x = T.imatrix('x')
y = T.ivector('y')
classifier = RNTN(
x,
y,
vocab_size=5,
embed_dim=3,
label_n=5,
)
x_input = np.asarray([[1, -1, -1], [2, -1, -1], [3, 1, 2]], dtype=np.int32)
y_input = labels[1:4]
original_embedding = classifier.embedding.get_value()
classifier.update_embedding(x_input)
new_embedding = classifier.embedding.get_value()
assert_matrix_neq(original_embedding, new_embedding, "update_embeding")
original_params = [p.get_value() for p in classifier.params]
classifier.train(x_input, y_input)
updated_params = [p for p in classifier.params]
for op, up in zip(original_params, updated_params):
assert_matrix_neq(op, up.get_value(), up.name)
classifier = RNTN(
x, y,
vocab_size = 5,
embed_dim = 3,
label_n = 5,
)
x_input = np.asarray([[1,-1,-1],
[2,-1,-1],
[3, 1, 2]],
dtype=np.int32)
y_input = labels[1:4]
original_embedding = classifier.embedding.get_value()
classifier.update_embedding(x_input)
new_embedding = classifier.embedding.get_value()
assert_matrix_neq(original_embedding,
new_embedding,
"update_embeding")
original_params = [p.get_value() for p in classifier.params]
classifier.train(x_input, y_input)
updated_params = [p for p in classifier.params]
for op, up in zip(original_params, updated_params):
assert_matrix_neq(op, up.get_value(), up.name)