# -*- coding: utf-8 -*-
#pylint: skip-file
import time
import sys
import numpy as np
import theano
import theano.tensor as T
from utils_pg import *
from rnn import *
use_gpu(1)
import data
drop_rate = 0.
batch_size = 1
seqs, i2w, w2i, data_xy = data.load_hlm("/data/hlm/hlm.txt", batch_size)
hidden_size = [400, 400]
dim_x = len(w2i)
dim_y = len(w2i)
print dim_x, dim_y
cell = "gru" # cell = "gru" or "lstm"
optimizer = "adadelta"
print "building..."
model = RNN(dim_x, dim_y, hidden_size, cell, optimizer, drop_rate)
print "load model..."
model = load_model("./model/rnn_hlm.model", model)
'''
num_x = 0.0
# __author__ = 'taowei'
# -*- coding: utf-8 -*-
import time
from rnn import *
import data
# seqs, i2w, w2i = data.char_sequence("./data/toy.txt")
seqs, i2w, w2i, data_xy = data.load_hlm("./data/toy.txt", 50)
lr = 0.5
# layers = []
hidden_size = [100, 100, 100]
cell = "gru"
dim_x = len(w2i)
dim_y = len(w2i)
print dim_x, dim_y
model = RNN(dim_x, dim_y, hidden_size, cell)
# batch train
start = time.time()
for i in xrange(100):
acc = 0.0
in_start = time.time()
# for s in xrange(len(seqs)):
# seq = seqs[s]
# X = seq[0 : len(seq) - 1, ]
# Y = seq[1 : len(seq), ]
# model.batch_train(X, Y, lr)
import data
# set use gpu programatically
use_gpu(0)
e = 0.01
lr = 0.1
drop_rate = 0.
batch_size = 50
hidden_size = [400, 400]
# try: gru, lstm
cell = "gru"
# try: sgd, momentum, rmsprop, adagrad, adadelta, adam
optimizer = "adadelta"
seqs, i2w, w2i, data_xy = data.load_hlm("/data/hlm/hlm.txt", batch_size)
dim_x = len(w2i)
dim_y = len(w2i)
print "#features = ", dim_x, "#labels = ", dim_y
print "compiling..."
model = RNN(dim_x, dim_y, hidden_size, cell, optimizer, drop_rate)
#model = load_model("./model/rnn_hlm.model", model)
print "training..."
start = time.time()
g_error = 9999.9999
for i in xrange(100):
error = 0.0
in_start = time.time()
for batch_id, xy in data_xy.items():
X, Y, mask, local_batch_size = data.index2seqs(seqs, xy[0], w2i)
