Creating a Simple Sequence-to-Sequence Model with LSTM in Keras

This function uses the Keras library's LSTM and Dense layers to create a simple sequence-to-sequence model to fit a random sequence.

Technology Stack : Keras, LSTM, Dense, Sequential, numpy

Code Type : The type of code

Code Difficulty : Intermediate

                
                    
def generate_random_sequence(arg1, arg2):
    from keras.layers import LSTM, Dense
    from keras.models import Sequential
    from numpy import random

    # Generate a random sequence of numbers
    sequence = random.rand(arg1, arg2)

    # Create a simple LSTM model
    model = Sequential()
    model.add(LSTM(units=50, activation='relu', input_shape=(arg1, 1)))
    model.add(Dense(1))

    # Compile the model
    model.compile(optimizer='adam', loss='mean_squared_error')

    # Fit the model to the random sequence
    model.fit(sequence, sequence, epochs=1, batch_size=1)

    return model