Applying CNN to time series data

[HARSHIT KUMAR GUPTA]

This is my caffee model for time series data

name: "AusticNet"
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  data_param {
    source: "austiclmdb"
    backend: LMDB
    batch_size: 200
   
  }
}

layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 11
    stride: 4
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "norm1"
  top: "conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "norm2"
  top: "conv3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "pool5"
  top: "fc6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "fc6"
  top: "fc6"
}
layer {
  name: "drop6"
  type: "Dropout"
  bottom: "fc6"
  top: "fc6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "fc7"
  top: "fc7"
}
layer {
  name: "drop7"
  type: "Dropout"
  bottom: "fc7"
  top: "fc7"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc8"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc8"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "fc8"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "fc8"
  bottom: "label"
  top: "loss"
}

Following error log  I got upon training

I0609 11:10:27.874151 23576 layer_factory.hpp:74] Creating layer data
I0609 11:10:27.874186 23576 net.cpp:84] Creating Layer data
I0609 11:10:27.874202 23576 net.cpp:338] data -> data
I0609 11:10:27.874233 23576 net.cpp:338] data -> label
I0609 11:10:27.874251 23576 net.cpp:113] Setting up data
I0609 11:10:27.874326 23576 db.cpp:34] Opened lmdb austiclmdb
I0609 11:10:27.874443 23576 data_layer.cpp:67] output data size: 200,1,1,65000
I0609 11:10:27.874670 23576 net.cpp:120] Top shape: 200 1 1 65000 (130000)
I0609 11:10:27.874689 23576 net.cpp:120] Top shape: 200
I0609 11:10:27.874707 23576 layer_factory.hpp:74] Creating layer conv1
I0609 11:10:27.874727 23576 net.cpp:84] Creating Layer conv1
I0609 11:10:27.874742 23576 net.cpp:380] conv1 <- data
I0609 11:10:27.874761 23576 net.cpp:338] conv1 -> conv1
I0609 11:10:27.874781 23576 net.cpp:113] Setting up conv1
F0609 11:10:27.875177 23576 blob.cpp:28] Check failed: shape[i] >= 0 (-1 vs. 0)
*** Check failure stack trace: ***
    @     0x7feb1d8b4f7d  google::LogMessage::Fail()
    @     0x7feb1d8b708f  google::LogMessage::SendToLog()
    @     0x7feb1d8b4b6c  google::LogMessage::Flush()
    @     0x7feb1d8b792d  google::LogMessageFatal::~LogMessageFatal()
    @     0x7feb1dbfd5ed  caffe::Blob<>::Reshape()
    @     0x7feb1dbfda8a  caffe::Blob<>::Reshape()
    @     0x7feb1dca7b88  caffe::BaseConvolutionLayer<>::Reshape()
    @     0x7feb1dceae59  caffe::Net<>::Init()
    @     0x7feb1dced5f1  caffe::Net<>::Net()
    @     0x7feb1dd05ccf  caffe::Solver<>::InitTrainNet()
    @     0x7feb1dd06322  caffe::Solver<>::Init()
    @     0x7feb1dd06945  caffe::Solver<>::Solver()
    @           0x40d788  caffe::GetSolver<>()
    @           0x406ca6  train()
    @           0x404d2b  main
    @     0x7feb1cfebec5  (unknown)
    @           0x4051af  (unknown)

[Abhishek Sharma]

How did you convert your time series data into lmdb??

...

[Abhishek Sharma]

Hey, I am also struck with the same problem. Did you figure this out?

On Tuesday, June 9, 2015 at 11:20:50 AM UTC+5:30, HARSHIT KUMAR GUPTA wrote:

[ee15res...@iith.ac.in]

Hey Harshit Can I know how you have converted time series data into images?
Thanks

On Tuesday, June 9, 2015 at 11:20:50 AM UTC+5:30, HARSHIT KUMAR GUPTA wrote: