def iter_predict(Cws, Qws, Cchs, Qchs):
s_preds = []
e_preds = []
for cwmb, qwmb, cchmb, qchmb in iter_data(Cws,
Qws,
Cchs,
Qchs,
n_batch=n_batch_train,
truncate=True,
verbose=True):
n = len(cwmb)
if n == n_batch_train:
s_p, e_p = sess.run([eval_mgpu_s_preds, eval_mgpu_e_preds], {
Cw_train: cwmb,
Qw_train: qwmb,
Cch_train: cchmb,
Qch_train: qchmb
})
else:
s_p, e_p = sess.run([eval_s_preds, eval_e_preds], {
Cw: cwmb,
Qw: qwmb,
Cch: cchmb,
Qch: qchmb
})
s_preds.append(s_p)
e_preds.append(e_p)
s_preds = np.concatenate(s_preds, 0)
e_preds = np.concatenate(e_preds, 0)
return s_preds, e_preds
def iter_apply(Xs, Ms, Ys):
fns = [lambda x: np.concatenate(x, 0), lambda x: float(np.sum(x))]
results = []
for xmb, mmb, ymb in iter_data(Xs,
Ms,
Ys,
n_batch=n_batch_train,
truncate=False,
verbose=True):
n = len(xmb)
if n == n_batch_train:
res = sess.run([eval_mgpu_logits, eval_mgpu_clf_loss], {
X_train: xmb,
M_train: mmb,
Y_train: ymb
})
else:
res = sess.run([eval_logits, eval_clf_loss], {
X: xmb,
M: mmb,
Y: ymb
})
res = [r * n for r in res]
results.append(res)
results = zip(*results)
return [fn(res) for res, fn in zip(results, fns)]
def iter_predict(Xs, Ms):
preds = []
for xmb, mmb in iter_data(Xs,
Ms,
n_batch=n_batch_train,
truncate=False,
verbose=True):
n = len(xmb)
idx_mask = np.equal(xmb[:, :, 0], delimiter).astype(int)
end_idxs = np.argmax(np.equal(xmb[:, :, 0], end).astype(int), 1)
delim_idxs = np.argmax(idx_mask, 1)
if n == n_batch_train:
for i in range(np.max(end_idxs - delim_idxs)):
pred = np.argmax(
sess.run(eval_mgpu_logits, {
X_train: xmb,
M_train: mmb
}), 1)
idx_mask = roll_mask(idx_mask)
xmb = next_xmb(xmb, pred, idx_mask)
if all_finished(np.reshape(pred, xmb[:, 1:, 0].shape), end):
break
else:
for i in range(np.max(end_idxs - delim_idxs)):
pred = np.argmax(sess.run(eval_logits, {X: xmb, M: mmb}), 1)
idx_mask = roll_mask(idx_mask)
xmb = next_xmb(xmb, pred, idx_mask)
if all_finished(np.reshape(pred, xmb[:, 1:, 0].shape), end):
break
preds.append(pred)
preds = np.concatenate(preds, 0)
return preds
def iter_predict(Xs, Ms):
logits = []
for xmb, mmb in iter_data(Xs, Ms, n_batch=n_batch_train, truncate=False, verbose=True):
n = len(xmb)
if n == n_batch_train:
logits.append(sess.run(eval_mgpu_logits, {X_train:xmb, M_train:mmb}))
else:
logits.append(sess.run(eval_logits, {X:xmb, M:mmb}))
logits = np.concatenate(logits, 0)
return logits
def iter_apply(Cws, Qws, Cchs, Qchs, Yss, Yes):
fns = [
lambda x: np.concatenate(x, 0), lambda x: np.concatenate(x, 0),
lambda x: float(np.sum(x))
]
results = []
for cwmb, qwmb, cchmb, qchmb, ysmb, yemb in iter_data(
Cws,
Qws,
Cchs,
Qchs,
Yss,
Yes,
n_batch=n_batch_train,
truncate=True,
verbose=True):
n = len(cwmb)
if n == n_batch_train:
res = sess.run(
[eval_mgpu_s_preds, eval_mgpu_e_preds, eval_mgpu_qa_loss], {
Cw_train: cwmb,
Qw_train: qwmb,
Cch_train: cchmb,
Qch_train: qchmb,
Ys_train: ysmb,
Ye_train: yemb
})
else:
res = sess.run([eval_s_preds, eval_e_preds, eval_qa_loss], {
Cw: cwmb,
Qw: qwmb,
Cch: cchmb,
Qch: qchmb,
Ys: ysmb,
Ye: yemb
})
res = [r * n for r in res]
results.append(res)
results = zip(*results)
return [fn(res) for res, fn in zip(results, fns)]
def iter_apply(Xs, Ms):
fns = [lambda x: np.concatenate(x, 0), lambda x: float(np.sum(x))]
results = []
for xmb, mmb in iter_data(Xs,
Ms,
n_batch=n_batch_train,
truncate=False,
verbose=True):
n = len(xmb)
idx_mask = np.equal(xmb[:, :, 0], delimiter).astype(int)
end_idxs = np.argmax(np.equal(xmb[:, :, 0], end).astype(int), 1)
delim_idxs = np.argmax(idx_mask, 1)
if n == n_batch_train:
for i in range(np.max(end_idxs - delim_idxs)):
ev_logits, ev_lm_loss = sess.run(
[eval_mgpu_logits, eval_mgpu_lm_loss], {
X_train: xmb,
M_train: mmb
})
pred = np.argmax(ev_logits, 1)
idx_mask = roll_mask(idx_mask)
xmb = next_xmb(xmb, pred, idx_mask)
if all_finished(np.reshape(pred, xmb[:, 1:, 0].shape), end):
break
else:
for i in range(np.max(end_idxs - delim_idxs)):
ev_logits, ev_lm_losses = sess.run([eval_logits, eval_lm_loss],
{
X: xmb,
M: mmb
})
pred = np.argmax(ev_logits, 1)
idx_mask = roll_mask(idx_mask)
xmb = next_xmb(xmb, pred, idx_mask)
if all_finished(np.reshape(pred, xmb[:, 1:, 0].shape), end):
break
res = [pred * n, ev_lm_loss * n]
results.append(res)
results = zip(*results)
return [fn(res) for res, fn in zip(results, fns)]
# params
params = find_trainable_variables('model')
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess.run(tf.global_variables_initializer())
# get saved params
if use_prev_best and os.path.isfile(os.path.join(save_dir, desc, 'best_params.jl')):
sess.run([p.assign(ip) for p, ip in zip(params, joblib.load(os.path.join(save_dir, desc, 'best_params.jl')))])
else:
# get the embedding matrix of the pretrained model
#emb = np.concatenate([np.load('{}params_{}.npy'.format(pretrained_lm_dir, n)) for n in range(3)], 0)[393216:31480320].reshape((40478,768))
emb = np.load('{}elmo_768_40478_matrix.npy'.format(elmo_dir))
emb = np.concatenate([emb, (np.random.randn(n_special, n_embd)*0.02).astype(np.float32)], 0)
sess.run(params[0].assign(emb))
del emb
# train, eval, test
n_updates = 0
n_epochs = 0
if submit:
save(os.path.join(save_dir, desc, 'best_params.jl'))
best_score = 0
for i in range(n_iter):
for xmb, mmb in iter_data(*shuffle(trX, trM, random_state=np.random), n_batch=n_batch_train, truncate=True, verbose=True):
cost, _ = sess.run([lm_loss, train], {X_train:xmb, M_train:mmb})
n_updates += 1
if n_updates in [1000, 2000, 4000, 8000, 16000, 32000] and n_epochs == 0:
log()
n_epochs += 1
log()
if submit:
sess.run([p.assign(ip) for p, ip in zip(params, joblib.load(os.path.join(save_dir, desc, 'best_params.jl')))])
predict()
params,
joblib.load(os.path.join(save_dir, desc, 'best_params.jl')))
])
# train, eval, test
n_updates = 0
n_epochs = 0
if submit:
save(os.path.join(save_dir, desc, 'best_params.jl'))
best_score = 0
for i in range(n_iter):
for cwmb, qwmb, cchmb, qchmb, ysmb, yemb in iter_data(
*shuffle(trCtxW,
trQW,
trCtxCh,
trQCh,
trYs,
trYe,
random_state=np.random),
n_batch=n_batch_train,
truncate=True,
verbose=True):
cost, _ = sess.run(
[qa_loss, train], {
Cw_train: cwmb,
Qw_train: qwmb,
Cch_train: cchmb,
Qch_train: qchmb,
Ys_train: ysmb,
Ye_train: yemb
})
n_updates += 1
