# please cite: # @article{SqueezeNet, # Author = {Forrest N. Iandola and Matthew W. Moskewicz and Khalid Ashraf and Song Han and William J. Dally and Kurt Keutzer}, # Title = {SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and $<$1MB model size}, # Journal = {arXiv:1602.07360}, # Year = {2016} # } test_iter: 2000 #not subject to iter_size test_interval: 1000 base_lr: 0.04 display: 40 max_iter: 170000 iter_size: 16 #global batch size = batch_size * iter_size lr_policy: "poly" power: 1.0 #linearly decrease LR momentum: 0.9 weight_decay: 0.0002 snapshot: 1000 snapshot_prefix: "train" solver_mode: GPU random_seed: 42 net: "train_val.prototxt" #we typically do `cd SqueezeNet_v1.0; caffe train ` test_initialization: false average_loss: 40