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This commit is contained in:
Connor Olding 2017-02-28 00:36:04 +00:00
parent fcdb7e1918
commit 65fe5cad85
2 changed files with 41 additions and 9 deletions
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2
optim_nn_core.py
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@ -723,7 +723,7 @@ class Ritual: # i'm just making up names at this point
batch_outputs = outputs[bi:bi+batch_size]
if not test_only and self.learner.per_batch:
self.learner.batch(b / batch_count)
self.learner.batch(b / batch_count)
predicted = self.learn(batch_inputs, batch_outputs)
if not test_only:

48
optim_nn_mnist.py
View file

@ -1,6 +1,7 @@
#!/usr/bin/env python3
from optim_nn import *
from optim_nn_core import _f
#np.random.seed(42069)
@ -13,8 +14,12 @@ from optim_nn import *
lr = 0.01
epochs = 24
starts = 2
restart_decay = 0.5
bs = 100
log_fn = 'mnist_losses.npz'
measure_every_epoch = True
mnist_dim = 28
mnist_classes = 10
def get_mnist(fn='mnist.npz'):
@ -60,7 +65,7 @@ model = Model(x, y, unsafe=True)
optim = Adam()
learner = SGDR(optim, epochs=epochs//starts, rate=lr,
restarts=starts - 1, restart_decay=0.5,
restarts=starts - 1, restart_decay=restart_decay,
expando=lambda i:0)
loss = CategoricalCrossentropy()
@ -72,15 +77,24 @@ log('parameters', model.param_count)
ritual.prepare(model)
def measure_error():
batch_losses, batch_mlosses = [], []
train_losses, train_mlosses = [], []
valid_losses, valid_mlosses = [], []
def measure_error(quiet=False):
def print_error(name, inputs, outputs, comparison=None):
loss, mloss, _, _ = ritual.test_batched(inputs, outputs, bs, return_losses='both')
log(name + " loss", "{:12.6e}".format(loss))
log(name + " accuracy", "{:6.2f}%".format(mloss * 100))
if not quiet:
log(name + " loss", "{:12.6e}".format(loss))
log(name + " accuracy", "{:6.2f}%".format(mloss * 100))
return loss, mloss
print_error("train", inputs, outputs)
print_error("valid", valid_inputs, valid_outputs)
loss, mloss = print_error("train", inputs, outputs)
train_losses.append(loss)
train_mlosses.append(mloss)
loss, mloss = print_error("valid", valid_inputs, valid_outputs)
valid_losses.append(loss)
valid_mlosses.append(mloss)
measure_error()
@ -90,7 +104,7 @@ while learner.next():
shuffled_inputs = inputs[indices]
shuffled_outputs = outputs[indices]
avg_loss, avg_mloss, _, _ = ritual.train_batched(
avg_loss, avg_mloss, losses, mlosses = ritual.train_batched(
shuffled_inputs, shuffled_outputs,
batch_size=bs,
return_losses='both')
@ -98,4 +112,22 @@ while learner.next():
log("epoch {}".format(learner.epoch + 1),
fmt.format(learner.rate, avg_loss, avg_mloss * 100))
measure_error()
batch_losses += losses
batch_mlosses += mlosses
if measure_every_epoch:
quiet = learner.epoch + 1 != learner.epochs
measure_error(quiet=quiet)
if not measure_every_epoch:
measure_error()
if log_fn:
log('saving losses', log_fn)
np.savez_compressed(log_fn,
batch_losses =np.array(batch_losses, dtype=_f),
batch_mlosses=np.array(batch_mlosses, dtype=_f),
train_losses =np.array(train_losses, dtype=_f),
train_mlosses=np.array(train_mlosses, dtype=_f),
valid_losses =np.array(valid_losses, dtype=_f),
valid_mlosses=np.array(valid_mlosses, dtype=_f))