def hyperopt(nofm_layer1, fshape_layer1, learning_rate_layer1, coef_layer1, fshape_layer2, stride_layer2, nofm_layer3, fshape_layer3, learning_rate_layer3, coef_layer3, fshape_layer4, stride_layer4, learning_rate_layer5, coef_layer5, learning_rate_output, coef_output, num_epochs, batch_size): basepath = "/Users/DOE6903584/NERSC/mantissa-new/AR/data" repo_path = os.path.join(basepath, "/results/") fland = os.path.join(basepath, "landmask_imgs.pkl") far = os.path.join(basepath, "atmosphericriver_TMQ.h5") dataset = AR(fland=fland, far=far, repo_path=repo_path) # metrics = {'train':[LogLossMean(), MisclassRate(), MSE()], 'validation':[LogLossMean(), MisclassRate(), MSE()], 'test':[]} experiment = FitPredictErrorExperiment(model=model_gen(nofm_layer1, fshape_layer1, learning_rate_layer1, coef_layer1, fshape_layer2, stride_layer2, nofm_layer3, fshape_layer3, learning_rate_layer3, coef_layer3, fshape_layer4, stride_layer4, learning_rate_layer5, coef_layer5, learning_rate_output, coef_output, num_epochs, batch_size), backend=gen_backend(),dataset=dataset, predictions = ['train', 'test']) metrics = experiment.run() result = float(metrics['test']['MisclassPercentage_TOP_1'] / 100) # # Experiment result is dict: result[metric_set][metric_name] # result = experiment.run() # for setname in result.keys(): # for metricname in result[setname].keys(): # print '%s_%s: %f' % (setname, metricname, result[setname][metricname]) # loss = result['validation']['MSE'] # return loss return {'hyperopt': result}
def run(): model = create_model(nin=784) backend = gen_backend(rng_seed=0) dataset = MNIST(repo_path='~/data/') experiment = FitPredictErrorExperiment(model=model, backend=backend, dataset=dataset) experiment.run()
def hyperopt(lrate, lrule_coef, num_epochs, batch_size): basepath = "/Users/DOE6903584/NERSC/mantissa-new/AR/data" fland = os.path.join(basepath, "landmask_imgs.pkl") far = os.path.join(basepath, "atmosphericriver_TMQ.h5") dataset = AR(fland=fland, far=far) experiment = FitPredictErrorExperiment(model=model_gen(lrate, lrule_coef, num_epochs, batch_size), backend=gen_backend(),dataset=dataset) metrics = experiment.run() result = float(metrics['test']['MisclassPercentage_TOP_1']) return {'hyperopt': result}
{'learning_rate': 0.01, 'weight_decay': 0.001, 'momentum_params': {'coef': 0.9, 'type': 'constant'} }, 'type': 'gradient_descent_momentum_weight_decay'}, weight_init=val_init.UniformValGen(low=-0.1,high=0.1), activation = Logistic() ) ) layers.append(CostLayer( name = 'cost', ref_layer = layers[0], cost = CrossEntropy() ) ) model = MLP(num_epochs=10, batch_size=100, layers=layers) return model basepath = "/Users/DOE6903584/NERSC/mantissa-new/AR/data" fland = os.path.join(basepath, "landmask_imgs.pkl") far = os.path.join(basepath, "atmosphericriver_TMQ.h5") dataset = AR(fland=fland, far=far) experiment = FitPredictErrorExperiment(model=model_gen(), backend=gen_backend(),dataset=dataset) experiment_run = experiment.run()