tf.contrib.layers.batch_norm(*args, **kwargs)

tf.contrib.layers.batch_norm(*args, **kwargs)

See the guide: Layers (contrib) > Higher level ops for building neural network layers

Adds a Batch Normalization layer from http://arxiv.org/abs/1502.03167.

"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift"

Sergey Ioffe, Christian Szegedy

Can be used as a normalizer function for conv2d and fully_connected.

update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) if update_ops: updates = tf.group(*update_ops) total_loss = control_flow_ops.with_dependencies([updates], total_loss)

One can set updates_collections=None to force the updates in place, but that can have speed penalty, specially in distributed settings.

Args:

  • inputs: a tensor with 2 or more dimensions, where the first dimension has batch_size. The normalization is over all but the last dimension if data_format is NHWC and the second dimension if data_format is NCHW.
  • decay: decay for the moving average. Reasonable values for decay are close to 1.0, typically in the multiple-nines range: 0.999, 0.99, 0.9, etc. Lower decay value (recommend trying decay=0.9) if model experiences reasonably good training performance but poor validation and/or test performance. Try zero_debias_moving_mean=True for improved stability.
  • center: If True, add offset of beta to normalized tensor. If False, beta is ignored.
  • scale: If True, multiply by gamma. If False, gamma is not used. When the next layer is linear (also e.g. nn.relu), this can be disabled since the scaling can be done by the next layer.
  • epsilon: small float added to variance to avoid dividing by zero.
  • activation_fn: activation function, default set to None to skip it and maintain a linear activation.
  • param_initializers: optional initializers for beta, gamma, moving mean and moving variance.
  • updates_collections: collections to collect the update ops for computation. The updates_ops need to be executed with the train_op. If None, a control dependency would be added to make sure the updates are computed in place.
  • is_training: whether or not the layer is in training mode. In training mode it would accumulate the statistics of the moments into moving_mean and moving_variance using an exponential moving average with the given decay. When it is not in training mode then it would use the values of the moving_mean and the moving_variance.
  • reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
  • variables_collections: optional collections for the variables.
  • outputs_collections: collections to add the outputs.
  • trainable: If True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
  • batch_weights: An optional tensor of shape [batch_size], containing a frequency weight for each batch item. If present, then the batch normalization uses weighted mean and variance. (This can be used to correct for bias in training example selection.)
  • fused: Use nn.fused_batch_norm if True, nn.batch_normalization otherwise.
  • data_format: A string. NHWC (default) and NCHW are supported.
  • zero_debias_moving_mean: Use zero_debias for moving_mean. It creates a new pair of variables 'moving_mean/biased' and 'moving_mean/local_step'.
  • scope: Optional scope for variable_scope.

Returns:

A Tensor representing the output of the operation.

Raises:

  • ValueError: if batch_weights is not None and fused is True.
  • ValueError: if data_format is neither NHWC nor NCHW.
  • ValueError: if the rank of inputs is undefined.
  • ValueError: if rank or channels dimension of inputs is undefined.

Defined in tensorflow/contrib/framework/python/ops/arg_scope.py.