Machine learning caffe中卷积核的可视化

我一直在遵循Caffe示例，从我的ConvNet中绘制卷积核。我在下面附上了一张我的内核的图片，但是它看起来与示例中的内核完全不同。我完全按照这个例子，有人知道问题是什么吗

我的网络是在一组模拟图像上训练的，有两个类，网络的性能非常好，大约80%的测试准确率

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嗯，在调用imshow时，可能需要将插值参数设置为“none”。这就是您所指的吗？

要获得更平滑的过滤器，您可以尝试向conv1层添加少量L2权重衰减

另见

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你用的是什么重量？这个网络是根据自然图像训练的吗？这张网的性能如何？你需要提供更多的细节。我已经用更多的信息更新了这个问题，包括网络本身。在绘制过滤器之前，你加载了什么caffemodel文件？你能展示一些你的培训示例吗？@AnoopK.Prabhu谢谢你的夸奖

layer {
  name: "input"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mean_file: "/tmp/stage5/mean/mean.binaryproto"
  }
  data_param {
    source: "/tmp/stage5/train/train-lmdb"
    batch_size: 100
    backend: LMDB
  }
}
layer {
  name: "input"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    mean_file: "/tmp/stage5/mean/mean.binaryproto"
  }
  data_param {
    source: "/tmp/stage5/validation/validation-lmdb"
    batch_size: 10
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  convolution_param {
    num_output: 40
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool1"
  top: "ip1"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  inner_product_param {
    num_output: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip2"
  bottom: "label"
  top: "loss"
}

layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  # learning rate and decay multipliers for the filters
  param { lr_mult: 1 decay_mult: 1 }
  # learning rate and decay multipliers for the biases
  param { lr_mult: 2 decay_mult: 0 }
  convolution_param {
    num_output: 96     # learn 96 filters
    kernel_size: 11    # each filter is 11x11
    stride: 4          # step 4 pixels between each filter application
    weight_filler {
      type: "gaussian" # initialize the filters from a Gaussian
      std: 0.01        # distribution with stdev 0.01 (default mean: 0)
    }
    bias_filler {
      type: "constant" # initialize the biases to zero (0)
      value: 0
    }
  }
}