def save(artist, model_path, num_save):
sample_save_dir = c.get_dir('../save/samples/')
sess = tf.Session()
print artist
data_reader = DataReader(artist)
vocab = data_reader.get_vocab()
print 'Init model...'
model = LSTMModel(sess,
vocab,
c.BATCH_SIZE,
c.SEQ_LEN,
c.CELL_SIZE,
c.NUM_LAYERS,
test=True)
saver = tf.train.Saver()
sess.run(tf.initialize_all_variables())
saver.restore(sess, model_path)
print 'Model restored from ' + model_path
artist_save_dir = c.get_dir(join(sample_save_dir, artist))
for i in xrange(num_save):
print i
path = join(artist_save_dir, str(i) + '.txt')
sample = model.generate()
processed_sample = process_sample(sample)
with open(path, 'w') as f:
f.write(processed_sample)
class LyricGenRunner:
def __init__(self, model_load_path, artist_name, test, prime_text):
self.sess = tf.Session()
self.artist_name = artist_name
print 'Process data...'
self.data_reader = DataReader(self.artist_name)
self.vocab = self.data_reader.get_vocab()
print 'Init model...'
self.model = LSTMModel(self.sess,
self.vocab,
c.BATCH_SIZE,
c.SEQ_LEN,
c.CELL_SIZE,
c.NUM_LAYERS,
test=test)
print 'Init variables...'
self.saver = tf.train.Saver(max_to_keep=None)
self.sess.run(tf.initialize_all_variables())
if model_load_path is not None:
self.saver.restore(self.sess, model_load_path)
print 'Model restored from ' + model_load_path
if test:
self.test(prime_text)
else:
self.train()
def train(self):
while True:
inputs, targets = self.data_reader.get_train_batch(
c.BATCH_SIZE, c.SEQ_LEN)
print 'Training model...'
feed_dict = {
self.model.inputs: inputs,
self.model.targets: targets
}
global_step, loss, _ = self.sess.run(
[self.model.global_step, self.model.loss, self.model.train_op],
feed_dict=feed_dict)
print 'Step: %d | loss: %f' % (global_step, loss)
if global_step % c.MODEL_SAVE_FREQ == 0:
print 'Saving model...'
self.saver.save(self.sess,
join(c.MODEL_SAVE_DIR,
self.artist_name + '.ckpt'),
global_step=global_step)
def test(self, prime_text):
sample = self.model.generate(prime=prime_text)
print sample
class LyricGenRunner:
def __init__(self, model_load_path, artist_name, test, prime_text):
"""
Initializes the Lyric Generation Runner.
@param model_load_path: The path from which to load a previously-saved model.
Default = None.
@param artist_name: The name of the artist on which to train. (Used to grab data).
Default = 'kanye_west'
@param test: Whether to test or train the model. Testing generates a sequence from the
provided model and artist. Default = False.
@param prime_text: The text with which to start the test sequence.
"""
self.sess = tf.Session()
self.artist_name = artist_name
print 'Process data...'
self.data_reader = DataReader(self.artist_name)
self.vocab = self.data_reader.get_vocab()
print 'Init model...'
self.model = LSTMModel(self.sess,
self.vocab,
c.BATCH_SIZE,
c.SEQ_LEN,
c.CELL_SIZE,
c.NUM_LAYERS,
test=test)
print 'Init variables...'
self.saver = tf.train.Saver(max_to_keep=None)
self.sess.run(tf.global_variables_initializer())
# if load path specified, load a saved model
if model_load_path is not None:
self.saver.restore(self.sess, model_load_path)
print 'Model restored from ' + model_load_path
if test:
self.test(prime_text)
else:
self.train()
def train(self):
"""
Runs a training loop on the model.
"""
keep_training = True
while keep_training:
inputs, targets = self.data_reader.get_train_batch(
c.BATCH_SIZE, c.SEQ_LEN)
print 'Training model...'
feed_dict = {
self.model.inputs: inputs,
self.model.targets: targets
}
global_step, loss, _ = self.sess.run(
[self.model.global_step, self.model.loss, self.model.train_op],
feed_dict=feed_dict)
if global_step == c.MAX_STEPS:
keep_training = False
print 'Step: %d | loss: %f' % (global_step, loss)
if global_step % c.MODEL_SAVE_FREQ == 0 or not keep_training:
print 'Saving model...'
self.saver.save(self.sess,
join(c.MODEL_SAVE_DIR,
self.artist_name + '.ckpt'),
global_step=global_step)
def test(self, prime_text):
"""
Generates a text sequence.
"""
# generate and save sample sequence
sample = self.model.generate(prime=prime_text, num_out=200).replace(
'.', '.\n').replace(',', ',\n ')
print sample
class LyricsGenerator():
def __init__(self, model_name='model', test=False):
self.session = tf.Session()
print('Process data...')
self.data_reader = DataReader()
self.vocab = self.data_reader.get_vocab()
print('Init model...')
self.model = Model(self.session, self.vocab, c.BATCH_SIZE, c.SEQ_LEN,
c.CELL_SIZE, c.NUM_LAYERS, test)
print('Init variables...')
self.test = test
self.saver = tf.train.Saver(max_to_keep=None)
self.session.run(tf.global_variables_initializer())
self.model_name = model_name
def train(self):
"""Runs a training loop on the model.
"""
while True:
inputs, targets = self.data_reader.get_train_batch(
c.BATCH_SIZE, c.SEQ_LEN)
print('Training model...')
feed_dict = {
self.model.inputs: inputs,
self.model.targets: targets
}
global_step, loss, _ = self.session.run(
[self.model.global_step, self.model.loss, self.model.train_op],
feed_dict=feed_dict)
print('Step: %d | loss: %f' % (global_step, loss))
if global_step % c.MODEL_SAVE_FREQ == 0:
print('Saving model...')
model_path = '{}{}.ckpt'.format(c.MODEL_SAVE_DIR,
self.model_name)
self.saver.save(self.session,
model_path,
global_step=global_step)
def generate(self):
"""Generate the lyrics
"""
return self.model.generate()
def run(self):
# if self.test and self._load_saved_model():
if self.test:
return self.generate()
else:
return self.train()
def _load_saved_model(self):
print("model loading ...")
ok = True
try:
model_path = '{}{}.ckpt-{}'.format(c.MODEL_SAVE_DIR,
self.model_name,
c.MODEL_SAVE_FREQ)
self.saver.restore(self.session, model_path)
except ValueError:
ok = False
print("Done!")
return ok
from data_reader import DataReader
d = DataReader('kanye_west')
print(len(d.get_vocab()))
class LyricGenRunner:
def __init__(self, model_load_path, artist_name, test, prime_text):
"""
Initializes the Lyric Generation Runner.
@param model_load_path: The path from which to load a previously-saved model.
Default = None.
@param artist_name: The name of the artist on which to train. (Used to grab data).
Default = 'kanye_west'
@param test: Whether to test or train the model. Testing generates a sequence from the
provided model and artist. Default = False.
@param prime_text: The text with which to start the test sequence.
"""
self.sess = tf.Session()
self.artist_name = artist_name
print('Process data...') #Py3Upgrade
self.data_reader = DataReader(self.artist_name)
self.vocab = self.data_reader.get_vocab()
print('Init model...') #Py3Upgrade
self.model = LSTMModel(self.sess,
self.vocab,
c.BATCH_SIZE,
c.SEQ_LEN,
c.CELL_SIZE,
c.NUM_LAYERS,
test=test)
print('Init variables...') #Py3Upgrade
self.saver = tf.train.Saver(max_to_keep=None)
self.sess.run(tf.global_variables_initializer())
# if load path specified, load a saved model
if model_load_path is not None:
self.saver.restore(self.sess, model_load_path)
print('Model restored from ', model_load_path) #Py3Upgrade
if test:
self.test(prime_text)
else:
self.train()
def train(self):
"""
Runs a training loop on the model.
"""
for i in range(
30000): #changed from True while loop to 30,000 for loop
inputs, targets = self.data_reader.get_train_batch(
c.BATCH_SIZE, c.SEQ_LEN)
print('Training model...') #Py3Upgrade
feed_dict = {
self.model.inputs: inputs,
self.model.targets: targets
}
global_step, loss, _ = self.sess.run(
[self.model.global_step, self.model.loss, self.model.train_op],
feed_dict=feed_dict)
print('Step: %d | loss: %f' % (global_step, loss)) #Py3Upgrade
if global_step % c.MODEL_SAVE_FREQ == 0:
print('Saving model...') #Py3Upgrade
self.saver.save(self.sess,
join(c.MODEL_SAVE_DIR,
self.artist_name + '.ckpt'),
global_step=global_step)
def test(self, prime_text):
"""
Generates a text sequence.
"""
# generate and save sample sequence
sample = self.model.generate(prime=prime_text)
print(sample) #Py3Upgrade
# save sample as txt file
f = open("../save/outputs/dirty/" + self.artist_name + ".txt", "w+")
f.write(sample)
f.close()
from data_reader import DataReader
d = DataReader('kanye_west')
print len(d.get_vocab())
# -*- coding: utf-8 -*-
from flask import Flask, request, render_template
from runner import LyricGenRunner
from LSTMModel import LSTMModel
from data_reader import DataReader
import tensorflow as tf
import constants as c
app = Flask(__name__)
sess = tf.Session()
artist_name = 'all_lyrics'
data_reader = DataReader(artist_name)
vocab = data_reader.get_vocab()
model = LSTMModel(sess, vocab, c.BATCH_SIZE, c.SEQ_LEN, c.CELL_SIZE, c.NUM_LAYERS, test=True)
saver = tf.train.Saver(max_to_keep=None)
sess.run(tf.global_variables_initializer())
load_path = '/home/ubuntu/eight/encore.ai/save/models/test/all_lyrics.ckpt-25000'
saver.restore(sess, load_path)
@app.route('/')
def eight():
with open('/home/ubuntu/eight/templates/valentine2.htm', 'r', encoding='cp1252') as f:
return f.read()
#return render_template('valentine2.htm')
#return render_template('predict.html')
@app.route('/', methods=['POST'])
def eight_post():
