I'm training caffenet with multilabel data. However the lost is not decreasing during training phase. I'm now trying to check if the backward() is not working properly. I have this code to check if there is a gradient.
import numpy as np
import os.path as osp
import matplotlib.pyplot as plt
from pprint import pprint
from copy import copy
% matplotlib inline
plt.rcParams['figure.figsize'] = (6, 6)
caffe_root = '../' # this file is expected to be in {caffe_root}/examples
sys.path.append(caffe_root + 'python')
import caffe # If you get "No module named _caffe", either you have not built pycaffe or you have the wrong path.
from caffe import layers as L, params as P # Shortcuts to define the net prototxt.
sys.path.append("pycaffe/layers") # the datalayers we will use are in this directory.
sys.path.append("pycaffe") # the tools file is in this folder
import tools #this contains some tools that we need
# set data root directory, e.g:
peta_root = osp.join('/root/data/PETA/')
# these are the PASCAL classes, we'll need them later.
#classes = np.asarray(['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'])
# make sure we have the caffenet weight downloaded.
if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):
print("Downloading pre-trained CaffeNet model...")
!../scripts/download_model_binary.py ../models/bvlc_reference_caffenet
# initialize caffe for gpu mode
caffe.set_mode_gpu()
caffe.set_device(1)
# helper function for common structures
def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):
conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
num_output=nout, pad=pad, group=group)#,weight_filler=dict(type='xavier'))
return conv, L.ReLU(conv, in_place=True)
# another helper function
def fc_relu(bottom, nout):
fc = L.InnerProduct(bottom, num_output=nout)
return fc, L.ReLU(fc, in_place=True)
# yet another helper function
def max_pool(bottom, ks, stride=1):
return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
# main netspec wrapper
def caffenet_multilabel(data_layer_params, datalayer):
# setup the python data layer
n = caffe.NetSpec()
n.data, n.label = L.Python(module = 'peta_multilabel_datalayers', layer = datalayer,
ntop = 2, param_str=str(data_layer_params))
# the net itself
n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4)
n.pool1 = max_pool(n.relu1, 3, stride=2)
n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)
n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2)
n.pool2 = max_pool(n.relu2, 3, stride=2)
n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)
n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1)
n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2)
n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2)
n.pool5 = max_pool(n.relu5, 3, stride=2)
n.fc6, n.relu6 = fc_relu(n.pool5, 4096)
n.drop6 = L.Dropout(n.relu6, in_place=True)
n.fc7, n.relu7 = fc_relu(n.drop6, 4096)
n.drop7 = L.Dropout(n.relu7, in_place=True)
n.score = L.InnerProduct(n.drop7, num_output=2)
n.loss = L.SigmoidCrossEntropyLoss(n.score, n.label)
return str(n.to_proto())
workdir = './peta_multilabel_with_datalayer'
if not os.path.isdir(workdir):
os.makedirs(workdir)
solverprototxt = tools.CaffeSolver(trainnet_prototxt_path = osp.join(workdir, "trainnet.prototxt"), testnet_prototxt_path = osp.join(workdir, "valnet.prototxt"))
solverprototxt.sp['display'] = "1"
solverprototxt.sp['base_lr'] = "0.0001"
solverprototxt.write(osp.join(workdir, 'solver.prototxt'))
# write train net.
with open(osp.join(workdir, 'trainnet.prototxt'), 'w') as f:
# provide parpeta_multilabel_with_datalayerameters to the data layer as a python dictionary. Easy as pie!
data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'train', peta_root = peta_root)
f.write(caffenet_multilabel(data_layer_params, 'PetaMultilabelDataLayerSync'))
# write validation net.
with open(osp.join(workdir, 'valnet.prototxt'), 'w') as f:
data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'val', peta_root = peta_root)
f.write(caffenet_multilabel(data_layer_params, 'PetaMultilabelDataLayerSync'))
solver = caffe.SGDSolver(osp.join(workdir, 'solver.prototxt'))
#solver.net.copy_from(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')
solver.test_nets[0].share_with(solver.net)
#solver.step(1)
solver.net.top_names
solver.net.backward()
solver.step(1)
print solver.net.params['fc6'][0].data[...]
print solver.net.blobs['fc6'].data[...]
print solver.net.blobs['fc6'].diff[...]
However the output of gradient seems to be zero and the weight is not update at all.
[[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]]
[[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]]
[[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]]
Anyone knows what happened?
