def main(opt_name): workspace.FeedBlob('input', np.random.randn(2, 16).astype(np.float32)) workspace.FeedBlob('label', np.array([0, 1]).astype(np.float32)) helper = ModelHelper("sample_model") fc = brew.fc(helper, "input", "fc", dim_in=16, dim_out=8) relu = helper.Relu(fc, 'relu') fc2 = brew.fc(helper, relu, "fc2", dim_in=8, dim_out=1) label_ex = helper.ExpandDims("label", "label_ex", dims=[1]) xent = helper.SigmoidCrossEntropyWithLogits([fc2, label_ex], 'xent') loss = helper.AveragedLoss(xent, 'loss') helper.AddGradientOperators([loss]) if opt_name == "manual": ONE = helper.param_init_net.ConstantFill([], "ONE", shape=[1], value=1.0) LR = helper.param_init_net.ConstantFill([], "LR", shape=[1], value=-0.03) for param in helper.params: param_grad = helper.param_to_grad[param] helper.WeightedSum([param, ONE, param_grad, LR], param) elif opt_name == "sgd": optimizer.build_sgd(helper, 0.03) elif opt_name == "adagrad": optimizer.build_adagrad(helper, 0.03) # caffe2 does not support rowwise adagrad for dense parameters # caffe2 seems not have lamb support yet elif opt_name == "adam": optimizer.build_adam(helper, 0.03) else: assert False, f"Unsupported optimizer {opt_name}" workspace.RunNetOnce(helper.param_init_net) workspace.RunNetOnce(helper.net) import pdb pdb.set_trace()