Repository: jinfagang/LSTM_learn
Branch: master
Commit: b6f54c9665cd
Files: 5
Total size: 5.4 KB
Directory structure:
gitextract_g9ev4h6w/
├── README.md
├── international-airline-passengers.csv
├── lstm_airline_predict.py
├── test.csv
└── test.py
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FILE CONTENTS
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FILE: README.md
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# LSTM_learn
a implement of LSTM using Keras for time series prediction regression problem
### Data
the data were from internet, this data was using for predict the number of people in a airline company, we use LSTM network to solve this problem
### Denpensies
for the implemention of code, we using Keras to establish LSTM network, as well as using numpy, pandas, so before you runing this tutorial, it is strongly recommended you install Anaconda which is a package inclueded them all.
### open source protocol
MIT
### contact
author: jinfagang19@163.com
Central South University, Mr. Jin
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FILE: international-airline-passengers.csv
================================================
time,passengers
"1949-01",112
"1949-02",118
"1949-03",132
"1949-04",129
"1949-05",121
"1949-06",135
"1949-07",148
"1949-08",148
"1949-09",136
"1949-10",119
"1949-11",104
"1949-12",118
"1950-01",115
"1950-02",126
"1950-03",141
"1950-04",135
"1950-05",125
"1950-06",149
"1950-07",170
"1950-08",170
"1950-09",158
"1950-10",133
"1950-11",114
"1950-12",140
"1951-01",145
"1951-02",150
"1951-03",178
"1951-04",163
"1951-05",172
"1951-06",178
"1951-07",199
"1951-08",199
"1951-09",184
"1951-10",162
"1951-11",146
"1951-12",166
"1952-01",171
"1952-02",180
"1952-03",193
"1952-04",181
"1952-05",183
"1952-06",218
"1952-07",230
"1952-08",242
"1952-09",209
"1952-10",191
"1952-11",172
"1952-12",194
"1953-01",196
"1953-02",196
"1953-03",236
"1953-04",235
"1953-05",229
"1953-06",243
"1953-07",264
"1953-08",272
"1953-09",237
"1953-10",211
"1953-11",180
"1953-12",201
"1954-01",204
"1954-02",188
"1954-03",235
"1954-04",227
"1954-05",234
"1954-06",264
"1954-07",302
"1954-08",293
"1954-09",259
"1954-10",229
"1954-11",203
"1954-12",229
"1955-01",242
"1955-02",233
"1955-03",267
"1955-04",269
"1955-05",270
"1955-06",315
"1955-07",364
"1955-08",347
"1955-09",312
"1955-10",274
"1955-11",237
"1955-12",278
"1956-01",284
"1956-02",277
"1956-03",317
"1956-04",313
"1956-05",318
"1956-06",374
"1956-07",413
"1956-08",405
"1956-09",355
"1956-10",306
"1956-11",271
"1956-12",306
"1957-01",315
"1957-02",301
"1957-03",356
"1957-04",348
"1957-05",355
"1957-06",422
"1957-07",465
"1957-08",467
"1957-09",404
"1957-10",347
"1957-11",305
"1957-12",336
"1958-01",340
"1958-02",318
"1958-03",362
"1958-04",348
"1958-05",363
"1958-06",435
"1958-07",491
"1958-08",505
"1958-09",404
"1958-10",359
"1958-11",310
"1958-12",337
"1959-01",360
"1959-02",342
"1959-03",406
"1959-04",396
"1959-05",420
"1959-06",472
"1959-07",548
"1959-08",559
"1959-09",463
"1959-10",407
"1959-11",362
"1959-12",405
"1960-01",417
"1960-02",391
"1960-03",419
"1960-04",461
"1960-05",472
"1960-06",535
"1960-07",622
"1960-08",606
"1960-09",508
"1960-10",461
"1960-11",390
"1960-12",432
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FILE: lstm_airline_predict.py
================================================
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import LSTM, Dense, Activation
def load_data(file_name, sequence_length=10, split=0.8):
df = pd.read_csv(file_name, sep=',', usecols=[1])
data_all = np.array(df).astype(float)
scaler = MinMaxScaler()
data_all = scaler.fit_transform(data_all)
data = []
for i in range(len(data_all) - sequence_length - 1):
data.append(data_all[i: i + sequence_length + 1])
reshaped_data = np.array(data).astype('float64')
np.random.shuffle(reshaped_data)
# 对x进行统一归一化，而y则不归一化
x = reshaped_data[:, :-1]
y = reshaped_data[:, -1]
split_boundary = int(reshaped_data.shape[0] * split)
train_x = x[: split_boundary]
test_x = x[split_boundary:]
train_y = y[: split_boundary]
test_y = y[split_boundary:]
return train_x, train_y, test_x, test_y, scaler
def build_model():
# input_dim是输入的train_x的最后一个维度，train_x的维度为(n_samples, time_steps, input_dim)
model = Sequential()
model.add(LSTM(input_dim=1, output_dim=50, return_sequences=True))
print(model.layers)
model.add(LSTM(100, return_sequences=False))
model.add(Dense(output_dim=1))
model.add(Activation('linear'))
model.compile(loss='mse', optimizer='rmsprop')
return model
def train_model(train_x, train_y, test_x, test_y):
model = build_model()
try:
model.fit(train_x, train_y, batch_size=512, nb_epoch=30, validation_split=0.1)
predict = model.predict(test_x)
predict = np.reshape(predict, (predict.size, ))
except KeyboardInterrupt:
print(predict)
print(test_y)
print(predict)
print(test_y)
try:
fig = plt.figure(1)
plt.plot(predict, 'r:')
plt.plot(test_y, 'g-')
plt.legend(['predict', 'true'])
except Exception as e:
print(e)
return predict, test_y
if __name__ == '__main__':
train_x, train_y, test_x, test_y, scaler = load_data('international-airline-passengers.csv')
train_x = np.reshape(train_x, (train_x.shape[0], train_x.shape[1], 1))
test_x = np.reshape(test_x, (test_x.shape[0], test_x.shape[1], 1))
predict_y, test_y = train_model(train_x, train_y, test_x, test_y)
predict_y = scaler.inverse_transform([[i] for i in predict_y])
test_y = scaler.inverse_transform(test_y)
fig2 = plt.figure(2)
plt.plot(predict_y, 'g:')
plt.plot(test_y, 'r-')
plt.show()
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FILE: test.csv
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1,2
2,3
3,4
5,6
7,8
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FILE: test.py
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import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
import pandas as pd
df = pd.read_csv('test.csv', sep=',')
print(df.head(5))
gitextract_g9ev4h6w/ ├── README.md ├── international-airline-passengers.csv ├── lstm_airline_predict.py ├── test.csv └── test.py
SYMBOL INDEX (3 symbols across 1 files) FILE: lstm_airline_predict.py function load_data (line 9) | def load_data(file_name, sequence_length=10, split=0.8): function build_model (line 32) | def build_model(): function train_model (line 45) | def train_model(train_x, train_y, test_x, test_y):
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This page contains the full source code of the jinfagang/LSTM_learn GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 5 files (5.4 KB), approximately 2.2k tokens, and a symbol index with 3 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
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