- `n * c_o * 1 * 1`
* Sample
- layers {
- name: "fc8"
- type: INNER_PRODUCT
- blobs_lr: 1 # learning rate multiplier for the filters
- blobs_lr: 2 # learning rate multiplier for the biases
- weight_decay: 1 # weight decay multiplier for the filters
- weight_decay: 0 # weight decay multiplier for the biases
- inner_product_param {
- num_output: 1000
- weight_filler {
- type: "gaussian"
- std: 0.01
- }
- bias_filler {
- type: "constant"
- value: 0
+ layers {
+ name: "fc8"
+ type: INNER_PRODUCT
+ blobs_lr: 1 # learning rate multiplier for the filters
+ blobs_lr: 2 # learning rate multiplier for the biases
+ weight_decay: 1 # weight decay multiplier for the filters
+ weight_decay: 0 # weight decay multiplier for the biases
+ inner_product_param {
+ num_output: 1000
+ weight_filler {
+ type: "gaussian"
+ std: 0.01
+ }
+ bias_filler {
+ type: "constant"
+ value: 0
+ }
}
+ bottom: "fc7"
+ top: "fc8"
}
- bottom: "fc7"
- top: "fc8"
- }
The `INNER_PRODUCT` layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob's height and width set to 1).