MaxPool1D
Description
Setup and add the max pooling 1D layer into the model during the definition graph step. Type : polymorphic.
Input parameters
Graph in : model architecture.
parameters : layer parameters.
size : integer, size of the average pooling windows.
Default value “2”. stride : integer, specifies how much the pooling window moves for each pooling step.
Default value “0”.
padding : boolean, “valid“or “same“. “valid” means no padding. “same” results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input.
Default value “False”.
data_format : enum, one of channels_last or channels_first (default) . The ordering of the dimensions in the inputs. channel_last corresponds to inputs with shape (batch, steps, features) while channels_first corresponds to inputs with shape (batch, features, steps).
Default value “channels_first”. training? : boolean, whether the layer is in training mode (can store data for backward).
Default value “True”.
lda_coeff : 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 “1”.
in/out param :
input_shape : integer array, shape (not including the batch axis). NB : To be used only if it is the first layer of the model. output_behavior : enum, setup if the layer is an output layer.
Default “Not Output”.
name (optional) : string, name of the layer.
Output parameters
Graph out : model architecture.
Dimension
Input shape
3D tensor with shape
- If data_format = ‘channels_last’ : (batch_size, steps, features).
- If data_format = ‘channels_first’ : (batch_size, features, steps).
Output shape
3D tensor with shape
- If data_format = ‘channels_last’ : 3D tensor with shape (batch_size, downsampled_steps, features).
- If data_format = ‘channels_first’ : 3D tensor with shape (batch_size, features, downsampled_steps).
Example
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).
MaxPool1D layer with explicit input layer
1 – Generate a set of data
We generate an array of data of type single and shape [batch size = 10, features = 7, steps = 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, steps = 5].
Then we add to the graph the MaxPool1D layer.
3 – Run graph
We call the forward method and retrieve the result with the “Prediction 3D” 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, features, downsampled_steps].
MaxPool1D layer with implicit input layer
1 – Generate a set of data
We generate an array of data of type single and shape [batch size = 10, features = 7, steps = 5].
2 – Define graph
First, we define the MaxPool1D layer as the input layer of the graph (implicit input layer method). To do this, we send in the “input_shape” variable of the “in/out param” cluster an array of shape [features = 7, steps = 5].
An input layer will be implicitly created and the name of this input layer will be the same name as its parent prefixed with “input_”.
Then we add to the graph the MaxPool1D layer.
3 – Run graph
We call the forward method and retrieve the result with the “Prediction 3D” 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, features, downsampled_steps].
MaxPool1D layer, batch and dimension
1 – Generate a set of data
We generate an array of data of type single and shape [number of batch = 9, batch size = 10, features = 7, steps = 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, steps = 5].
Then we add to the graph the MaxPool1D layer.
3 – Run graph
We call the forward method and retrieve the result with the “Prediction 3D” 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, features, downsampled_steps].