LayerNorm¶
Apply layer normalization to the given data.
This z-scores data across the spatial and typically also the feature dimensions, but separately per instance (train or test example). Layer normalization can be the best choice when dealing with data that has a small batch size, or when used inside an RNN (both cases rendering the batch norm potentially inapplicable) and/or when no additional large spatial axes are present (rendering the instance norm inapplicable). Also consider the group norm for a variant that can partition the norm across feature groups and the RMS norm for a variant that does not affect the mean of the data and is in that sense less computationally costly. Like most normalizations, layer normalization typically includes a learned scale and bias parameter, whose shape (and thus dimensionality) can be configured, and which in case of the layer norm varies between different NN suites. 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.
scale_init¶
Initializer for the trainable scale.
bias_init¶
Initializer for the trainable bias.
scale_prior¶
Optional prior distribution for the scale.
bias_prior¶
Optional prior distribution for the bias.
axes¶
"Optional comma-separated list of axis names or indices over which to accumulate the normalization statistics. If unspecified, the statistics will be accumulated over all except the instance axes. This parameter is not limited to the predefined choices.
param_axes¶
List of axis names/indices across which to learn separate per-element scale and bias parameters. Since the layer norm as originally introduced does not discuss additional spatial (non-feature) axes, different NN libraries use different conventions for this parameter. Haiku and Sonnet use the feature axis (or last axis) by default, meaning that each feature is post-scaled independently as in the batch norm, but some other ML set this to the same as the axes parameter, which causes a separate scale/bias to be learned also across all entries of the spatial axes. Like axis, this parameter is not limited to the predefined choices.
epsilon¶
Small value to add to the variance to avoid division by zero.
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.
learnable_bias¶
Whether to learn a trainable bias parameter. See the learnable scale for more details.
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.
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.
fast_variance¶
If True, use a faster but less accurate variance calculation.
layername¶
Name of the layer. Used for naming of the trainable parameters.
set_breakpoint¶
Set a breakpoint on this node. If this is enabled, your debugger (if one is attached) will trigger a breakpoint.
metadata¶
User-definable meta-data associated with the node. Usually reserved for technical purposes.