Created
September 30, 2015 13:00
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Cannot copy param 1 weights from layer 'conv1'; shape mismatch. Source param shape is 1 1 1 96 (96); target param shape is 96 (96).
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| import numpy as np | |
| import caffe | |
| MODEL_FILE = '../val.prototxt' | |
| PRETRAINED = '../food_alexnet_train_iter_25000.caffemodel' | |
| IMAGE_MEAN = '../imagenet_mean.binaryproto' | |
| INPUT_IMAGE = '~/code/fundus/data/train/cent_crop_227/1000016.png' | |
| net = caffe.Classifier(MODEL_FILE, PRETRAINED, image_dims=(256,256)) | |
| # net = caffe.Classifier(MODEL_FILE, PRETRAINED, image_dims=(227,227)) | |
| # loading the mean image and passing it in (with mean=) leads to the same error |
Hi check0104, I am facing the same difficulty. Did you resolved it? need your insight.
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I have stumbled upon the same problem: I trained a network using digits and tried to deploy it using happynear on windows. For some reason the initialization for the BatchNorm layer fails with the exact same error (only difference: 64 parameters instead of 96). Did you per chance happen to resolve this issue?