raw_data['max_item'],
name='Test')
model = VisualCML(batch_size=batch_size,
max_user=raw_data['max_user'],
max_item=raw_data['max_item'],
l2_reg=0.001,
l2_reg_mlp=0.001,
dropout_rate=0.5,
dim_embed=50,
item_f_source=raw_data['item_features'],
dims=[1028, 128, 50],
sess_config=sess_config,
opt='Adam')
sampler = PairwiseSampler(batch_size=batch_size,
dataset=train_dataset,
num_process=5)
model_trainer = ImplicitModelTrainer(batch_size=batch_size,
test_batch_size=test_batch_size,
item_serving_size=item_serving_size,
train_dataset=train_dataset,
model=model,
sampler=sampler)
auc_evaluator = AUC()
recall_evaluator = Recall(recall_at=[10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
model_trainer.train(num_itr=int(1e5),
display_itr=display_itr,
eval_datasets=[val_dataset, test_dataset],
evaluators=[auc_evaluator, recall_evaluator],
max_user=max_users,
max_item=max_items,
name='Val')
test_dataset = ImplicitDataset(raw_data=csv,
max_user=max_users,
max_item=max_items,
name='Test')
bpr_model = BPR(batch_size=1000,
max_user=train_dataset.max_user(),
max_item=train_dataset.max_item(),
dim_embed=20,
opt='Adam')
print("before sampler")
sampler = PairwiseSampler(batch_size=1000, dataset=train_dataset)
print("after sampler")
auc_evaluator = AUC()
print("after evaluator")
model_trainer = ImplicitModelTrainer(batch_size=1000,
test_batch_size=100,
train_dataset=train_dataset,
model=bpr_model,
sampler=sampler)
print("after implicit")
model_trainer.train(num_itr=10,
display_itr=10,
eval_datasets=[val_dataset, test_dataset],