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)
x = T.imatrix('x')
y = T.ivector('y')
th_model = TheanoRNTN(x, y, vocab_size, embed_dim, label_n)
np_model = NumpyRNTN.load_from_theano_model(
th_model, word2id) # (embedding = th_model.embedding.get_value(),
# rntn_layer = RNTNLayer(th_model.rntn_layer.V.get_value(), th_model.rntn_layer.W.get_value()),
# logreg_layer = LogisticRegression(th_model.logreg_layer.W.get_value(), th_model.logreg_layer.b.get_value()),
# word2id = word2id)
x_input = np.asarray([[4, 2, 5], [3, 1, 4]], dtype=np.int32)
tree_input = (5, "love", (3, (3, "you"), (3, "bro")))
actual = np_model.get_node_vector(tree_input)
th_model.update_embedding(x_input)
expected = th_model.embedding.get_value()[3]
assert_matrix_eq(actual, expected, "node vector")
get_label = theano.function(inputs=[x], outputs=th_model.logreg_layer.pred_y)
score = np_model.predict_top_node(tree_input)
assert isinstance(score, np.int64)
assert_matrix_eq(score, get_label(x_input[1:2, :]), 'logreg.predict')
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)
th_model = TheanoRNTN(x, y, vocab_size, embed_dim, label_n)
np_model = NumpyRNTN.load_from_theano_model(th_model, word2id)# (embedding = th_model.embedding.get_value(),
# rntn_layer = RNTNLayer(th_model.rntn_layer.V.get_value(), th_model.rntn_layer.W.get_value()),
# logreg_layer = LogisticRegression(th_model.logreg_layer.W.get_value(), th_model.logreg_layer.b.get_value()),
# word2id = word2id)
x_input = np.asarray([[4, 2, 5],
[3, 1, 4]],
dtype=np.int32)
tree_input = (5, "love", (3, (3, "you"), (3, "bro")))
actual = np_model.get_node_vector(tree_input)
th_model.update_embedding(x_input)
expected = th_model.embedding.get_value()[3]
assert_matrix_eq(actual, expected, "node vector")
get_label = theano.function(inputs = [x],
outputs = th_model.logreg_layer.pred_y)
score = np_model.predict_top_node(tree_input)
assert isinstance(score, np.int64)
assert_matrix_eq(score, get_label(x_input[1:2,:]), 'logreg.predict')
