machine learning - Convolution issue in Caffe -


I have 96x96 pixel images in grayscale format stored in HDF 5 files. I use multi-output regression using caf I am trying, though the configuration is not working. What exactly is the problem here? Why are not you doing Campology? I0122 17: 18: 39.474860 5074 NetQuality: 67] Creating Layer FCP I0122 17: 18: 39.47488 9 5074 NETQP: 356] FKP - & gt; Data I0122 17: 18: 39.474 9 30 5074 NetQuality: 356] FKP - & gt; Labels I0l22 17: 18 3 9 K474 9 67 5074 Netkcpp: 96] established FKP I0l22 17: 18 3 9 K474 9 87 5074 Hdf5_data_layerkcpp: 57] is loading train.txt I0122 file name 17: 18: 3 9 .475103 5074 hdf5_data_layer.cpp: 69] number 1 I0122 17 :: 18: 3 9 .475131 5074 hdf5_data_layer.cpp: 29] HDF5 Loading filefacialkp-train.hd5 I0122 17: 18: 40.337786 5074 hdf5_data_layer.cpp: 49] 476 rows of successful files loaded I0122 17: 18: 40.337862 5074 hdf5_data_layer. CPP: 81] Output data size: 100,9216,1,1 I0122 17: 18: 40.337 9 6 5074 Netcup: 103] Top Size: 100 9216 1 1 (921600) I0122 17: 18: 40.337 929 5074 Netcup: 103] Top Size: 100 30 1 1 (3000) I0122 17: 18: 40.337 971 5074 NetQuality: 67] Layer con 1 I0122 17: 18: 40.338001 5074 NetQuality: 394] Conf 1 & lt; Data I0122 17:18: 40.338069 5074 .Net. Cpp: 356] conv1 - & gt; Conv1 I0122 17: 18: 40.338109 5074 Netkyupi: 96] Confiviti set up F0122 17: 18: 40.599761 5074 BLOB CPP: 13] fail to examine the height> gt = 0 (-3 vs. 0) < P> My prototype layer file is like this 

  name: "logreg" layers {top: "data" heading: "label" name: "façapee" type: hdf5data hdf5_data_arm { Source: "train.txt" batch_size: train}} {layers bottom: "data" heading: "conv1" name: "conv1" type: blobs_lr of rotation: 1 blobs_lr: 100} {include phase 2 convolution_param {num_output: 64 kernel_size: 5 steps: 1 weight_fi Type: "xavier"} bias_filler {type: "static"}}} {bottom: "conv1" head: "pool1" name: "pool 1" type: pulling pooling_param {pool: Max Colonel_size: 2 steps: 2}} layers {bottom: "pool 1" top: "Conf 2" name: "Conf 2" type: CONVOLUTION blobs_lr: 1 blobs_lr: 2 Knfolusn_prm {num_output: 256 Krnl_akar: 5 cents ride: 1 Weight_filler {type: "Xavier "Pool2" name: "pool2" type: Pooling pooling_param {pool: Max kernel_size: 2 steps: 2}} Layers {bottom: "pool2"} type: "continuous"}} layers {type: "continuous"}}} layers { "Pool2" heading: "IP1" Name: "IP1" type: INNER_PRODUCT blobs_lr: 1 blobs_lr: 2 inner_product_param {num_output: 500 weight_filler {type: "Javier"} Bias_filler {type: "constant"}}} Layers {bottom: "Ipl" Name: "Relul" type: RELU} layers {bottom: "Ipl" top "Ip2" name: "Ip2" type: INNER_PRODUCT blobs_lr: 1 blobs_lr: 2 inner_product_param {num_output: 30 weight_filler {type: "Javier"} bias_filler {type: "continuous" }}} Layers {below: "IP2" below: "label" head: "loss" name: "loss" type: EUCLIDEAN_LOSS}

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Top sizes: 100 30 1 1 (3000) Top size: 100 9216 1 1 (921600) I0122 17: 1 8: 40.337929 5074 NetQupi: 103] Top Size: 100 30 1 1 (3000) << Ex> I0122 17:18 18:40 403737 65074 Net. / Pre>

Suggest that your input data is not in the correct size 96x96 Gray-scale image should have a size of 100 batch input: 100 1 96 96. Try changing it (My guess is that For size: NCHW, where N numbers batches, C channels, H height, weight)


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