research/improve_nas/trainer/nasnet.py [141:160]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  batch_norm_params = {
      # Decay for the moving averages.
      'decay': batch_norm_decay,
      # epsilon to prevent 0s in variance.
      'epsilon': batch_norm_epsilon,
      'scale': True,
      'fused': True,
  }
  weights_regularizer = tf.contrib.layers.l2_regularizer(weight_decay)
  weights_initializer = tf.contrib.layers.variance_scaling_initializer(
      mode='FAN_OUT')
  with arg_scope([slim.fully_connected, slim.conv2d, slim.separable_conv2d],
                 weights_regularizer=weights_regularizer,
                 weights_initializer=weights_initializer):
    with arg_scope([slim.fully_connected],
                   activation_fn=None, scope='FC'):
      with arg_scope([slim.conv2d, slim.separable_conv2d],
                     activation_fn=None, biases_initializer=None):
        with arg_scope([slim.batch_norm], **batch_norm_params) as sc:
          return sc
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



research/improve_nas/trainer/nasnet.py [213:232]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  batch_norm_params = {
      # Decay for the moving averages.
      'decay': batch_norm_decay,
      # epsilon to prevent 0s in variance.
      'epsilon': batch_norm_epsilon,
      'scale': True,
      'fused': True,
  }
  weights_regularizer = tf.contrib.layers.l2_regularizer(weight_decay)
  weights_initializer = tf.contrib.layers.variance_scaling_initializer(
      mode='FAN_OUT')
  with arg_scope([slim.fully_connected, slim.conv2d, slim.separable_conv2d],
                 weights_regularizer=weights_regularizer,
                 weights_initializer=weights_initializer):
    with arg_scope([slim.fully_connected],
                   activation_fn=None, scope='FC'):
      with arg_scope([slim.conv2d, slim.separable_conv2d],
                     activation_fn=None, biases_initializer=None):
        with arg_scope([slim.batch_norm], **batch_norm_params) as sc:
          return sc
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -