tensorflow time series mode training : Only one input size may be -1, not both 0 and 1

I was trying to do a tutorial on time series model with tensorflow and I got an error regarding reshaping presumably coming from a reshape layer.

train_dataset = tf.keras.utils.timeseries_dataset_from_array( train_norm, targets=temperature_train[delay:], sampling_rate=sampling_rate, sequence_length=sequence_length, shuffle=True, batch_size=batch_size, ) valid_dataset = tf.keras.utils.timeseries_dataset_from_array( valid_norm, targets=temperature_valid[delay:], sampling_rate=sampling_rate, sequence_length=sequence_length, shuffle=True, batch_size=batch_size, ) for batch in train_dataset.take(1): inputs, label = batch print(inputs.shape) print(label.shape) break

The print results are

(64, 120, 14) (64,)

The following assertion block raised no error for train_dataset and valid_dataset:

for batch in train_dataset: inputs, label = batch assert inputs.shape[1:] == (120, 14), f"{inputs.shape[1:]}"

When I build a model and start training

inputs = tf.keras.Input(shape=(inputs.shape[1:])) x = tf.keras.layers.Flatten()(inputs) x = tf.keras.layers.Dense(16, activation="relu")(x) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) model.compile("adam", loss="mse", metrics=["mae"]) callbacks = [ tf.keras.callbacks.ModelCheckpoint("jena_dense.keras", save_best_only=True)] history = model.fit(train_dataset, validation_data = valid_dataset, epochs = 15, callbacks=callbacks )

It throws a following error

InvalidArgumentError: Graph execution error: Detected at node functional_10_1/flatten_13_1/Reshape defined at (most recent call last) ... ... Only one input size may be -1, not both 0 and 1 [[{{node functional_10_1/flatten_13_1/Reshape}}]] [Op:__inference_multi_step_on_iterator_40343]

The input shape for the flatten layer shouldn't be unambiguous confirming from the assertion test. I don't know where it comes from. Could someone help me?