def training_batch(batch_index, model, sess, batches):
for index in batch_index:
user_input, num_idx, item_input, labels = data.batch_gen(batches, index)
feed_dict = {model.user_input: user_input, model.num_idx: num_idx[:, None],
model.item_input: item_input[:, None],
model.labels: labels[:, None]}
sess.run([model.loss, model.optimizer], feed_dict)
def training_loss(model, sess, batches):
train_loss = 0.0
num_batch = len(batches[1])
for index in range(num_batch):
user_input, num_idx, item_input, labels = data.batch_gen(batches, index)
feed_dict = {model.user_input: user_input, model.num_idx: num_idx[:, None], model.item_input: item_input[:, None],model.labels: labels[:, None]}
train_loss += sess.run(model.loss, feed_dict)
return train_loss / num_batch
def training_loss(model, batches):
train_loss = 0.0
num_batch = len(batches[1])
optimizer = optim.Adam(model.parameters(), lr=0.01)
for index in range(num_batch):
user_input, num_idx, item_input, labels = data.batch_gen(
batches, index)
user_input = torch.Tensor(user_input)
num_idx = torch.Tensor(num_idx)
item_input = torch.Tensor(item_input)
labels = torch.Tensor(labels)
optimizer.zero_grad()
loss = model(user_input, num_idx, item_input, labels)
print("epoch %d : loss %f" % (index + 1, loss))
train_loss += loss
loss.backward()
optimizer.step()
return train_loss / num_batch