def main(_):
PS = U.Params(params).init_comps()
model = model_for(PS)
@Q.function
def train_step(x, y):
with Q.GradientTape() as tape:
logits = model(x)
loss = PS.losses(y, logits)
acc = PS.metrics(y, logits)
grads = tape.gradient(loss, model.trainable_variables)
PS.optimizer.apply_gradients(zip(grads, model.trainable_variables))
return loss, acc
def train():
step, loss, acc = 0, 0.0, 0.0
for x, y in dset_for(PS, 'train'):
step += 1
loss, acc = train_step(x, y)
if Q.equal(step % 10, 0):
m = PS.metrics.result()
Q.print('Step:', step, ', loss:', loss, ', acc:', m)
return step, loss, acc
step, loss, acc = train()
print('Final step:', step, ', loss:', loss, ', acc:', PS.metrics.result())
def __init__(self):
self.ps = U.Params(params).init_comps()
self.pre = None
self.post = None
i = tf.constant([0.] * (4 * 10), shape=(4, 10))
self.src_b = tf.Variable(initial_value=i)
i = tf.constant([0.] * (4 * 10), shape=(4, 10))
self.mem_b = tf.Variable(initial_value=i)
def main(_):
ps = utils.Params(params).init_comps()
ds = dset_for(ps, TRAIN)
# with T.distribute.MirroredStrategy().scope():
mdl = model_for(ps, compiled=True)
mdl.train_on_batch(ds)
mp = pth.Path.cwd() / ps.dir_model / ps.model
assert tf.get_checkpoint_state(str(mp))
mdl.load_weights(str(mp / TRAIN))
c = tf.Checkpoint(model=mdl, optimizer=ps.optimizer)
c.restore(str(mp / TRAIN)).expect_partial() # .assert_consumed()
for n, s in tf.list_variables(str(mp)):
print(n)
mp2 = pth.Path.cwd() / ps.dir_model / 'mnist_2'
print('saving...')
c.save(str(mp2 / TRAIN))
for n, s in tf.list_variables(str(mp2)):
print(n)
assert tf.get_checkpoint_state(str(mp2))
mdl.load_weights(str(mp2 / 'train-1'))
PAD=0,
brackets=None,
dim_embed=4,
dim_hidden=8,
emb_one_hot=None,
num_toks=16,
num_types=4,
len_src=3,
len_tgt=3,
pos_max_len=None,
pos_max=1.0e4,
pos_min=1.0,
pos_start=0,
)
ps = U.Params(params).init_comps()
def test_tokembed():
e = TokEmbed(ps)
e.build((1, 5))
src = tf.constant([1, 2, 0, 3, 0], shape=(1, 5))
e.call(src)
ps.emb_one_hot = True
e = TokEmbed(ps)
e.build((1, 5))
e.call(src)
def test_w_grad():
e = TokEmbed(ps)
def load_params():
return utils.Params(params).init_comps()
def main(_):
ps = utils.Params(params).init_comps()
# tf.autograph.set_verbosity(1)
# print(tf.autograph.to_code(Trafo.embed.python_function))
session_for(ps)(dset_for, model_for)
def load_params():
f = 'channels_first' if T.test.is_built_with_cuda() else 'channels_last'
return U.Params(_params, data_format=f)
import qnarre.neura.utils as U
from qnarre.feeds.prep import utils
from qnarre.feeds.prep import encoder
ps = dict(
lower_case=True,
model='',
tok_max_chars=None,
vocab_pairs=None,
bert_vocab='.model/bert/uncased_L-12_H-768_A-12/vocab.txt',
gpt_2_vocab='.model/gpt_2/117M/encoder.json',
gpt_2_pairs='.model/gpt_2/117M/vocab.bpe',
)
ps = U.Params(ps)
def test_encoders():
txt = "sf!fg dfg'sdf?dfg xcxb'sdfg!sdg 324sdf.sdfa"
ce = encoder.CharE(ps)
ts, os, _ = zip(*ce(txt))
d = ce.decode(ts, os)
assert d == txt
we = encoder.WordE(ps)
ts, os, _ = zip(*we(txt))
d = we.decode(ts, os)
assert d == txt
be = encoder.BertE(ps)
ge = encoder.Gpt2E(ps)
with zipfile.ZipFile('.data/text8/text8.zip') as z:
