エラーの意味を教えてください

Question

(image_train,label_train),(image_test,label_test)=mnist.load_data()
print('Image\n',image_train[0])
print("画像データの要素数",image_train.shape)
print("ラベルデータの要素数",label_train.shape)

#ラベルと画像データを表示
for i in range(0,10):
 print("ラベル",label_train[i])
 plt.imshow(image_train[i].reshape(28,28))
 plt.show()

print('Train: num of imeges, width,height:',image_train.shape)
print('Test: num of imeges, width,height:',image_test.shape)
image_train=image_train.reshape(60000,784)
image_train=image_train/255.0
image_test=image_test.reshape(10000,784)
image_test=image_test/255.0

print('Train: num of labeled images:',label_train.shape)
print('Test: num of labeled images:',label_test.shape)

for i in range(0,9):
  print('Train Case:',i,'Label:',label_train[i])
for i in range(0,9):
  print('Test Case:',i,'Label:',label_test[i])

import numpy as np
from tensorflow.python.keras.utils.np_utils import to_categorical
from matplotlib import pyplot as plt
label_train=to_categorical(label_train,10)
label_test=to_categorical(label_test,10)
print('Onehot Label:',label_train[7])
print('Onehot Label:',label_test[1])

from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense

model=Sequential()

model.add(
    Dense(units=64,input_shape=(748,),activation='relu')
)
model.add(
    Dense(units=10, activation='softmax')
)

model.compile(
	optimizer='adam',
	loss='categorical_crossentropy',
	metrics=['accuracy']
)

history_adam=model.fit(
	image_train,label_train,batch_size=32,
	epochs=10,validation_split=0.2
)
コード

エラーメッセージ
ValueError Traceback (most recent call last)
<ipython-input-97-7bbb81a5a9de> in <module>()
2 history_adam=model.fit(
3 image_train,label_train,batch_size=64,
----> 4 epochs=10,validation_split=0.2
5 )

10 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
64 def _method_wrapper(self, *args, **kwargs):
65 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
---> 66 return method(self, *args, **kwargs)
67
68 # Running inside run_distribute_coordinator already.

/usr/local/lib/python3.6/dist-packages/tensorflow/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_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
846 batch_size=batch_size):
847 callbacks.on_train_batch_begin(step)
--> 848 tmp_logs = train_function(iterator)
849 # Catch OutOfRangeError for Datasets of unknown size.
850 # This blocks until the batch has finished executing.

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in call(self, *args, **kwds)
578 xla_context.Exit()
579 else:
--> 580 result = self._call(*args, **kwds)
581
582 if tracing_count == self._get_tracing_count():

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
625 # This is the first call of call, so we have to initialize.
626 initializers = []
--> 627 self._initialize(args, kwds, add_initializers_to=initializers)
628 finally:
629 # At this point we know that the initialization is complete (or less

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
504 self._concrete_stateful_fn = (
505 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 506 *args, **kwds))
507
508 def invalid_creator_scope(*unused_args, **unused_kwds):

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2444 args, kwargs = None, None
2445 with self._lock:
-> 2446 graph_function, _, _ = self._maybe_define_function(args, kwargs)
2447 return graph_function
2448

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
2775
2776 self._function_cache.missed.add(call_context_key)
-> 2777 graph_function = self._create_graph_function(args, kwargs)
2778 self._function_cache.primary[cache_key] = graph_function
2779 return graph_function, args, kwargs

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2665 arg_names=arg_names,
2666 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2667 capture_by_value=self._capture_by_value),
2668 self._function_attributes,
2669 # Tell the ConcreteFunction to clean up its graph once it goes out of

/usr/local/lib/python3.6/dist-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,

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
439 # wrapped allows AutoGraph to swap in a converted function. We give
440 # the function a weak reference to itself to avoid a reference cycle.
--> 441 return weak_wrapped_fn().wrapped(*args, **kwds)
442 weak_wrapped_fn = weakref.ref(wrapped_fn)
443

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
966 except Exception as e: # pylint:disable=broad-except
967 if hasattr(e, "ag_error_metadata"):
--> 968 raise e.ag_error_metadata.to_exception(e)
969 else:
970 raise

ValueError: in user code:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:571 train_function  *
    outputs = self.distribute_strategy.run(
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:951 run  **
    return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica
    return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica
    return fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:531 train_step  **
    y_pred = self(x, training=True)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:886 __call__
    self.name)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/input_spec.py:216 assert_input_compatibility
    ' but received input with shape ' + str(shape))

ValueError: Input 0 of layer sequential_3 is incompatible with the layer: expected axis -1 of input shape to have value 748 but received input with shape [64, 784]

Answer 1

google翻訳
ValueError: レイヤーsequence_3の入力0はレイヤーと互換性がありません：入力シェイプの軸-1の値は748であることが期待されますが、シェイプ[64、784]の入力を受け取りました

Answer 2

エラーメッセージ通り、shapeが一致していません。

以下では784になっていますが、

image_train=image_train.reshape(60000,784)
image_train=image_train/255.0
image_test=image_test.reshape(10000,784)
image_test=image_test/255.0

以下は748になっております。

model.add(
    Dense(units=64,input_shape=(748,),activation='relu')
)

Comments

[fiveHundred]

って以下で解決してますね。
https://teratail.com/questions/262378

同じような質問をして、この質問を放置するのは、マナーとどうかと。
運営でも「過去に投稿した質問と同じ内容の質問」は推奨していないと明言されていますしね。
https://teratail.com/help/avoid-asking