231 lines
6.7 KiB
Python
Executable file
231 lines
6.7 KiB
Python
Executable file
#!/usr/bin/env python3
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from optim_nn import *
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from optim_nn_core import _f
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#np.random.seed(42069)
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use_emnist = False
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measure_every_epoch = True
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if use_emnist:
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lr = 0.01
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epochs = 48
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starts = 2
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bs = 200
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sgdr = True
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restart_decay = 0.5
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CLR = SineCLR
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n_dense = 0
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n_denses = 2
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new_dims = (28, 28)
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activation = GeluApprox
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reg = None
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final_reg = None
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actreg_lamb = None
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load_fn = None
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save_fn = 'emnist.h5'
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log_fn = 'emnist_losses.npz'
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fn = 'emnist-balanced.npz'
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mnist_dim = 28
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mnist_classes = 47
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else:
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lr = 0.0032
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epochs = 60
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starts = 3
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bs = 200
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sgdr = True
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restart_decay = 0.5
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CLR = SineCLR
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n_dense = 2
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n_denses = 1
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new_dims = (4, 12)
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activation = Relu
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reg = L1L2(3.2e-5, 3.2e-4)
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final_reg = L1L2(3.2e-5, 1e-3)
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actreg_lamb = None # 1e-3
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load_fn = None
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save_fn = 'mnist.h5'
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log_fn = 'mnist_losses.npz'
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fn = 'mnist.npz'
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mnist_dim = 28
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mnist_classes = 10
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def get_mnist(fn='mnist.npz'):
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import os.path
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if fn == 'mnist.npz' and not os.path.exists(fn):
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from keras.datasets import mnist
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from keras.utils.np_utils import to_categorical
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(X_train, y_train), (X_test, y_test) = mnist.load_data()
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X_train = X_train.reshape(X_train.shape[0], 1, mnist_dim, mnist_dim)
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X_test = X_test.reshape(X_test.shape[0], 1, mnist_dim, mnist_dim)
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X_train = X_train.astype('float32') / 255
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X_test = X_test.astype('float32') / 255
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Y_train = to_categorical(y_train, mnist_classes)
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Y_test = to_categorical(y_test, mnist_classes)
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np.savez_compressed(fn,
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X_train=X_train,
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Y_train=Y_train,
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X_test=X_test,
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Y_test=Y_test)
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lament("mnist successfully saved to", fn)
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lament("please re-run this program to continue")
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sys.exit(1)
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with np.load(fn) as f:
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return f['X_train'], f['Y_train'], f['X_test'], f['Y_test']
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inputs, outputs, valid_inputs, valid_outputs = get_mnist(fn)
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def actreg(y):
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if not actreg_lamb:
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return y
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lamb = actreg_lamb # * np.prod(y.output_shape)
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reg = SaturateRelu(lamb)
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act = ActivityRegularizer(reg)
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reg.lamb_orig = reg.lamb # HACK
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return y.feed(act)
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x = Input(shape=inputs.shape[1:])
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y = x
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y = y.feed(Reshape(new_shape=(mnist_dim, mnist_dim)))
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for i in range(n_denses):
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if i > 0:
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y = actreg(y)
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y = y.feed(activation())
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y = y.feed(Denses(new_dims[0], axis=0, init=init_he_normal,
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reg_w=reg, reg_b=reg))
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y = y.feed(Denses(new_dims[1], axis=1, init=init_he_normal,
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reg_w=reg, reg_b=reg))
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y = y.feed(Flatten())
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for i in range(n_dense):
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if i > 0:
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y = actreg(y)
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y = y.feed(activation())
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y = y.feed(Dense(y.output_shape[0], init=init_he_normal,
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reg_w=reg, reg_b=reg))
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y = actreg(y)
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y = y.feed(activation())
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y = y.feed(Dense(mnist_classes, init=init_glorot_uniform,
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reg_w=final_reg, reg_b=final_reg))
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y = y.feed(Softmax())
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model = Model(x, y, unsafe=True)
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optim = Adam()
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if sgdr:
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learner = SGDR(optim, epochs=epochs//starts, rate=lr,
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restarts=starts-1, restart_decay=restart_decay,
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expando=lambda i:0)
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else:
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learner = CLR(optim, epochs=epochs, lower_rate=0, upper_rate=lr,
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frequency=epochs//starts)
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loss = CategoricalCrossentropy()
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mloss = Accuracy()
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ritual = Ritual(learner=learner, loss=loss, mloss=mloss)
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#ritual = NoisyRitual(learner=learner, loss=loss, mloss=mloss,
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# input_noise=1e-1, output_noise=3.2e-2, gradient_noise=1e-1)
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for node in model.ordered_nodes:
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children = [str(n) for n in node.children]
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if children:
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sep = '->'
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print(str(node) + sep + ('\n' + str(node) + sep).join(children))
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log('parameters', model.param_count)
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ritual.prepare(model)
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batch_losses, batch_mlosses = [], []
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train_losses, train_mlosses = [], []
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valid_losses, valid_mlosses = [], []
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train_confid, valid_confid = [], []
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def measure_error(quiet=False):
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def print_error(name, inputs, outputs, comparison=None):
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loss, mloss, _, _ = ritual.test_batched(inputs, outputs, bs, return_losses='both')
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c = Confidence()
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predicted = ritual.model.forward(inputs)
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confid = c.forward(predicted)
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if not quiet:
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log(name + " loss", "{:12.6e}".format(loss))
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log(name + " accuracy", "{:6.2f}%".format(mloss * 100))
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log(name + " confidence", "{:6.2f}%".format(confid * 100))
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return loss, mloss, confid
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#if not quiet:
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loss, mloss, confid = print_error("train", inputs, outputs)
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train_losses.append(loss)
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train_mlosses.append(mloss)
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train_confid.append(confid)
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loss, mloss, confid = print_error("valid", valid_inputs, valid_outputs)
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valid_losses.append(loss)
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valid_mlosses.append(mloss)
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valid_confid.append(confid)
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measure_error()
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while learner.next():
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act_t = (learner.epoch - 1) / (learner.epochs - 1)
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if actreg_lamb:
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for node in model.ordered_nodes:
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if isinstance(node, ActivityRegularizer):
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node.reg.lamb = act_t * node.reg.lamb_orig # HACK
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indices = np.arange(inputs.shape[0])
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np.random.shuffle(indices)
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shuffled_inputs = inputs[indices]
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shuffled_outputs = outputs[indices]
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avg_loss, avg_mloss, losses, mlosses = ritual.train_batched(
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shuffled_inputs, shuffled_outputs,
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batch_size=bs,
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return_losses='both')
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fmt = "rate {:10.8f}, loss {:12.6e}, accuracy {:6.2f}%"
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log("epoch {}".format(learner.epoch),
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fmt.format(learner.rate, avg_loss, avg_mloss * 100))
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batch_losses += losses
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batch_mlosses += mlosses
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if measure_every_epoch:
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quiet = learner.epoch != learner.epochs
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measure_error(quiet=quiet)
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if not measure_every_epoch:
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measure_error()
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if save_fn is not None:
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log('saving weights', save_fn)
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model.save_weights(save_fn, overwrite=True)
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if log_fn:
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log('saving losses', log_fn)
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np.savez_compressed(log_fn,
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batch_losses =np.array(batch_losses, dtype=_f),
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batch_mlosses=np.array(batch_mlosses, dtype=_f),
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train_losses =np.array(train_losses, dtype=_f),
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train_mlosses=np.array(train_mlosses, dtype=_f),
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valid_losses =np.array(valid_losses, dtype=_f),
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valid_mlosses=np.array(valid_mlosses, dtype=_f),
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train_confid =np.array(train_confid, dtype=_f),
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valid_confid =np.array(valid_confid, dtype=_f))
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