#!/usr/bin/env python3 from onn import * from onn.float import * from dotmap import DotMap lower_priority() np.random.seed(42069) measure_every_epoch = True target_boost = lambda y: y use_emnist = False if use_emnist: lr = 1.0 epochs = 48 starts = 2 bs = 400 learner_class = SGDR restart_decay = 0.5 n_dense = 2 n_denses = 0 new_dims = (28, 28) activation = GeluApprox output_activation = Softmax normalize = True optim = MomentumClip(mu=0.7, nesterov=True) restart_optim = False reg = None # L1L2(2.0e-5, 1.0e-4) final_reg = None # L1L2(2.0e-5, 1.0e-4) dropout = 0.33 actreg_lamb = None load_fn = None save_fn = 'emnist.h5' log_fn = 'emnist_losses.npz' fn = 'emnist-balanced.npz' mnist_dim = 28 mnist_classes = 47 else: lr = 0.01 epochs = 60 starts = 3 bs = 500 learner_class = SGDR restart_decay = 0.5 n_dense = 2 n_denses = 1 new_dims = (4, 12) activation = GeluApprox output_activation = Softmax normalize = True optim = MomentumClip(0.8, 0.8) restart_optim = False reg = None # L1L2(1e-6, 1e-5) # L1L2(3.2e-5, 3.2e-4) final_reg = None # L1L2(1e-6, 1e-5) # L1L2(3.2e-5, 1e-3) dropout = None # 0.05 actreg_lamb = None #1e-4 load_fn = None save_fn = 'mnist.h5' log_fn = 'floss{}.npz' fn = 'mnist.npz' mnist_dim = 28 mnist_classes = 10 def get_mnist(fn='mnist.npz'): with np.load(fn) as f: return f['X_train'], f['Y_train'], f['X_test'], f['Y_test'] inputs, outputs, valid_inputs, valid_outputs = get_mnist(fn) outputs = target_boost(outputs) valid_outputs = target_boost(valid_outputs) def regulate(y): if actreg_lamb: assert activation == Relu, activation lamb = actreg_lamb # * np.prod(y.output_shape) reg = SaturateRelu(lamb) act = ActivityRegularizer(reg) reg.lamb_orig = reg.lamb # HACK y = y.feed(act) if normalize: y = y.feed(LayerNorm()) if dropout: y = y.feed(Dropout(dropout)) return y x = Input(shape=inputs.shape[1:]) y = x y = y.feed(Reshape(new_shape=(mnist_dim, mnist_dim))) for i in range(n_denses): if i > 0: y = regulate(y) y = y.feed(activation()) y = y.feed(Denses(new_dims[0], axis=0, init=init_he_normal, reg_w=reg, reg_b=reg)) y = y.feed(Denses(new_dims[1], axis=1, init=init_he_normal, reg_w=reg, reg_b=reg)) y = y.feed(Flatten()) for i in range(n_dense): if i > 0: y = regulate(y) y = y.feed(activation()) y = y.feed(Dense(y.output_shape[0], init=init_he_normal, reg_w=reg, reg_b=reg)) y = regulate(y) y = y.feed(activation()) y = y.feed(Dense(mnist_classes, init=init_glorot_uniform, reg_w=final_reg, reg_b=final_reg)) y = y.feed(output_activation()) if output_activation in (Softmax, Sigmoid): loss = CategoricalCrossentropy() else: loss = SquaredHalved() mloss = Accuracy() model = Model(x, y, loss=loss, mloss=mloss, unsafe=True) def rscb(restart): log("restarting", restart) if restart_optim: optim.reset() if learner_class == SGDR: learner = learner_class(optim, epochs=epochs//starts, rate=lr, restarts=starts-1, restart_decay=restart_decay, expando=lambda i:0, callback=rscb) elif learner_class in (TriangularCLR, SineCLR, WaveCLR): learner = learner_class(optim, epochs=epochs, lower_rate=0, upper_rate=lr, frequency=epochs//starts, callback=rscb) elif learner_class is AnnealingLearner: learner = learner_class(optim, epochs=epochs, rate=lr, halve_every=epochs//starts) elif learner_class is DumbLearner: learner = learner_class(self, optim, epochs=epochs//starts, rate=lr, halve_every=epochs//(2*starts), restarts=starts-1, restart_advance=epochs//starts, callback=rscb) elif learner_class is Learner: learner = Learner(optim, epochs=epochs, rate=lr) else: if not isinstance(optim, YellowFin): lament('WARNING: no learning rate schedule selected.') learner = Learner(optim, epochs=epochs) ritual = Ritual(learner=learner) model.print_graph() log('parameters', model.param_count) ritual.prepare(model) logs = DotMap( batch_losses = [], batch_mlosses = [], train_losses = [], train_mlosses = [], valid_losses = [], valid_mlosses = [], learning_rate = [], momentum = [], ) def measure_error(quiet=False): def print_error(name, inputs, outputs): loss, mloss, _, _ = ritual.test_batched(inputs, outputs, bs, return_losses='both') if not quiet: log(name + " loss", "{:12.6e}".format(loss)) log(name + " accuracy", "{:6.2f}%".format(mloss * 100)) return loss, mloss loss, mloss = print_error("train", inputs, outputs) logs.train_losses.append(loss) logs.train_mlosses.append(mloss) loss, mloss = print_error("valid", valid_inputs, valid_outputs) logs.valid_losses.append(loss) logs.valid_mlosses.append(mloss) measure_error() while learner.next(): if actreg_lamb: act_t = (learner.epoch - 1) / (learner.epochs - 1) for node in model.ordered_nodes: if isinstance(node, ActivityRegularizer): node.reg.lamb = act_t * node.reg.lamb_orig # HACK avg_loss, avg_mloss, losses, mlosses = ritual.train_batched( inputs, outputs, batch_size=bs, return_losses='both') fmt = "rate {:10.8f}, loss {:12.6e}, accuracy {:6.2f}%" log("epoch {}".format(learner.epoch), fmt.format(learner.rate, avg_loss, avg_mloss * 100)) logs.batch_losses += losses logs.batch_mlosses += mlosses if measure_every_epoch: quiet = learner.epoch != learner.epochs measure_error(quiet=quiet) logs.learning_rate.append(optim.lr) if getattr(optim, 'mu', None): logs.momentum.append(optim.mu) if not measure_every_epoch: measure_error() if save_fn is not None: log('saving weights', save_fn) model.save_weights(save_fn, overwrite=True) if log_fn: kwargs = dict() for k, v in logs.items(): if len(v) > 0: kwargs[k] = np.array(v, dtype=_f) if '{}' in log_fn: from os.path import exists for i in range(10000): candidate = log_fn.format(i) if not exists(candidate): log_fn = candidate break log('saving losses', log_fn) np.savez_compressed(log_fn, **kwargs)