def test_model_prediction(self):
seed_value = 1234
seed(seed_value)
tf.set_random_seed(seed_value)
MAX_LENGTH = 300
NUM_WORDS = 1000
h = vae.Hyper(vocab_size=NUM_WORDS, max_length=MAX_LENGTH)
temp = np.zeros((self.X.shape[0], MAX_LENGTH, NUM_WORDS))
temp[np.expand_dims(np.arange(self.X.shape[0]), axis=0
).reshape(self.X.shape[0], 1),
np.repeat(
np.array([np.arange(MAX_LENGTH)]), self.X.shape[0], axis=0),
self.X] = 1
self.X_one_hot = temp
model = VAEAlexAdam(h)
model.autoencoder.evaluate(x=self.X,
y={
'decoded_mean': self.X_one_hot,
'pred': self.y
},
batch_size=1)
pred = model.autoencoder.predict(self.X[0].reshape(1, -1))[1][0][0]
expected = 0.4988190531730652
self.assertEqual(np.float(pred), np.float(expected))
def test_model_evaluation(self):
seed_value = 1234
seed(seed_value)
tf.set_random_seed(seed_value)
session_conf = tf.ConfigProto(intra_op_parallelism_threads=1,
inter_op_parallelism_threads=1)
sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)
MAX_LENGTH = 300
NUM_WORDS = 1000
h = vae.Hyper(vocab_size=NUM_WORDS, max_length=MAX_LENGTH)
temp = np.zeros((self.X.shape[0], MAX_LENGTH, NUM_WORDS))
temp[np.expand_dims(np.arange(self.X.shape[0]), axis=0
).reshape(self.X.shape[0], 1),
np.repeat(
np.array([np.arange(MAX_LENGTH)]), self.X.shape[0], axis=0),
self.X] = 1
self.X_one_hot = temp
model = VAEAlexAdam(h)
out = model.autoencoder.evaluate(x=self.X,
y={
'decoded_mean': self.X_one_hot,
'pred': self.y
},
batch_size=10)
expected = [
3.0580520629882812, 2.3692352771759033, 0.6888167858123779,
0.0011111111380159855, 0.6666666865348816
]
self.assertEqual(expected, out)
def test_create_vae(self):
MAX_LENGTH = 300
NUM_WORDS = 1000
h = vae.Hyper(vocab_size=NUM_WORDS, max_length=MAX_LENGTH)
model = VAEAlexAdam(h)
preds = model.autoencoder.predict(x=self.X)
expected = (3, 300, 1000)
self.assertEqual(expected, preds[0].shape)
expected = (3, 1)
self.assertEqual(expected, preds[1].shape)
expected = self.expected_shapes
actual = [w.shape for w in model.autoencoder.get_weights()]
self.assertEqual(expected, actual)
self.assertEqual(preds[0].shape, (3, 300, 1000))
self.assertEqual(preds[1].shape, (3, 1))
def test_random_init(self):
MAX_LENGTH = 300
NUM_WORDS = 1000
h = vae.Hyper(vocab_size=NUM_WORDS, max_length=MAX_LENGTH)
expected = 0.023570226039551584
actual = random_init.scale([100, 200])
self.assertAlmostEqual(expected, actual)
r0 = np.random.RandomState(42)
expected = [
random_init.scale(shape) * r0.uniform(size=shape)
for shape in self.expected_shapes
]
model = VAEAlexAdam(h)
r1 = np.random.RandomState(42)
actual = random_init.random_w(r1, model.autoencoder)
for u, v in zip(expected, actual):
self.assertAlmostEqual(0, norm(u - v))
def test_save_and_load_vae(self):
MAX_LENGTH = 300
NUM_WORDS = 1000
h = vae.Hyper(vocab_size=NUM_WORDS, max_length=MAX_LENGTH)
r0 = np.random.RandomState(42)
expected = [
random_init.scale(shape) * r0.uniform(size=shape)
for shape in self.expected_shapes
]
model = VAEAlexAdam(h)
model.autoencoder.set_weights(expected)
model.autoencoder.save('data/autoencode.h5')
model2 = VAEAlexAdam(h)
model2.autoencoder.load_weights('data/autoencode.h5')
actual = model2.autoencoder.get_weights()
for u, v in zip(expected, actual):
self.assertAlmostEqual(0, norm(u - v), 6)