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Convolution 2D

Description

Setup and add the convolution 2D layer into the model during the definition graph step. Type : polymorphic.

 

Input parameters

 

Graph in : model architecture.

parameters : layer parameters.

n_filters : integer, the dimensionality of the output space.
Default value “3”.
sizeΒ : array integer, specify the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
Default value “[3,3]”. Never more 2 values
strideΒ : array integer, specify the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions.
Default value “[1,1]”. Never more 2 values
dilation_rate : integer, specifying the dilation rate to use for dilated convolution.
Default value “[1,1]”. Never more 2 values
activation : enum, activation function to use.
Default value “relu”.
use_bias? : boolean, whether the layer uses a bias vector.
Default value “True”.
paddingΒ : boolean, False = “valid” means no padding. True = “same” results in padding with zeros 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”.
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”.
optimizer :

Β algorithm : enum, (name of optimizer) for optimizer instance.
Default value “adam”.
learning_rate : float, define the learning rate to use.
Default value “0.001”.
beta_1 : float, define the exponential decay rate for the 1st moment estimates.
Default value “0.9”.
beta_2 : float, define the exponential decay rate for the 2nd moment estimates.
Default value “0.999”.

filter_initializerΒ : enum, initializer for the kernel weights matrix.
Default value “glorot_uniform”.
bias_initializerΒ : enum, initializer for the bias vector.
Default value “zero”.
training?Β : boolean, whether the layer is in training mode (can store data for backward).
Default value “True”.
store?Β : boolean, whether the layer stores the last iteration gradient (accessible via the “get_gradients” function).
Default value “False”.
update?Β : boolean, whether the layer’s variables should be updated during backward. Equivalent to freeze the layer.
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.
Β output_behaviorΒ :Β enum, setup if the layer is an output layer.
Default β€œNot Output”​​​.

name (optional) : string, name of the layer.

 

Output parameters

 

Graph out : model architecture.

Dimension

Input shape

4-Dimension tensor with shape : [batch_size, channel, row, column] (default “channel_first” parameters).
In case of “channel_last” setup, forward function will input shape [batch_size, row, column, channel].

 

Output shape

Same shape as input 4-Dimension tensor with shape : [batch_size, channel, row, column] (default “channel_first” parameters).
In case of “channel_last” setup, forward function will input shape [batch_size, row, column, channel].

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).

Convolution 2D layer with explicit input layer

1 – Generate a set of data

We generate an array of data of type single and shape [batch_size, channel, row, column] (channel first is default layer configuration).
In case of channel last layer configuration, shape is [batch_size, row, column, channel].

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 [channel = 5, row = 128, column = 128].
Then we add to the graph the Conv2D layer.

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, filter, new_row, new_column].

 

Convolution 2D layer with implicit input layer

1 – Generate a set of data

We generate an array of data of type single and shape [batch_size, channel, row, column] (channel first is default layer configuration).
In case of channel last layer configuration, shape is [batch_size, row, column, channel].

2 – Define graph

First, we define the Conv2D layer as the input layer of the graph (implicit input layer method). To do this, we send in the β€œinput_shape” variable of the β€œin/out param” cluster an array of shape [channel = 5, row = 128, column = 128].
An input layer will be implicitly created and the name of this input layer will be the same name as its parent prefixed with β€œinput_”.
Then we add to the graph the Conv2D layer.

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, filter, new_row, new_column].

 

Convolution 2D 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, channel = 5, row = 128, column = 128] (channel first default layer configuration).
In case of channel last layer configuration, shape is [batch_size, row, column, channel].

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 [channel = 5, row = 128, column = 128].
Then we add to the graph the Conv2D layer.

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, filter, new_row, new_column].

 

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