def load_pretrain_weights(self):
"""Loading weights from trained MLP model & GMF model"""
config = self.config
config['latent_dim'] = config['latent_dim_mlp']
mlp_model = MLP(config)
if config['use_cuda'] is True:
mlp_model.cuda()
resume_checkpoint(mlp_model,
model_dir=config['pretrain_mlp'],
device_id=config['device_id'])
self.embedding_user_mlp.weight.data = mlp_model.embedding_user.weight.data
self.embedding_item_mlp.weight.data = mlp_model.embedding_item.weight.data
for idx in range(len(self.fc_layers)):
self.fc_layers[idx].weight.data = mlp_model.fc_layers[
idx].weight.data
config['latent_dim'] = config['latent_dim_mf']
gmf_model = GMF(config)
if config['use_cuda'] is True:
gmf_model.cuda()
resume_checkpoint(gmf_model,
model_dir=config['pretrain_mf'],
device_id=config['device_id'])
self.embedding_user_mf.weight.data = gmf_model.embedding_user.weight.data
self.embedding_item_mf.weight.data = gmf_model.embedding_item.weight.data
self.affine_output.weight.data = 0.5 * torch.cat([
mlp_model.affine_output.weight.data,
gmf_model.affine_output.weight.data
],
dim=-1)
self.affine_output.bias.data = 0.5 * (
mlp_model.affine_output.bias.data +
gmf_model.affine_output.bias.data)
def load_pretrain_weights(self):
"""Loading weights from trained MLP model & GMF model"""
config = self.config
mlp_model = MLP(config)
device_id = -1
if config['use_cuda'] is True:
mlp_model.cuda()
device_id = config['device_id']
resume_checkpoint(mlp_model,
model_dir=config['pretrain_mlp'],
device_id=device_id)
self.embedding_account_mlp.weight.data = mlp_model.embedding_account.weight.data
self.embedding_location_mlp.weight.data = mlp_model.embedding_location.weight.data
for idx in range(len(self.fc_layers)):
self.fc_layers[idx].weight.data = mlp_model.fc_layers[
idx].weight.data
config['latent_dim'] = config['latent_dim_mf']
gmf_model = GMF(config)
if config['use_cuda'] is True:
gmf_model.cuda()
resume_checkpoint(gmf_model,
model_dir=config['pretrain_mf'],
device_id=device_id)
self.embedding_account_mf.weight.data = gmf_model.embedding_account.weight.data
self.embedding_location_mf.weight.data = gmf_model.embedding_location.weight.data
self.embedding_account_mlp.require = False
self.embedding_location_mlp.require = False
self.embedding_account_mf.require = False
self.embedding_location_mf.require = False
def load_pretrain_weights(self):
"""Loading weights from trained GMF model"""
config = self.config
gmf_model = GMF(config)
if config['use_cuda'] is True:
gmf_model.cuda()
resume_checkpoint(gmf_model, model_dir=config['pretrain_mf'], device_id=config['device_id'])
self.embedding_user.weight.data = gmf_model.embedding_user.weight.data
self.embedding_item.weight.data = gmf_model.embedding_item.weight.data
def load_pretrain_weights(self):
"""Loading weights from trained GMF model"""
config = self.config
gmf_model = GMF(config)
if config['use_cuda'] is True:
gmf_model.cuda()
resume_checkpoint(gmf_model, model_dir=config['pretrain_mf'], device_id=config['device_id'])
self.embedding_user.weight.data = gmf_model.embedding_user.weight.data
self.embedding_item.weight.data = gmf_model.embedding_item.weight.data
# class MLPEngine(Engine):
# """Engine for training & evaluating GMF model"""
# def __init__(self, config):
# self.model = MLP(config)
# if config['use_cuda'] is True:
# use_cuda(True, config['device_id'])
# self.model.cuda()
# super(MLPEngine, self).__init__(config)
# print(self.model)
# if config['pretrain']:
# self.model.load_pretrain_weights()