LSTM_network.py

#long short term memory neural network
from __future__ import absolute_import,division, print_function
import os
from six import moves
import ssl
import tflearn
from tflearn.data_utils import *

#step 1- retrieve the data
path="US_cities.txt"
if not os.path.isfile(path):
	context = ssl._create_unverified_context()
	#get dataset
	moves.urllib.request.urlretrieve("https://raw.githubusercontent.com/tflearn/tflearn.github.io/master/resources/US_Cities.txt", path, context=context)

maxlen=20

#vectorize the text file
X, Y, char_idx= \
	textfile_to_semi_redundant_sequences(path,seq_maxlen=maxlen,redun_step=3)

#create LSTM
g = tflearn.input_data(shape=[None,maxlen,len(char_idx)])
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(char_idx), activation='softmax')
g = tflearn.regression(g, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.001)
#generate cities
m = tflearn.SequenceGenerator(g, dictionary=char_idx,seq_maxlen=maxlen,
	clip_gradients=5.0,
	checkpoint_path='model_us_cities')
for i in range(40):
	seed=random_sequence_from_textfile(path, maxlen)
	m.fit(X,Y, validation_set=0.1, batch_size=128, n_epoch=1, run_id='us_cities')
	print("testing with temp 1.2")
	print(m.generate(30,temperature=1.2, seq_seed=seed))
	print("testing with temp 1.0")
	print(m.generate(30,temperature=1.0, seq_seed=seed))
	print("testing with temp 0.5")
	print(m.generate(30,temperature=0.5, seq_seed=seed))