learn_count.py

from __future__ import absolute_import, division, print_function

import os
import pickle
from six.moves import urllib

import tflearn
from tflearn.data_utils import *
import random

# path = "shakespeare_input.txt"
char_idx_file = 'numbers.pickle'
#
# if not os.path.isfile(path):
#     urllib.request.urlretrieve("https://raw.githubusercontent.com/tflearn/tflearn.github.io/master/resources/shakespeare_input.txt", path)
#
maxlen = 24
#
dictionary = None
if os.path.isfile(char_idx_file):
  print('Loading previous char_idx')
  dictionary = pickle.load(open(char_idx_file, 'rb'))
#
# X, Y, char_idx = \
#     textfile_to_semi_redundant_sequences(path, seq_maxlen=maxlen, redun_step=3,
#                                          pre_defined_char_idx=char_idx)
#

def gen_data(start, end):
    txt = ""
    for i in range(start, end, 2):
        txt+="{:05}-{:05}-".format(i,i+1)
    return txt

start = 0
end = 9999

txt = gen_data(0, 9999)


# for i in range(0, len(txt) - maxlen, 1):
#     print([txt[i: i+ maxlen]])
#     print([txt[i+ maxlen]])
#     if(i == 11):
#         exit()

print("------------------")
print(txt[:100])
print(maxlen)
print("------------------")

# exit()

X, Y, dictionary = string_to_semi_redundant_sequences(txt, seq_maxlen=maxlen, char_idx=dictionary, redun_step=1)

pickle.dump(dictionary, open(char_idx_file,'wb'))

# X=pad_sequences(X, maxlen=maxlen, value=0.)
# Y=pad_sequences(Y, maxlen=maxlen, value=0.)

# Y=pad_sequences(Y)

g = tflearn.input_data([None, maxlen, len(dictionary)])
g = tflearn.lstm(g, 512, return_seq=True)
g = tflearn.dropout(g, 0.5)
g = tflearn.lstm(g, 512, return_seq=True)
g = tflearn.dropout(g, 0.5)
g = tflearn.lstm(g, 512)
g = tflearn.dropout(g, 0.5)
g = tflearn.fully_connected(g, len(dictionary), activation='softmax')
g = tflearn.regression(g, optimizer='adam', loss='categorical_crossentropy',
                       learning_rate=0.0005)

m = tflearn.SequenceGenerator(g, dictionary=dictionary,
                              seq_maxlen=maxlen,
                              clip_gradients=5.0,
                              checkpoint_path='model_shakespeare')

def txtOut(txt):
    txt.split(";")

n_epoch = 3
# m.load("lstm_count.tflearn")
print("")
print("")
print("")
print("")
print("")
print("")
m.load("lstm_count.tflearn")
for i in range(1500):
    r = random.randrange(start, (end-maxlen)-5)
    print(r, r+maxlen+5)
    seed = gen_data(r, r+maxlen+5)

    seed = seed[seed.index("-"):seed.index("-")+maxlen]

    print(seed)
    print(len(seed))



    m.fit(X, Y, validation_set=0.1, batch_size=128,
            n_epoch=n_epoch, run_id='shakespeare', snapshot_epoch=False)
    print("-- TESTING...")
    print("-- Test with temperature of 1.0 --")
    print(m.generate(600, temperature=1.0, seq_seed=seed))
    print("-- Test with temperature of 0.5 --")
    print(m.generate(600, temperature=0.5, seq_seed=seed))

    m.save("lstm_count.tflearn")













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