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Define the zero padding 1D layer according to its parameters. To be used for the TimeDistributed layer. Type : polymorphic.


Input parameters


parameters : layer parameters.

padd : cluster, how many zeros to add at the beginning and the end of the padding dimension ((padd_left, padd_right)).
Default value “(1,1)”.
Β data_format :Β enum, one ofΒ channels_lastΒ orΒ channels_firstΒ (default) . The ordering of the dimensions in the inputs.Β channel_lastΒ corresponds to inputs with shapeΒ (batch, steps, features)Β whileΒ channels_firstΒ corresponds to inputs with shapeΒ (batch, features, steps).
Default value β€œchannels_first”.
Β training?Β :Β boolean, whether the layer is in training mode (can store data for backward).
Default value β€œTrue”.
Β lda_coeff :Β float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value β€œ1”.


Output parameters


ZeroPadding1D out : layer zero padding 1D architecture.


All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install HAIBAL library to run it).

ZeroPadding1D layer inside TimeDistributed layer

1 – Generate a set of data

We generate an array of data of type single and shape [batch size = 10, time = 6, features = 7, axis_to_pad = 5].

2 – Define graph

First, we define the first layer of the graph which is an Input layer (explicit input layer method). This layer is setup as an input array shaped [features = 7, axis_to_pad = 5].
Then, we add to the graph the TimeDistributed layer which we setup with a ZeroPadding1D layer using the define method.

3 – Run graph

We call the forward method and retrieve the result with the β€œPrediction 4D” method.
This method returns two variables, the first one is the layer information (cluster composed of the layer name, the graph index and the shape of the output layer) and the second one is the prediction with a shape of [batch_size, time, features, padded_axis].


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