CNN Pooling Operation Example
Pooling is an essential operation in Convolutional Neural Networks (CNNs) that reduces the spatial dimensions of feature maps while retaining important features. Let's break down the Max Pooling and Average Pooling operations mathematically using the given input tensor:
- Kernel size: 2×2 matrix
- Stride: 2
Max Pooling
Steps: The 2×2 kernel slides over the input tensor, computes the maximum value for each region, and forms the pooled matrix.
1. Top-left:
2. Top-right:
3. Bottom-left:
4. Bottom-right:
Average Pooling
Steps: The 2×2 kernel slides over the input tensor, computes the average value for each region, and forms the pooled matrix.
1. Top-left:
2. Top-right:
3. Bottom-left:
4. Bottom-right:
CNN Pooling Operation Example
Pooling is an essential operation in Convolutional Neural Networks (CNNs) that reduces the spatial dimensions of feature maps while retaining important features. Let's break down the Max Pooling and Average Pooling operations mathematically using the given input tensor:
- Kernel size: 2×2 matrix
- Stride: 2
Max Pooling
Steps: The 2×2 kernel slides over the input tensor, computes the maximum value for each region, and forms the pooled matrix.
1. Top-left:
2. Top-right:
3. Bottom-left:
4. Bottom-right:
Average Pooling
Steps: The 2×2 kernel slides over the input tensor, computes the average value for each region, and forms the pooled matrix.
1. Top-left:
2. Top-right:
3. Bottom-left:
4. Bottom-right:
CNN Pooling Operation Example
Pooling is an essential operation in Convolutional Neural Networks (CNNs) that reduces the spatial dimensions of feature maps while retaining important features. Let's break down the Max Pooling and Average Pooling operations mathematically using the given input tensor:
- Kernel size: 2×2 matrix
- Stride: 2
Max Pooling
Steps: The 2×2 kernel slides over the input tensor, computes the maximum value for each region, and forms the pooled matrix.
1. Top-left:
2. Top-right:
3. Bottom-left:
4. Bottom-right:
Average Pooling
Steps: The 2×2 kernel slides over the input tensor, computes the average value for each region, and forms the pooled matrix.
1. Top-left:
2. Top-right:
3. Bottom-left:
4. Bottom-right:
CNN Pooling Operation Example
Pooling is an essential operation in Convolutional Neural Networks (CNNs) that reduces the spatial dimensions of feature maps while retaining important features. Let's break down the Max Pooling and Average Pooling operations mathematically using the given input tensor:
- Kernel size: 2×2 matrix
- Stride: 2
Max Pooling
Steps: The 2×2 kernel slides over the input tensor, computes the maximum value for each region, and forms the pooled matrix.
1. Top-left:
2. Top-right:
3. Bottom-left:
4. Bottom-right:
Average Pooling
Steps: The 2×2 kernel slides over the input tensor, computes the average value for each region, and forms the pooled matrix.
1. Top-left:
2. Top-right:
3. Bottom-left:
4. Bottom-right:
