def testComputation(self):
model_rnn = snt.ModelRNN(self.model)
inputs = tf.random_normal([self.batch_size, 5])
prev_state_data = np.random.randn(self.batch_size, self.hidden_size)
prev_state = tf.convert_to_tensor(prev_state_data)
outputs, next_state = model_rnn(inputs, prev_state)
outputs_value = self.evaluate([outputs, next_state])
outputs_value, next_state_value = outputs_value
self.assertAllClose(prev_state_data, outputs_value)
self.assertAllClose(outputs_value, next_state_value)
def testShape(self):
model_rnn = snt.ModelRNN(self.model)
inputs = tf.random_normal([self.batch_size, 5])
prev_state = tf.placeholder(tf.float32,
shape=[self.batch_size, self.hidden_size])
outputs, next_state = model_rnn(inputs, prev_state)
batch_size_shape = tf.TensorShape(self.batch_size)
expected_shape = batch_size_shape.concatenate(self.model.output_size)
self.assertNotEqual(expected_shape, inputs.get_shape())
self.assertEqual(expected_shape, prev_state.get_shape())
self.assertEqual(expected_shape, next_state.get_shape())
self.assertEqual(expected_shape, outputs.get_shape())
def testComputation(self):
model_rnn = snt.ModelRNN(self.model)
inputs = tf.random_normal([self.batch_size, 5])
prev_state = tf.placeholder(tf.float32,
shape=[self.batch_size, self.hidden_size])
outputs, next_state = model_rnn(inputs, prev_state)
with self.test_session() as sess:
prev_state_data = np.random.randn(self.batch_size, self.hidden_size)
feed_dict = {prev_state: prev_state_data}
sess.run(tf.global_variables_initializer())
outputs_value = sess.run([outputs, next_state], feed_dict=feed_dict)
outputs_value, next_state_value = outputs_value
self.assertAllClose(prev_state_data, outputs_value)
self.assertAllClose(outputs_value, next_state_value)
def testBadArguments(self):
with self.assertRaises(AttributeError):
snt.ModelRNN(tf.identity)
with self.assertRaises(TypeError):
snt.ModelRNN(np.array([42]))