Python chainer.functions.flatten() Examples

The following are 8 code examples of chainer.functions.flatten(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module chainer.functions , or try the search function .
Example #1
Source File: network.py    From chainer-PGGAN with MIT License 6 votes vote down vote up 
def __call__(self, x, alpha=1.0):
        if self.depth > 0 and alpha < 1:
            h1 = self['b%d'%(7-self.depth)](x, True)
            x2 = F.average_pooling_2d(x, 2, 2)
            h2 = F.leaky_relu(self['b%d'%(7-self.depth+1)].fromRGB(x2))
            h = h2 * (1 - alpha) + h1 * alpha
        else:
            h = self['b%d'%(7-self.depth)](x, True)
                
        for i in range(self.depth):
            h = self['b%d'%(7-self.depth+1+i)](h)

        h = self.l(h)
        h = F.flatten(h)

        return h
Example #2
Source File: soft_actor_critic.py    From chainerrl with MIT License 5 votes vote down vote up 
def update_q_func(self, batch):
        """Compute loss for a given Q-function."""

        batch_next_state = batch['next_state']
        batch_rewards = batch['reward']
        batch_terminal = batch['is_state_terminal']
        batch_state = batch['state']
        batch_actions = batch['action']
        batch_discount = batch['discount']

        with chainer.no_backprop_mode(), chainer.using_config('train', False):
            next_action_distrib = self.policy(batch_next_state)
            next_actions, next_log_prob =\
                next_action_distrib.sample_with_log_prob()
            next_q1 = self.target_q_func1(batch_next_state, next_actions)
            next_q2 = self.target_q_func2(batch_next_state, next_actions)
            next_q = F.minimum(next_q1, next_q2)
            entropy_term = self.temperature * next_log_prob[..., None]
            assert next_q.shape == entropy_term.shape

            target_q = batch_rewards + batch_discount * \
                (1.0 - batch_terminal) * F.flatten(next_q - entropy_term)

        predict_q1 = F.flatten(self.q_func1(batch_state, batch_actions))
        predict_q2 = F.flatten(self.q_func2(batch_state, batch_actions))

        loss1 = 0.5 * F.mean_squared_error(target_q, predict_q1)
        loss2 = 0.5 * F.mean_squared_error(target_q, predict_q2)

        # Update stats
        self.q1_record.extend(cuda.to_cpu(predict_q1.array))
        self.q2_record.extend(cuda.to_cpu(predict_q2.array))
        self.q_func1_loss_record.append(float(loss1.array))
        self.q_func2_loss_record.append(float(loss2.array))

        self.q_func1_optimizer.update(lambda: loss1)
        self.q_func2_optimizer.update(lambda: loss2)
Example #3
Source File: td3.py    From chainerrl with MIT License 5 votes vote down vote up 
def update_q_func(self, batch):
        """Compute loss for a given Q-function."""

        batch_next_state = batch['next_state']
        batch_rewards = batch['reward']
        batch_terminal = batch['is_state_terminal']
        batch_state = batch['state']
        batch_actions = batch['action']
        batch_discount = batch['discount']

        with chainer.no_backprop_mode(), chainer.using_config('train', False):
            next_actions = self.target_policy_smoothing_func(
                self.target_policy(batch_next_state).sample().array)
            next_q1 = self.target_q_func1(batch_next_state, next_actions)
            next_q2 = self.target_q_func2(batch_next_state, next_actions)
            next_q = F.minimum(next_q1, next_q2)

            target_q = batch_rewards + batch_discount * \
                (1.0 - batch_terminal) * F.flatten(next_q)

        predict_q1 = F.flatten(self.q_func1(batch_state, batch_actions))
        predict_q2 = F.flatten(self.q_func2(batch_state, batch_actions))

        loss1 = F.mean_squared_error(target_q, predict_q1)
        loss2 = F.mean_squared_error(target_q, predict_q2)

        # Update stats
        self.q1_record.extend(cuda.to_cpu(predict_q1.array))
        self.q2_record.extend(cuda.to_cpu(predict_q2.array))
        self.q_func1_loss_record.append(float(loss1.array))
        self.q_func2_loss_record.append(float(loss2.array))

        self.q_func1_optimizer.update(lambda: loss1)
        self.q_func2_optimizer.update(lambda: loss2)
Example #4
Source File: trpo.py    From chainerrl with MIT License 5 votes vote down vote up 
def _flatten_and_concat_variables(vs):
    """Flatten and concat variables to make a single flat vector variable."""
    return F.concat([F.flatten(v) for v in vs], axis=0)
Example #5
Source File: agents.py    From EPG with MIT License 5 votes vote down vote up 
def _compute_ppo_loss(self, obs, acts, at, vt, old_params):
        params = self._pi_f(obs)
        cv = F.flatten(self._vf_f(obs))
        ratio = F.exp(self._logp(params, acts) - self._logp(old_params, acts))
        surr1 = ratio * at
        surr2 = F.clip(ratio, 1 - self._ppo_clipparam, 1 + self._ppo_clipparam) * at
        ppo_surr_loss = (
                -sym_mean(F.minimum(surr1, surr2))
                + self._ppo_klcoeff * sym_mean(self.kl(old_params, params))
                + sym_mean(F.square(cv - vt))
        )
        return ppo_surr_loss
Example #6
Source File: test_flatten.py    From chainer with MIT License 5 votes vote down vote up 
def forward_expected(self, inputs):
        x, = inputs
        return x.flatten(),
Example #7
Source File: test_flatten.py    From chainer with MIT License 5 votes vote down vote up 
def forward(self, inputs, device):
        x, = inputs
        y = functions.flatten(x)
        return y,
Example #8
Source File: multibox_loss.py    From chainercv with MIT License 5 votes vote down vote up 
def _elementwise_softmax_cross_entropy(x, t):
    assert x.shape[:-1] == t.shape
    shape = t.shape
    x = F.reshape(x, (-1, x.shape[-1]))
    t = F.flatten(t)
    return F.reshape(
        F.softmax_cross_entropy(x, t, reduce='no'), shape)