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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- """
- BGCF training script.
- """
- import time
-
- from mindspore import Tensor
- import mindspore.context as context
- from mindspore.common import dtype as mstype
- from mindspore.train.serialization import save_checkpoint
-
- from src.bgcf import BGCF
- from src.config import parser_args
- from src.utils import convert_item_id
- from src.callback import TrainBGCF
- from src.dataset import load_graph, create_dataset
-
-
- def train():
- """Train"""
- num_user = train_graph.graph_info()["node_num"][0]
- num_item = train_graph.graph_info()["node_num"][1]
- num_pairs = train_graph.graph_info()['edge_num'][0]
-
- bgcfnet = BGCF([parser.input_dim, num_user, num_item],
- parser.embedded_dimension,
- parser.activation,
- parser.neighbor_dropout,
- num_user,
- num_item,
- parser.input_dim)
-
- train_net = TrainBGCF(bgcfnet, parser.num_neg, parser.l2, parser.learning_rate,
- parser.epsilon, parser.dist_reg)
- train_net.set_train(True)
-
- itr = train_ds.create_dict_iterator(parser.num_epoch, output_numpy=True)
- num_iter = int(num_pairs / parser.batch_pairs)
-
- for _epoch in range(1, parser.num_epoch + 1):
-
- epoch_start = time.time()
- iter_num = 1
-
- for data in itr:
-
- u_id = Tensor(data["users"], mstype.int32)
- pos_item_id = Tensor(convert_item_id(data["items"], num_user), mstype.int32)
- neg_item_id = Tensor(convert_item_id(data["neg_item_id"], num_user), mstype.int32)
- pos_users = Tensor(data["pos_users"], mstype.int32)
- pos_items = Tensor(convert_item_id(data["pos_items"], num_user), mstype.int32)
-
- u_group_nodes = Tensor(data["u_group_nodes"], mstype.int32)
- u_neighs = Tensor(convert_item_id(data["u_neighs"], num_user), mstype.int32)
- u_gnew_neighs = Tensor(convert_item_id(data["u_gnew_neighs"], num_user), mstype.int32)
-
- i_group_nodes = Tensor(convert_item_id(data["i_group_nodes"], num_user), mstype.int32)
- i_neighs = Tensor(data["i_neighs"], mstype.int32)
- i_gnew_neighs = Tensor(data["i_gnew_neighs"], mstype.int32)
-
- neg_group_nodes = Tensor(convert_item_id(data["neg_group_nodes"], num_user), mstype.int32)
- neg_neighs = Tensor(data["neg_neighs"], mstype.int32)
- neg_gnew_neighs = Tensor(data["neg_gnew_neighs"], mstype.int32)
-
- train_loss = train_net(u_id,
- pos_item_id,
- neg_item_id,
- pos_users,
- pos_items,
- u_group_nodes,
- u_neighs,
- u_gnew_neighs,
- i_group_nodes,
- i_neighs,
- i_gnew_neighs,
- neg_group_nodes,
- neg_neighs,
- neg_gnew_neighs)
-
- if iter_num == num_iter:
- print('Epoch', '%03d' % _epoch, 'iter', '%02d' % iter_num,
- 'loss',
- '{}, cost:{:.4f}'.format(train_loss, time.time() - epoch_start))
- iter_num += 1
-
- if _epoch % parser.eval_interval == 0:
- save_checkpoint(bgcfnet, parser.ckptpath + "/bgcf_epoch{}.ckpt".format(_epoch))
-
-
- if __name__ == "__main__":
- parser = parser_args()
- context.set_context(mode=context.GRAPH_MODE,
- device_target="Ascend",
- save_graphs=False,
- device_id=parser.device)
-
- train_graph, _, sampled_graph_list = load_graph(parser.datapath)
- train_ds = create_dataset(train_graph, sampled_graph_list, parser.workers, batch_size=parser.batch_pairs,
- num_samples=parser.raw_neighs, num_bgcn_neigh=parser.gnew_neighs, num_neg=parser.num_neg)
-
- train()
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