LSTM training multiclass with Keras

lstm_keras.py

# X_train contains word indices (single int between 0 and max_words)
# Y_train0 contains class indices (single int between 0 and nb_classes)

X_train = sequence.pad_sequences(X_train, maxlen=maxlen, padding='post')
X_test = sequence.pad_sequences(X_test, maxlen=maxlen, padding='post')

Y_train = np.zeros((batchSize,globvars.nb_classes))#,dtype=np.float32)
for t in range(batchSize):
    Y_train[t][Y_train0[t]]=1
Y_test = np.zeros((len(Y_test0),globvars.nb_classes))#,dtype=np.float32)
for t in range(len(Y_test)):
    Y_test[t][Y_test0[t]]=1

model = Sequential()
model.add(Embedding(globvars.max_words, globvars.embedsize, input_length=maxlen))
model.add(LSTM(globvars.hidden,return_sequences=False))
model.add(Dropout(0.5))
model.add(Dense(globvars.nb_classes))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adam')

model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=3)
score, acc = model.evaluate(X_test, Y_test,
                            batch_size=batch_size,
                            show_accuracy=True)