KERAS: Recurrent network

from keras.models import Sequential
from keras.layers import LSTM, Dropout, Activation
import numpy as np

ils = 10            # input layer size
ilt = 11            # input layer time steps
hls = 12            # hidden layer size
nhl = 2             # number of hidden layers
ols = 1             # output layer size
p = 0.2             # dropout probability
f_a = 'relu'        # activation function
opt = 'rmsprop'     # optimizing function

#
# Building the model
#
model = Sequential()

# The input layer
model.add(LSTM(hls, input_shape=(ilt, ils), return_sequences=True))
model.add(Activation(f_a))
model.add(Dropout(p))

# Hidden layers
for i in range(nhl - 1):
    model.add(LSTM(hls, return_sequences=True))
    model.add(Activation(f_a))
    model.add(Dropout(p))

# Output layer
model.add(LSTM(ols, return_sequences=False))
model.add(Activation('softmax'))

model.compile(optimizer=opt, loss='binary_crossentropy')

#
# Making test data and fitting the model
#

m_train, n_class = 1000, ols
data = np.array(np.random.random((m_train, ilt, ils)))
labels = np.random.randint(n_class, size=(m_train, 1))
# Make labels onehot
onehot_labels = np.zeros(shape=(labels.shape[0], ols))
onehot_labels[np.arange(labels.shape[0]), labels.astype(np.int)] = 1

model.fit(data, labels, nb_epoch=10, batch_size=32)