das.kapre.filterbank#
- class das.kapre.filterbank.Filterbank(*args, **kwargs)[source]#
###
Filterbank
- `kapre.filterbank.Filterbank(n_fbs, trainable_fb, sr=None, init=’mel’, fmin=0., fmax=None,
bins_per_octave=12, image_data_format=’default’, **kwargs)`
- #### Notes
Input/output are 2D image format. E.g., if channel_first,
input_shape:
(None, n_ch, n_freqs, n_time)
output_shape:
(None, n_ch, n_mels, n_time)
#### Parameters * n_fbs: int
Number of filterbanks
- sr: int
sampling rate. It is used to initialize
freq_to_mel
.
- init: str
if
'mel'
, init with mel center frequencies and stds.
- fmin: float
min frequency of filterbanks.
If
init == 'log'
, fmin should be > 0. UseNone
if you got no idea.
- fmax: float
max frequency of filterbanks.
If
init == 'log'
, fmax is ignored.
- trainable_fb: bool,
Whether the filterbanks are trainable or not.
TODO: is sr necessary? is fmax necessary? init with None?
- build(input_shape)[source]#
Creates the variables of the layer (optional, for subclass implementers).
This is a method that implementers of subclasses of
Layer
orModel
can override if they need a state-creation step in-between layer instantiation and layer call. It is invoked automatically before the first execution ofcall()
.This is typically used to create the weights of
Layer
subclasses (at the discretion of the subclass implementer).- Parameters
input_shape – Instance of
TensorShape
, or list of instances ofTensorShape
if the layer expects a list of inputs (one instance per input).
- call(x)[source]#
This is where the layer’s logic lives.
The
call()
method may not create state (except in its first invocation, wrapping the creation of variables or other resources intf.init_scope()
). It is recommended to create state in__init__()
, or thebuild()
method that is called automatically beforecall()
executes the first time.- Parameters
inputs –
Input tensor, or dict/list/tuple of input tensors. The first positional
inputs
argument is subject to special rules: -inputs
must be explicitly passed. A layer cannot have zeroarguments, and
inputs
cannot be provided via the default value of a keyword argument.NumPy array or Python scalar values in
inputs
get cast as tensors.Keras mask metadata is only collected from
inputs
.Layers are built (
build(input_shape)
method) using shape info frominputs
only.input_spec
compatibility is only checked againstinputs
.Mixed precision input casting is only applied to
inputs
. If a layer has tensor arguments in*args
or**kwargs
, their casting behavior in mixed precision should be handled manually.The SavedModel input specification is generated using
inputs
only.Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for
inputs
and not for tensors in positional and keyword arguments.
*args – Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above.
**kwargs –
Additional keyword arguments. May contain tensors, although this is not recommended, for the reasons above. The following optional keyword arguments are reserved: -
training
: Boolean scalar tensor of Python boolean indicatingwhether the
call
is meant for training or inference.mask
: Boolean input mask. If the layer’scall()
method takes amask
argument, its default value will be set to the mask generated forinputs
by the previous layer (ifinput
did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).
- Returns
A tensor or list/tuple of tensors.
- compute_output_shape(input_shape)[source]#
Computes the output shape of the layer.
This method will cause the layer’s state to be built, if that has not happened before. This requires that the layer will later be used with inputs that match the input shape provided here.
- Parameters
input_shape – Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.
- Returns
An input shape tuple.
- get_config()[source]#
Returns the config of the layer.
A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.
The config of a layer does not include connectivity information, nor the layer class name. These are handled by
Network
(one layer of abstraction above).Note that
get_config()
does not guarantee to return a fresh copy of dict every time it is called. The callers should make a copy of the returned dict if they want to modify it.- Returns
Python dictionary.