pythonでのエラーについて回答をお願いします

Question

以下のコードを入力するとエラーが出ます。どこがおかしいのか教えてください。```python
コード

from tensorflow import keras
import matplotlib.pyplot as plt
batch_size = 128
num_class = 10
epochs = 20
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
for i in range(10):
    plt.subplot(2, 5, i+1)
    plt.title("Label:" + str(i))
    plt.imshow(x_train[i].reshape(28, 28), cmap=None)
x_train, x_test = x_train / 255.0, x_test / 255.0
model = keras.models.Sequential([
    keras.layers.Flatten(),
    keras.layers.Dense(512, activation="relu"),
    keras.layers.Dropout(0.2),
    keras.layers.Dense(10, activation="softmax")
])
model.compile(optimizer="adam", loss="sparse_categorical_clossentropy", metrics=["accuracy"])
model.fit(x_train, y_train, epochs=5)

エラー内容
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-1-b7b249b23d96> in <module>
     18 ])
     19 model.compile(optimizer="adam", loss="sparse_categorical_clossentropy", metrics=["accuracy"])
---> 20 model.fit(x_train, y_train, epochs=5)

Comments

[y_waiwai]

エラーが出たなら、エラーメッセージを提示しましょう
エラーメッセージは、いらぬ翻訳省略しないで、出たそのママをコピペで提示してください

[Ren516]

エラー全文は以下の通りです

Epoch 1/5
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-1-b7b249b23d96> in <module>
18 ])
19 model.compile(optimizer="adam", loss="sparse_categorical_clossentropy", metrics=["accuracy"])
---> 20 model.fit(x_train, y_train, epochs=5)

~\anaconda3\envs\tensorflow\lib\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)
1182 _r=1):
1183 callbacks.on_train_batch_begin(step)
-> 1184 tmp_logs = self.train_function(iterator)
1185 if data_handler.should_sync:
1186 context.async_wait()

~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
883
884 with OptionalXlaContext(self._jit_compile):
--> 885 result = self._call(*args, **kwds)
886
887 new_tracing_count = self.experimental_get_tracing_count()

~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
931 # This is the first call of __call__, so we have to initialize.
932 initializers = []
--> 933 self._initialize(args, kwds, add_initializers_to=initializers)
934 finally:
935 # At this point we know that the initialization is complete (or less

~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to)
757 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
758 self._concrete_stateful_fn = (
--> 759 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
760 *args, **kwds))
761

~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
3064 args, kwargs = None, None
3065 with self._lock:
-> 3066 graph_function, _ = self._maybe_define_function(args, kwargs)
3067 return graph_function
3068

~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self, args, kwargs)
3461
3462 self._function_cache.missed.add(call_context_key)
-> 3463 graph_function = self._create_graph_function(args, kwargs)
3464 self._function_cache.primary[cache_key] = graph_function
3465

~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3296 arg_names = base_arg_names + missing_arg_names
3297 graph_function = ConcreteFunction(
-> 3298 func_graph_module.func_graph_from_py_func(
3299 self._name,
3300 self._python_function,

~\anaconda3\envs\tensorflow\lib\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)
1005 _, original_func = tf_decorator.unwrap(python_func)
1006
-> 1007 func_outputs = python_func(*func_args, **func_kwargs)
1008
1009 # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args, **kwds)
666 # the function a weak reference to itself to avoid a reference cycle.
667 with OptionalXlaContext(compile_with_xla):
--> 668 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
669 return out
670

~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\func_graph.py in wrapper(*args, **kwargs)
992 except Exception as e: # pylint:disable=broad-except
993 if hasattr(e, "ag_error_metadata"):
--> 994 raise e.ag_error_metadata.to_exception(e)
995 else:
996 raise

ValueError: in user code:

C:\Users\ren_t\anaconda3\envs\tensorflow\lib\site-packages\keras\engine\training.py:853 train_function *
return step_function(self, iterator)
C:\Users\ren_t\anaconda3\envs\tensorflow\lib\site-packages\keras\engine\training.py:842 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
C:\Users\ren_t\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1286 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\Users\ren_t\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2849 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
C:\Users\ren_t\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3632 _call_for_each_replica
return fn(*args, **kwargs)
C:\Users\ren_t\anaconda3\envs\tensorflow\lib\site-packages\keras\engine\training.py:835 run_step **
outputs = model.train_step(data)
C:\Users\ren_t\anaconda3\envs\tensorflow\lib\site-packages\keras\engine\training.py:788 train_step
loss = self.compiled_loss(
C:\Users\ren_t\anaconda3\envs\tensorflow\lib\site-packages\keras\engine\compile_utils.py:184 __call__
self.build(y_pred)
C:\Users\ren_t\anaconda3\envs\tensorflow\lib\site-packages\keras\engine\compile_utils.py:133 build
self._losses = tf.nest.map_structure(self._get_loss_object, self._losses)
C:\Users\ren_t\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\util\nest.py:869 map_structure
structure[0], [func(*x) for x in entries],
C:\Users\ren_t\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\util\nest.py:869 <listcomp>
structure[0], [func(*x) for x in entries],
C:\Users\ren_t\anaconda3\envs\tensorflow\lib\site-packages\keras\engine\compile_utils.py:273 _get_loss_object
loss = losses_mod.get(loss)
C:\Users\ren_t\anaconda3\envs\tensorflow\lib\site-packages\keras\losses.py:2136 get
return deserialize(identifier)
C:\Users\ren_t\anaconda3\envs\tensorflow\lib\site-packages\keras\losses.py:2091 deserialize
return deserialize_keras_object(
C:\Users\ren_t\anaconda3\envs\tensorflow\lib\site-packages\keras\utils\generic_utils.py:704 deserialize_keras_object
raise ValueError(

ValueError: Unknown loss function: sparse_categorical_clossentropy. Please ensure this object is passed to the `custom_objects` argument. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.

Answer 1

Unknown loss function: sparse_categorical_clossentropy.

sparse_categorical_clossentropy

↓ 修正

sparse_categorical_crossentropy