batch_size = 100
train_batches = 20000
charset = "data/charset.txt"
train_corpus = "data/train.txt"
tag_name = "2_256"
seq_length = 128
save_on_every = 100
epoches = 50
cwd = os.getcwd()
charset = os.path.join(cwd, charset)
train_corpus = os.path.join(cwd, train_corpus)
d = DataGen2(charset, batch_size, seq_length)
gen = d.generate_v1(train_corpus, batches=train_batches)
input_shape = (seq_length, len(d.char2int) + 1)
output_shape = len(d.char2int) + 1
model = get_model(input_shape, output_shape, lstm_cell=True)
model_name = "single_input_single_task"
trainer = TrainingHandler(model, model_name)
trainer.train(tag_name, gen, epoches,
train_batches, save_on_every,
save_model=True)
from data_generator import DataGen
from data_gen2 import DataGen2
from models import *
from training_handler import TrainingHandler
batch_size = 100
batches = 100
seuqnce_length = 100
epoches = 50
charset = "data/charset.txt"
corpus = "data/big.txt"
tag_name = "char_train_128"
save_on_every = 10
cwd = os.getcwd()
charset = os.path.join(cwd, charset)
corpus = os.path.join(cwd, corpus)
d = DataGen2(charset, batch_size, seuqnce_length)
gen = d.generate_vowels_xy(corpus, batches=batches)
input_shape = (seuqnce_length, len(d.char2int) + 1)
char_model = get_model(input_shape, d.n_vowels, lstm_cell=True)
model_name = "char_model"
save_model(char_model, model_name, tag_name)
trainer = TrainingHandler(char_model, model_name)
trainer.train(tag_name, gen, epoches, batches,
save_on_every, save_model=True)