def testComputation(self):
inputs = tf.placeholder(tf.float32, shape=[self.batch_size, self.in_size])
prev_state = tf.placeholder(tf.float32,
shape=[self.batch_size, self.in_size])
vanilla_rnn = snt.VanillaRNN(name="rnn", hidden_size=self.in_size)
residual = snt.ResidualCore(vanilla_rnn, name="residual")
output, new_state = residual(inputs, prev_state)
in_to_hid = vanilla_rnn.in_to_hidden_variables
hid_to_hid = vanilla_rnn.hidden_to_hidden_variables
with self.test_session() as sess:
# With random data, check the TF calculation matches the Numpy version.
input_data = np.random.randn(self.batch_size, self.in_size)
prev_state_data = np.random.randn(self.batch_size, self.in_size)
tf.global_variables_initializer().run()
fetches = [output, new_state, in_to_hid[0], in_to_hid[1],
hid_to_hid[0], hid_to_hid[1]]
output = sess.run(fetches,
{inputs: input_data, prev_state: prev_state_data})
output_v, new_state_v, in_to_hid_w, in_to_hid_b = output[:4]
hid_to_hid_w, hid_to_hid_b = output[4:]
real_in_to_hid = np.dot(input_data, in_to_hid_w) + in_to_hid_b
real_hid_to_hid = np.dot(prev_state_data, hid_to_hid_w) + hid_to_hid_b
vanilla_output = np.tanh(real_in_to_hid + real_hid_to_hid)
residual_output = vanilla_output + input_data
self.assertAllClose(residual_output, output_v)
self.assertAllClose(vanilla_output, new_state_v)
def testShape(self):
inputs = tf.placeholder(tf.float32, shape=[self.batch_size, self.in_size])
prev_state = tf.placeholder(
tf.float32, shape=[self.batch_size, self.in_size])
vanilla_rnn = snt.VanillaRNN(self.in_size)
residual_wrapper = snt.ResidualCore(vanilla_rnn, name="residual")
output, next_state = residual_wrapper(inputs, prev_state)
shape = np.ndarray((self.batch_size, self.in_size))
self.assertEqual(self.in_size, residual_wrapper.output_size)
self.assertShapeEqual(shape, output)
self.assertShapeEqual(shape, next_state)
def testHeterogeneousState(self):
"""Checks that the shape and type of the initial state are preserved."""
core = HeterogeneousStateCore(name="rnn", hidden_size=self.in_size)
residual = snt.ResidualCore(core, name="residual")
core_state = core.initial_state(self.batch_size)
residual_state = residual.initial_state(self.batch_size)
self.assertEqual(core_state[0].shape.as_list(),
residual_state[0].shape.as_list())
self.assertEqual(core_state[1].shape.as_list(),
residual_state[1].shape.as_list())
self.assertEqual(core_state[0].dtype, residual_state[0].dtype)
self.assertEqual(core_state[1].dtype, residual_state[1].dtype)