Hi everyone,
I am trying to train a  LSTM   network with input (train data shape)  (80,27) (batch size not included) since each sequence in my training data has 80 timesteps and 27 features
For the prediction part I want to predict each timestep separately and not the 80 together.
For example my prediction batch has size (1,1,27) but the result is wrong (MAE Value 4.0000) since the model was trained with 80 timesteps in the input data.
I tried to switch into a stateful Model when predicting and transfering the weights from the trained model to the new one but  still MAE value is 4. So what am I missing?
Network Architecture:
def Model():
    inputs = keras.Input(shape=(None,27))
    x = layers.Bidirectional(layers.LSTM(512,return_sequences=True))(inputs)
    x = layers.Bidirectional(layers.LSTM(512,return_sequences=True))(x)
    x = layers.Bidirectional(layers.LSTM(256,return_sequences=True))(x)
    x = layers.Bidirectional(layers.LSTM(256,return_sequences=True))(x)
    x = layers.Bidirectional(layers.LSTM(128,return_sequences=True))(x)
    x = layers.Bidirectional(layers.LSTM(128,return_sequences=True))(x)
    x = layers.Bidirectional(layers.LSTM(128,return_sequences=True,))(x)
    
    x = layers.Dense(512,activation="selu")(x)
    x = layers.Dense(256,activation="selu")(x)
    
    x = layers.Dense(1)(x)
    
    return keras.Model(inputs=inputs,outputs=x)

Thanks in advance