das.tcn.tcn_new#
- class das.tcn.tcn_new.ResidualBlock(*args, **kwargs)[source]#
Defines the residual block for the WaveNet TCN
- Parameters
x – The previous layer in the model
training – boolean indicating whether the layer should behave in training mode or in inference mode
dilation_rate – The dilation power of 2 we are using for this residual block
nb_filters – The number of convolutional filters to use in this block
kernel_size – The size of the convolutional kernel
padding – The padding used in the convolutional layers, ‘same’ or ‘causal’.
activation – The final activation used in o = Activation(x + F(x))
dropout_rate – Float between 0 and 1. Fraction of the input units to drop.
kernel_initializer – Initializer for the kernel weights matrix (Conv1D).
use_batch_norm – Whether to use batch normalization in the residual layers or not.
use_layer_norm – Whether to use layer normalization in the residual layers or not.
use_separable – Whether to use separabel conv1d.
kwargs – Any initializers for Layer class.
- 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(inputs, training=None)[source]#
- Returns: A tuple where the first element is the residual model tensor, and the second
is the skip connection tensor.
- 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.
- class das.tcn.tcn_new.TCN(*args, **kwargs)[source]#
Creates a TCN layer.
- Input shape:
A tensor of shape (batch_size, timesteps, input_dim).
- Parameters
nb_filters – The number of filters to use in the convolutional layers. Can be a list.
kernel_size – The size of the kernel to use in each convolutional layer.
dilations – The list of the dilations. Example is: [1, 2, 4, 8, 16, 32, 64].
nb_stacks – The number of stacks of residual blocks to use.
padding – The padding to use in the convolutional layers, ‘causal’ or ‘same’.
use_skip_connections – Boolean. If we want to add skip connections from input to each residual blocK.
return_sequences – Boolean. Whether to return the last output in the output sequence, or the full sequence.
activation – The activation used in the residual blocks o = Activation(x + F(x)).
dropout_rate – Float between 0 and 1. Fraction of the input units to drop.
kernel_initializer – Initializer for the kernel weights matrix (Conv1D).
use_batch_norm – Whether to use batch normalization in the residual layers or not.
use_separable – Whether to use separable convolution in the residual layers or not.
kwargs – Any other arguments for configuring parent class Layer. For example “name=str”, Name of the model. Use unique names when using multiple TCN.
- Returns
A TCN layer.
- 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(inputs, training=None)[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.
- das.tcn.tcn_new.channel_normalization(x: keras.engine.base_layer.Layer) keras.engine.base_layer.Layer [source]#
Normalize a layer to the maximum activation
This keeps a layers values between zero and one. It helps with relu’s unbounded activation
- Parameters
x – The layer to normalize
- Returns
A maximal normalized layer