我想用 keras 对 MINST 数据集 (csv) 进行分类。这是我的代码，但运行后出现此错误。你知道我该如何解决吗ValueError：Shapes (None, 10) and (None, 28, 10) is incompatible
from keras import models
import numpy as np
from keras import layers
import tensorflow as tf
from tensorflow.keras.models import Sequential
from keras.utils import np_utils
from tensorflow.keras.layers import Dense, Dropout, LSTM, BatchNormalization
from keras.utils import to_categorical, plot_model
mnist = tf.keras.datasets.mnist
#Load dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
model = Sequential()
model.add(Dense(units=32, activation='sigmoid',input_shape=(x_train.shape[1:])))
model.add(Dense(units=64, activation='sigmoid'))
model.add(Dense(units=10,activation='softmax'))
model.compile(optimizer="sgd", loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit(x_train, y_train, batch_size=32, epochs=100, validation_split=.3)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['training', 'validation'], loc='best')
plt.show()
这里我从代码中得到了错误。我知道这是因为输入形状的原因，但我不知道应该如何定义它。 x_train.shape 是 (60000, 28, 28) y_train.shape 是 (60000, 10)
ValueError Traceback (most recent call last)
<ipython-input-112-7c9220a71c0e> in <module>
1 model.compile(optimizer="sgd", loss='categorical_crossentropy', metrics=['accuracy'])
----> 2 history = model.fit(x_train, y_train, batch_size=32, epochs=100, validation_split=.3)
3
4 plt.plot(history.history['accuracy'])
5 plt.plot(history.history['val_accuracy'])
~\anaconda3\lib\site-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.
~\anaconda3\lib\site-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.
~\anaconda3\lib\site-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():
~\anaconda3\lib\site-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
~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to)
503 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
504 self._concrete_stateful_fn = (
--> 505 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
506 *args, **kwds))
507
~\anaconda3\lib\site-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
~\anaconda3\lib\site-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
~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2655 arg_names = base_arg_names + missing_arg_names
2656 graph_function = ConcreteFunction(
-> 2657 func_graph_module.func_graph_from_py_func(
2658 self._name,
2659 self._python_function,
~\anaconda3\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)
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\lib\site-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
~\anaconda3\lib\site-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
th
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (None, 10) and (None, 28, 10) are incompatible
答案 0 :(得分：0)
由于密集层无法处理图像等二维数据，您应该首先将输入展平为向量，然后将其传递给您的模型，否则，您将在输出中获得其他维度，然后是您的标签和logits（模型输出）不兼容，你会得到错误。
像这样向模型添加一个展平层：
model.add(Flatten(input_shape=(x_train.shape[1:]))) #add this
model.add(Dense(units=32, activation='sigmoid'))
model.add(Dense(units=64, activation='sigmoid'))
model.add(Dense(units=10,activation='softmax'))