CSV MNIST 数据集：ValueError：Shapes (None, 10) 和 (None, 28, 10) 不兼容

Question

我想用 keras 对 MINST 数据集 (csv) 进行分类。这是我的代码，但运行后我得到了这个错误。你知道我该如何解决 ValueError: Shapes (None, 10) 和 (None, 28, 10) 不兼容

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错误。我知道这是由于输入形状的原因，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

Answer 1

由于密集层无法处理像图像这样的 2D 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，会报错。

像这样向您的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'))