parameters: (self, features, feature_columns, is_training)
return_statement: return logits
docstring: See base class.
docstring_summary: See base class.
docstring_tokens: See base class .
function: 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_op_scope(
          features.values(),
          self._scope + "/input_from_feature_columns",
          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_op_scope(
            [net], self._scope + "/hiddenlayer_%d" % layer_id,
            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_op_scope(
          [net], self._scope + "/logits",
          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
function_tokens: def build_model ( self , features , feature_columns , is_training ) : 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_op_scope ( features . values ( ) , self . _scope + "/input_from_feature_columns" , 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_op_scope ( [ net ] , self . _scope + "/hiddenlayer_%d" % layer_id , 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_op_scope ( [ net ] , self . _scope + "/logits" , 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