def initialize_learner(self):
optimization_function = partial(optim.Adam, betas=(0.8, 0.99))
self.learner = RNN_Learner(data=self.model_data, models=TextModel(to_gpu(self.model)),
opt_fn=optimization_function)
self.learner.reg_fn = partial(seq2seq_reg, alpha=2, beta=1)
self.learner.clip = 25.
self.learner.metrics = [accuracy]
def _train_classifier(self,
train_ids,
train_labels,
batch_size=4,
val_ids=None,
val_labels=None):
# change from multi-label to multi-class:
def one_hot_idxs(idxs, n_classes):
res = np.zeros(n_classes)
res[idxs] = 1.
return res
onehot_train_labels = np.array(
[one_hot_idxs(l, self._n_classes) for l in train_labels])
onehot_val_labels = np.array(
[one_hot_idxs(l, self._n_classes) for l in val_labels])
train_ds = TextDataset(train_ids, onehot_train_labels)
val_ds = TextDataset(val_ids, onehot_val_labels)
train_sampler = SortishSampler(train_ids,
key=lambda x: len(train_ids[x]),
bs=batch_size)
val_sampler = SortSampler(val_ids, key=lambda x: len(val_ids[x]))
train_dl = DataLoader(train_ds,
batch_size,
num_workers=1,
transpose=True,
pad_idx=1,
sampler=train_sampler)
val_dl = DataLoader(val_ds,
batch_size,
num_workers=1,
transpose=True,
pad_idx=1,
sampler=val_sampler)
md = ModelData("tmp", train_dl, val_dl)
m = get_rnn_classifier(
self._bptt,
20 * 70,
self._n_classes,
self._vocab.size,
emb_sz=self._embedding_size,
n_hid=self._n_hidden_activations,
n_layers=self._n_layers,
pad_token=1,
layers=[self._embedding_size * 3, 128, self._n_classes],
drops=[self._dropouts_classifier[4], 0.1],
dropouti=self._dropouts_classifier[0],
wdrop=self._dropouts_classifier[1],
dropoute=self._dropouts_classifier[2],
dropouth=self._dropouts_classifier[3])
self._classifier_model = RNN_Learner(md,
TextModel(to_gpu(m)),
opt_fn=self.OPT_FN)
self._classifier_model.reg_fn = partial(seq2seq_reg, alpha=2, beta=1)
self._classifier_model.clip = 25. # or 0.3 ?!
def binary_ce_wrapper(predicted, gt):
out = F.sigmoid(predicted)
return binary_cross_entropy(out, gt)
self._classifier_model.crit = binary_ce_wrapper
jaccard_0_5 = partial(self.func_metric, func=jaccard_index)
jaccard_0_5.__name__ = "jaccard_0_5"
precision_0_5 = partial(self.func_metric, func=precision)
precision_0_5.__name__ = "precision_0_5"
recall_0_5 = partial(self.func_metric, func=recall)
recall_0_5.__name__ = "recall_0_5"
f1_0_5 = partial(self.func_metric, func=f1)
f1_0_5.__name__ = "f1_0_5"
self._classifier_model.metrics = [
jaccard_0_5, precision_0_5, recall_0_5, f1_0_5
]
lr = 3e-3
lrm = 2.6
lrs = np.array(
[lr / (lrm**4), lr / (lrm**3), lr / (lrm**2), lr / lrm, lr])
self._classifier_model.load_encoder('enc_weights')
self._classifier_model.freeze_to(-1)
self._classifier_model.fit(
lrs,
1,
cycle_len=1,
use_clr=(8, 3),
callbacks=[LoggingCallback(save_path="./tmp/log")])
self._classifier_model.freeze_to(-2)
self._classifier_model.fit(
lrs,
1,
cycle_len=1,
use_clr=(8, 3),
callbacks=[LoggingCallback(save_path="./tmp/log")])
self._classifier_model.unfreeze()
self._classifier_model.fit(
lrs,
1,
cycle_len=24,
use_clr=(32, 10),
callbacks=[LoggingCallback(save_path="./tmp/log")])
self._classifier_model.save('classifier_weights')
3,
n_toks,
emb_sz=EMB_DIM,
n_hid=500,
n_layers=3,
pad_token=2,
layers=[EMB_DIM * 3, 50, 3],
drops=[dps[4], 0.1],
dropouti=dps[0],
wdrop=dps[1],
dropoute=dps[2],
dropouth=dps[3])
# In[42]:
learn = RNN_Learner(model_data, TextModel(to_gpu(model)), opt_fn=opt_fn)
learn.reg_fn = partial(seq2seq_reg, alpha=2, beta=1)
learn.clip = 25.
learn.metrics = [accuracy]
learn.load_encoder('lm1_enc')
# In[43]:
learn.freeze_to(-1)
learn.lr_find(lrs / 1000)
learn.sched.plot()
# In[44]:
lr = 2e-4
lrm = 2.6