import caffe
import missinglink
missinglink_callback = missinglink.PyCaffeCallback(
owner_id="OWNER_ID", project_token="PROJECT_TOKEN")
solver = missinglink_callback.create_wrapped_solver(
caffe.SGDSolver, "poolmean_solver.prototxt")
missinglink_callback.set_properties(display_name="Videos 2 Language")
missinglink_callback.set_monitored_blobs(["softmax_loss", "accuracy"])
caffe.set_mode_cpu()
solver.solve()
import caffe
import missinglink
caffe.set_mode_cpu()
missinglink_callback = missinglink.PyCaffeCallback(
owner_id="485aee1a-7f13-0dab-c470-0be21d273407",
project_token="KuqiSOcHQkzhavxl")
missinglink_callback.set_properties(display_name="BVLC CaffeNet")
solver = missinglink_callback.create_wrapped_solver(caffe.SGDSolver,
'solver.prototxt')
missinglink_callback.set_expected_predictions_layers("label", "fc8")
missinglink_callback.set_monitored_blobs(["loss", "accuracy"])
solver.solve()
OWNER_ID = args.owner_id or OWNER_ID
PROJECT_TOKEN = args.project_token or PROJECT_TOKEN
def start_new_experiment():
# Write code here that starts a new experiment
pass
def log_experiment_to_internal_log():
# Write code here that logs important information
# to your internal logs
pass
def stopped_callback():
start_new_experiment()
log_experiment_to_internal_log()
missinglink_callback = missinglink.PyCaffeCallback(
owner_id=OWNER_ID,
project_token=PROJECT_TOKEN,
stopped_callback=stopped_callback)
missinglink_callback.set_properties(display_name='MNIST',
description='LeNet network')
solver = missinglink_callback.create_wrapped_solver(
caffe.SGDSolver, 'mnist/lenet_auto_solver.prototxt')
solver.solve()
import caffe
import missinglink
net_model = 'models/vgg16/VGG16-deploy.prototxt'
net_weights = 'models/vgg16/VGG16.caffemodel'
net = caffe.Net(net_model, net_weights, caffe.TEST)
missinglink_callback = missinglink.PyCaffeCallback(
owner_id='replace with owner id',
project_token='replace with project token')
path = 'http://l7.alamy.com' + \
'/zooms/b76d255dd51e493e8c0fd5d5aa85f96f/lumbermill-cp93p7.jpg'
missinglink_callback.generate_grad_cam(path, model=net)
