I use features as inputs in a ConvNN, dimensions 4096x19x32. Input layer looks like
layer{
name: "data"
type: "Data"
top: "data"
#input_param { shape: {dim:20 dim: 4096 dim: 19 dim: 32 } }
data_param {
source: "train1lmdb"
batch_size: 20
backend: LMDB
}
include {
phase: TRAIN
}
}
The error I get is Check failed: datum_channels > 0 (0 vs. 0), so I guess caffe requies a Datum object; I created a blobproto object with pycaffe's array_to_blobproto().
It works if I change the layer type to "Input" and uncomment the input_param, but I know it's wrong and there's some error, as training/validation errors go to 0.
What am I doing wrong?