[Lab 1 Part 2] - Missing softmax argument in Dense layer

[ksadura]

In the solution for this task the final RNN model is created as follows:

def build_model(vocab_size, embedding_dim, rnn_units, batch_size):
    model = tf.keras.Sequential([
        tf.keras.layers.Embedding(vocab_size, embedding_dim, batch_input_shape=[batch_size, None]),
        LSTM(rnn_units), 
        tf.keras.layers.Dense(vocab_size)
    ])

    return model

model = build_model(len(vocab), embedding_dim=256, rnn_units=1024, batch_size=32)

Why there's no activation function (softmax) defined in the Dense layer? In the task it's said:

The final output of the LSTM is then fed into a fully connected Dense layer where we'll output a softmax over each character in the vocabulary, and then sample from this distribution to predict the next character.

According to the docs activation function is None if not explicitly declared.