example.py

from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.layers.recurrent import LSTM
from keras.layers.core import Dense, Activation, Dropout

model = Sequential()
model.add(Embedding(10, 20))
model.add(LSTM(20, 10)) 
model.add(Dropout(0.5))
model.add(Dense(10, 5))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='sgd')

import numpy as np
X = np.random.randint(0, 10, size=(5, 10))
Y = np.random.randint(0, 5, size=(5, 5))
model.fit(X, Y, nb_epoch=2)
print model.predict(X)
print model.predict_classes(X)

"""
Epoch 0
5/5 [==============================] - 0s - loss: 10.9368
Epoch 1
5/5 [==============================] - 0s - loss: 10.9237
5/5 [==============================] - 0s
[[ 0.19954167  0.2032648   0.20230913  0.20168537  0.19319904]
 [ 0.19952643  0.20361215  0.20237164  0.20141158  0.1930782 ]
 [ 0.20005952  0.20260406  0.20279311  0.20199704  0.19254628]
 [ 0.19930014  0.20366372  0.20246421  0.20164138  0.19293055]
 [ 0.20006272  0.20291759  0.20239519  0.20154302  0.19308148]]
5/5 [==============================] - 0s
[1 1 2 1 1]
"""