composable_model.py 文件源码

def build_model(self, features, feature_columns, is_training):
    """See base class."""
    self._feature_columns = feature_columns

    input_layer_partitioner = (
        partitioned_variables.min_max_variable_partitioner(
            max_partitions=self._num_ps_replicas,
            min_slice_size=64 << 20))
    with variable_scope.variable_scope(
        self._scope + "/input_from_feature_columns",
        values=features.values(),
        partitioner=input_layer_partitioner) as scope:
      net = layers.input_from_feature_columns(
          features,
          self._get_feature_columns(),
          weight_collections=[self._scope],
          scope=scope)

    hidden_layer_partitioner = (
        partitioned_variables.min_max_variable_partitioner(
            max_partitions=self._num_ps_replicas))
    for layer_id, num_hidden_units in enumerate(self._hidden_units):
      with variable_scope.variable_scope(
          self._scope + "/hiddenlayer_%d" % layer_id,
          values=[net],
          partitioner=hidden_layer_partitioner) as scope:
        net = layers.fully_connected(
            net,
            num_hidden_units,
            activation_fn=self._activation_fn,
            variables_collections=[self._scope],
            scope=scope)
        if self._dropout is not None and is_training:
          net = layers.dropout(
              net,
              keep_prob=(1.0 - self._dropout))
      self._add_hidden_layer_summary(net, scope.name)

    with variable_scope.variable_scope(
        self._scope + "/logits",
        values=[net],
        partitioner=hidden_layer_partitioner) as scope:
      logits = layers.fully_connected(
          net,
          self._num_label_columns,
          activation_fn=None,
          variables_collections=[self._scope],
          scope=scope)
    self._add_hidden_layer_summary(logits, "logits")
    return logits