Python sonnet.Module方法代码示例

# 需要导入模块: import sonnet [as 别名]
# 或者: from sonnet import Module [as 别名]
def transformer_at_state(base_model, new_variables):
  """Get the base_model that has been transformed to use the variables
  in final_state.
  Args:
    base_model: snt.Module
      Goes from batch to features
    new_variables: list
      New list of variables to use
  Returns:
    func: callable of same api as base_model.
  """
  assert not variable_replace.in_variable_replace_scope()

  def _feature_transformer(input_data):
    """Feature transformer at the end of training."""
    initial_variables = base_model.get_variables()
    replacement = collections.OrderedDict(
        utils.eqzip(initial_variables, new_variables))
    with variable_replace.variable_replace(replacement):
      features = base_model(input_data)
    return features

  return _feature_transformer 

# 需要导入模块: import sonnet [as 别名]
# 或者: from sonnet import Module [as 别名]
def make_ensemble(num_actions: int,
                  num_ensemble: int = 20,
                  num_hidden_layers: int = 2,
                  num_units: int = 50,
                  prior_scale: float = 3.) -> Sequence[snt.Module]:
  """Convenience function to make an ensemble from flags."""
  output_sizes = [num_units] * num_hidden_layers + [num_actions]
  ensemble = []
  for _ in range(num_ensemble):
    network = snt.Sequential([
        snt.Flatten(),
        snt.nets.MLP(output_sizes),
    ])
    prior_network = snt.Sequential([
        snt.Flatten(),
        snt.nets.MLP(output_sizes),
    ])
    ensemble.append(NetworkWithPrior(network, prior_network, prior_scale))
  return ensemble 

# 需要导入模块: import sonnet [as 别名]
# 或者: from sonnet import Module [as 别名]
def custom_build(inputs, is_training, keep_prob):
  x_inputs = tf.reshape(inputs, [-1, 28, 28, 1])
  """A custom build method to wrap into a sonnet Module."""
  outputs = snt.Conv2D(output_channels=32, kernel_shape=4, stride=2)(x_inputs)
  outputs = snt.BatchNorm()(outputs, is_training=is_training)
  outputs = tf.nn.relu(outputs)
  outputs = tf.nn.max_pool(outputs, ksize=[1, 2, 2, 1],
                           strides=[1, 2, 2, 1], padding='SAME')
  outputs = snt.Conv2D(output_channels=64, kernel_shape=4, stride=2)(outputs)
  outputs = snt.BatchNorm()(outputs, is_training=is_training)
  outputs = tf.nn.relu(outputs)
  outputs = tf.nn.max_pool(outputs, ksize=[1, 2, 2, 1],
                           strides=[1, 2, 2, 1], padding='SAME')
  outputs = snt.Conv2D(output_channels=1024, kernel_shape=1, stride=1)(outputs)
  outputs = snt.BatchNorm()(outputs, is_training=is_training)
  outputs = tf.nn.relu(outputs)
  outputs = snt.BatchFlatten()(outputs)
  outputs = tf.nn.dropout(outputs, keep_prob=keep_prob)
  outputs = snt.Linear(output_size=10)(outputs)
#  _activation_summary(outputs)
  return outputs 

# 需要导入模块: import sonnet [as 别名]
# 或者: from sonnet import Module [as 别名]
def custom_build(self, inputs):
        """A custom build method to wrap into a sonnet Module."""
        outputs = snt.Conv2D(output_channels=16, kernel_shape=[7, 7], stride=[1, 1])(inputs)
        outputs = tf.nn.relu(outputs)
        outputs = snt.Conv2D(output_channels=16, kernel_shape=[5, 5], stride=[1, 2])(outputs)
        outputs = tf.nn.relu(outputs)
        outputs = snt.Conv2D(output_channels=16, kernel_shape=[5, 5], stride=[1, 2])(outputs)
        outputs = tf.nn.relu(outputs)
        outputs = snt.Conv2D(output_channels=16, kernel_shape=[5, 5], stride=[2, 2])(outputs)
        outputs = tf.nn.relu(outputs)
        outputs = tf.nn.dropout(outputs,  self.placeholders['keep_prob'])
        outputs = snt.BatchFlatten()(outputs)
        outputs = snt.Linear(128)(outputs)
        outputs = tf.nn.relu(outputs)

        return outputs 

# 需要导入模块: import sonnet [as 别名]
# 或者: from sonnet import Module [as 别名]
def __init__(self,
               network: snt.Module,
               prior_network: snt.Module,
               prior_scale: float = 1.):
    super().__init__(name='network_with_prior')
    self._network = network
    self._prior_network = prior_network
    self._prior_scale = prior_scale 

# 需要导入模块: import sonnet [as 别名]
# 或者: from sonnet import Module [as 别名]
def __init__(
      self,
      action_spec: specs.DiscreteArray,
      network: snt.Module,
      batch_size: int,
      discount: float,
      replay_capacity: int,
      min_replay_size: int,
      sgd_period: int,
      target_update_period: int,
      optimizer: snt.Optimizer,
      epsilon: float,
      seed: int = None,
  ):

    # Internalise hyperparameters.
    self._num_actions = action_spec.num_values
    self._discount = discount
    self._batch_size = batch_size
    self._sgd_period = sgd_period
    self._target_update_period = target_update_period
    self._epsilon = epsilon
    self._min_replay_size = min_replay_size

    # Seed the RNG.
    tf.random.set_seed(seed)
    self._rng = np.random.RandomState(seed)

    # Internalise the components (networks, optimizer, replay buffer).
    self._optimizer = optimizer
    self._replay = replay.Replay(capacity=replay_capacity)
    self._online_network = network
    self._target_network = copy.deepcopy(network)
    self._forward = tf.function(network)
    self._total_steps = tf.Variable(0) 

# 需要导入模块: import sonnet [as 别名]
# 或者: from sonnet import Module [as 别名]
def evaluate():
    """Eval MNIST for a number of steps."""
    with tf.Graph().as_default():
        # Get images and labels for MNIST.
        mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=False)
        images = mnist.test.images
        labels = mnist.test.labels

        # Build a Graph that computes the logits predictions from the
        # inference model.
        # The line below takes custom_build and wraps it to construct a sonnet Module.
        module_with_build_args = snt.Module(custom_build, name='simple_net')
        test_model_outputs = module_with_build_args(images, is_training=False,
                                                    keep_prob=tf.constant(1.0))

        # Calculate predictions.
        top_k_op = tf.nn.in_top_k(predictions=test_model_outputs, targets=labels, k=1)

        # Create saver to restore the learned variables for eval.
        saver = tf.train.Saver()

        with tf.Session() as sess:
            ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
            if ckpt and ckpt.model_checkpoint_path:
                # Restores from checkpoint
                saver.restore(sess, ckpt.model_checkpoint_path)
            else:
                print('No checkpoint file found')
                return

            predictions = np.sum(sess.run([top_k_op]))

            # Compute precision.
            print('%s: precision = %.3f' % (datetime.now(), predictions)) 

# 需要导入模块: import sonnet [as 别名]
# 或者: from sonnet import Module [as 别名]
def classification_probe(features, labels, n_classes, labeled=None):
  """Classification probe with stopped gradient on features."""

  def _classification_probe(features):
    logits = snt.Linear(n_classes)(tf.stop_gradient(features))
    xe = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
                                                        labels=labels)
    if labeled is not None:
      xe = xe * tf.to_float(labeled)
    xe = tf.reduce_mean(xe)
    acc = tf.reduce_mean(tf.to_float(tf.equal(tf.argmax(logits, axis=1),
                                              labels)))
    return xe, acc

  return snt.Module(_classification_probe)(features) 

# 需要导入模块: import sonnet [as 别名]
# 或者: from sonnet import Module [as 别名]
def tower_loss(scope):
    """Calculate the total loss on a single tower running the MNIST model.

    Args:
      scope: unique prefix string identifying the MNIST tower, e.g. 'tower_0'

    Returns:
       Tensor of shape [] containing the total loss for a batch of data
    """
    # Input images and labels.

    images, labels = inputs(train=True, batch_size=FLAGS.batch_size,
                            num_epochs=(FLAGS.num_epochs / FLAGS.num_gpus))
    # Build inference Graph.
    # The line below takes custom_build and
    # wraps it to construct a sonnet Module.
    module_with_build_args = snt.Module(custom_build, name='simple_net')

    train_model_outputs = module_with_build_args(images, is_training=True,
                                                 keep_prob=tf.constant(0.5))

    # Build the portion of the Graph calculating the losses. Note that we will
    # assemble the total_loss using a custom function below.
    _ = loss(train_model_outputs, labels)

    # Assemble all of the losses for the current tower only.
    losses = tf.get_collection('losses', scope)

    # Calculate the total loss for the current tower.
    total_loss = tf.add_n(losses, name='total_loss')

    # Attach a scalar summary to all individual losses and the total loss; do
    # the same for the averaged version of the losses.
    if FLAGS.tb_logging:
        for l in losses + [total_loss]:
            # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU
            # training session. This helps the clarity of presentation on
            # tensorboard.
            loss_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', l.op.name)
            tf.summary.scalar(loss_name, l)

    return total_loss 

# 需要导入模块: import sonnet [as 别名]
# 或者: from sonnet import Module [as 别名]
def build_modules(self, min_obs_likelihood, proposer_keep_ratio, learn_gaussian_mle):
        """
        :param min_obs_likelihood:
        :param proposer_keep_ratio:
        :return: None
        """

        # MEASUREMENT MODEL

        # conv net for encoding the image
        self.encoder = snt.Sequential([
            snt.nets.ConvNet2D([16, 16, 16, 16], [[7, 7], [5, 5], [5, 5], [5, 5]], [[1,1], [1, 2], [1, 2], [2, 2]], [snt.SAME], activate_final=True, name='encoder/convnet'),
            snt.BatchFlatten(),
            lambda x: tf.nn.dropout(x,  self.placeholders['keep_prob']),
            snt.Linear(128, name='encoder/linear'),
            tf.nn.relu
        ])

        # observation likelihood estimator that maps states and image encodings to probabilities
        self.obs_like_estimator = snt.Sequential([
            snt.Linear(128, name='obs_like_estimator/linear'),
            tf.nn.relu,
            snt.Linear(128, name='obs_like_estimator/linear'),
            tf.nn.relu,
            snt.Linear(1, name='obs_like_estimator/linear'),
            tf.nn.sigmoid,
            lambda x: x * (1 - min_obs_likelihood) + min_obs_likelihood
        ], name='obs_like_estimator')

        # motion noise generator used for motion sampling
        if learn_gaussian_mle:
            self.mo_noise_generator = snt.nets.MLP([32, 32, 4], activate_final=False, name='mo_noise_generator')
        else:
            self.mo_noise_generator = snt.nets.MLP([32, 32, 2], activate_final=False, name='mo_noise_generator')

        # odometry model (if we want to learn it)
        if self.learn_odom:
            self.mo_transition_model = snt.nets.MLP([128, 128, 128, self.state_dim], activate_final=False, name='mo_transition_model')

        # particle proposer that maps encodings to particles (if we want to use it)
        if self.use_proposer:
            self.particle_proposer = snt.Sequential([
                snt.Linear(128, name='particle_proposer/linear'),
                tf.nn.relu,
                lambda x: tf.nn.dropout(x,  proposer_keep_ratio),
                snt.Linear(128, name='particle_proposer/linear'),
                tf.nn.relu,
                snt.Linear(128, name='particle_proposer/linear'),
                tf.nn.relu,
                snt.Linear(128, name='particle_proposer/linear'),
                tf.nn.relu,
                snt.Linear(4, name='particle_proposer/linear'),
                tf.nn.tanh,
            ])

        self.noise_scaler1 = snt.Module(lambda x: x * tf.exp(10 * tf.get_variable('motion_sampler/noise_scaler1', initializer=np.array(0.0, dtype='float32'))))
        self.noise_scaler2 = snt.Module(lambda x: x * tf.exp(10 * tf.get_variable('motion_sampler/noise_scaler2', initializer=np.array(0.0, dtype='float32'))))