import time
import logging
import numpy as np
from baseline.utils import export, optional_params, register, listify
import math
__all__ = []
exporter = export(__all__)
BASELINE_LR_SCHEDULERS = {}
logger = logging.getLogger('baseline')
@exporter
class LearningRateScheduler(object):
def __init__(self, **kwargs):
self.lr = kwargs.get('lr', kwargs.get('eta', 1.0))
@staticmethod
def _identity(x):
return x
def __str__(self):
return "{}(eta={})".format(type(self).__name__, self.lr)
@exporter
class WarmupLearningRateScheduler(LearningRateScheduler):
def __init__(self, warmup_steps=16000, **kwargs):
import os
import shutil
import datetime
from tensorflow.python.framework.errors_impl import NotFoundError
import mead.exporters
from mead.exporters import register_exporter
from baseline.tf.embeddings import *
from baseline.tf.tfy import transition_mask
from baseline.utils import (export, read_json, ls_props, Offsets, write_json)
from collections import namedtuple
FIELD_NAME = 'text/tokens'
ASSET_FILE_NAME = 'model.assets'
__all__ = []
exporter = export(__all__)
SignatureOutput = namedtuple("SignatureOutput", ("classes", "scores"))
@exporter
class TensorFlowExporter(mead.exporters.Exporter):
def __init__(self, task):
super(TensorFlowExporter, self).__init__(task)
def _run(self, sess, basename):
pass
def _restore_checkpoint(self, sess, basename):
saver = tf.train.Saver()
sess.run(tf.tables_initializer())
