Factors Affecting Parameter Calculation
- Filter size: Larger filters have more parameters.
- Number of filters: More filters increase the parameters.
- Stride and padding: These do not affect the number of parameters but affect the output size.
- Number of layers: More layers increase the total parameters.
- Input size: Larger input sizes can lead to larger fully connected layers if the output of the final convolutional layer is large.
How to calculate the number of parameters in CNN?
Calculating the number of parameters in Convolutional Neural Networks (CNNs) is important for understanding the model complexity, computational requirements, and potential overfitting.
Parameters in CNNs are primarily the weights and biases learned during training. This article will walk you through calculating these parameters in various layers of a CNN.
Table of Content
- Steps of Calculate the number of Parameter in CNN
- 1. Convolutional Layer
- 2. Fully Connected (Dense) Layers
- 3. Batch Normalization Layers
- 4. Pooling Layers
- 5. Combining All Layers
- Example: Calculating the Number of Parameter in CNN
- Parameter Calculation for 3D Convolutions
- Factors Affecting Parameter Calculation
- Conclusion