def test_example_embed_embed_minibatch_with_different_number_of_inputs(
self):
embed = ExampleEmbed(2, 2, 1, (np.arange(5) + 1).reshape((5, 1)))
"""
EmbedId
0 (-2) -> 1
1 (-1) -> 2
2 ( 0) -> 3
3 ( 1) -> 4
4 (NULL) -> 5
"""
metadata = DatasetMetadata(2, set([]), 2, 2)
e0 = examples_encoding(
[Example([[0, 1]], 0), Example([[1]], 1)], metadata)
e1 = examples_encoding(
[Example([1, [0, 1]], [0]),
Example([0, [0, 1]], [])], metadata)
state_embeddings = embed.forward(np.array([e0.types, e1.types]),
np.array([e0.values, e1.values]))
self.assertEqual((2, 2, 3, 2 + 2 * 1), state_embeddings.shape)
self.assertTrue(
np.allclose([0, 1, 3, 4],
state_embeddings.array[0, 0, 0])) # Input of e00
self.assertTrue(
np.allclose([0, 0, 5, 5],
state_embeddings.array[0, 0, 1])) # Input of e00
# Output of e00
self.assertTrue(
np.allclose([1, 0, 3, 5], state_embeddings.array[0, 0, 2]))
self.assertTrue(
np.allclose([0, 1, 4, 5],
state_embeddings.array[0, 1, 0])) # Input of e01
self.assertTrue(
np.allclose([0, 0, 5, 5],
state_embeddings.array[0, 1, 1])) # Input of e01
# Output of e01
self.assertTrue(
np.allclose([1, 0, 4, 5], state_embeddings.array[0, 1, 2]))
self.assertTrue(
np.allclose([1, 0, 4, 5],
state_embeddings.array[1, 0, 0])) # Input of e10
self.assertTrue(
np.allclose([0, 1, 3, 4],
state_embeddings.array[1, 0, 1])) # Input of e10
# Output of e10
self.assertTrue(
np.allclose([0, 1, 3, 5], state_embeddings.array[1, 0, 2]))
self.assertTrue(
np.allclose([1, 0, 3, 5],
state_embeddings.array[1, 1, 0])) # Input of e11
self.assertTrue(
np.allclose([0, 1, 3, 4],
state_embeddings.array[1, 1, 1])) # Input of e11
# Output of e11
self.assertTrue(
np.allclose([0, 1, 5, 5], state_embeddings.array[1, 1, 2]))
def test_TrainingClassifier(self):
embed = ExampleEmbed(1, 2, 2)
encoder = Encoder(10)
decoder = Decoder(2)
classifier = TrainingClassifier(ch.Sequential(embed, encoder, decoder))
metadata = DatasetMetadata(1, set([]), 2, 2)
e = examples_encoding(
[Example([[0, 1]], 0), Example([[1]], 1)], metadata)
labels = np.array([[1, 1]])
loss = classifier(np.array([e.types]), np.array([e.values]), labels)
loss.grad = np.ones(loss.shape, dtype=np.float32)
# backward does not throw an error
loss.backward()
def test_predict_with_neural_network(self):
examples = [
Example([2, [10, 20, 30]], 30),
Example([1, [-10, 30, 40]], 30)
]
metadata = DatasetMetadata(2, set(["MAP", "HEAD"]), 256, 5)
model_shape = ModelShapeParameters(metadata, 3, 2, 10)
m = InferenceModel(model_shape)
pred = predict_with_neural_network(model_shape, m)
prob = pred(examples)
encoding = examples_encoding(examples, metadata)
prob_dnn = m.model(np.array([encoding.types]),
np.array([encoding.values])).array[0]
self.assertAlmostEqual(prob_dnn[0], prob["HEAD"])
self.assertAlmostEqual(prob_dnn[1], prob["MAP"])
def test_Encoder(self):
embed = ExampleEmbed(1, 2, 1, (np.arange(5) + 1).reshape((5, 1)))
encoder = Encoder(1,
initialW=ch.initializers.One(),
initial_bias=ch.initializers.Zero())
self.assertEqual(6, len(list(encoder.params())))
"""
state_embeddings: (N, e, 2, 4) -> h1: (N, e, 1) -> h2: (N, e, 2) -> output: (N, e, 2)
"""
metadata = DatasetMetadata(1, set([]), 2, 2)
e = examples_encoding(
[Example([[0, 1]], 0), Example([[1]], 1)], metadata)
state_embeddings = embed(np.array([e.types]), np.array([e.values]))
layer_encodings = encoder(state_embeddings)
self.assertEqual((1, 2, 1), layer_encodings.shape)
for i in range(1):
for j in range(2):
h = np.array(state_embeddings[i, j, :, :].array.sum())
h = F.sigmoid(F.sigmoid(F.sigmoid(h)))
self.assertEqual(h.array, layer_encodings.array[i, j])
def test_example_embed_embed_one_sample(self):
embed = ExampleEmbed(1, 2, 1, (np.arange(5) + 1).reshape((5, 1)))
self.assertEqual(1, len(list(embed.params())))
"""
EmbedId
0 (-2) -> 1
1 (-1) -> 2
2 ( 0) -> 3
3 ( 1) -> 4
4 (NULL) -> 5
"""
e = examples_encoding(
[Example([[0, 1]], 0), Example([[1]], 1)],
DatasetMetadata(1, set([]), 2, 2))
state_embeddings = embed.forward(np.array([e.types]),
np.array([e.values]))
self.assertEqual((1, 2, 2, 2 + 2 * 1), state_embeddings.shape)
self.assertTrue(
np.allclose([0, 1, 3, 4],
state_embeddings.array[0, 0, 0])) # Input of e1
self.assertTrue(
np.allclose([1, 0, 3, 5],
state_embeddings.array[0, 0, 1])) # Output of e1
self.assertTrue(
np.allclose([0, 1, 4, 5],
state_embeddings.array[0, 1, 0])) # Input of e2
self.assertTrue(
np.allclose([1, 0, 4, 5],
state_embeddings.array[0, 1, 1])) # Output of e2
# backward does not throw an error
state_embeddings.grad = np.ones(state_embeddings.shape,
dtype=np.float32)
state_embeddings.backward()
def test_examples_encoding_if_num_inputs_is_too_large(self):
metadata = DatasetMetadata(0, set([]), 2, 2)
self.assertRaises(
RuntimeError, lambda: examples_encoding(
[Example([1, [0, 1]], [0]),
Example([0, [0, 1]], [])], metadata))