model = FullyConnectedNet([hidden_size,hidden_size],input_dim=1*5, weight_scale=weight_scale,reg=rg, dtype=np.float64,normalization="batchnorm",dropout = dp) solver = Solver(model,data, print_every=10, num_epochs=num_epochs, batch_size=batch_size, update_rule="rmsprop",verbose=True, optim_config={ 'learning_rate': lr, } ) solver.train() result = solver.check_accuracy_for_saccarde( X, y2, num_samples=None, batch_size=546) if result > acc: acc = result marker = "cs231n/data_record/3layer_itteration_%d_hidden_%d_lr_%f_end_rg_%f_end_dp_%f_acc_%f"%(i,hidden_size,lr,rg,dp,acc) solver.checkpoint_name = marker solver._save_checkpoint() print("\niteration = %d\n"%(i)) print("\nlearning rate = %f\n"%(lr)) print("\nreg = %f\n"%(rg)) print("\n dropout = %f\n"%(dp)) print("\ntest accuracy = %f\n"%(acc)) else: model = None #path="cs231n/data_record/2_layer/set_1/set1_2layer_itteration_0_hidden_20_lr_0.000005_end_rg_0.633019_end_dp_0.500000_acc_1.000000_epoch_8.pkl" path="cs231n/data_record/3layer_itteration_0_hidden_20_lr_0.000009_end_rg_0.834396_end_dp_0.500000_acc_1.000000_epoch_8.pkl" with open(path,"rb") as f: record = pk.load(f)