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InstanceNorm

Apply instance normalization to the given data.

This z-scores data across the spatial (non-instance/feature) dimensions of a given instance only. This is like batch norm without going over the batch dimension, and is suitable to data with large spatial dimensions but possibly small batch sizes; see also layer and group norm for variants that offer more control. Like most normalizations, instance normalization typically includes a learned scale and bias parameter per feature. These can also be optionally overridden with externally generated values. If packet data is given, this node ensures that the instance axes come first and the feature axes come last. More Info... Version 0.2.0

Ports/Properties

data

Data to process.

  • verbose name: Data
  • default value: None
  • port type: DataPort
  • value type: AnyNumeric (can be None)
  • data direction: INOUT

scale_init

Initializer for the trainable scale.

  • verbose name: Scale Init
  • default value: None
  • port type: DataPort
  • value type: BaseNode (can be None)
  • data direction: IN

bias_init

Initializer for the trainable bias.

  • verbose name: Bias Init
  • default value: None
  • port type: DataPort
  • value type: BaseNode (can be None)
  • data direction: IN

scale_prior

Optional prior distribution for the scale.

  • verbose name: Scale Prior
  • default value: None
  • port type: DataPort
  • value type: Distribution (can be None)
  • data direction: IN

bias_prior

Optional prior distribution for the bias.

  • verbose name: Bias Prior
  • default value: None
  • port type: DataPort
  • value type: Distribution (can be None)
  • data direction: IN

epsilon

Small value to add to the variance to avoid division by zero.

  • verbose name: Epsilon
  • default value: 1e-05
  • port type: FloatPort
  • value type: float (can be None)

learnable_scale

Whether to learn a trainable scale parameter. Normalizations typically include such a parameter in order to drive the subsequent activation function in a regime that is desirable for downstream computations (e.g., saturating or linear). Note the shape (and thus dimensionality) of the learned parameter is governed by the param_axes (learn scale/bias across axes) parameter.

  • verbose name: Learnable Scale
  • default value: True
  • port type: BoolPort
  • value type: bool (can be None)

learnable_bias

Whether to learn a trainable bias parameter. See the learnable scale for more details.

  • verbose name: Learnable Bias
  • default value: True
  • port type: BoolPort
  • value type: bool (can be None)

data_format

Format of the input data. This is only respected when working with plain arrays and is ignored for packet data, which always normalizes the data to 'channels_last' layout. If 'channels_last', the data is assumed to be in the format ({batch}, ..., channels). If 'channels_first', the data is assumed to be in the format ({batch}, channels, ...).

  • verbose name: Array Data Format
  • default value: auto
  • port type: EnumPort
  • value type: str (can be None)

scale_initializer

Choice of scale initializer. This can either be one of the provided initializers, or the value "custom", in which case one of the Initializer nodes must be wired into the respective input port. For beginners it is recommended to stick to the defaults, since initialization of deep net layers is nuanced and can be tricky, otherwise be prepared to experiment with different choices. In general, the variance-scaling (lecun, glorot/xavier, he/kaiming) initializers are recommended, except for very simple/small layers where you may have a good default assumption as to the distribution of the weights (e.g., truncated_normal or uniform). Bias layers are typically zero-initialized. For initializers that take arguments, you can also type out the arguments positionally as in "truncated_normal(1.0,0.0)" (note reversed order of stddev, mean). The following initializers have arguments (here listed with their defaults): constant(value), those ending in normal(stddev=1, mean=0), those ending in uniform(min=0,max=1), orthogonal(scale=1,axis=-1), identity(gain=1), and variance_scaling(scale=1, "fan_in" (default)/"fan_avg"/"fan_out", "truncated_normal"(default)/"normal"/"uniform",optional-axis-indices=auto). Note that glorot and xavier are aliases for each other, and likewise he and kaiming are aliases for each other.

  • verbose name: Scale Initializer
  • default value: ones
  • port type: ComboPort
  • value type: str (can be None)

bias_initializer

Choice of bias initializer. This can either be one of the provided initializers, or the value "custom", in which case one of the Initializer nodes must be wired into the respective input port. For beginners it is recommended to stick to the defaults, since initialization of deep net layers is nuanced and can be tricky, otherwise be prepared to experiment with different choices. In general, the variance-scaling (lecun, glorot/xavier, he/kaiming) initializers are recommended, except for very simple/small layers where you may have a good default assumption as to the distribution of the weights (e.g., truncated_normal or uniform). Bias layers are typically zero-initialized. For initializers that take arguments, you can also type out the arguments positionally as in "truncated_normal(1.0,0.0)" (note reversed order of stddev, mean). The following initializers have arguments (here listed with their defaults): constant(value), those ending in normal(stddev=1, mean=0), those ending in uniform(min=0,max=1), orthogonal(scale=1,axis=-1), identity(gain=1), and variance_scaling(scale=1, "fan_in" (default)/"fan_avg"/"fan_out", "truncated_normal"(default)/"normal"/"uniform",optional-axis-indices=auto). Note that glorot and xavier are aliases for each other, and likewise he and kaiming are aliases for each other.

  • verbose name: Bias Initializer
  • default value: zeros
  • port type: ComboPort
  • value type: str (can be None)

fast_variance

If True, use a faster but less accurate variance calculation.

  • verbose name: Fast Variance
  • default value: False
  • port type: BoolPort
  • value type: bool (can be None)

layername

Name of the layer. Used for naming of the trainable parameters.

  • verbose name: Layer Name
  • default value: instancenorm
  • port type: StringPort
  • value type: str (can be None)

set_breakpoint

Set a breakpoint on this node. If this is enabled, your debugger (if one is attached) will trigger a breakpoint.

  • verbose name: Set Breakpoint (Debug Only)
  • default value: False
  • port type: BoolPort
  • value type: bool (can be None)

metadata

User-definable meta-data associated with the node. Usually reserved for technical purposes.

  • verbose name: Metadata
  • default value: {}
  • port type: DictPort
  • value type: dict (can be None)