densenet python keras

>>> # Create a `Sequential` model and add a Dense layer as the first layer.  
>>> model = tf.keras.models.Sequential()
>>> model.add(tf.keras.Input(shape=(16,)))
>>> model.add(tf.keras.layers.Dense(32, activation='relu'))
>>> # Now the model will take as input arrays of shape (None, 16)  
>>> # and output arrays of shape (None, 32).  
>>> # Note that after the first layer, you don't need to specify  
>>> # the size of the input anymore:  
>>> model.add(tf.keras.layers.Dense(32))
>>> model.output_shape
(None, 32)

Here is what the above code is Doing:
1. Creating a Sequential model.
2. Adding an Input layer with an input_shape of (16,).
3. Adding a Dense layer with 32 output units and an activation function of relu.
4. Adding a Dense layer with 32 output units.

The model will take as input arrays of shape (None, 16) and output arrays of shape (None, 32). Note that after the first layer, you don’t need to specify the size of the input anymore.

Now let’s try to build a model that takes multiple inputs.