def run_op(self, argmap):
import original
import augmented
# Original data just returns the actual samples
origdata = utility.PickleData(original, 'sample').load()
# Augmented saves a bunch more stuff.
_, augmdata, tknprbs, loggies = utility.PickleData(
augmented, 'sample').load()
utility.assert_zero(origdata - augmdata)
print(
"Success, checked data of size {} against original sample".format(
origdata.shape))
for bi in range(augmdata.shape[0]):
for pi in range(1, augmdata.shape[1]):
logits = loggies[bi, :, pi]
token = augmdata[bi, pi]
probs = utility.softmax(logits)
aprb = probs[token]
bprb = tknprbs[bi, pi]
utility.assert_small(aprb - bprb, epsilon=1e-4)
print(
"Checked correspondence between logit values and token probabilities"
)
def run_op(self, argmap):
modname = argmap.get_str(('modname', 'original'))
seed = argmap.get_int(('seed', 10000))
relmod = importlib.import_module(modname)
print("Going to do Basic Model with module {}".format(modname))
# Load the sample data, and peel off the initial layer of <start_token> data.
origdata = self.load_data(seed)
origdata = origdata[:, 1:]
assert origdata.shape[1] == utility.SAMPLE_LENGTH
enc = utility.get_encoder()
hparams = utility.get_hparams()
with tf.Session(graph=tf.Graph()) as sess:
np.random.seed(seed)
tf.set_random_seed(seed)
tfop = relmod.model_or_sample(origdata.shape[0], origdata)
ckpt = tf.train.latest_checkpoint(
os.path.join('models', utility.MODEL_NAME))
tf.train.Saver().restore(sess, ckpt)
alpha = time.time()
result = sess.run(tfop)
print("Basic Model successful, took {:.03f} seconds".format(
time.time() - alpha))
utility.PickleData(relmod, 'modsample', seed, result=result).save()
def run_op(self, argmap):
modname = argmap.get_str(('modname', 'original'))
relmod = importlib.import_module(modname)
print("Going model hard-coded data with module {}".format(modname))
# Load the hard-coded sample data
origdata = utility.get_encoded_sents()
#enc = utility.get_encoder()
hparams = utility.get_hparams()
with tf.Session(graph=tf.Graph()) as sess:
# Notice!!! You don't need these set-seed operations here!!!
# np.random.seed(seed)
# tf.set_random_seed(seed)
tfop = relmod.model_or_sample(origdata.shape[0], origdata)
ckpt = tf.train.latest_checkpoint(
os.path.join('models', utility.MODEL_NAME))
tf.train.Saver().restore(sess, ckpt)
alpha = time.time()
result = sess.run(tfop)
print("Basic Model successful, took {:.03f} seconds".format(
time.time() - alpha))
utility.PickleData(relmod, 'modhcode', 0, result=result).save()
def run_op(self, argmap):
modname = argmap.get_str(('modname', 'original'))
seed = argmap.get_int(('seed', 10000))
batch_size = argmap.get_int(('batchsize', 100))
enc = utility.get_encoder()
hparams = utility.get_hparams()
relmod = importlib.import_module(modname)
with tf.Session(graph=tf.Graph()) as sess:
np.random.seed(seed)
tf.set_random_seed(seed)
tfop = relmod.model_or_sample(batch_size)
ckpt = tf.train.latest_checkpoint(
os.path.join('models', utility.MODEL_NAME))
tf.train.Saver().restore(sess, ckpt)
alpha = time.time()
result = sess.run(tfop)
print(
"Sample successful, took {:.03f} seconds".format(time.time() -
alpha))
utility.PickleData(relmod, 'sample', seed, result=result).save()
def run_op(self, argmap):
import expl_modl
import augmented
# Okay, load the SAMPLES data from the augmented operation
_, adata, aprobs, aloggs = utility.PickleData(augmented,
'sample').load()
# Load the MODSAMPLE data from the expl_model operation
_, bdata, bprobs, bloggs = utility.PickleData(expl_modl,
'modsample').load()
utility.assert_zero(adata - bdata)
utility.assert_small(aprobs - bprobs)
utility.assert_small(aloggs - bloggs)
print(
"Success, explicit model operation produces exactly the same probabilities as sample operation"
)
def check_hcode_model(self):
import expl_model
import bmdl_augm
# Okay, load the SAMPLES data from the augmented operation
_, adata, aprobs, aloggs = utility.PickleData(expl_model, 'modhcode',
0).load()
# Load the MODSAMPLE data from the expl_model operation
_, bdata, bprobs, bloggs, bstep = utility.PickleData(
bmdl_augm, 'modhcode', 0).load()
utility.assert_zero(adata - bdata)
utility.assert_small(aprobs - bprobs)
utility.assert_small(aloggs - bloggs)
print(
"Success, explicit model and augmented bimodel produce identical results on hard-coded inputs"
)
def check_sample(self, opcode):
import bmdl_augm
import augmented
# Okay, load the SAMPLES data from the augmented operation
_, adata, aprobs, aloggs = utility.PickleData(augmented,
'sample').load()
# Load the MODSAMPLE data from the expl_model operation
_, bdata, bprobs, bloggs, bstep = utility.PickleData(
bmdl_augm, opcode).load()
utility.assert_zero(adata - bdata)
utility.assert_small(aprobs - bprobs)
utility.assert_small(aloggs - bloggs)
assert bstep.shape == (1, ) and bstep[0] == utility.SAMPLE_LENGTH
print(
"Success, explicit model operation produces exactly the same probabilities as sample operation"
)
def check_against_augm(self, opcode):
import bmdl_augm
import bimodel
seed = 0 if opcode == 'modhcode' else 10000
# Okay, load the SAMPLES data from the augmented operation
_, adata, aprobs, astep = utility.PickleData(bimodel, opcode,
seed).load()
# Load the MODSAMPLE data from the expl_model operation
_, bdata, bprobs, _, bstep = utility.PickleData(
bmdl_augm, opcode, seed).load()
utility.assert_zero(adata - bdata)
utility.assert_zero(astep - bstep)
utility.assert_small(aprobs - bprobs)
print("Success, checked against augmented model for opcode {}".format(
opcode.upper()))
def run_op(self, argmap):
import bimodel
_, adata, aprobs, _ = utility.PickleData(bimodel, 'modhcode', 0).load()
utility.assert_small(aprobs[:, 0] + 1.513)
aprobs = aprobs[:, 1:]
for idx in range(adata.shape[0]):
thesent = utility.get_encoder().decode(adata[idx, 1:])
logprob = -np.sum(np.log(aprobs[idx, :]))
print("Sentence/LogProb: \n\t{}\n\t{:.05f}".format(
thesent, logprob))
def load_data(self, seedid):
# Load the data from the original model
import original
return utility.PickleData(original, 'sample', seedid).load()