%matplotlib inline
%config InlineBackend.figure_formats = {'png','retina'}
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.python.framework import dtypes
from tensorflow.contrib import learn
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.layers.recurrent import LSTM
from keras.layers import Dropout
# 我们先取了前 1500 个样本,先观察一下数据
data = pd.read_csv('./elec_load.csv',error_bad_lines=False)
plt.subplot()
plot_test, = plt.plot(data.values[:1500],label='Load')
plt.legend(handles= [plot_test] )
# 从下面的图中可以大致看出,每 100 个采样呈现一个循环,这跟我们每天 96 个采样点吻合。
# 数据预处理
# 为了使得反向传播更加容易收敛,我们一般都会对数据进行正规化,
# 即放缩和数据中心化,即每个数据减去均值,并按照最大值进行缩放(将最大值限定到某个固定值,如 1)
# 通过 pandas.describe() 查看数据情况
print data.describe()
array = (data.values - 147.0) / 339.0
plt.subplot()
plot_test, = plt.plot(array[:1500],label='Normalized Load')
plt.legend(handles=[plot_test])
Load
count 140256.000000
mean 145.332503
std 48.477976
min 0.000000
25% 106.850998
50% 151.428571
75% 177.557604
max 338.218126
# 构建数据集 (本例中,通过最近的 5 个值预测下一个值)
listX = []
listy = []
X = {}
y = {}
for i in range(0,len(array) - 6):
listX.append(array[i:i+5].reshape([5,1]))
listy.append(array[i+6])
arrayX = np.array(listX) # (140250, 5, 1)
arrayy = np.array(listy) # (140250, 1)
# 划分数据集
X['train']=arrayX[0:13000]
X['test']=arrayX[13000:14000]
y['train']=arrayy[0:13000]
y['test']=arrayy[13000:14000]
# 建立模型,使用了连接 10 个串联的 LSTM multicell,
# 每个 multicell 的输出都是一个线性回归
model = Sequential()
model.add(LSTM(input_dim=1,output_dim=50, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(input_dim=100,output_dim=200,return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(output_dim=1))
model.add(Activation('linear'))
model.compile(loss='mse',optimizer='rmsprop')
# Fit the model to the data
model.fit(X['train'],y['train'],batch_size=512,nb_epoch=10,validation_split=0.08)
test_results = model.predict(X['test'])
# Rescale the test dataset and predicted data
test_results = test_results * 339 + 147
y['test'] = y['test'] * 339 + 147
Train on 11960 samples, validate on 1040 samples
Epoch 1/10
11960/11960 [==============================] - 3s 218us/step - loss: 0.0032 - val_loss: 0.0022
Epoch 2/10
11960/11960 [==============================] - 1s 123us/step - loss: 0.0020 - val_loss: 0.0021
Epoch 3/10
11960/11960 [==============================] - 1s 119us/step - loss: 0.0018 - val_loss: 0.0019
Epoch 4/10
11960/11960 [==============================] - 1s 124us/step - loss: 0.0017 - val_loss: 0.0016
Epoch 5/10
11960/11960 [==============================] - 1s 122us/step - loss: 0.0016 - val_loss: 0.0014
Epoch 6/10
11960/11960 [==============================] - 1s 118us/step - loss: 0.0015 - val_loss: 0.0014
Epoch 7/10
11960/11960 [==============================] - 1s 115us/step - loss: 0.0014 - val_loss: 0.0013
Epoch 8/10
11960/11960 [==============================] - 1s 115us/step - loss: 0.0013 - val_loss: 0.0012
Epoch 9/10
11960/11960 [==============================] - 1s 115us/step - loss: 0.0013 - val_loss: 0.0011
Epoch 10/10
11960/11960 [==============================] - 1s 116us/step - loss: 0.0013 - val_loss: 0.0011
plt.subplot()
plot_predicted, = plt.plot(test_results,label='predicted')
plot_test, = plt.plot(y['test'],label='test')
plt.legend(handles= [plot_predicted,plot_test])
ABC 格式的构成:由两部分构成,文件头和文件体。
数据集生成
# coding: utf-8
import random
input = open('original.txt','r').read().split('X:')
# print len(input)
for i in range(1,1000):
print 'X: ' + input[random.randint(1,6)] + '\n _____________________________\n'
python generate_dataset.py > input.txt
之后就可以将其作为输入了。最后,在得到 .abc 格式的音乐字符后,可以到 这里,转换五线谱
源代码如下:
model.py
import tensorflow as tf
from tensorflow.contrib import rnn
from tensorflow.contrib import legacy_seq2seq
import numpy as np
class Model():
def __init__(self, args, training=True):
self.args = args
if not training:
args.batch_size = 1
args.seq_length = 1
if args.model == 'rnn':
cell_fn = rnn.BasicRNNCell
elif args.model == 'gru':
cell_fn = rnn.GRUCell
elif args.model == 'lstm':
cell_fn = rnn.BasicLSTMCell
elif args.model == 'nas':
cell_fn = rnn.NASCell
else:
raise Exception("model type not supported: {}".format(args.model))
cells = []
for _ in range(args.num_layers):
cell = cell_fn(args.rnn_size)
if training and (args.output_keep_prob < 1.0 or args.input_keep_prob < 1.0):
cell = rnn.DropoutWrapper(cell,
input_keep_prob=args.input_keep_prob,
output_keep_prob=args.output_keep_prob)
cells.append(cell)
self.cell = cell = rnn.MultiRNNCell(cells, state_is_tuple=True)
self.input_data = tf.placeholder(
tf.int32, [args.batch_size, args.seq_length])
self.targets = tf.placeholder(
tf.int32, [args.batch_size, args.seq_length])
self.initial_state = cell.zero_state(args.batch_size, tf.float32)
with tf.variable_scope('rnnlm'):
softmax_w = tf.get_variable("softmax_w",
[args.rnn_size, args.vocab_size])
softmax_b = tf.get_variable("softmax_b", [args.vocab_size])
embedding = tf.get_variable("embedding", [args.vocab_size, args.rnn_size])
inputs = tf.nn.embedding_lookup(embedding, self.input_data)
# dropout beta testing: double check which one should affect next line
if training and args.output_keep_prob:
inputs = tf.nn.dropout(inputs, args.output_keep_prob)
inputs = tf.split(inputs, args.seq_length, 1)
inputs = [tf.squeeze(input_, [1]) for input_ in inputs]
def loop(prev, _):
prev = tf.matmul(prev, softmax_w) + softmax_b
prev_symbol = tf.stop_gradient(tf.argmax(prev, 1))
return tf.nn.embedding_lookup(embedding, prev_symbol)
outputs, last_state = legacy_seq2seq.rnn_decoder(inputs, self.initial_state, cell, loop_function=loop if not training else None, scope='rnnlm')
output = tf.reshape(tf.concat(outputs, 1), [-1, args.rnn_size])
self.logits = tf.matmul(output, softmax_w) + softmax_b
self.probs = tf.nn.softmax(self.logits)
loss = legacy_seq2seq.sequence_loss_by_example(
[self.logits],
[tf.reshape(self.targets, [-1])],
[tf.ones([args.batch_size * args.seq_length])])
self.cost = tf.reduce_sum(loss) / args.batch_size / args.seq_length
with tf.name_scope('cost'):
self.cost = tf.reduce_sum(loss) / args.batch_size / args.seq_length
self.final_state = last_state
self.lr = tf.Variable(0.0, trainable=False)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tvars),
args.grad_clip)
with tf.name_scope('optimizer'):
optimizer = tf.train.AdamOptimizer(self.lr)
self.train_op = optimizer.apply_gradients(zip(grads, tvars))
# instrument tensorboard
tf.summary.histogram('logits', self.logits)
tf.summary.histogram('loss', loss)
tf.summary.scalar('train_loss', self.cost)
def sample(self, sess, chars, vocab, num=200, prime='The ', sampling_type=1):
state = sess.run(self.cell.zero_state(1, tf.float32))
for char in prime[:-1]:
x = np.zeros((1, 1))
x[0, 0] = vocab[char]
feed = {self.input_data: x, self.initial_state: state}
[state] = sess.run([self.final_state], feed)
def weighted_pick(weights):
t = np.cumsum(weights)
s = np.sum(weights)
return(int(np.searchsorted(t, np.random.rand(1)*s)))
ret = prime
char = prime[-1]
for n in range(num):
x = np.zeros((1, 1))
x[0, 0] = vocab[char]
feed = {self.input_data: x, self.initial_state: state}
[probs, state] = sess.run([self.probs, self.final_state], feed)
p = probs[0]
if sampling_type == 0:
sample = np.argmax(p)
elif sampling_type == 2:
if char == ' ':
sample = weighted_pick(p)
else:
sample = np.argmax(p)
else: # sampling_type == 1 default:
sample = weighted_pick(p)
pred = chars[sample]
ret += pred
char = pred
return ret
import codecs
import os
import collections
from six.moves import cPickle
import numpy as np
class TextLoader():
def __init__(self, data_dir, batch_size, seq_length, encoding='utf-8'):
self.data_dir = data_dir
self.batch_size = batch_size
self.seq_length = seq_length
self.encoding = encoding
input_file = os.path.join(data_dir, "input.txt")
vocab_file = os.path.join(data_dir, "vocab.pkl")
tensor_file = os.path.join(data_dir, "data.npy")
if not (os.path.exists(vocab_file) and os.path.exists(tensor_file)):
print("reading text file")
self.preprocess(input_file, vocab_file, tensor_file)
else:
print("loading preprocessed files")
self.load_preprocessed(vocab_file, tensor_file)
self.create_batches()
self.reset_batch_pointer()
def preprocess(self, input_file, vocab_file, tensor_file):
with codecs.open(input_file, "r", encoding=self.encoding) as f:
data = f.read()
counter = collections.Counter(data)
count_pairs = sorted(counter.items(), key=lambda x: -x[1])
self.chars, _ = zip(*count_pairs)
self.vocab_size = len(self.chars)
self.vocab = dict(zip(self.chars, range(len(self.chars))))
with open(vocab_file, 'wb') as f:
cPickle.dump(self.chars, f)
self.tensor = np.array(list(map(self.vocab.get, data)))
np.save(tensor_file, self.tensor)
def load_preprocessed(self, vocab_file, tensor_file):
with open(vocab_file, 'rb') as f:
self.chars = cPickle.load(f)
self.vocab_size = len(self.chars)
self.vocab = dict(zip(self.chars, range(len(self.chars))))
self.tensor = np.load(tensor_file)
self.num_batches = int(self.tensor.size / (self.batch_size *
self.seq_length))
def create_batches(self):
self.num_batches = int(self.tensor.size / (self.batch_size *
self.seq_length))
# When the data (tensor) is too small,
# let's give them a better error message
if self.num_batches == 0:
assert False, "Not enough data. Make seq_length and batch_size small."
self.tensor = self.tensor[:self.num_batches * self.batch_size * self.seq_length]
xdata = self.tensor
ydata = np.copy(self.tensor)
ydata[:-1] = xdata[1:]
ydata[-1] = xdata[0]
self.x_batches = np.split(xdata.reshape(self.batch_size, -1),
self.num_batches, 1)
self.y_batches = np.split(ydata.reshape(self.batch_size, -1),
self.num_batches, 1)
def next_batch(self):
x, y = self.x_batches[self.pointer], self.y_batches[self.pointer]
self.pointer += 1
return x, y
def reset_batch_pointer(self):
self.pointer = 0
from __future__ import print_function
import tensorflow as tf
import argparse
import time
import os
from six.moves import cPickle
from utils import TextLoader
from model import Model
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--data_dir', type=str, default='data/tinyshakespeare',
help='data directory containing input.txt')
parser.add_argument('--save_dir', type=str, default='save',
help='directory to store checkpointed models')
parser.add_argument('--log_dir', type=str, default='logs',
help='directory to store tensorboard logs')
parser.add_argument('--rnn_size', type=int, default=128,
help='size of RNN hidden state')
parser.add_argument('--num_layers', type=int, default=2,
help='number of layers in the RNN')
parser.add_argument('--model', type=str, default='lstm',
help='rnn, gru, lstm, or nas')
parser.add_argument('--batch_size', type=int, default=50,
help='minibatch size')
parser.add_argument('--seq_length', type=int, default=50,
help='RNN sequence length')
parser.add_argument('--num_epochs', type=int, default=50,
help='number of epochs')
parser.add_argument('--save_every', type=int, default=1000,
help='save frequency')
parser.add_argument('--grad_clip', type=float, default=5.,
help='clip gradients at this value')
parser.add_argument('--learning_rate', type=float, default=0.002,
help='learning rate')
parser.add_argument('--decay_rate', type=float, default=0.97,
help='decay rate for rmsprop')
parser.add_argument('--output_keep_prob', type=float, default=1.0,
help='probability of keeping weights in the hidden layer')
parser.add_argument('--input_keep_prob', type=float, default=1.0,
help='probability of keeping weights in the input layer')
parser.add_argument('--init_from', type=str, default=None,
help="""continue training from saved model at this path. Path must contain files saved by previous training process:
'config.pkl' : configuration;
'chars_vocab.pkl' : vocabulary definitions;
'checkpoint' : paths to model file(s) (created by tf).
Note: this file contains absolute paths, be careful when moving files around;
'model.ckpt-*' : file(s) with model definition (created by tf)
""")
args = parser.parse_args()
train(args)
def train(args):
data_loader = TextLoader(args.data_dir, args.batch_size, args.seq_length)
args.vocab_size = data_loader.vocab_size
# check compatibility if training is continued from previously saved model
if args.init_from is not None:
# check if all necessary files exist
assert os.path.isdir(args.init_from)," %s must be a a path" % args.init_from
assert os.path.isfile(os.path.join(args.init_from,"config.pkl")),"config.pkl file does not exist in path %s"%args.init_from
assert os.path.isfile(os.path.join(args.init_from,"chars_vocab.pkl")),"chars_vocab.pkl.pkl file does not exist in path %s" % args.init_from
ckpt = tf.train.get_checkpoint_state(args.init_from)
assert ckpt, "No checkpoint found"
assert ckpt.model_checkpoint_path, "No model path found in checkpoint"
# open old config and check if models are compatible
with open(os.path.join(args.init_from, 'config.pkl'), 'rb') as f:
saved_model_args = cPickle.load(f)
need_be_same = ["model", "rnn_size", "num_layers", "seq_length"]
for checkme in need_be_same:
assert vars(saved_model_args)[checkme]==vars(args)[checkme],"Command line argument and saved model disagree on '%s' "%checkme
# open saved vocab/dict and check if vocabs/dicts are compatible
with open(os.path.join(args.init_from, 'chars_vocab.pkl'), 'rb') as f:
saved_chars, saved_vocab = cPickle.load(f)
assert saved_chars==data_loader.chars, "Data and loaded model disagree on character set!"
assert saved_vocab==data_loader.vocab, "Data and loaded model disagree on dictionary mappings!"
if not os.path.isdir(args.save_dir):
os.makedirs(args.save_dir)
with open(os.path.join(args.save_dir, 'config.pkl'), 'wb') as f:
cPickle.dump(args, f)
with open(os.path.join(args.save_dir, 'chars_vocab.pkl'), 'wb') as f:
cPickle.dump((data_loader.chars, data_loader.vocab), f)
model = Model(args)
with tf.Session() as sess:
# instrument for tensorboard
summaries = tf.summary.merge_all()
writer = tf.summary.FileWriter(
os.path.join(args.log_dir, time.strftime("%Y-%m-%d-%H-%M-%S")))
writer.add_graph(sess.graph)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
# restore model
if args.init_from is not None:
saver.restore(sess, ckpt.model_checkpoint_path)
for e in range(args.num_epochs):
sess.run(tf.assign(model.lr,
args.learning_rate * (args.decay_rate ** e)))
data_loader.reset_batch_pointer()
state = sess.run(model.initial_state)
for b in range(data_loader.num_batches):
start = time.time()
x, y = data_loader.next_batch()
feed = {model.input_data: x, model.targets: y}
for i, (c, h) in enumerate(model.initial_state):
feed[c] = state[i].c
feed[h] = state[i].h
train_loss, state, _ = sess.run([model.cost, model.final_state, model.train_op], feed)
# instrument for tensorboard
summ, train_loss, state, _ = sess.run([summaries, model.cost, model.final_state, model.train_op], feed)
writer.add_summary(summ, e * data_loader.num_batches + b)
end = time.time()
print("{}/{} (epoch {}), train_loss = {:.3f}, time/batch = {:.3f}"
.format(e * data_loader.num_batches + b,
args.num_epochs * data_loader.num_batches,
e, train_loss, end - start))
if (e * data_loader.num_batches + b) % args.save_every == 0\
or (e == args.num_epochs-1 and
b == data_loader.num_batches-1):
# save for the last result
checkpoint_path = os.path.join(args.save_dir, 'model.ckpt')
saver.save(sess, checkpoint_path,
global_step=e * data_loader.num_batches + b)
print("model saved to {}".format(checkpoint_path))
if __name__ == '__main__':
main()
from __future__ import print_function
import tensorflow as tf
import argparse
import os
from six.moves import cPickle
from model import Model
from six import text_type
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--save_dir', type=str, default='save',
help='model directory to store checkpointed models')
parser.add_argument('-n', type=int, default=500,
help='number of characters to sample')
parser.add_argument('--prime', type=text_type, default=u' ',
help='prime text')
parser.add_argument('-- ', type=int, default=1,
help='0 to use max at each timestep, 1 to sample at '
'each timestep, 2 to sample on spaces')
args = parser.parse_args()
sample(args)
def sample(args):
with open(os.path.join(args.save_dir, 'config.pkl'), 'rb') as f:
saved_args = cPickle.load(f)
with open(os.path.join(args.save_dir, 'chars_vocab.pkl'), 'rb') as f:
chars, vocab = cPickle.load(f)
model = Model(saved_args, training=False)
with tf.Session() as sess:
tf.global_variables_initializer().run()
saver = tf.train.Saver(tf.global_variables())
ckpt = tf.train.get_checkpoint_state(args.save_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print(model.sample(sess, chars, vocab, args.n, args.prime,
args.sample).encode('utf-8'))
if __name__ == '__main__':
main()