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* Remove python_version < '3.12' for tensorflow * Reenable dynamic_programming/k_means_clustering_tensorflow.py * updating DIRECTORY.md * Try to fix ruff * Try to fix ruff * Try to fix ruff * Try to fix ruff * Try to fix ruff * Reenable machine_learning/lstm/lstm_prediction.py * updating DIRECTORY.md * Try to fix ruff * Reenable computer_vision/cnn_classification.py * updating DIRECTORY.md * Reenable neural_network/input_data.py * updating DIRECTORY.md * Try to fix ruff * Try to fix ruff * Try to fix mypy * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Try to fix ruff * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: MaximSmolskiy <MaximSmolskiy@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
55 lines
2.2 KiB
Python
55 lines
2.2 KiB
Python
"""
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Create a Long Short Term Memory (LSTM) network model
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An LSTM is a type of Recurrent Neural Network (RNN) as discussed at:
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* https://colah.github.io/posts/2015-08-Understanding-LSTMs
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* https://en.wikipedia.org/wiki/Long_short-term_memory
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"""
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import numpy as np
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import pandas as pd
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from sklearn.preprocessing import MinMaxScaler
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from tensorflow.keras.layers import LSTM, Dense
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from tensorflow.keras.models import Sequential
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if __name__ == "__main__":
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"""
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First part of building a model is to get the data and prepare
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it for our model. You can use any dataset for stock prediction
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make sure you set the price column on line number 21. Here we
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use a dataset which have the price on 3rd column.
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"""
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sample_data = pd.read_csv("sample_data.csv", header=None)
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len_data = sample_data.shape[:1][0]
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# If you're using some other dataset input the target column
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actual_data = sample_data.iloc[:, 1:2]
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actual_data = actual_data.to_numpy().reshape(len_data, 1)
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actual_data = MinMaxScaler().fit_transform(actual_data)
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look_back = 10
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forward_days = 5
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periods = 20
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division = len_data - periods * look_back
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train_data = actual_data[:division]
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test_data = actual_data[division - look_back :]
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train_x, train_y = [], []
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test_x, test_y = [], []
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for i in range(len(train_data) - forward_days - look_back + 1):
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train_x.append(train_data[i : i + look_back])
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train_y.append(train_data[i + look_back : i + look_back + forward_days])
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for i in range(len(test_data) - forward_days - look_back + 1):
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test_x.append(test_data[i : i + look_back])
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test_y.append(test_data[i + look_back : i + look_back + forward_days])
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x_train = np.array(train_x)
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x_test = np.array(test_x)
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y_train = np.array([list(i.ravel()) for i in train_y])
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y_test = np.array([list(i.ravel()) for i in test_y])
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model = Sequential()
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model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
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model.add(LSTM(64, input_shape=(128, 1)))
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model.add(Dense(forward_days))
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model.compile(loss="mean_squared_error", optimizer="adam")
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history = model.fit(
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x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
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)
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pred = model.predict(x_test)
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