用于多标签文本分类的keras参数

我在我的多类文本分类中使用keras，数据集包含25000个带有10个类标签的阿拉伯语推文我使用此代码：

model = Sequential()
model.add(Dense(512, input_shape=(10902,)))#10902
model.add(Activation('relu'))
model.add(Dropout(0.3))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.3))
model.add(Dense(10))
model.add(Activation('softmax'))
model.summary()
 #categorical_crossentropy
model.compile(loss='sparse_categorical_crossentropy',        optimizer='rmsprop',
              metrics=['accuracy'])
..
history = model.fit(X_train, y_train,
                    batch_size=100,
                    epochs=30,
                    verbose=1,
                    validation_split=0.5)

摘要：

Layer (type)                 Output Shape              Param #   
=================================================================
dense_23 (Dense)             (None, 512)               5582336   
_________________________________________________________________
activation_22 (Activation)   (None, 512)               0         
_________________________________________________________________
dropout_15 (Dropout)         (None, 512)               0         
_________________________________________________________________
dense_24 (Dense)             (None, 512)               262656    
_________________________________________________________________
activation_23 (Activation)   (None, 512)               0         
_________________________________________________________________
dropout_16 (Dropout)         (None, 512)               0         
_________________________________________________________________
dense_25 (Dense)             (None, 10)                5130      
_________________________________________________________________
activation_24 (Activation)   (None, 10)                0         
=================================================================
Total params: 5,850,122
Trainable params: 5,850,122
Non-trainable params: 0

但我得到错误：无法将字符串转换为浮点数：'food'其中食物是类名

当我将损失更改为categorical_crossentropy时，我在检查目标时得到错误错误：期望activation_24具有形状（10，）但是得到了具有形状的数组（1，）

Update

'
nd=data.replace(['ads', 'Politic', 'eco', 'food', 'health', 'porno', 'religion', 'sports', 'tech','tv'], 
                     [1, 2, 3, 4, 5,6,7,8,9,10]) 
model = Sequential()
    model.add(Dense(512, input_shape=(10902,10)))#no. of words
    model.add(Activation('relu'))
    model.add(Dropout(0.3))
    model.add(Dense(512))
    model.add(Activation('relu'))
    model.add(Dropout(0.3))
    model.add(Dense(10))
    model.add(Activation('softmax'))
    model.summary()
     #categorical_crossentropy
    model.compile(loss='categorical_crossentropy',        optimizer='rmsprop',
                  metrics=['accuracy'])
    y_train=keras.utils.to_categorical(y_train) 
    history = model.fit(X_train, y_train,
                        batch_size=100,
                        epochs=30,
                        verbose=1,
                        validation_split=0.5)'