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# Average Pooling 2D

The

`Average Pooling 2D`

node in AugeLab Studio performs average pooling on a 2D input tensor. It reduces the spatial dimensions of the input by taking the average value within each pooling window.The

`Average Pooling 2D`

node takes a 2D input tensor and applies average pooling to it. It provides the following outputs:- Output Tensor: The result of applying average pooling to the input tensor.

The node allows you to configure the pooling size and stride, which determine the size of the pooling window and the stride between consecutive pooling windows.

- Node Title: Average Pooling 2D
- Node ID: OP_NODE_AI_2D_AVG_POOL

The

`Average Pooling 2D`

node has one input socket:- 1.Input Tensor: The 2D input tensor to be subjected to average pooling.

The

`Average Pooling 2D`

node has one output socket:- 1.Output Tensor: The result of applying average pooling to the input tensor.

- 1.Drag and drop the
`Average Pooling 2D`

node from the node library onto the canvas in AugeLab Studio. - 2.Connect the input tensor to the Input Tensor input socket of the
`Average Pooling 2D`

node. - 3.Configure the pooling size and stride using the Pooling Size and Stride dropdown menus in the node properties panel.
- 4.Run the pipeline.
- 5.The output socket of the
`Average Pooling 2D`

node will provide the output tensor after applying average pooling.

The

`Average Pooling 2D`

node uses the `AveragePooling2D`

layer from the Keras API to perform average pooling. The node creates an instance of the `AveragePooling2D`

layer with the specified pooling size and stride values. The layer is then applied to the input tensor to produce the output tensor.The pooling size determines the size of the pooling window, which determines the spatial extent over which the average pooling is applied. The stride determines the stride length, which determines the spacing between consecutive pooling windows. The pooling size and stride can be configured through the node properties.

- 1.Drag and drop the
`Average Pooling 2D`

node from the node library onto the canvas in AugeLab Studio. - 2.Connect the input tensor to the Input Tensor input socket of the
`Average Pooling 2D`

node. - 3.Configure the pooling size and stride using the Pooling Size and Stride dropdown menus in the node properties panel.
- 4.Run the pipeline.
- 5.The output socket of the
`Average Pooling 2D`

node will provide the output tensor after applying average pooling.

- The
`Average Pooling 2D`

node is commonly used in convolutional neural networks (CNNs) to reduce the spatial dimensions of feature maps. - Average pooling helps in reducing the spatial dimensions while retaining important features by taking the average value within each pooling window.
- Ensure that the input tensor is correctly connected to the Input Tensor input socket of the
`Average Pooling 2D`

node. - Adjust the pooling size and stride values according to the requirements of your model and the desired level of pooling.
- The output tensor can be further processed or used as input to other nodes in the pipeline.

The

`Max Pooling 2D`

node in AugeLab Studio performs max pooling on a 2D input tensor. It reduces the spatial dimensions of the input by taking the maximum value within each pooling window.The

`Max Pooling 2D`

node takes a 2D input tensor and applies max pooling to it. It provides the following outputs:- Output Tensor: The result of applying max pooling to the input tensor.

The node allows you to configure the pooling size and stride, which determine the size of the pooling window and the stride between consecutive pooling windows.

- Node Title: Max Pooling 2D
- Node ID: OP_NODE_AI_2D_MAX_POOL

The

`Max Pooling 2D`

node has one input socket:- 1.Input Tensor: The 2D input tensor to be subjected to max pooling.

The

`Max Pooling 2D`

node has one output socket:- 1.Output Tensor: The result of applying max pooling to the input tensor.

- 1.Drag and drop the
`Max Pooling 2D`

node from the node library onto the canvas in AugeLab Studio. - 2.Connect the input tensor to the Input Tensor input socket of the
`Max Pooling 2D`

node. - 3.Configure the pooling size and stride using the Pooling Size and Stride dropdown menus in the node properties panel.
- 4.Run the pipeline.
- 5.The output socket of the
`Max Pooling 2D`

node will provide the output tensor after applying max pooling.

The

`Max Pooling 2D`

node uses the `MaxPooling2D`

layer from the Keras API to perform max pooling. The node creates an instance of the `MaxPooling2D`

layer with the specified pooling size and stride values. The layer is then applied to the input tensor to produce the output tensor.The pooling size determines the size of the pooling window, which determines the spatial extent over which the max pooling is applied. The stride determines the stride length, which determines the spacing between consecutive pooling windows. The pooling size and stride can be configured through the node properties.

- 1.Drag and drop the
`Max Pooling 2D`

node from the node library onto the canvas in AugeLab Studio. - 2.Connect the input tensor to the Input Tensor input socket of the
`Max Pooling 2D`

node. - 4.Run the pipeline.
- 5.The output socket of the
`Max Pooling 2D`

node will provide the output tensor after applying max pooling.

- The
`Max Pooling 2D`

node is commonly used in convolutional neural networks (CNNs) to reduce the spatial dimensions of feature maps. - Max pooling helps in reducing the spatial dimensions while retaining important features by taking the maximum value within each pooling window.
- Ensure that the input tensor is correctly connected to the Input Tensor input socket of the
`Max Pooling 2D`

node. - Adjust the pooling size and stride values according to the requirements of your model and the desired level of pooling.
- The output tensor can be further processed or used as input to other nodes in the pipeline.