def __call__(self, pred, target, action_list, exp_sizes):
batch_size, alpha_iter, target_dim = pred.size()
target = target.unsqueeze(1).expand_as(pred)
losses = (pred - target).pow(2).mean(2, keepdim=True)
self.saved_losses = [[i.data[0] for i in row] for row in losses]
self.saved_baselines = [compute_baseline(i) for i in self.saved_losses]
for actions, rewards in zip(action_list, self.saved_baselines):
for action, reward in zip(actions, rewards):
action.reinforce(reward)
if self.reg:
reg_loss = self.reg * (
(torch.stack(exp_sizes) - self.reg_mean)**2).sum()
reg_loss.backward(retain_variables=True)
# backpropagate through kernel network
pseudo_loss = torch.stack(
[torch.stack(actions) for actions in action_list]).sum()
pseudo_loss.backward(None)
# backpropgate through prediction network
loss.backward()
return loss
def train(self, train_iter, batch_size, lr, alpha_iter=1, baseline=True):
"""
Training the model.
Doesn't use the forward pass as want to sample repeatedly!
"""
set_size = self.set_size
kernel_in = self.kernel_in
kernel_out = self.kernel_out
loss_log = 100
optimizer = optim.Adam(self.kernel_net.parameters(), lr=lr)
for t in range(train_iter):
actions = self.saved_subsets = [[] for i in range(batch_size)]
rewards = self.saved_losses = [[] for i in range(batch_size)]
cum_loss = 0.
words, context, target = self.generate(batch_size)
# Concatenate individual words and set context
# Dimensions are batch_size x set_size x kernel_in
batch_x = Variable(torch.cat([words, context], dim = 2))
# Compute embedding of DPP kernel
batch_kernel = self.kernel_net(batch_x.view(-1, kernel_in))
batch_kernel = batch_kernel.view(-1, set_size, kernel_out)
for i, kernel in enumerate(batch_kernel):
vals, vecs = custom_decomp()(kernel)
for j in range(alpha_iter):
subset = DPP()(vals, vecs)
actions[i].append(subset)
loss, _, _, _, _ = self._assess(target[i], subset.data)
rewards[i].append(loss)
cum_loss += loss
if baseline:
self.saved_baselines = [compute_baseline(i) for i in self.saved_losses]
else:
self.saved_baselines = self.saved_losses
# Register the baselines
for actions, rewards in zip(self.saved_subsets, self.saved_baselines):
for action, reward in zip(actions, rewards):
action.reinforce(reward)
pseudo_loss = torch.stack([torch.stack(subsets) for subsets in self.saved_subsets]).sum()
pseudo_loss.backward(None)
optimizer.step()
optimizer.zero_grad()
self.loss_dict[t].append(cum_loss / (batch_size * alpha_iter))
if not ((t + 1) % loss_log):
print("Loss at it ", t+1, " is: ", cum_loss / (batch_size * alpha_iter))
def forward(self, words, target):
kernel, words = self.kernel_net(
words) # returned words are masked now!
self.sampler.s_ix = self.kernel_net.s_ix
self.sampler.e_ix = self.kernel_net.e_ix
weighted_words = self.sampler(kernel, words)
self.pred_net.s_ix = self.sampler.s_ix
self.pred_net.e_ix = self.sampler.e_ix
self.pred = self.pred_net(weighted_words)
if self.activation:
self.pred = self.activation(self.pred)
self.pred_loss = self.criterion(self.pred, target)
if self.reg:
perc_extract = (torch.stack(self.sampler.exp_sizes) /
Variable(self.kernel_net.lengths.squeeze(2).data))
self.reg_loss = self.reg * (perc_extract -
(self.reg_mean / 100)).pow(2).mean()
self.loss = self.pred_loss + self.reg_loss
else:
self.loss = self.pred_loss
# add computation of baselines and registering rewards here!!
losses = (self.pred - target).pow(2).view(-1, self.alpha_iter,
target.size(-1)).mean(
2, keepdim=True)
self.saved_losses = [[i.data[0] for i in row] for row in losses]
if self.alpha_iter > 1:
self.saved_baselines = [
compute_baseline(i) for i in self.saved_losses
]
else:
self.saved_baselines = self.saved_losses
self.saved_subsets = self.sampler.saved_subsets
#print("executed")
#print('We have so many subsets: ',len(self.saved_subsets))
for actions, rewards in zip(self.saved_subsets, self.saved_baselines):
for action, reward in zip(actions, rewards):
action.reinforce(reward)
return self.loss
if reg:
exp_ssize = (vals / (1 + vals)).sum()
reg_loss += reg * (exp_ssize - reg_mean)**2
picks = torch.stack([torch.stack(pick) for pick in picks]).view(-1, embd_dim)
preds = self.pred_net(picks).view(batch_size, alpha_iter, -1)
targets = target.unsqueeze(1).expand_as(preds)
loss = criterion(preds, Variable(targets))
# Compute indivdual losses and baseline
losses = ((preds - Variable(targets))**2).mean(2)
self.saved_losses = [[i.data[0] for i in row] for row in losses]
if baseline:
self.saved_baselines = [compute_baseline(i) for i in self.saved_losses]
else:
self.saved_baselines = self.saved_losses
# Register the baselines
for actions, rewards in zip(self.saved_subsets, self.saved_baselines):
for action, reward in zip(actions, rewards):
action.reinforce(reward)
if reg:
(loss + reg_loss).backward()
else:
loss.backward(None)
optimizer.step()
optimizer.zero_grad()
def train(self,
train_steps,
batch_size=1,
sample_iter=1,
lr=1e-3,
baseline=False,
reg=0,
reg_mean=0):
if baseline:
assert sample_iter > 1
params = [{
'params': self.kernel_net.parameters()
}, {
'params': self.pred_net.parameters()
}]
optimizer = optim.Adam(params, lr=lr)
train_iter = train_steps * batch_size
cum_loss = 0
cum_prec = 0
cum_rec = 0
cum_size = 0
for t in range(train_iter):
actions = self.saved_subsets = []
rewards = self.saved_losses = []
picks = []
words, context, ixs, target = self.generate()
input_x = torch.cat([words, context], dim=1)
kernel = self.kernel_net(input_x)
vals, vecs = custom_decomp()(kernel)
pred_loss = 0
for j in range(sample_iter):
subset = DPP()(vals, vecs)
actions.append(subset)
pick = subset.diag().mm(words).sum(0, keepdim=True)
self.pred = self.pred_net(pick).squeeze()
loss = nn.BCELoss()(self.pred, target)
rewards.append(loss.data[0])
pred_loss += loss
# For the statistics
precision, recall, set_size = self.assess(subset.data, ixs)
cum_loss += loss.data[0]
cum_prec += precision
cum_rec += recall
cum_size += set_size
# Compute baselines
if baseline:
self.saved_baselines = compute_baseline(self.saved_losses)
else:
self.saved_baselines = self.saved_losses
# Register rewards
for action, reward in zip(self.saved_subsets,
self.saved_baselines):
action.reinforce(reward)
# Apply Regularization
total_loss = pred_loss
if reg:
card = (vals / (1 + vals)).sum()
reg_loss = sample_iter * reg * ((card - reg_mean)**2)
total_loss += reg_loss
total_loss.backward()
if not ((t + 1) % batch_size):
optimizer.step()
optimizer.zero_grad()
if not ((t + 1) % (batch_size * 100)):
print(cum_loss / (batch_size * sample_iter))
self.loss_dict[self.counter].append(cum_loss /
(batch_size * sample_iter))
self.prec_dict[self.counter].append(cum_prec /
(batch_size * sample_iter))
self.rec_dict[self.counter].append(cum_rec /
(batch_size * sample_iter))
self.ssize_dict[self.counter].append(
cum_size / (batch_size * sample_iter))
self.counter += 1
cum_loss = 0
cum_prec = 0
cum_rec = 0
cum_size = 0
def forward(self, reviews, target):
batch_size = target.size(0)
alpha_iter = self.alpha_iter
try:
target_dim = target.size(1)
except RuntimeError:
target = target.unsqueeze(1)
target_dim = target.size(1)
print(target_dim)
target = target.unsqueeze(1).expand(batch_size, alpha_iter,
target_dim).contiguous().view(
batch_size * alpha_iter,
target_dim)
print(target.size())
words = self.embd(reviews)
kernel, words = self.kernel_net(words)
self.sampler.s_ix = self.kernel_net.s_ix
self.sampler.e_ix = self.kernel_net.e_ix
weighted_words = self.sampler(kernel, words)
self.pred_net.s_ix = self.sampler.s_ix
self.pred_net.e_ix = self.sampler.e_ix
self.pred = self.pred_net(weighted_words)
if self.activation:
self.pred = self.activation(self.pred)
self.pred_loss = self.criterion(self.pred, target)
if self.reg:
self.reg_loss = self.reg * (torch.stack(self.sampler.exp_sizes) -
self.reg_mean).pow(2).mean()
self.loss = self.pred_loss + self.reg_loss
# print("reg_loss is:", reg_loss.data[0])
else:
self.loss = self.pred_loss
# add computation of baselines and registering reawards here!!
losses = (self.pred - target).pow(2).view(
batch_size, alpha_iter, target_dim).mean(2, keepdim=True)
self.saved_losses = [[i.data[0] for i in row] for row in losses]
if self.alpha_iter > 1:
self.saved_baselines = [
compute_baseline(i) for i in self.saved_losses
]
else:
self.saved_baselines = self.saved_losses
self.saved_subsets = self.sampler.saved_subsets
for actions, rewards in zip(self.saved_subsets, self.saved_baselines):
for action, reward in zip(actions, rewards):
action.reinforce(reward)
return self.loss