我尝试按照这里的说明进行操作，其中提到使用了Reuter数据集。

from keras.datasets import reuters(X_train, y_train), (X_test, y_test) = reuters.load_data(path="reuters.pkl",                                                         nb_words=None,                                                         skip_top=0,                                                         maxlen=None,                                                         test_split=0.1)from keras.models import Sequentialfrom keras.layers import Dense, Dropout, Activationfrom keras.optimizers import SGDmodel = Sequential()# Dense(64) 是一个具有64个隐藏单元的全连接层。# 在第一层中，您必须指定预期的输入数据形状：# 这里是20维向量。model.add(Dense(64, input_dim=20, init='uniform'))model.add(Activation('tanh'))model.add(Dropout(0.5))model.add(Dense(64, init='uniform'))model.add(Activation('tanh'))model.add(Dropout(0.5))model.add(Dense(10, init='uniform'))model.add(Activation('softmax'))sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)model.compile(loss='categorical_crossentropy',              optimizer=sgd,              metrics=['accuracy'])#在这里报错model.fit(X_train, y_train,          nb_epoch=20,          batch_size=16)score = model.evaluate(X_test, y_test, batch_size=16)

但是在模型拟合时代码报错了。如何解决这个问题？

更新：这是我得到的错误信息。

In [21]: model.fit(X_train, y_train,   ....:           nb_epoch=20,   ....:           batch_size=16)---------------------------------------------------------------------------Exception                                 Traceback (most recent call last)<ipython-input-21-4b227e56e5a9> in <module>()      1 model.fit(X_train, y_train,      2           nb_epoch=20,----> 3           batch_size=16)//anaconda/lib/python2.7/site-packages/keras/models.pyc in fit(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, **kwargs)    400                               shuffle=shuffle,    401                               class_weight=class_weight,--> 402                               sample_weight=sample_weight)    403    404     def evaluate(self, x, y, batch_size=32, verbose=1,//anaconda/lib/python2.7/site-packages/keras/engine/training.pyc in fit(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight)    969                                                            class_weight=class_weight,    970                                                            check_batch_dim=False,--> 971                                                            batch_size=batch_size)    972         # prepare validation data    973         if validation_data://anaconda/lib/python2.7/site-packages/keras/engine/training.pyc in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_dim, batch_size)    909                           in zip(y, sample_weights, class_weights, self.sample_weight_modes)]    910         check_array_lengths(x, y, sample_weights)--> 911         check_loss_and_target_compatibility(y, self.loss_functions, self.internal_output_shapes)    912         if self.stateful and batch_size:    913             if x[0].shape[0] % batch_size != 0://anaconda/lib/python2.7/site-packages/keras/engine/training.pyc in check_loss_and_target_compatibility(targets, losses, output_shapes)    182             if y.shape[1] == 1:    183                 raise Exception('You are passing a target array of shape ' + str(y.shape) +--> 184                                 ' while using as loss `categorical_crossentropy`. '    185                                 '`categorical_crossentropy` expects '    186                                 'targets to be binary matrices (1s and 0s) 'Exception: You are passing a target array of shape (10105, 1) while using as loss `categorical_crossentropy`. `categorical_crossentropy` expects targets to be binary matrices (1s and 0s) of shape (samples, classes). If your targets are integer classes, you can convert them to the expected format via:```from keras.utils.np_utils import to_categoricaly_binary = to_categorical(y_int)```Alternatively, you can use the loss function `sparse_categorical_crossentropy` instead, which does expect integer targets.

回答：

这是一个在使用Keras时的常见初学者错误。与其他深度学习框架不同，Keras在使用常规的交叉熵损失时不使用整数标签，而是期望一个二进制向量（称为“one-hot”），其中向量只有0和在正确类别的索引位置上的1。

您可以使用以下代码轻松地将您的标签转换为这种格式：

from keras.utils.np_utils import to_categoricaly_train = to_categorical(y_train)y_test = to_categorical(y_test)

在model.fit之前。另一种方法是将损失函数改为“sparse_categorical_crossentropy”，它确实期望整数标签。