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161 changes: 122 additions & 39 deletions training/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,27 +3,67 @@
"""
import theano
import theano.tensor as tensor

import cPickle as pkl
#import Pickle as pkl
import numpy
import copy

import os
import tensorflow as tf
from collections import Counter
import _pickle as pkl
import warnings
warnings.filterwarnings(action='ignore', category=UserWarning, module='gensim')
import collections
import sys
from skip_thoughts.data import special_words
import time

#import pickle
import homogeneous_data

#from special_words import *
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams

from utils import *
#from utils import*
from layers import get_layer, param_init_fflayer, fflayer, param_init_gru, gru_layer
from optim import adam
from model import init_params, build_model
from vocab import load_dictionary
input_directory='C:/Users/Hp 15/Desktop/pos/'
input_files1=glob.glob(input_directory+"*.txt")
for path in input_files1:
def del_all_flags(FLAGS):
flags_dict = FLAGS._flags()
keys_list = [keys for keys in flags_dict]
for keys in keys_list:
FLAGS.__delattr__(keys)

del_all_flags(tf.flags.FLAGS)
# because path is object not string
input_files= str(path)
FLAGS = tf.flags.FLAGS
word_idict="C:/Users/Hp 15/Desktop/vocab/vocab.txt"
tf.flags.DEFINE_string("vocab_file", word_idict,
"(Optional) existing vocab file. Otherwise, a new vocab "
"file is created and written to the output directory. "
"The file format is a list of newline-separated words, "
"where the word id is the corresponding 0-based index "
"in the file.")

def _build_vocabulary(input_files):

if FLAGS.vocab_file:

tf.logging.info("Loading existing word_idict file.")
word_idict = collections.OrderedDict()
with tf.gfile.GFile(FLAGS.vocab_file, mode="r") as f:
for i, line in enumerate(f):
word = line.strip()
assert word not in word_idict, "Attempting to add word twice: %s" % word
word_idict[word] = i
tf.logging.info("Read word_idict of size %d from %s",
len(word_idict), FLAGS.vocab_file)
return word_idict



# main trainer
def trainer(X,
dim_word=620, # word vector dimensionality
dim=2400, # the number of GRU units
Expand All @@ -37,11 +77,12 @@ def trainer(X,
maxlen_w=30,
optimizer='adam',
batch_size = 64,
saveto='/u/rkiros/research/semhash/models/toy.npz',
dictionary='/ais/gobi3/u/rkiros/bookgen/book_dictionary_large.pkl',
saveto='C:/Users/Hp 15/Desktop/vocab/',

dictionary='C:/Users/Hp 15/Desktop/vocab/vocab.txt',
saveFreq=1000,
reload_=False):

print ("Done heloooooooshabnam")
# Model options
model_options = {}
model_options['dim_word'] = dim_word
Expand All @@ -61,26 +102,45 @@ def trainer(X,
model_options['saveFreq'] = saveFreq
model_options['reload_'] = reload_

print model_options
print (model_options)

# reload options
if reload_ and os.path.exists(saveto):
print 'reloading...' + saveto
with open('%s.pkl'%saveto, 'rb') as f:
models_options = pkl.load(f)

# load dictionary
print 'Loading dictionary...'
print ("reloading...") + saveto


file = open('C:/Users/Hp 15/Desktop/vocab/vocab.txt', "r")
models_options=file.read()
# with open('C:/Users/Hp 15/Desktop/vocab/vocab.txt', 'r') as f:
# models_options =pkl.load(f)
file = open('C:/Users/Hp 15/Desktop/vocab/word_counts.txt', "r")
wordcount=file.read()
words = list(wordcount)
freqs = Counter(file.read().split())
# freqs = list(wordcount.values())
sorted_indices = np.argsort(freqs)[::-1]
#have to look again # load dictionary
print ("Loading dictionary...")
worddict = load_dictionary(dictionary)

# Inverse dictionary
word_idict = dict()
for kk, vv in worddict.iteritems():
word_idict[vv] = kk
word_idict[0] = '<eos>'
word_idict[1] = 'UNK'

print 'Building model'
word_idict = collections.OrderedDict()
word_idict[special_words.EOS] = special_words.EOS_ID
word_idict[special_words.UNK] = special_words.UNK_ID
for kk , vv in enumerate(sorted_indices[0:-2]):
word_idict[words[vv]] = kk + 2 # 0: EOS, 1: UNK.




#
# for kk, vv in worddict.iteritems():
# word_idict[vv] = kk
# word_idict[0] = '<eos>'
# word_idict[1] = 'UNK'

print ("Building model")
params = init_params(model_options)
# reload parameters
if reload_ and os.path.exists(saveto):
Expand All @@ -95,9 +155,9 @@ def trainer(X,
inps = [x, x_mask, y, y_mask, z, z_mask]

# before any regularizer
print 'Building f_log_probs...',
print ("Building f_log_probs..."),
f_log_probs = theano.function(inps, cost, profile=False)
print 'Done'
print ("Done")

# weight decay, if applicable
if decay_c > 0.:
Expand All @@ -109,12 +169,12 @@ def trainer(X,
cost += weight_decay

# after any regularizer
print 'Building f_cost...',
print ("Building f_cost..."),
f_cost = theano.function(inps, cost, profile=False)
print 'Done'
print ("Done")

print 'Done'
print 'Building f_grad...',
print ("Done")
print ("Building f_grad..."),
grads = tensor.grad(cost, wrt=itemlist(tparams))
f_grad_norm = theano.function(inps, [(g**2).sum() for g in grads], profile=False)
f_weight_norm = theano.function([], [(t**2).sum() for k,t in tparams.iteritems()], profile=False)
Expand All @@ -131,11 +191,11 @@ def trainer(X,
grads = new_grads

lr = tensor.scalar(name='lr')
print 'Building optimizers...',
print ("Building optimizers..."),
# (compute gradients), (updates parameters)
f_grad_shared, f_update = eval(optimizer)(lr, tparams, grads, inps, cost)

print 'Optimization'
print ("Optimization")

# Each sentence in the minibatch have same length (for encoder)
trainX = homogeneous_data.grouper(X)
Expand All @@ -146,7 +206,7 @@ def trainer(X,
for eidx in xrange(max_epochs):
n_samples = 0

print 'Epoch ', eidx
print ("Epoch "), eidx

for x, y, z in train_iter:
n_samples += len(x)
Expand All @@ -155,7 +215,7 @@ def trainer(X,
x, x_mask, y, y_mask, z, z_mask = homogeneous_data.prepare_data(x, y, z, worddict, maxlen=maxlen_w, n_words=n_words)

if x == None:
print 'Minibatch with zero sample under length ', maxlen_w
print ("Minibatch with zero sample under length "), maxlen_w
uidx -= 1
continue

Expand All @@ -165,23 +225,46 @@ def trainer(X,
ud = time.time() - ud_start

if numpy.isnan(cost) or numpy.isinf(cost):
print 'NaN detected'
print ("NaN detected")
return 1., 1., 1.

if numpy.mod(uidx, dispFreq) == 0:
print 'Epoch ', eidx, 'Update ', uidx, 'Cost ', cost, 'UD ', ud
print ("Epoch"), eidx, 'Update ', uidx, 'Cost ', cost, 'UD ', ud

if numpy.mod(uidx, saveFreq) == 0:
print 'Saving...',
print ("Saving..."),

params = unzip(tparams)
numpy.savez(saveto, history_errs=[], **params)
pkl.dump(model_options, open('%s.pkl'%saveto, 'wb'))
print 'Done'
pkl.dump(model_options, open('C:/Users/Hp 15/Desktop/vocab/', 'wb'))
print ("Done")

print 'Seen %d samples'%n_samples
print ("Seen %d samples")+n_samples

if __name__ == '__main__':
print ("Done shabnam")
file = open('C:/Users/Hp 15/Desktop/11525.txt', "r")
X=file.read()
trainer(X,
dim_word=620, # word vector dimensionality
dim=2400, # the number of GRU units
encoder='gru',
decoder='gru',
max_epochs=5,
dispFreq=1,
decay_c=0.,
grad_clip=5.,
n_words=20000,
maxlen_w=30,
optimizer='adam',
batch_size = 64,
saveto='C:/Users/Hp 15/Desktop/vocab/',

dictionary='C:/Users/Hp 15/Desktop/vocab/vocab.txt',
saveFreq=1000,
reload_=False)
print (".npz shabnam")
pass