Python/machine_learning/lstm/lstm_prediction.py_tf
Christian Clauss 3fdbf9741d
Python 3.9 (#3926)
* Upgrade to Python 3.9

* pip install wheel for faster builds

* updating DIRECTORY.md

* requirements.txt: tensorflow; python_version < '3.9'

* keras requires tensorflow

* Rename lstm_prediction.py to lstm_prediction.py_tf

* Update requirements.txt

* updating DIRECTORY.md

* Update requirements.txt

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
Co-authored-by: Dhruv Manilawala <dhruvmanila@gmail.com>
2020-11-24 12:41:10 +01:00

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"""
Create a Long Short Term Memory (LSTM) network model
An LSTM is a type of Recurrent Neural Network (RNN) as discussed at:
* http://colah.github.io/posts/2015-08-Understanding-LSTMs
* https://en.wikipedia.org/wiki/Long_short-term_memory
"""
import numpy as np
import pandas as pd
from keras.layers import LSTM, Dense
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
if __name__ == "__main__":
"""
First part of building a model is to get the data and prepare
it for our model. You can use any dataset for stock prediction
make sure you set the price column on line number 21. Here we
use a dataset which have the price on 3rd column.
"""
df = pd.read_csv("sample_data.csv", header=None)
len_data = df.shape[:1][0]
# If you're using some other dataset input the target column
actual_data = df.iloc[:, 1:2]
actual_data = actual_data.values.reshape(len_data, 1)
actual_data = MinMaxScaler().fit_transform(actual_data)
look_back = 10
forward_days = 5
periods = 20
division = len_data - periods * look_back
train_data = actual_data[:division]
test_data = actual_data[division - look_back :]
train_x, train_y = [], []
test_x, test_y = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
x_train = np.array(train_x)
x_test = np.array(test_x)
y_train = np.array([list(i.ravel()) for i in train_y])
y_test = np.array([list(i.ravel()) for i in test_y])
model = Sequential()
model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(128, 1)))
model.add(Dense(forward_days))
model.compile(loss="mean_squared_error", optimizer="adam")
history = model.fit(
x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
)
pred = model.predict(x_test)