tflearn_rnn_sherlock.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 *

path = "data/sherlock_edited.txt"
char_idx_file = 'char_idx.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 = 25

char_idx = None
if os.path.isfile(char_idx_file):
    print('Loading previous char_idx')
    char_idx = pickle.load(open(char_idx_file, 'rb'))

X, Y, char_idx = \
    textfile_to_semi_redundant_sequences(path, seq_maxlen=maxlen, redun_step=3)

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

# input
i = tflearn.input_data([None, maxlen, len(char_idx)])

# path a
a = tflearn.conv_1d(i, 16, 10)
a = tflearn.lstm(a, 16, return_seq=True)
a = tflearn.dropout(a, 0.5)
a = tflearn.gru(a, 32)
a = tflearn.dropout(a, 0.5)

# path b
b = tflearn.lstm(i, 48, return_seq=True)
b = tflearn.dropout(b, 0.5)
b = tflearn.gru(b, 64)
b = tflearn.dropout(b, 0.5)

# path c
c = tflearn.lstm(i, 96, return_seq=True)
c = tflearn.dropout(c, 0.5)
c = tflearn.gru(c, 128)
c = tflearn.dropout(c, 0.5)

# merge
m = tflearn.merge_outputs([a, b, c])

# fully connected
fc = tflearn.highway(m, 224, activation='relu')
fc = tflearn.dropout(fc, 0.5)
fc = tflearn.fully_connected(fc, len(char_idx), activation='softmax')
fc = tflearn.regression(fc, optimizer='adam', loss='categorical_crossentropy',
                       learning_rate=0.002)

# final model
m = tflearn.SequenceGenerator(fc, dictionary=char_idx,
                              seq_maxlen=maxlen,
                              clip_gradients=5.0,
                              checkpoint_path='model_aiw',
                              tensorboard_verbose=1)

for i in range(10):
    seed = random_sequence_from_textfile(path, maxlen)
    m.fit(X, Y, validation_set=0.1, batch_size=128,
          n_epoch=1, run_id='aiw')
    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))