputs, *args, **kwargs):
--> 557 return layer_call(inputs, *args, **kwargs), layer.get_losses_for(inputs)
558 return _create_call_fn_decorator(layer, call_and_return_conditional_losses)
559
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/layers/wrappers.py in call(self, inputs, training, mask)
218 def call(self, inputs, training=None, mask=None):
219 kwargs = {}
--> 220 if generic_utils.has_arg(self.layer.call, 'training'):
221 kwargs['training'] = training
222
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/utils/generic_utils.py in has_arg(fn, name, accept_all)
302 bool, whether `fn` accepts a `name` keyword argument.
303 """
--> 304 arg_spec = tf_inspect.getfullargspec(fn)
305 if accept_all and arg_spec.varkw is not None:
306 return True
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/util/tf_inspect.py in getfullargspec(obj)
255 if d.decorator_argspec is not None:
256 return _convert_maybe_argspec_to_fullargspec(d.decorator_argspec)
--> 257 return _getfullargspec(target)
258
259
~/anaconda3/envs/topic_forecaster/lib/python3.7/inspect.py in getfullargspec(func)
1130 # else. So to be fully backwards compatible, we catch all
1131 # possible exceptions here, and reraise a TypeError.
-> 1132 raise TypeError('unsupported callable') from ex
1133
1134 args = []
TypeError: unsupported callable
However, when saving a regular model without a TimeDistributed layer as below, it worked fine:
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10)
])
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer='adam',
loss=loss_fn,
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test, verbose=2)
model.save('temp', save_format='tf')
By upgrading Tensorflow to 2.1 with CUDA10 using Anaconda as below, the problem is solved.
conda create -n tf-gpu-cuda10 tensorflow-gpu=2.1 cudatoolkit=10
conda activate tf-gpu-cuda10
Related
I use TFRS build a custom hybrid recommender; the model was trained and is able to make prediction well. But when I save it:
model = ...
filepath = 'saved_model/model0'
tf.saved_model.save(model, filepath)
error:
--------------------------------------------------------------------------- FailedPreconditionError Traceback (most recent call last) /tmp/ipykernel_2678112/2773535809.py in <module>
5 #!mkdir -p saved_model
6 filepath = 'saved_model/model0'
----> 7 tf.saved_model.save(model, filepath)
/napi/k_working_dir/ds_test/shared-space/venv/lib/python3.9/site-packages/tensorflow/python/saved_model/save.py in save(obj, export_dir, signatures, options) 1278 # pylint: enable=line-too-long 1279 metrics.IncrementWriteApi(_SAVE_V2_LABEL)
-> 1280 save_and_return_nodes(obj, export_dir, signatures, options) 1281 metrics.IncrementWrite(write_version="2") 1282
/napi/k_working_dir/ds_test/shared-space/venv/lib/python3.9/site-packages/tensorflow/python/saved_model/save.py in save_and_return_nodes(obj, export_dir, signatures, options, experimental_skip_checkpoint) 1313 1314 _, exported_graph, object_saver, asset_info, saved_nodes, node_paths = (
-> 1315 _build_meta_graph(obj, signatures, options, meta_graph_def)) 1316 saved_model.saved_model_schema_version = ( 1317 constants.SAVED_MODEL_SCHEMA_VERSION)
/napi/k_working_dir/ds_test/shared-space/venv/lib/python3.9/site-packages/tensorflow/python/saved_model/save.py in _build_meta_graph(obj, signatures, options, meta_graph_def) 1485 1486 with save_context.save_context(options):
-> 1487 return _build_meta_graph_impl(obj, signatures, options, meta_graph_def)
/napi/k_working_dir/ds_test/shared-space/venv/lib/python3.9/site-packages/tensorflow/python/saved_model/save.py in _build_meta_graph_impl(obj, signatures, options, meta_graph_def) 1434 1435 # Use _SaveableView to provide a frozen listing of properties and functions.
-> 1436 saveable_view = _SaveableView(checkpoint_graph_view, options, 1437 wrapped_functions) 1438 object_saver = util.TrackableSaver(checkpoint_graph_view)
/napi/k_working_dir/ds_test/shared-space/venv/lib/python3.9/site-packages/tensorflow/python/saved_model/save.py in __init__(self, checkpoint_view, options, wrapped_functions)
200 # Run through the nodes in the object graph first for side effects of
201 # creating variables.
--> 202 self._trace_all_concrete_functions()
203
204 (self._trackable_objects, self.node_paths, self._node_ids,
/napi/k_working_dir/ds_test/shared-space/venv/lib/python3.9/site-packages/tensorflow/python/saved_model/save.py in _trace_all_concrete_functions(self)
304 for function in self.checkpoint_view.list_functions(obj).values():
305 if isinstance(function, def_function.Function):
--> 306 function._list_all_concrete_functions_for_serialization() # pylint: disable=protected-access
307
308 #property
/napi/k_working_dir/ds_test/shared-space/venv/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py in _list_all_concrete_functions_for_serialization(self) 1194 A list of instances of `ConcreteFunction`. 1195 """
-> 1196 concrete_functions = self._list_all_concrete_functions() 1197 seen_signatures = [] 1198 for concrete_function in concrete_functions:
/napi/k_working_dir/ds_test/shared-space/venv/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py in _list_all_concrete_functions(self) 1176 """Returns all concrete functions.""" 1177 if self.input_signature is not None:
-> 1178 self.get_concrete_function() 1179 concrete_functions = [] 1180 # pylint: disable=protected-access
/napi/k_working_dir/ds_test/shared-space/venv/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py in get_concrete_function(self, *args, **kwargs) 1257 def get_concrete_function(self, *args, **kwargs): 1258 # Implements GenericFunction.get_concrete_function.
-> 1259 concrete = self._get_concrete_function_garbage_collected(*args, **kwargs) 1260 concrete._garbage_collector.release() # pylint: disable=protected-access 1261 return concrete
/napi/k_working_dir/ds_test/shared-space/venv/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py in _get_concrete_function_garbage_collected(self, *args, **kwargs) 1237 if self._stateful_fn is None: 1238 initializers
= []
-> 1239 self._initialize(args, kwargs, add_initializers_to=initializers) 1240 self._initialize_uninitialized_variables(initializers) 1241
/napi/k_working_dir/ds_test/shared-space/venv/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
778 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
779 self._concrete_stateful_fn = (
--> 780 self._stateful_fn._get_concrete_function_internal_garbage_collected(
# pylint: disable=protected-access
781 *args, **kwds))
782
/napi/k_working_dir/ds_test/shared-space/venv/lib/python3.9/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args,
**kwargs) 3155 args, kwargs = None, None 3156 with self._lock:
-> 3157 graph_function, _ = self._maybe_define_function(args, kwargs) 3158 return graph_function 3159
/napi/k_working_dir/ds_test/shared-space/venv/lib/python3.9/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs) 3555 3556 self._function_cache.missed.add(call_context_key)
-> 3557 graph_function = self._create_graph_function(args, kwargs) 3558 self._function_cache.primary[cache_key] = graph_function 3559
/napi/k_working_dir/ds_test/shared-space/venv/lib/python3.9/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes) 3390 arg_names = base_arg_names + missing_arg_names 3391 graph_function = ConcreteFunction(
-> 3392 func_graph_module.func_graph_from_py_func( 3393 self._name, 3394 self._python_function,
/napi/k_working_dir/ds_test/shared-space/venv/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, acd_record_initial_resource_uses) 1141
_, original_func = tf_decorator.unwrap(python_func) 1142
-> 1143 func_outputs = python_func(*func_args, **func_kwargs) 1144 1145 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
/napi/k_working_dir/ds_test/shared-space/venv/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
/napi/k_working_dir/ds_test/shared-space/venv/lib/python3.9/site-packages/tensorflow/python/training/tracking/tracking.py in _creator()
279 #def_function.function(input_signature=[], autograph=False)
280 def _creator():
--> 281 resource = self._create_resource()
282 return resource
283
/napi/k_working_dir/ds_test/shared-space/venv/lib/python3.9/site-packages/tensorflow/python/data/ops/dataset_ops.py in <lambda>()
241 # to have a weak reference to the Dataset to avoid creating
242 # reference cycles and making work for the garbage collector.
--> 243 lambda: weak_self._trace_variant_creation()()), # pylint: disable=unnecessary-lambda,protected-access
244 name="_variant_tracker")
245 self._graph_attr = ops.get_default_graph()
/napi/k_working_dir/ds_test/shared-space/venv/lib/python3.9/site-packages/tensorflow/python/data/ops/dataset_ops.py in _trace_variant_creation(self)
363 # pylint: disable=protected-access
364 graph_def = graph_pb2.GraphDef().FromString(
--> 365 self._as_serialized_graph(external_state_policy=options_lib
366 .ExternalStatePolicy.FAIL).numpy())
367 output_node_names = []
/napi/k_working_dir/ds_test/shared-space/venv/lib/python3.9/site-packages/tensorflow/python/util/deprecation.py in new_func(*args, **kwargs)
550 'in a future version' if date is None else ('after %s' % date),
551 instructions)
--> 552 return func(*args, **kwargs)
553
554 doc = _add_deprecated_arg_notice_to_docstring(
/napi/k_working_dir/ds_test/shared-space/venv/lib/python3.9/site-packages/tensorflow/python/data/ops/dataset_ops.py in _as_serialized_graph(self, allow_stateful, strip_device_assignment, external_state_policy)
302 if external_state_policy:
303 policy = external_state_policy.value
--> 304 return gen_dataset_ops.dataset_to_graph_v2(
305 self._variant_tensor,
306 external_state_policy=policy,
/napi/k_working_dir/ds_test/shared-space/venv/lib/python3.9/site-packages/tensorflow/python/ops/gen_dataset_ops.py in dataset_to_graph_v2(input_dataset, external_state_policy, strip_device_assignment, name) 1158 return _result 1159 except _core._NotOkStatusException as e:
-> 1160 _ops.raise_from_not_ok_status(e, name) 1161 except _core._FallbackException: 1162 pass
/napi/k_working_dir/ds_test/shared-space/venv/lib/python3.9/site-packages/tensorflow/python/framework/ops.py in raise_from_not_ok_status(e, name) 7105 def raise_from_not_ok_status(e, name): 7106 e.message += (" name: " + name if name is not None else "")
-> 7107 raise core._status_to_exception(e) from None # pylint: disable=protected-access 7108 7109
FailedPreconditionError: Failed to serialize the input pipeline graph: ResourceGather is stateful. [Op:DatasetToGraphV2]
Solution:
# define create_model()
def create_model():
"""instantiate a model and compile it"""
model = MyTFModel(...)
model.compile(optimizer=tf.keras.optimizers.Adagrad(0.1))
return model
# instantiate & compile your model by custom function create_model()
model = create_model()
# set model checkpoint to record the weight during & after fitting
model_checkpoint=tf.keras.callbacks.ModelCheckpoint('DeepCrossMultitaskModel{epoch:02d}',period=2,save_weights_only=True)
# fit the model & save weights during & after the fitting using model_checkpoint
history = model.fit(
cached_train,
validation_data=cached_test,
validation_freq=5,
epochs=epochs,
callbacks=[model_checkpoint],
verbose=0)
#!mkdir -p saved_model
filepath = 'saved_model/model_weight'
model.save_weights(filepath=filepath, save_format='tf')
# newly instantiate & compile a model
new_model = create_model()
# load the weights back to the new model
new_model.load_weights(filepath)
#double check: expect to produce the same thing as model(dict(test_df.head(3)))
new_model(dict(test_df.head(3)))
References & Credits
The cause of the error, check out this post: Can't save custom subclassed model
Solution: https://androidkt.com/how-to-save-and-load-model-weights-in-keras/
I had the same problem in my multitask model (TFRS) and following this solution posted here resolved the issue.
The metrics used in the retrieval task is the issue.
After training and evaluating the model I run this code to save it:
model.retrieval_task = tfrs.tasks.Retrieval() # Removes the metrics.
model.compile()
model.save("./models/my_rec_model")
and when I need to perform inference, I use this:
model = tf.keras.models.load_model("./models/my_rec_model")
I was trying to reproach the examples Transfer learning and fine-tuning adapting for my problem My colab with GPU. When I use a softmax in the last Dense Layers, this error not ocorrer. But with 'from_logits=True', this error ocorrer, my imagens are jpg and they are divided in folders:
InvalidArgumentError Traceback (most recent call last)
<ipython-input-85-7ea61d5df8ec> in <module>()
----> 1 loss0, accuracy0, auc0, precision0, recall0 = model.evaluate(val_ds)
5 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in evaluate(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict)
1387 with trace.Trace('test', step_num=step, _r=1):
1388 callbacks.on_test_batch_begin(step)
-> 1389 tmp_logs = self.test_function(iterator)
1390 if data_handler.should_sync:
1391 context.async_wait()
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
826 tracing_count = self.experimental_get_tracing_count()
827 with trace.Trace(self._name) as tm:
--> 828 result = self._call(*args, **kwds)
829 compiler = "xla" if self._experimental_compile else "nonXla"
830 new_tracing_count = self.experimental_get_tracing_count()
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
893 # If we did not create any variables the trace we have is good enough.
894 return self._concrete_stateful_fn._call_flat(
--> 895 filtered_flat_args, self._concrete_stateful_fn.captured_inputs) # pylint: disable=protected-access
896
897 def fn_with_cond(inner_args, inner_kwds, inner_filtered_flat_args):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1917 # No tape is watching; skip to running the function.
1918 return self._build_call_outputs(self._inference_function.call(
-> 1919 ctx, args, cancellation_manager=cancellation_manager))
1920 forward_backward = self._select_forward_and_backward_functions(
1921 args,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager)
558 inputs=args,
559 attrs=attrs,
--> 560 ctx=ctx)
561 else:
562 outputs = execute.execute_with_cancellation(
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
58 ctx.ensure_initialized()
59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
62 if name is not None:
InvalidArgumentError: 2 root error(s) found.
(0) Invalid argument: assertion failed: [predictions must be >= 0] [Condition x >= y did not hold element-wise:] [x (Dense121/dense_1/BiasAdd:0) = ] [[0.853173912 1.97515857 0.608713508...]...] [y (Cast_4/x:0) = ] [0]
[[{{node assert_greater_equal/Assert/AssertGuard/else/_1/assert_greater_equal/Assert/AssertGuard/Assert}}]]
(1) Invalid argument: assertion failed: [predictions must be >= 0] [Condition x >= y did not hold element-wise:] [x (Dense121/dense_1/BiasAdd:0) = ] [[0.853173912 1.97515857 0.608713508...]...] [y (Cast_4/x:0) = ] [0]
[[{{node assert_greater_equal/Assert/AssertGuard/else/_1/assert_greater_equal/Assert/AssertGuard/Assert}}]]
[[assert_greater_equal_2/Assert/AssertGuard/branch_executed/_65/_167]]
0 successful operations.
0 derived errors ignored. [Op:__inference_test_function_61870]
Function call stack:
test_function -> test_function
I tried several things to solve and nothing
I Was following a post on 'Training a transformer model for a chatbot with TensorFlow 2.0'. I have encountered an error on my local machine although the code seems to work fine in colab. Below is the code snippet.
def encoder_layer(units, d_model, num_heads, dropout, name="encoder_layer"):
inputs = tf.keras.Input(shape=(None, d_model), name="inputs")
padding_mask = tf.keras.Input(shape=(1, 1, None), name="padding_mask")
attention = MultiHeadAttention(
d_model, num_heads, name="attention")({
'query': inputs,
'key': inputs,
'value': inputs,
'mask': padding_mask
})
attention = tf.keras.layers.Dropout(rate=dropout)(attention)
attention = tf.keras.layers.LayerNormalization(
epsilon=1e-6)(inputs + attention)
outputs = tf.keras.layers.Dense(units=units, activation='relu')(attention)
outputs = tf.keras.layers.Dense(units=d_model)(outputs)
outputs = tf.keras.layers.Dropout(rate=dropout)(outputs)
outputs = tf.keras.layers.LayerNormalization(
epsilon=1e-6)(attention + outputs)
return tf.keras.Model(
inputs=[inputs, padding_mask], outputs=outputs, name=name)
I called above function with the following function call;
sample_encoder_layer = encoder_layer(
units=512,
d_model=128,
num_heads=4,
dropout=0.3,
name="sample_encoder_layer")
Below is the traceback of the error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~/anaconda3/envs/tf-chatbot/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py in _AssertCompatible(values, dtype)
323 try:
--> 324 fn(values)
325 except ValueError as e:
~/anaconda3/envs/tf-chatbot/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py in _check_not_tensor(values)
275 def _check_not_tensor(values):
--> 276 _ = [_check_failed(v) for v in nest.flatten(values)
277 if isinstance(v, ops.Tensor)]
~/anaconda3/envs/tf-chatbot/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py in <listcomp>(.0)
276 _ = [_check_failed(v) for v in nest.flatten(values)
--> 277 if isinstance(v, ops.Tensor)]
278 # pylint: enable=invalid-name
~/anaconda3/envs/tf-chatbot/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py in _check_failed(v)
247 # it is safe to use here.
--> 248 raise ValueError(v)
249
ValueError: Tensor("attention_1/Identity:0", shape=(None, None, 128), dtype=float32)
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-20-3fa05a9bbfda> in <module>
----> 1 sample_encoder_layer = encoder_layer(units=512, d_model=128, num_heads=4, dropout=0.3, name='sample_encoder_layer')
2
3 tf.keras.utils.plot_model(
4 sample_encoder_layer, to_file='encoder_layer.png', show_shapes=True)
<ipython-input-18-357ca53de1c0> in encoder_layer(units, d_model, num_heads, dropout, name)
10 'mask': padding_mask
11 })
---> 12 attention = tf.keras.layers.Dropout(rate=dropout)(attention)
13 attention = tf.keras.layers.LayerNormalization(
14 epsilon=1e-6)(inputs + attention)
~/anaconda3/envs/tf-chatbot/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
920 not base_layer_utils.is_in_eager_or_tf_function()):
921 with auto_control_deps.AutomaticControlDependencies() as acd:
--> 922 outputs = call_fn(cast_inputs, *args, **kwargs)
923 # Wrap Tensors in `outputs` in `tf.identity` to avoid
924 # circular dependencies.
~/anaconda3/envs/tf-chatbot/lib/python3.6/site-packages/tensorflow/python/keras/layers/core.py in call(self, inputs, training)
209 output = tf_utils.smart_cond(training,
210 dropped_inputs,
--> 211 lambda: array_ops.identity(inputs))
212 return output
213
~/anaconda3/envs/tf-chatbot/lib/python3.6/site-packages/tensorflow/python/keras/utils/tf_utils.py in smart_cond(pred, true_fn, false_fn, name)
63 pred, true_fn=true_fn, false_fn=false_fn, name=name)
64 return smart_module.smart_cond(
---> 65 pred, true_fn=true_fn, false_fn=false_fn, name=name)
66
67
~/anaconda3/envs/tf-chatbot/lib/python3.6/site-packages/tensorflow/python/framework/smart_cond.py in smart_cond(pred, true_fn, false_fn, name)
57 else:
58 return control_flow_ops.cond(pred, true_fn=true_fn, false_fn=false_fn,
---> 59 name=name)
60
61
~/anaconda3/envs/tf-chatbot/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py in new_func(*args, **kwargs)
505 'in a future version' if date is None else ('after %s' % date),
506 instructions)
--> 507 return func(*args, **kwargs)
508
509 doc = _add_deprecated_arg_notice_to_docstring(
~/anaconda3/envs/tf-chatbot/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py in cond(pred, true_fn, false_fn, strict, name, fn1, fn2)
1175 if (util.EnableControlFlowV2(ops.get_default_graph()) and
1176 not context.executing_eagerly()):
-> 1177 return cond_v2.cond_v2(pred, true_fn, false_fn, name)
1178
1179 # We needed to make true_fn/false_fn keyword arguments for
~/anaconda3/envs/tf-chatbot/lib/python3.6/site-packages/tensorflow/python/ops/cond_v2.py in cond_v2(pred, true_fn, false_fn, name)
82 true_name, collections=ops.get_default_graph()._collections), # pylint: disable=protected-access
83 add_control_dependencies=add_control_dependencies,
---> 84 op_return_value=pred)
85 false_graph = func_graph_module.func_graph_from_py_func(
86 false_name,
~/anaconda3/envs/tf-chatbot/lib/python3.6/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)
979 _, original_func = tf_decorator.unwrap(python_func)
980
--> 981 func_outputs = python_func(*func_args, **func_kwargs)
982
983 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
~/anaconda3/envs/tf-chatbot/lib/python3.6/site-packages/tensorflow/python/keras/layers/core.py in dropped_inputs()
205 noise_shape=self._get_noise_shape(inputs),
206 seed=self.seed,
--> 207 rate=self.rate)
208
209 output = tf_utils.smart_cond(training,
~/anaconda3/envs/tf-chatbot/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py in new_func(*args, **kwargs)
505 'in a future version' if date is None else ('after %s' % date),
506 instructions)
--> 507 return func(*args, **kwargs)
508
509 doc = _add_deprecated_arg_notice_to_docstring(
~/anaconda3/envs/tf-chatbot/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py in dropout(x, keep_prob, noise_shape, seed, name, rate)
4341 raise ValueError("You must provide a rate to dropout.")
4342
-> 4343 return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)
4344
4345
~/anaconda3/envs/tf-chatbot/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py in dropout_v2(x, rate, noise_shape, seed, name)
4422 raise ValueError("rate must be a scalar tensor or a float in the "
4423 "range [0, 1), got %g" % rate)
-> 4424 x = ops.convert_to_tensor(x, name="x")
4425 x_dtype = x.dtype
4426 if not x_dtype.is_floating:
~/anaconda3/envs/tf-chatbot/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)
1339
1340 if ret is None:
-> 1341 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
1342
1343 if ret is NotImplemented:
~/anaconda3/envs/tf-chatbot/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
319 as_ref=False):
320 _ = as_ref
--> 321 return constant(v, dtype=dtype, name=name)
322
323
~/anaconda3/envs/tf-chatbot/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py in constant(value, dtype, shape, name)
260 """
261 return _constant_impl(value, dtype, shape, name, verify_shape=False,
--> 262 allow_broadcast=True)
263
264
~/anaconda3/envs/tf-chatbot/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast)
298 tensor_util.make_tensor_proto(
299 value, dtype=dtype, shape=shape, verify_shape=verify_shape,
--> 300 allow_broadcast=allow_broadcast))
301 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
302 const_tensor = g._create_op_internal( # pylint: disable=protected-access
~/anaconda3/envs/tf-chatbot/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape, allow_broadcast)
449 nparray = np.empty(shape, dtype=np_dt)
450 else:
--> 451 _AssertCompatible(values, dtype)
452 nparray = np.array(values, dtype=np_dt)
453 # check to them.
~/anaconda3/envs/tf-chatbot/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py in _AssertCompatible(values, dtype)
326 [mismatch] = e.args
327 if dtype is None:
--> 328 raise TypeError("Expected any non-tensor type, got a tensor instead.")
329 else:
330 raise TypeError("Expected %s, got %s of type '%s' instead." %
TypeError: Expected any non-tensor type, got a tensor instead.
I had this error when I converted a function argument of int datatype to tf.constant . I resolved the issue in my case by undoing it. I faced this issue when I was converting TF1 codes to TF2.3.0 . Looking at your error trace I can see it's pointed to handling some constants in tf-chatbot. Kindly check how that constant is handled.
This is a fixed issue in TensorFlow 2.3.0 onwards. Can you upgrade your TensorFlow version?
pip install tensorflow==2.3.0
pip install --upgrade tensorflow
How do I feed a tf.keras model, that includes a 1D input TF Hub module, with a tf.data.Dataset?
(Ultimately, the aim is to use a single tf.data.Dataset with a multi-input, multi-output keras funtional api model.)
Tried this:
import tensorflow as tf
import tensorflow_hub as hub
embed = "https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1"
hub_layer = hub.KerasLayer(embed, output_shape=[20], input_shape=[],
dtype=tf.string, trainable=True, name='hub_layer')
# From tf hub webpage: "The module takes a batch of sentences in a 1-D tensor of strings as input."
input_tensor = tf.keras.Input(shape=(), dtype=tf.string)
hub_tensor = hub_layer(input_tensor)
x = tf.keras.layers.Dense(16, activation='relu')(hub_tensor)#(x)
main_output = tf.keras.layers.Dense(units=4, activation='softmax', name='main_output')(x)
model = tf.keras.models.Model(inputs=[input_tensor], outputs=[main_output])
# This works as expected.
X_tensor = tf.constant(['Hello World', 'The Quick Brown Fox'])
model(X_tensor)
# This fails
X_ds = tf.data.Dataset.from_tensors(X_tensor)
X_ds.element_spec
model(X_ds)
Expectation was that the 1D tensor in the dataset would be automatically extracted and consumed by the model.
Error message:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
in
21 X_ds = tf.data.Dataset.from_tensors(X_tensor)
22 X_ds.element_spec
---> 23 model(X_ds)
24
25
~/projects/email_analysis/email_venv/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
966 with base_layer_utils.autocast_context_manager(
967 self._compute_dtype):
--> 968 outputs = self.call(cast_inputs, *args, **kwargs)
969 self._handle_activity_regularization(inputs, outputs)
970 self._set_mask_metadata(inputs, outputs, input_masks)
~/projects/email_analysis/email_venv/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py in call(self, inputs, training, mask)
717 return self._run_internal_graph(
718 inputs, training=training, mask=mask,
--> 719 convert_kwargs_to_constants=base_layer_utils.call_context().saving)
720
721 def compute_output_shape(self, input_shape):
~/projects/email_analysis/email_venv/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py in _run_internal_graph(self, inputs, training, mask, convert_kwargs_to_constants)
835 tensor_dict = {}
836 for x, y in zip(self.inputs, inputs):
--> 837 y = self._conform_to_reference_input(y, ref_input=x)
838 x_id = str(id(x))
839 tensor_dict[x_id] = [y] * self._tensor_usage_count[x_id]
~/projects/email_analysis/email_venv/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py in _conform_to_reference_input(self, tensor, ref_input)
959 # Dtype handling.
960 if isinstance(ref_input, (ops.Tensor, composite_tensor.CompositeTensor)):
--> 961 tensor = math_ops.cast(tensor, dtype=ref_input.dtype)
962
963 return tensor
~/projects/email_analysis/email_venv/lib/python3.6/site-packages/tensorflow/python/util/dispatch.py in wrapper(*args, **kwargs)
178 """Call target, and fall back on dispatchers if there is a TypeError."""
179 try:
--> 180 return target(*args, **kwargs)
181 except (TypeError, ValueError):
182 # Note: convert_to_eager_tensor currently raises a ValueError, not a
~/projects/email_analysis/email_venv/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py in cast(x, dtype, name)
785 # allows some conversions that cast() can't do, e.g. casting numbers to
786 # strings.
--> 787 x = ops.convert_to_tensor(x, name="x")
788 if x.dtype.base_dtype != base_type:
789 x = gen_math_ops.cast(x, base_type, name=name)
~/projects/email_analysis/email_venv/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)
1339
1340 if ret is None:
-> 1341 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
1342
1343 if ret is NotImplemented:
~/projects/email_analysis/email_venv/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
319 as_ref=False):
320 _ = as_ref
--> 321 return constant(v, dtype=dtype, name=name)
322
323
~/projects/email_analysis/email_venv/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py in constant(value, dtype, shape, name)
260 """
261 return _constant_impl(value, dtype, shape, name, verify_shape=False,
--> 262 allow_broadcast=True)
263
264
~/projects/email_analysis/email_venv/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast)
268 ctx = context.context()
269 if ctx.executing_eagerly():
--> 270 t = convert_to_eager_tensor(value, ctx, dtype)
271 if shape is None:
272 return t
~/projects/email_analysis/email_venv/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py in convert_to_eager_tensor(value, ctx, dtype)
94 dtype = dtypes.as_dtype(dtype).as_datatype_enum
95 ctx.ensure_initialized()
---> 96 return ops.EagerTensor(value, ctx.device_name, dtype)
97
98
ValueError: Attempt to convert a value () with an unsupported type () to a Tensor.
The point of a dataset is to provide a sequence of tensors, like here:
all_data = tf.constant([['Hello', 'World'], ['Brown Fox', 'lazy dog']])
ds = tf.data.Dataset.from_tensor_slices(all_data)
for tensor in ds:
print(tensor)
which outputs
tf.Tensor([b'Hello' b'World'], shape=(2,), dtype=string)
tf.Tensor([b'Brown Fox' b'lazy dog'], shape=(2,), dtype=string)
Instead of just printing tensor, you can compute with it:
for tensor in ds:
print(hub_layer(tensor))
which outputs 2 tensors of shape (2,20) each.
For more, see https://www.tensorflow.org/guide/data.
I am following this guide by Google (https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/machine_learning/tensorflow/d_experiment.ipynb) to build a simple linear regression model.
In the notebook it has used the Experiment class and learn_runner (a class that I cannot find any documentation) to train up the model. I am now trying to use the model for prediction. I tried the following but i got an error. Would you please let me know the correct way to do it? Thanks.
Code added to the bottom:
# load the saved model
estimator = tflearn.LinearRegressor(feature_columns=feature_cols, model_dir='taxi_trained')
estimator.predict(input_fn=get_test)
Error got:
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_is_chief': True, '_model_dir': None, '_save_checkpoints_secs': 600, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x00000218611630F0>, '_master': '', '_task_id': 0, '_keep_checkpoint_every_n_hours': 10000, '_evaluation_master': '', '_environment': 'local', '_num_worker_replicas': 0, '_tf_random_seed': None, '_tf_config': gpu_options {
per_process_gpu_memory_fraction: 1
}
, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_task_type': None, '_num_ps_replicas': 0, '_save_summary_steps': 100}
WARNING:tensorflow:From c:\users\tommy\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\util\deprecation.py:335: calling LinearRegressor.predict (from tensorflow.contrib.learn.python.learn.estimators.linear) with outputs=None is deprecated and will be removed after 2017-03-01.
Instructions for updating:
Please switch to predict_scores, or set `outputs` argument.
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-5-7f1903437174> in <module>()
1 with tf.Session() as sess:
2 estimator = tflearn.LinearRegressor(feature_columns=feature_cols, model_dir='taxi_trained')
----> 3 estimator.predict(input_fn=get_test)
c:\users\tommy\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\util\deprecation.py in new_func(*args, **kwargs)
333 _call_location(), decorator_utils.get_qualified_name(func),
334 func.__module__, arg_name, arg_value, date, instructions)
--> 335 return func(*args, **kwargs)
336 new_func.__doc__ = _add_deprecated_arg_notice_to_docstring(
337 func.__doc__, date, instructions)
c:\users\tommy\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\util\deprecation.py in new_func(*args, **kwargs)
333 _call_location(), decorator_utils.get_qualified_name(func),
334 func.__module__, arg_name, arg_value, date, instructions)
--> 335 return func(*args, **kwargs)
336 new_func.__doc__ = _add_deprecated_arg_notice_to_docstring(
337 func.__doc__, date, instructions)
c:\users\tommy\appdata\local\programs\python\python35\lib\site-packages\tensorflow\contrib\learn\python\learn\estimators\linear.py in predict(self, x, input_fn, batch_size, outputs, as_iterable)
755 input_fn=input_fn,
756 batch_size=batch_size,
--> 757 as_iterable=as_iterable)
758 return super(LinearRegressor, self).predict(
759 x=x,
c:\users\tommy\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\util\deprecation.py in new_func(*args, **kwargs)
333 _call_location(), decorator_utils.get_qualified_name(func),
334 func.__module__, arg_name, arg_value, date, instructions)
--> 335 return func(*args, **kwargs)
336 new_func.__doc__ = _add_deprecated_arg_notice_to_docstring(
337 func.__doc__, date, instructions)
c:\users\tommy\appdata\local\programs\python\python35\lib\site-packages\tensorflow\contrib\learn\python\learn\estimators\linear.py in predict_scores(self, x, input_fn, batch_size, as_iterable)
790 batch_size=batch_size,
791 outputs=[key],
--> 792 as_iterable=as_iterable)
793 if as_iterable:
794 return _as_iterable(preds, output=key)
c:\users\tommy\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\util\deprecation.py in new_func(*args, **kwargs)
279 _call_location(), decorator_utils.get_qualified_name(func),
280 func.__module__, arg_name, date, instructions)
--> 281 return func(*args, **kwargs)
282 new_func.__doc__ = _add_deprecated_arg_notice_to_docstring(
283 func.__doc__, date, instructions)
c:\users\tommy\appdata\local\programs\python\python35\lib\site-packages\tensorflow\contrib\learn\python\learn\estimators\estimator.py in predict(self, x, input_fn, batch_size, outputs, as_iterable)
563 feed_fn=feed_fn,
564 outputs=outputs,
--> 565 as_iterable=as_iterable)
566
567 def get_variable_value(self, name):
c:\users\tommy\appdata\local\programs\python\python35\lib\site-packages\tensorflow\contrib\learn\python\learn\estimators\estimator.py in _infer_model(self, input_fn, feed_fn, outputs, as_iterable, iterate_batches)
855 contrib_framework.create_global_step(g)
856 features = self._get_features_from_input_fn(input_fn)
--> 857 infer_ops = self._get_predict_ops(features)
858 predictions = self._filter_predictions(infer_ops.predictions, outputs)
859 mon_sess = monitored_session.MonitoredSession(
c:\users\tommy\appdata\local\programs\python\python35\lib\site-packages\tensorflow\contrib\learn\python\learn\estimators\estimator.py in _get_predict_ops(self, features)
1186 labels = tensor_signature.create_placeholders_from_signatures(
1187 self._labels_info)
-> 1188 return self._call_model_fn(features, labels, model_fn_lib.ModeKeys.INFER)
1189
1190 def export_savedmodel(
c:\users\tommy\appdata\local\programs\python\python35\lib\site-packages\tensorflow\contrib\learn\python\learn\estimators\estimator.py in _call_model_fn(self, features, labels, mode)
1101 if 'model_dir' in model_fn_args:
1102 kwargs['model_dir'] = self.model_dir
-> 1103 model_fn_results = self._model_fn(features, labels, **kwargs)
1104
1105 if isinstance(model_fn_results, model_fn_lib.ModelFnOps):
c:\users\tommy\appdata\local\programs\python\python35\lib\site-packages\tensorflow\contrib\learn\python\learn\estimators\linear.py in _linear_model_fn(features, labels, mode, params, config)
159 num_outputs=head.logits_dimension,
160 weight_collections=[parent_scope],
--> 161 scope=scope)
162
163 def _train_op_fn(loss):
c:\users\tommy\appdata\local\programs\python\python35\lib\site-packages\tensorflow\contrib\layers\python\layers\feature_column_ops.py in weighted_sum_from_feature_columns(columns_to_tensors, feature_columns, num_outputs, weight_collections, trainable, scope)
529 # pylint: disable=protected-access
530 for column in sorted(set(feature_columns), key=lambda x: x.key):
--> 531 transformed_tensor = transformer.transform(column)
532 try:
533 embedding_lookup_arguments = column._wide_embedding_lookup_arguments(
c:\users\tommy\appdata\local\programs\python\python35\lib\site-packages\tensorflow\contrib\layers\python\layers\feature_column_ops.py in transform(self, feature_column)
880 return self._columns_to_tensors[feature_column]
881
--> 882 feature_column.insert_transformed_feature(self._columns_to_tensors)
883
884 if feature_column not in self._columns_to_tensors:
c:\users\tommy\appdata\local\programs\python\python35\lib\site-packages\tensorflow\contrib\layers\python\layers\feature_column.py in insert_transformed_feature(self, columns_to_tensors)
1406 """
1407 # Transform the input tensor according to the normalizer function.
-> 1408 input_tensor = self._normalized_input_tensor(columns_to_tensors[self.name])
1409 columns_to_tensors[self] = math_ops.to_float(input_tensor)
1410
KeyError: 'dropofflat'
I am using TensorFlow 1.1 with Python 3.5 on Windows 10. GPU enabled.