def main():
data_root = '../data/wordinds/'
print 'starting L matrix construction'
gw = GloveWrapper(verbose=True)
L0 = gw.L
train_files = os.listdir(data_root+ '/train/')
print 'getting train and test data'
train_x, train_y, train_D, _ = get_data(gw, train_files, 'train/')
pdb.set_trace()
test_x, test_y, test_D, _ = get_data(gw, train_files, 'test/')
D0 = np.random.randn(train_D[-1] + 1, 300)
print 'got train and test data'
n_epochs = 25
train_x = train_x*n_epochs
train_y = train_y*n_epochs
train_D = train_D*n_epochs
model = SimpleDRNNLM(L0, D0, U0 = L0, alpha=0.05, rseed=10, bptt=3)
print 'constructed model, training...'
model.custom_train_sgd(train_x,train_y, train_D, apply_to=['H','U','L','D'], printevery=5, costevery=25)
print 'training done'
print 'saving model'
with open('../data/simple_drnnlm_model.pkl', 'w') as model_file:
pkl.dump(model, model_file)
print 'model saved'
for i in range(10):
seq, J = model.generate_sequence(1, gw.get_index("SSTART"), gw.get_index("EEND"), maxlen=100)
print " ".join(seq_to_words(seq))
import os
import numpy as np
from theano import tensor as T
import cPickle as cpkl
from rnn_lang import get_data
from rnn_slu import RNNSLU
from glove_wrapper import GloveWrapper
data_root = '../data/wordinds/'
print 'starting L matrix construction'
gw = GloveWrapper(verbose=True)
train_files = os.listdir(data_root+ '/train/')
print 'getting train and test data'
train_x, train_y, train_D, train_class = get_data(gw, train_files, 'train/')
print 'initializing'
drnn = RNNSLU(train_D[-1]+1, gw)
print 'intialized'
num_epochs = 25
learn_rate = 0.01
for e in range(num_epochs):
for i in range(len(train_x)):
x = np.array(train_x[i]).astype('int32')
y = np.array(train_y[i]).astype('int32')
drnn.sentence_train(x, y, learn_rate, train_D[i])
drnn.normalize()
def __init__(self):
self.glove_vecs = GloveWrapper(verbose=True)
