def test_auto_batch_epoch():
check = {
"1": (1, 200),
"10": (10, 200),
"100": (16, 160),
"1000": (32, 64),
"10000": (64, 25),
"100000": (128, 20),
"1000000": (256, 20),
"10000000": (512, 20),
}
observe = {}
# for n_data in [10, int(1e3), int(1e6)]:
for n_data, (batch_size, epochs) in check.items():
n_data = int(n_data)
c = configure.Train(
learning_rate=None,
epochs=None,
batch_size=None,
loss_func="mse",
ar_sparsity=None,
optimizer="SGD",
)
c.set_auto_batch_epoch(n_data=n_data)
observe["{}".format(n_data)] = (c.batch_size, c.epochs)
log.debug("[config] n_data: {}, batch: {}, epoch: {}".format(
n_data, c.batch_size, c.epochs))
log.debug("[should] n_data: {}, batch: {}, epoch: {}".format(
n_data, batch_size, epochs))
assert c.batch_size == batch_size
assert c.epochs == epochs
def test_auto_batch_epoch(self):
check = {
"1": (1, 500),
"10": (10, 500),
"100": (16, 320),
"1000": (32, 181),
"10000": (64, 102),
"100000": (128, 57),
"1000000": (256, 50),
"10000000": (256, 50),
}
for n_data in [10, int(1e3), int(1e6)]:
c = configure.Train(
learning_rate=None,
epochs=None,
batch_size=None,
loss_func="mse",
ar_sparsity=None,
train_speed=0,
optimizer="SGD",
)
c.set_auto_batch_epoch(n_data)
log.debug("n_data: {}, batch: {}, epoch: {}".format(n_data, c.batch_size, c.epochs))
batch, epoch = check["{}".format(n_data)]
assert c.batch_size == batch
assert c.epochs == epoch
def test_auto_batch_epoch(self):
check = {
"1": (1, 1000),
"10": (2, 1000),
"100": (8, 320),
"1000": (32, 64),
"10000": (128, 12),
"100000": (128, 5),
}
for n_data in [1, 10, int(1e2), int(1e3), int(1e4), int(1e5)]:
c = configure.Train(learning_rate=None,
epochs=None,
batch_size=None,
loss_func="mse",
ar_sparsity=None,
train_speed=0)
c.set_auto_batch_epoch(n_data)
log.debug("n_data: {}, batch: {}, epoch: {}".format(
n_data, c.batch_size, c.epochs))
batch, epoch = check["{}".format(n_data)]
assert c.batch_size == batch
assert c.epochs == epoch
def test_auto_batch_epoch(self):
n2b = lambda x: int(400 / (1 + np.log(x / 100)))
check = {
"3": (3, 400),
"10": (10, 400),
"30": (16, 400),
"100": (16, 400),
"300": (16, 190),
"1000": (32, 121),
"10000": (64, 71),
"100000": (128, 50),
"1000000": (256, 40),
"10000000": (256, 40),
}
for n_data in [
3, 10, 30,
int(1e2),
int(1e3),
int(1e4),
int(1e5),
int(1e6),
int(1e7)
]:
c = configure.Train(
learning_rate=None,
epochs=None,
batch_size=None,
loss_func="mse",
ar_sparsity=None,
train_speed=0,
optimizer="SGD",
)
c.set_auto_batch_epoch(n_data)
log.debug("n_data: {}, batch: {}, epoch: {}".format(
n_data, c.batch_size, c.epochs))
batch, epoch = check["{}".format(n_data)]
assert c.batch_size == batch
assert c.epochs == epoch