5分钟用keras实验LSTM时间序列预测最简单版，简单就是一切！能运行就是一切！

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我是靠谱客的博主可耐招牌，这篇文章主要介绍5分钟用keras实验LSTM时间序列预测最简单版，简单就是一切！能运行就是一切！，现在分享给大家，希望可以做个参考。

输入测试数据，这里以sin函数为例：

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data = np.sin(x)
print(data)

[0. 0.14943813 0.29552021 ... 0.96101339 0.90890233 0.83637928]
这个函数你可以随便写，也可以展开应用，开个脑洞：

一，把一首歌的单声部时序录入，如果是多声部，如钢琴，录入多次运行多次好了。

比如理查德的经典和弦套路，梦中婚礼，星空，6253进行，都可以理解为是时间序列，预测和弦是简单的，还可以预测具体的音阶时长，最后做一万首理查德风格的钢琴曲！汪峰，许巍，Yiruma，肖邦都可以！~

声音可以描述成12分律，用相对音高，首调来描述，Do Re Mi Fa So La Si分别对应，0和12同唱名

0，2，4，5，7，9，11，12 然后可以转成MusicXML,放到OVE,GuitaPro 里播放。

二，古诗，小说亦何尝不是时间序列？录入三国演义，全唐诗，会输出什么？

三，图片亦何尝不是时间序列？录入大量Ru1mm图片，会输出什么？

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import matplotlib.pyplot as plt
import numpy as np
from numpy import newaxis
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.layers.recurrent import LSTM
import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
step = 0.15
steps = 750
x = np.arange(0, steps, step)
data = np.sin(x)
print(data)
SEQ_LENGTH = 100
sequence_length = SEQ_LENGTH + 1
result = []
for index in range(len(data) - sequence_length):
result.append(data[index: index + sequence_length])
result = np.array(result)
row = round(0.9 * result.shape[0])
train = result[:int(row), :]
np.random.shuffle(train)
x_train = train[:, :-1]
y_train = train[:, -1]
x_test = result[int(row):, :-1]
y_test = result[int(row):, -1]
# LSTM层的输入必须是三维的
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
print(x_train)
# Neural Network model
HIDDEN_DIM = 512
LAYER_NUM = 10
model = Sequential()
model.add(LSTM(50, input_shape=(x_train.shape[1], x_train.shape[2]), return_sequences=True))
model.add(LSTM(100, return_sequences=False))
model.add(Dense(1))
model.add(Activation('linear'))
model.compile(loss="mse", optimizer="rmsprop")
model.summary()
BATCH_SIZE = 32
epoch = 1
model.fit(x_train, y_train, batch_size=BATCH_SIZE, verbose=1, epochs=epoch, validation_split=0.05)
# start with first frame
curr_frame = x_test[0]
# start with zeros
# curr_frame = np.zeros((100,1))
predicted = []
for i in range(len(x_test)):
predicted.append(model.predict(curr_frame[newaxis, :, :])[0, 0])
curr_frame = curr_frame[1:]
curr_frame = np.insert(curr_frame, [SEQ_LENGTH - 1], predicted[-1], axis=0)
predicted1 = model.predict(x_test)
predicted1 = np.reshape(predicted1, (predicted1.size,))
plt.figure(1)
plt.subplot(211)
plt.plot(predicted)
plt.plot(y_test)
plt.subplot(212)
plt.plot(predicted1)
plt.plot(y_test)
plt.show()

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import matplotlib.pyplot as plt
import numpy as np
from numpy import newaxis
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.layers.recurrent import LSTM
import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
step = 0.15
steps = 750
x = np.arange(0, steps, step)
data = np.sin(x)
print(data)
SEQ_LENGTH = 100
sequence_length = SEQ_LENGTH + 1
result = []
for index in range(len(data) - sequence_length):
result.append(data[index: index + sequence_length])
result = np.array(result)
row = round(0.9 * result.shape[0])
train = result[:int(row), :]
np.random.shuffle(train)
x_train = train[:, :-1]
y_train = train[:, -1]
x_test = result[int(row):, :-1]
y_test = result[int(row):, -1]
# LSTM层的输入必须是三维的
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
print(x_train)
# Neural Network model
HIDDEN_DIM = 512
LAYER_NUM = 10
model = Sequential()
model.add(LSTM(50, input_shape=(x_train.shape[1], x_train.shape[2]), return_sequences=True))
model.add(LSTM(100, return_sequences=False))
model.add(Dense(1))
model.add(Activation('linear'))
model.compile(loss="mse", optimizer="rmsprop")
model.summary()
BATCH_SIZE = 32
epoch = 1
model.fit(x_train, y_train, batch_size=BATCH_SIZE, verbose=1, epochs=epoch, validation_split=0.05)
# start with first frame
curr_frame = x_test[0]
# start with zeros
# curr_frame = np.zeros((100,1))
predicted = []
for i in range(len(x_test)):
predicted.append(model.predict(curr_frame[newaxis, :, :])[0, 0])
curr_frame = curr_frame[1:]
curr_frame = np.insert(curr_frame, [SEQ_LENGTH - 1], predicted[-1], axis=0)
predicted1 = model.predict(x_test)
predicted1 = np.reshape(predicted1, (predicted1.size,))
plt.figure(1)
plt.subplot(211)
plt.plot(predicted)
plt.plot(y_test)
plt.subplot(212)
plt.plot(predicted1)
plt.plot(y_test)
plt.show()