Snippet example of LSTM implementation using tensorflow

lstm_example.py

lstm_model = tf.keras.Sequential([
    layers.LSTM(32, return_sequences=True, input_shape=(n_steps, 1)),
    layers.LSTM(32, return_sequences=True),
    layers.Dropout(0.2),
    layers.LSTM(32, return_sequences=True),
    layers.LSTM(32),
    layers.Dropout(0.2),
    layers.Dense(1)
])

lstm_model.compile(optimizer=tf.keras.optimizers.Adam(),
                  loss=tf.losses.MeanSquaredError(),
                  metrics=['mae', 'mse'])

early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss',
                                                  patience=60,
                                                  mode='min')

lstm_hist = lstm_model.fit(
    X_train, y_train, epochs=500, validation_split=0.2,
    batch_size=32, verbose=0,
    callbacks=[tfdocs.modeling.EpochDots(), early_stopping]
)