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)