# Path: dlm/eval.py
# class Evaluator():
# 	def __init__(self, dataset, classifier):
# 	def classification_error(self):
# 	def mean_neg_log_likelihood(self):
# 	def mean_unnormalized_neg_log_likelihood(self):
# 	def perplexity(self):
# 	def unnormalized_perplexity(self):
# 	def get_sequence_log_prob(self, index):
# 	def get_unnormalized_sequence_log_prob(self, index):
# 	def get_ngram_log_prob(self, x, y):
# 	def get_denominator(self):
# 	def get_class(self, index, restricted_ids = []):
# 
# Path: dlm/algorithms/lr_tuner.py
# class LRTuner:
# 	def __init__(self, low, high, inc):
# 		self.low = low
# 		self.high = high
# 		self.inc = inc
# 		self.prev_ppl = numpy.inf
# 	
# 	def adapt_lr(self, curr_ppl, curr_lr):
# 		if curr_ppl >= self.prev_ppl:
# 			lr = max(curr_lr / 2, self.low)
# 		else:
# 			lr = min(curr_lr + self.inc, self.high)
# 		self.prev_ppl = curr_ppl
# 		return lr


import theano
import theano.tensor as T
import dlm.utils as U
import dlm.io.logging as L
import time
import numpy as np
import sys
import time
from dlm import eval
from dlm.algorithms.lr_tuner import LRTuner
		from dlm.algorithms.sgd import SGD as Trainer
			if minibatch_index % verbose_freq == 0:
				grad_norms = [np.linalg.norm(gparam) for gparam in gparams]
				L.info(U.blue("[" + time.ctime() + "] ") + '%i/%i, cost=%.2f, lr=%f'
					% (minibatch_index, num_train_batches, minibatch_avg_cost_sum/(minibatch_index+1), trainer.get_learning_rate()))
				L.info('Grad Norms: [' + ', '.join(['%.6f' % gnorm for gnorm in grad_norms]) + ']')
			curr_iter = (epoch - 1) * num_train_batches + minibatch_index
			if curr_iter > 0 and curr_iter % validation_frequency == 0:
				hook.evaluate(curr_iter)

		L.info(U.blue("[" + time.ctime() + "] ") + '%i/%i, cost=%.2f, lr=%f'
			% (num_train_batches, num_train_batches, minibatch_avg_cost_sum/num_train_batches, trainer.get_learning_rate()))
		dev_ppl = hook.evaluate(curr_iter)
		lr = trainer.get_learning_rate()
		if args.enable_lr_adjust:
			lr = lr_tuner.adapt_lr(dev_ppl, lr)
		trainer.set_learning_rate(lr)
		classifier.save_model(args.out_dir + '/model.epoch_' + str(epoch) + '.gz', zipped=True)

	end_time = time.time()
	hook.evaluate(total_num_iter)
	L.info('Optimization complete')
	L.info('Ran for %.2fm' % ((end_time - start_time) / 60.))


class Hook:
	def __init__(self, classifier, devset, testset, total_num_iter, out_dir):
		self.classifier = classifier
		self.dev_eval = eval.Evaluator(dataset=devset, classifier=classifier)
		self.test_eval = None
		if testset:

# Path: dlm/eval.py
# class Evaluator():
# 	def __init__(self, dataset, classifier):
# 	def classification_error(self):
# 	def mean_neg_log_likelihood(self):
# 	def mean_unnormalized_neg_log_likelihood(self):
# 	def perplexity(self):
# 	def unnormalized_perplexity(self):
# 	def get_sequence_log_prob(self, index):
# 	def get_unnormalized_sequence_log_prob(self, index):
# 	def get_ngram_log_prob(self, x, y):
# 	def get_denominator(self):
# 	def get_class(self, index, restricted_ids = []):
# 
# Path: dlm/algorithms/lr_tuner.py
# class LRTuner:
# 	def __init__(self, low, high, inc):
# 		self.low = low
# 		self.high = high
# 		self.inc = inc
# 		self.prev_ppl = numpy.inf
# 	
# 	def adapt_lr(self, curr_ppl, curr_lr):
# 		if curr_ppl >= self.prev_ppl:
# 			lr = max(curr_lr / 2, self.low)
# 		else:
# 			lr = min(curr_lr + self.inc, self.high)
# 		self.prev_ppl = curr_ppl
# 		return lr


# Path: dlm/trainer.py
import theano
import theano.tensor as T
import dlm.utils as U
import dlm.io.logging as L
import time
import numpy as np
import sys
import time
from dlm import eval
from dlm.algorithms.lr_tuner import LRTuner
		from dlm.algorithms.sgd import SGD as Trainer

			if minibatch_index % verbose_freq == 0:
				grad_norms = [np.linalg.norm(gparam) for gparam in gparams]
				L.info(U.blue("[" + time.ctime() + "] ") + '%i/%i, cost=%.2f, lr=%f'
					% (minibatch_index, num_train_batches, minibatch_avg_cost_sum/(minibatch_index+1), trainer.get_learning_rate()))
				L.info('Grad Norms: [' + ', '.join(['%.6f' % gnorm for gnorm in grad_norms]) + ']')
			curr_iter = (epoch - 1) * num_train_batches + minibatch_index
			if curr_iter > 0 and curr_iter % validation_frequency == 0:
				hook.evaluate(curr_iter)

		L.info(U.blue("[" + time.ctime() + "] ") + '%i/%i, cost=%.2f, lr=%f'
			% (num_train_batches, num_train_batches, minibatch_avg_cost_sum/num_train_batches, trainer.get_learning_rate()))
		dev_ppl = hook.evaluate(curr_iter)
		lr = trainer.get_learning_rate()
		if args.enable_lr_adjust:
			lr = lr_tuner.adapt_lr(dev_ppl, lr)
		trainer.set_learning_rate(lr)
		classifier.save_model(args.out_dir + '/model.epoch_' + str(epoch) + '.gz', zipped=True)

	end_time = time.time()
	hook.evaluate(total_num_iter)
	L.info('Optimization complete')
	L.info('Ran for %.2fm' % ((end_time - start_time) / 60.))


class Hook:
	def __init__(self, classifier, devset, testset, total_num_iter, out_dir):
		self.classifier = classifier
		self.dev_eval = eval.Evaluator(dataset=devset, classifier=classifier)
		self.test_eval = None
		if testset:

			self.test_eval = eval.Evaluator(dataset=testset, classifier=classifier)