# examples/cifar10/cifar10_quick_train_test.prototxt

name: "cifar10 quick train"

data_transform {
    mean_data_file: "cifar10-data.mean"
}

network {
  name: "main" 

  sequence {
  
    item { convolutional {
      name: "conv1"
      
      param {
        out_channels: 32
        
        kernel {
            shape { x:5 ; y:5 }
            padding: PADDING_SAME
        }
        
        weights { gaussian { std: 1.e-04 } }
        biases { constant {} }
      }
    } }

    item { pooling {
      name: "pool1"
        
      param {
        type: POOL_MAX
    
        kernel {
            shape { x:3 ; y:3 }
            stride { x:2 ; y:2 }
            padding: PADDING_SAME
        }
      }
      
      activation { type: RELU }
    } }

    item { convolutional {
      name: "conv2"
      
      param {
        out_channels: 32
        
        kernel {
            shape { x:5 ; y:5 }
            padding: PADDING_SAME
        }
        
        weights { gaussian { std: 1.e-02 } }
        biases { constant {} }
      }
      
      activation { type: RELU }
    } }

    item { pooling {
      name: "pool2"
        
      param {
        type: POOL_AVE
    
        kernel {
            shape { x:3 ; y:3 }
            stride { x:2 ; y:2 }
            padding: PADDING_SAME
        }
      }
    } }
  
    item { convolutional {
      name: "conv3"
      
      param {
        out_channels: 64
        
        kernel {
            shape { x:5 ; y:5 }
            padding: PADDING_SAME
        }
        
        weights { gaussian { std: 1.e-02 } }
        biases { constant {} }
      }
      
      activation { type: RELU }
    } }

    item { pooling {
      name: "pool3"
        
      param {
        type: POOL_AVE
    
        kernel {
            shape { x:3 ; y:3 }
            stride { x:2 ; y:2 }
            padding: PADDING_SAME
        }
      }
    } }

    item { fully_connected {
      name: "ip1"
        
      param { 
        weights { gaussian { std: 0.1 } }
        biases { constant {} }
      }
      
      output: 64
    } }

    item { fully_connected {
      name: "ip2"
        
      param { 
        weights { gaussian { std: 0.1 } }
        biases { constant {} }
      }
      
      output: 10
    } }

  }
}

cost: CROSS_ENTROPY_MULTICLASS

regression: SOFTMAX

optimizer {
  sgd { momentum: 9.e-01 }
  
  learning_rate: 1.e-03
  L2_loss: 4.e-03
  
  bias_factor: 2
}