【发布时间】:2017-07-06 12:06:36
【问题描述】:
我有一个 HDF5 类型的数据层。它包含预期的训练和测试阶段
name: "LogisticRegressionNet"
layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
hdf5_data_param {
source: "examples/hdf5_classification/data/train.txt"
batch_size: 10
}
}
layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "label"
include {
phase: TEST
}
hdf5_data_param {
source: "examples/hdf5_classification/data/test.txt"
batch_size: 10
}
}
我想用python来生成它。这是我的代码
import caffe
from caffe import layers as L # pseudo module using __getattr__ magic to generate protobuf messages
from caffe import params as P # pseudo module using __getattr__ magic to generate protobuf messages
n = caffe.NetSpec()
n.data, n.label = L.HDF5Data(batch_size=batch_size, source='examples/hdf5_classification/data/train.txt', ntop=2, include={'phase': caffe.TRAIN})
n.data, n.label = L.HDF5Data(batch_size=batch_size, source='examples/hdf5_classification/data/test.txt',ntop=2, include={'phase': caffe.TEST})
但是,我的输出只是测试阶段。我该如何解决?谢谢
layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "label"
include {
phase: TEST
}
hdf5_data_param {
source: "examples/hdf5_classification/data/test.txt"
batch_size: 2
}
}
【问题讨论】:
标签: python neural-network deep-learning caffe pycaffe