import numpy as np from keras.models import Sequential from keras.layers import LSTM, Dense
# Define the long sequence long_sequence = [8,1,8,4,8,8,8,8,6,7,5,8,4,4,2,7,1,6,8,5,4,3,3,4,1,1,7,3,4,6,7,5,0,7,7,5,8,2,9,0,0,9,3,4,6,5,2,3,0,9,6,3,0,3,6,9,9,6,0,8,2,1,0,5,0,9,2,0,7,2,2,4,6,9,5,2,6,2,7,4,1,4,4,8,4,9,2,1,8,9,0,9,4,4,7,2,6,1,2,0,5,4,6,3,3,5,9,4,4,2,5,8,9,4,7,2,7,7,9,6,3,4,4,3,9,1,8,1,4,0,8,4,7,4,7,2,4,6,0,1,6,9,6,1,2,8,5,4,4,3,9,2,7,3,7,6,3,4,8,5,4,2,7,7,9,2,0,2,6,8,1,7,7,7,0,3,8,4,7,5,4,1,4,7,8,1,5,3,2,0,4,4,9,7,1,7,4,8,1,2]
# Define the length of sequences for training sequence_length = 20
# Split the long sequence into smaller sequences sequences = [long_sequence[i:i+sequence_length] for i in range(len(long_sequence) - sequence_length)]
# Create input and output data for the LSTM model X = np.array(sequences) y = np.array(long_sequence[sequence_length:])
# Reshape the input data to fit the LSTM input shape X = np.reshape(X, (X.shape[0], X.shape[1], 1))
# Define the LSTM model architecture model = Sequential() model.add(LSTM(units=50, input_shape=(X.shape[1], 1))) model.add(Dense(units=1)) model.compile(optimizer='adam', loss='mean_squared_error')
# Train the LSTM model model.fit(X, y, epochs=100, batch_size=32, verbose=2)
# Save the trained model model.save("sequence_prediction_model.h5")