keras parametes for multilabel text classification

Question

I am using keras in my multiclass text classifcation, the dataset contains 25000 arabic tweets with 10 class labels I use this code :

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)

Summary:

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

but i get error: could not convert string to float: 'food' where food is a class name

when i change loss to categorical_crossentropy i get the error Error when checking target: expected activation_24 to have shape (10,) but got array with shape (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)'

Answer 1

You correctly used Dense(10) at the end, in order to produce ten results, one for each class.

But you should have your output y_train shaped also with 10 classes.

It should have shape (numberOfTweets, 10).

For this you should:

• If you have an array with indices, transform them using the keras function y_train=to_categorical(y_train).
• If you have them as strings, you must transform them in indices, and then use to_categorical