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)