Python sonnet.SAME属性代码示例

# 需要导入模块: import sonnet [as 别名]
# 或者: from sonnet import SAME [as 别名]
def _build(self, inputs, is_training):
    """Connects the module to inputs.

    Args:
      inputs: Inputs to the Unit3D component.
      is_training: whether to use training mode for snt.BatchNorm (boolean).

    Returns:
      Outputs from the module.
    """
    net = snt.Conv3D(output_channels=self._output_channels,
                     kernel_shape=self._kernel_shape,
                     stride=self._stride,
                     padding=snt.SAME,
                     use_bias=self._use_bias)(inputs)
    if self._use_batch_norm:
      bn = snt.BatchNorm()
      net = bn(net, is_training=is_training, test_local_stats=False)
    if self._activation_fn is not None:
      net = self._activation_fn(net)
    return net 

# 需要导入模块: import sonnet [as 别名]
# 或者: from sonnet import SAME [as 别名]
def _build(self, inputs, is_training):
    """Connects the module to inputs.

    Args:
      inputs: Inputs to the Unit3D component.
      is_training: whether to use training mode for snt.BatchNorm (boolean).

    Returns:
      Outputs from the module.
    """
    net = snt.Conv3D(output_channels=self._output_channels,
                     kernel_shape=self._kernel_shape,
                     stride=self._stride,
                     padding=snt.SAME,
                     use_bias=self._use_bias)(inputs)
    if self._use_batch_norm:
      bn = snt.BatchNorm()
      #################### Warning batchnorm is hard coded to is_training=False #################
      # net = bn(net, is_training=is_training, test_local_stats=False)
      net = bn(net, is_training=False, test_local_stats=False)
    if self._activation_fn is not None:
      net = self._activation_fn(net)
    return net 

# 需要导入模块: import sonnet [as 别名]
# 或者: from sonnet import SAME [as 别名]
def _build(self, inputs, is_training):
        """Connects the module to inputs.

    Args:
    inputs: Inputs to the Unit3Dtf component.
    is_training: whether to use training mode for snt.BatchNorm (boolean).

    Returns:
    Outputs from the module.
        """
        net = snt.Conv3D(
            output_channels=self._output_channels,
            kernel_shape=self._kernel_shape,
            stride=self._stride,
            padding=snt.SAME,
            use_bias=self._use_bias)(inputs)
        if self._use_batch_norm:
            bn = snt.BatchNorm()
            net = bn(net, is_training=is_training, test_local_stats=False)
        if self._activation_fn is not None:
            net = self._activation_fn(net)

        return net 

# 需要导入模块: import sonnet [as 别名]
# 或者: from sonnet import SAME [as 别名]
def _build(self, inputs):

    if FLAGS.l2_reg:
      regularizers = {'w': lambda w: FLAGS.l2_reg*tf.nn.l2_loss(w),
                      'b': lambda w: FLAGS.l2_reg*tf.nn.l2_loss(w),}
    else:
      regularizers = None

    reshape = snt.BatchReshape([28, 28, 1])

    conv = snt.Conv2D(2, 5, padding=snt.SAME, regularizers=regularizers)
    act = _NONLINEARITY(conv(reshape(inputs)))

    pool = tf.nn.pool(act, window_shape=(2, 2), pooling_type=_POOL,
                      padding=snt.SAME, strides=(2, 2))

    conv = snt.Conv2D(4, 5, padding=snt.SAME, regularizers=regularizers)
    act = _NONLINEARITY(conv(pool))

    pool = tf.nn.pool(act, window_shape=(2, 2), pooling_type=_POOL,
                      padding=snt.SAME, strides=(2, 2))

    flatten = snt.BatchFlatten()(pool)

    linear = snt.Linear(32, regularizers=regularizers)(flatten)

    return snt.Linear(10, regularizers=regularizers)(linear) 

# 需要导入模块: import sonnet [as 别名]
# 或者: from sonnet import SAME [as 别名]
def __init__(self, init_with_true_state=False, model='2lstm', **unused_kwargs):

        self.placeholders = {'o': tf.placeholder('float32', [None, None, 24, 24, 3], 'observations'),
                     'a': tf.placeholder('float32', [None, None, 3], 'actions'),
                     's': tf.placeholder('float32', [None, None, 3], 'states'),
                     'keep_prob': tf.placeholder('float32')}
        self.pred_states = None
        self.init_with_true_state = init_with_true_state
        self.model = model

        # build models
        # <-- observation
        self.encoder = snt.Sequential([
            snt.nets.ConvNet2D([16, 32, 64], [[3, 3]], [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,
        ])

        # <-- action
        if self.model == '2lstm':
            self.rnn1 = snt.LSTM(512)
            self.rnn2 = snt.LSTM(512)
        if self.model == '2gru':
            self.rnn1 = snt.GRU(512)
            self.rnn2 = snt.GRU(512)
        elif self.model == 'ff':
            self.ff_lstm_replacement = snt.Sequential([
                snt.Linear(512),
                tf.nn.relu,
                snt.Linear(512),
                tf.nn.relu])

        self.belief_decoder = snt.Sequential([
            snt.Linear(256),
            tf.nn.relu,
            snt.Linear(256),
            tf.nn.relu,
            snt.Linear(3)
        ]) 

# 需要导入模块: import sonnet [as 别名]
# 或者: from sonnet import SAME [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')))) 

# 需要导入模块: import sonnet [as 别名]
# 或者: from sonnet import SAME [as 别名]
def build_modules(self, min_obs_likelihood, proposer_keep_ratio):
        """
        :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, 32, 64], [[3, 3]], [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
        self.mo_noise_generator = snt.nets.MLP([32, 32, self.state_dim], 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,
            ])