Correct minor issue in the guide "Understanding masking & padding"

[palc001]

Correct the axis for computing the denominator of softmax in the example which creates a TemporalSoftmax class.

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[palc001]

@fchollet As per my understanding, a softmax over the time dimension (axis 1) of an input sequence implies that the softmax should be applied along the sequence length dimension individually over each embedding dimension.

To illustrate with a better example:
You'd typically use such a TemporalSoftmax function to compute weights over the given input sequence (to, say, get a single embedding for the entire sequence). Here, you'd want the weights across the sequence to sum to 1 (and not the embeddings for each time-step to sum to 1, as is done in the guide using axis=-1)
Rewriting the code snippet given in the example to compute weights (I've only changed the variable names, everything else remains the same):

inputs = tf.keras.Input(shape=(None,), dtype="int32")
embeddings = tf.keras.layers.Embedding(input_dim=10, output_dim=32, mask_zero=True)(inputs)
weights = tf.keras.layers.Dense(1)(embeddings)
weights = TemporalSoftmax()(weights)

model = tf.keras.Model(inputs, weights)
y = model(np.random.randint(0, 10, size=(32, 100)))

With my suggested correction to axis, you get the weights correctly; and you can further use them to weigh the embeddings for each time-step OR say, to combine the embeddings of all time-steps in a sequence into a single embedding for the sequence.

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Edit: Also saw from git blame and PR #1424 that it was previously implemented correctly; unsure as to why it was changed. In case my understanding is incorrect, please let me know.