import unittest
import numpy as np
import theano
from keras.layers import recurrent
nb_samples, timesteps, input_dim, output_dim = 3, 3, 10, 5
def _runner(layer_class):
"""
All the recurrent layers share the same interface, so we can run through them with a single
function.
"""
for weights in [None, [np.ones((input_dim, output_dim))]]:
for ret_seq in [True, False]:
layer = layer_class(input_dim, output_dim, return_sequences=ret_seq, weights=weights)
layer.input = theano.shared(value=np.ones((nb_samples, timesteps, input_dim)))
config = layer.get_config()
for train in [True, False]:
out = layer.get_output(train).eval()
# Make sure the output has the desired shape
if ret_seq:
assert(out.shape == (nb_samples, timesteps, output_dim))
else:
assert(out.shape == (nb_samples, output_dim))
mask = layer.get_output_mask(train)
class TestRNNS(unittest.TestCase):
"""
Test all the RNNs using a generic test runner function defined above.
"""
def test_simple(self):
_runner(recurrent.SimpleRNN)
def test_simple_deep(self):
_runner(recurrent.SimpleDeepRNN)
def test_gru(self):
_runner(recurrent.GRU)
def test_lstm(self):
_runner(recurrent.LSTM)
def test_jzs1(self):
_runner(recurrent.JZS1)
def test_jzs2(self):
_runner(recurrent.JZS2)
def test_jzs3(self):
_runner(recurrent.JZS3)
if __name__ == '__main__':
unittest.main()
