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def convert_to_caffe(self, name): caffe_net = caffe.NetSpec() layer = L.Input(shape=dict(dim=[1, 3, 224, 224])) caffe_net.tops['data'] = layer generate_caffe_prototxt(self, caffe_net, layer) …
I assume you mean how to define phase when writing a prototxt using caffe.NetSpec? from caffe import layers as L, params as P, to_proto import caffe ns = …
layer. top. append (self. _get_top_name (top, names, autonames)) layer. name = self. _get_name (names, autonames) for k, v in six. iteritems (self. params): # special case to handle generic …
Then locate the file .prototxt of you model path_model. Finally replace in the following code: path_caffe = "path/to/caffe/python" import sys sys.path.append (path_caffe) …
To explicitly specify blob names, use the NetSpec. class -- assign to its attributes directly to name layers, and call. NetSpec.to_proto to serialize all assigned layers. This interface is expected to …
def convert_to_caffe(self, name): caffe_net = caffe.NetSpec() layer = L.Input(shape=dict(dim=[1, 3, args.image_hw, args.image_hw])) caffe_net.tops['data'] = layer …
Python NetSpec.loss - 6 examples found.These are the top rated real world Python examples of caffe.NetSpec.loss extracted from open source projects. You can rate examples to help us …
These are the top rated real world Python examples of caffe.NetSpec.accuracy_top5 extracted from open source projects. You can rate examples to help us improve the quality of examples. …
def test_relu(): from lego.base import BaseLegoFunction n = caffe.NetSpec() n.data, n.label = L.ImageData(image_data_param=dict(source='tmp' , batch_size=100), ntop=2, …
from caffe import layers as L from caffe import params as P def lenet (lmdb, batch_size): # our version of LeNet: a series of linear and simple nonlinear transformations n = …
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