我正在跟隨一個教程，并且正在使用 tensorflow 構建一個簡單的回歸模型。我希望 tf 能夠順利地擬合模型。相反，我收到一個值錯誤。
模型和編譯步驟看起來與教程相同。
資料相似（兩個 numpy 陣列）。我在陣列中使用了不同的數字，但我認為這不是問題所在。任何兩個長度相等的陣列都應該有效，對吧？
X = tf.constant(np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]))
y = tf.constant(np.array([1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31]))
model = tf.keras.Sequential([
tf.keras.layers.Dense(1)
])
model.compile(
loss=tf.keras.losses.mae,
optimizer=tf.keras.optimizers.SGD(),
metrics=["mae"]
)
model.fit(X, y, epochs=10)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-41-2da4b2bd3c5c> in <module>
12 )
13
---> 14 model.fit(X, y, epochs=10)
~/opt/anaconda3/lib/python3.8/site-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
59 def error_handler(*args, **kwargs):
60 if not tf.debugging.is_traceback_filtering_enabled():
---> 61 return fn(*args, **kwargs)
62
63 filtered_tb = None
~/opt/anaconda3/lib/python3.8/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1562 ):
1563 callbacks.on_train_batch_begin(step)
-> 1564 tmp_logs = self.train_function(iterator)
1565 if data_handler.should_sync:
1566 context.async_wait()
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/util/traceback_utils.py in error_handler(*args, **kwargs)
139 try:
140 if not is_traceback_filtering_enabled():
--> 141 return fn(*args, **kwargs)
142 except NameError:
143 # In some very rare cases,
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
913
914 with OptionalXlaContext(self._jit_compile):
--> 915 result = self._call(*args, **kwds)
916
917 new_tracing_count = self.experimental_get_tracing_count()
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
961 # This is the first call of __call__, so we have to initialize.
962 initializers = []
--> 963 self._initialize(args, kwds, add_initializers_to=initializers)
964 finally:
965 # At this point we know that the initialization is complete (or less
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
783 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
784 self._concrete_stateful_fn = (
--> 785 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
786 *args, **kwds))
787
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2521 args, kwargs = None, None
2522 with self._lock:
-> 2523 graph_function, _ = self._maybe_define_function(args, kwargs)
2524 return graph_function
2525
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
2758 # Only get placeholders for arguments, not captures
2759 args, kwargs = placeholder_dict["args"]
-> 2760 graph_function = self._create_graph_function(args, kwargs)
2761
2762 graph_capture_container = graph_function.graph._capture_func_lib # pylint: disable=protected-access
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs)
2668 arg_names = base_arg_names missing_arg_names
2669 graph_function = ConcreteFunction(
-> 2670 func_graph_module.func_graph_from_py_func(
2671 self._name,
2672 self._python_function,
~/opt/anaconda3/lib/python3.8/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, acd_record_initial_resource_uses)
1245 _, original_func = tf_decorator.unwrap(python_func)
1246
-> 1247 func_outputs = python_func(*func_args, **func_kwargs)
1248
1249 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
675 # the function a weak reference to itself to avoid a reference cycle.
676 with OptionalXlaContext(compile_with_xla):
--> 677 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
678 return out
679
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in autograph_handler(*args, **kwargs)
1231 except Exception as e: # pylint:disable=broad-except
1232 if hasattr(e, "ag_error_metadata"):
-> 1233 raise e.ag_error_metadata.to_exception(e)
1234 else:
1235 raise
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in autograph_handler(*args, **kwargs)
1220 # TODO(mdan): Push this block higher in tf.function's call stack.
1221 try:
-> 1222 return autograph.converted_call(
1223 original_func,
1224 args,
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py in converted_call(f, args, kwargs, caller_fn_scope, options)
437 try:
438 if kwargs is not None:
--> 439 result = converted_f(*effective_args, **kwargs)
440 else:
441 result = converted_f(*effective_args)
~/opt/anaconda3/lib/python3.8/site-packages/keras/engine/training.py in tf__train_function(iterator)
13 try:
14 do_return = True
---> 15 retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
16 except:
17 do_return = False
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py in converted_call(f, args, kwargs, caller_fn_scope, options)
375
376 if not options.user_requested and conversion.is_allowlisted(f):
--> 377 return _call_unconverted(f, args, kwargs, options)
378
379 # internal_convert_user_code is for example turned off when issuing a dynamic
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py in _call_unconverted(f, args, kwargs, options, update_cache)
457 if kwargs is not None:
458 return f(*args, **kwargs)
--> 459 return f(*args)
460
461
~/opt/anaconda3/lib/python3.8/site-packages/keras/engine/training.py in step_function(model, iterator)
1144 )
1145 data = next(iterator)
-> 1146 outputs = model.distribute_strategy.run(run_step, args=(data,))
1147 outputs = reduce_per_replica(
1148 outputs, self.distribute_strategy, reduction="first"
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py in run(***failed resolving arguments***)
1313 fn = autograph.tf_convert(
1314 fn, autograph_ctx.control_status_ctx(), convert_by_default=False)
-> 1315 return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
1316
1317 def reduce(self, reduce_op, value, axis):
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py in call_for_each_replica(self, fn, args, kwargs)
2889 kwargs = {}
2890 with self._container_strategy().scope():
-> 2891 return self._call_for_each_replica(fn, args, kwargs)
2892
2893 def _call_for_each_replica(self, fn, args, kwargs):
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py in _call_for_each_replica(self, fn, args, kwargs)
3690 def _call_for_each_replica(self, fn, args, kwargs):
3691 with ReplicaContext(self._container_strategy(), replica_id_in_sync_group=0):
-> 3692 return fn(*args, **kwargs)
3693
3694 def _reduce_to(self, reduce_op, value, destinations, options):
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py in wrapper(*args, **kwargs)
687 try:
688 with conversion_ctx:
--> 689 return converted_call(f, args, kwargs, options=options)
690 except Exception as e: # pylint:disable=broad-except
691 if hasattr(e, 'ag_error_metadata'):
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py in converted_call(f, args, kwargs, caller_fn_scope, options)
375
376 if not options.user_requested and conversion.is_allowlisted(f):
--> 377 return _call_unconverted(f, args, kwargs, options)
378
379 # internal_convert_user_code is for example turned off when issuing a dynamic
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py in _call_unconverted(f, args, kwargs, options, update_cache)
456
457 if kwargs is not None:
--> 458 return f(*args, **kwargs)
459 return f(*args)
460
~/opt/anaconda3/lib/python3.8/site-packages/keras/engine/training.py in run_step(data)
1133
1134 def run_step(data):
-> 1135 outputs = model.train_step(data)
1136 # Ensure counter is updated only if `train_step` succeeds.
1137 with tf.control_dependencies(_minimum_control_deps(outputs)):
~/opt/anaconda3/lib/python3.8/site-packages/keras/engine/training.py in train_step(self, data)
991 # Run forward pass.
992 with tf.GradientTape() as tape:
--> 993 y_pred = self(x, training=True)
994 loss = self.compute_loss(x, y, y_pred, sample_weight)
995 self._validate_target_and_loss(y, loss)
~/opt/anaconda3/lib/python3.8/site-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
59 def error_handler(*args, **kwargs):
60 if not tf.debugging.is_traceback_filtering_enabled():
---> 61 return fn(*args, **kwargs)
62
63 filtered_tb = None
~/opt/anaconda3/lib/python3.8/site-packages/keras/engine/training.py in __call__(self, *args, **kwargs)
555 layout_map_lib._map_subclass_model_variable(self, self._layout_map)
556
--> 557 return super().__call__(*args, **kwargs)
558
559 @doc_controls.doc_in_current_and_subclasses
~/opt/anaconda3/lib/python3.8/site-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
59 def error_handler(*args, **kwargs):
60 if not tf.debugging.is_traceback_filtering_enabled():
---> 61 return fn(*args, **kwargs)
62
63 filtered_tb = None
~/opt/anaconda3/lib/python3.8/site-packages/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
1095 self._compute_dtype_object
1096 ):
-> 1097 outputs = call_fn(inputs, *args, **kwargs)
1098
1099 if self._activity_regularizer:
~/opt/anaconda3/lib/python3.8/site-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
153 else:
154 new_e = e
--> 155 raise new_e.with_traceback(e.__traceback__) from None
156 finally:
157 del signature
~/opt/anaconda3/lib/python3.8/site-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
94 bound_signature = None
95 try:
---> 96 return fn(*args, **kwargs)
97 except Exception as e:
98 if hasattr(e, "_keras_call_info_injected"):
~/opt/anaconda3/lib/python3.8/site-packages/keras/engine/sequential.py in call(self, inputs, training, mask)
423 kwargs["training"] = training
424
--> 425 outputs = layer(inputs, **kwargs)
426
427 if len(tf.nest.flatten(outputs)) != 1:
~/opt/anaconda3/lib/python3.8/site-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
59 def error_handler(*args, **kwargs):
60 if not tf.debugging.is_traceback_filtering_enabled():
---> 61 return fn(*args, **kwargs)
62
63 filtered_tb = None
~/opt/anaconda3/lib/python3.8/site-packages/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
1063 ):
1064
-> 1065 input_spec.assert_input_compatibility(
1066 self.input_spec, inputs, self.name
1067 )
~/opt/anaconda3/lib/python3.8/site-packages/keras/engine/input_spec.py in assert_input_compatibility(input_spec, inputs, layer_name)
248 ndim = x.shape.rank
249 if ndim is not None and ndim < spec.min_ndim:
--> 250 raise ValueError(
251 f'Input {input_index} of layer "{layer_name}" '
252 "is incompatible with the layer: "
ValueError: in user code:
File "/Users/mcm66103/opt/anaconda3/lib/python3.8/site-packages/keras/engine/training.py", line 1160, in train_function *
return step_function(self, iterator)
File "/Users/mcm66103/opt/anaconda3/lib/python3.8/site-packages/keras/engine/training.py", line 1146, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/Users/mcm66103/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py", line 1315, in run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
File "/Users/mcm66103/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py", line 2891, in call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
File "/Users/mcm66103/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py", line 3692, in _call_for_each_replica
return fn(*args, **kwargs)
File "/Users/mcm66103/opt/anaconda3/lib/python3.8/site-packages/keras/engine/training.py", line 1135, in run_step **
outputs = model.train_step(data)
File "/Users/mcm66103/opt/anaconda3/lib/python3.8/site-packages/keras/engine/training.py", line 993, in train_step
y_pred = self(x, training=True)
File "/Users/mcm66103/opt/anaconda3/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 61, in error_handler
return fn(*args, **kwargs)
File "/Users/mcm66103/opt/anaconda3/lib/python3.8/site-packages/keras/engine/training.py", line 557, in __call__
return super().__call__(*args, **kwargs)
File "/Users/mcm66103/opt/anaconda3/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 61, in error_handler
return fn(*args, **kwargs)
File "/Users/mcm66103/opt/anaconda3/lib/python3.8/site-packages/keras/engine/base_layer.py", line 1097, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/Users/mcm66103/opt/anaconda3/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 155, in error_handler
raise new_e.with_traceback(e.__traceback__) from None
File "/Users/mcm66103/opt/anaconda3/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 96, in error_handler
return fn(*args, **kwargs)
File "/Users/mcm66103/opt/anaconda3/lib/python3.8/site-packages/keras/engine/sequential.py", line 425, in call
outputs = layer(inputs, **kwargs)
File "/Users/mcm66103/opt/anaconda3/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 61, in error_handler
return fn(*args, **kwargs)
File "/Users/mcm66103/opt/anaconda3/lib/python3.8/site-packages/keras/engine/base_layer.py", line 1065, in __call__
input_spec.assert_input_compatibility(
File "/Users/mcm66103/opt/anaconda3/lib/python3.8/site-packages/keras/engine/input_spec.py", line 250, in assert_input_compatibility
raise ValueError(
ValueError: Exception encountered when calling layer "sequential_24" " f"(type Sequential).
Input 0 of layer "dense_25" is incompatible with the layer: expected min_ndim=2, found ndim=1. Full shape received: (None,)
Call arguments received by layer "sequential_24" " f"(type Sequential):
? inputs=tf.Tensor(shape=(None,), dtype=int64)
? training=True
? mask=None
uj5u.com熱心網友回復：
您缺少圖層所需的特征維度Dense，因為您的模型正在根據您提供的資料推斷輸入形狀，因此請嘗試：
import tensorflow as tf
import numpy as np
X = tf.constant(np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]))[:, None]
y = tf.constant(np.array([1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31]))
model = tf.keras.Sequential([
tf.keras.layers.Dense(1)
])
model.compile(
loss=tf.keras.losses.mae,
optimizer=tf.keras.optimizers.SGD(),
metrics=["mae"]
)
model.fit(X, y, epochs=10)
您也可以使用X = tf.expand_dims(X, axis=-1).
uj5u.com熱心網友回復：
嘗試在 Sequential 模型中添加一個輸入層：
X = tf.constant(np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]))
y = tf.constant(np.array([1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31]))
model = tf.keras.Sequential([
tf.keras.layers.InputLayer(input_shape=(1,)), # This will take care of dimensions
tf.keras.layers.Dense(1)
])
model.compile(
loss=tf.keras.losses.mae,
optimizer=tf.keras.optimizers.SGD(),
metrics=["mae"]
)
model.fit(X, y, epochs=10)
有關參考，請參閱：tf.keras.InputLayer
uj5u.com熱心網友回復：
你應該試試這個代碼......
X = tf.constant(np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]))
y = tf.constant(np.array([1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31]))
model = tf.keras.Sequential([
tf.keras.layers.Dense(1)
])
model.compile(
loss=tf.keras.losses.mae,
optimizer=tf.keras.optimizers.SGD(),
metrics=["mae"]
)
X = tf.expand_dims(X , axis=0)
y = tf.expand_dims(y , axis=0)
model.fit(X, y, epochs=10)
輸出
Epoch 1/10
1/1 [==============================] - 1s 777ms/step - loss: 14.0288 - mae: 14.0288
Epoch 2/10
1/1 [==============================] - 0s 8ms/step - loss: 11.6894 - mae: 11.6894
Epoch 3/10
1/1 [==============================] - 0s 9ms/step - loss: 10.2720 - mae: 10.2720
Epoch 4/10
1/1 [==============================] - 0s 9ms/step - loss: 9.4745 - mae: 9.4745
Epoch 5/10
1/1 [==============================] - 0s 9ms/step - loss: 8.9153 - mae: 8.9153
Epoch 6/10
1/1 [==============================] - 0s 10ms/step - loss: 8.6282 - mae: 8.6282
Epoch 7/10
1/1 [==============================] - 0s 9ms/step - loss: 8.4167 - mae: 8.4167
Epoch 8/10
1/1 [==============================] - 0s 5ms/step - loss: 8.3848 - mae: 8.3848
Epoch 9/10
1/1 [==============================] - 0s 9ms/step - loss: 8.3529 - mae: 8.3529
Epoch 10/10
1/1 [==============================] - 0s 10ms/step - loss: 8.3210 - mae: 8.3210
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