Question: I'm following one of the online courses about time series predictions using Tensorflow. The function used to convert Numpy array (TS) into a Tensorflow dataset used is LSTM-based model is already given (with my comment lines): def windowed_dataset(series, window_size, batch_size, shuffle_buffer): # creating a tensor from an array dataset = tf.data.Dataset.from_tensor_slices(series) # cutting the tensor into fixed-size windows dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True) # joining windows into a batch? dataset = dataset.flat_map(lambda window: window.batch(window_size + 1)) # separating row into features/label dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1])) dataset = dataset.batch(batch_size).prefetch(1) return dataset  This code work fine but I want to understand it better to modify/adapt it for my needs. If I remove dataset.flat_map(lambda window: window.batch(window_size + 1)) operation, I receive the TypeError: '_VariantDataset' object is not subscriptable pointing to the line: lambda window: (window[:-1], window[-1])) I managed to rewrite part of this code (skipping shuffling) to Numpy-based one: def windowed_dataset_np(series, window_size): values = sliding_window_view(series, window_size) X = values[:, :-1] X = tf.convert_to_tensor(np.expand_dims(X, axis=-1)) y = values[:,-1] return X, y  Syntax of fitting of the model looks a bit differently but it works fine. My two questions are:  What does dataset.flat_map(lambda window: window.batch(window_size + 1)) achieves? Is the second code really equivalent to the three first operations in the original function? 

Answer: I would break down the operations into smaller parts to really understand what is happening, since applying window to a dataset actually creates a dataset of windowed datasets containing tensor sequences: import tensorflow as tf window_size = 2 dataset = tf.data.Dataset.range(7) dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True) for i, window in enumerate(dataset): print('{}. windowed dataset'.format(i + 1)) for w in window: print(w)  1. windowed dataset tf.Tensor(0, shape=(), dtype=int64) tf.Tensor(1, shape=(), dtype=int64) tf.Tensor(2, shape=(), dtype=int64) 2. windowed dataset tf.Tensor(1, shape=(), dtype=int64) tf.Tensor(2, shape=(), dtype=int64) tf.Tensor(3, shape=(), dtype=int64) 3. windowed dataset tf.Tensor(2, shape=(), dtype=int64) tf.Tensor(3, shape=(), dtype=int64) tf.Tensor(4, shape=(), dtype=int64) 4. windowed dataset tf.Tensor(3, shape=(), dtype=int64) tf.Tensor(4, shape=(), dtype=int64) tf.Tensor(5, shape=(), dtype=int64) 5. windowed dataset tf.Tensor(4, shape=(), dtype=int64) tf.Tensor(5, shape=(), dtype=int64) tf.Tensor(6, shape=(), dtype=int64)  Notice how the window is always shifted by one position due to the parameter shift=1. Now, the operation flat_map is used here to flatten the dataset of datasets into a dataset of elements; however, you still want to keep the windowed sequences you created so you divide the dataset according to the window parameters using dataset.batch: dataset = dataset.flat_map(lambda window: window.batch(window_size + 1)) for w in dataset: print(w)  tf.Tensor([0 1 2], shape=(3,), dtype=int64) tf.Tensor([1 2 3], shape=(3,), dtype=int64) tf.Tensor([2 3 4], shape=(3,), dtype=int64) tf.Tensor([3 4 5], shape=(3,), dtype=int64) tf.Tensor([4 5 6], shape=(3,), dtype=int64)  You could also first flatten the dataset of datasets and then apply batch if you want to create the windowed sequences: dataset = dataset.flat_map(lambda window: window).batch(window_size + 1)  Or only flatten the dataset of datasets: dataset = dataset.flat_map(lambda window: window) for w in dataset: print(w)  tf.Tensor(0, shape=(), dtype=int64) tf.Tensor(1, shape=(), dtype=int64) tf.Tensor(2, shape=(), dtype=int64) tf.Tensor(1, shape=(), dtype=int64) tf.Tensor(2, shape=(), dtype=int64) tf.Tensor(3, shape=(), dtype=int64) tf.Tensor(2, shape=(), dtype=int64) tf.Tensor(3, shape=(), dtype=int64) tf.Tensor(4, shape=(), dtype=int64) tf.Tensor(3, shape=(), dtype=int64) tf.Tensor(4, shape=(), dtype=int64) tf.Tensor(5, shape=(), dtype=int64) tf.Tensor(4, shape=(), dtype=int64) tf.Tensor(5, shape=(), dtype=int64) tf.Tensor(6, shape=(), dtype=int64)  But that is probably not what you want. Regarding this line in your question: dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1])), it is pretty trivial. It simply splits the data into sequences and labels, using the last element of each sequence as the label: dataset = dataset.shuffle(2).map(lambda window: (window[:-1], window[-1])) for w in dataset: print(w)  (<tf.Tensor: shape=(2,), dtype=int64, numpy=array([1, 2])>, <tf.Tensor: shape=(), dtype=int64, numpy=3>) (<tf.Tensor: shape=(2,), dtype=int64, numpy=array([2, 3])>, <tf.Tensor: shape=(), dtype=int64, numpy=4>) (<tf.Tensor: shape=(2,), dtype=int64, numpy=array([3, 4])>, <tf.Tensor: shape=(), dtype=int64, numpy=5>) (<tf.Tensor: shape=(2,), dtype=int64, numpy=array([4, 5])>, <tf.Tensor: shape=(), dtype=int64, numpy=6>) (<tf.Tensor: shape=(2,), dtype=int64, numpy=array([0, 1])>, <tf.Tensor: shape=(), dtype=int64, numpy=2>)