commit 1227a9446db345ad3f96e56097e01aaf30c93168
author Francois Chollet <fchollet@google.com> Sun Apr 12 16:00:50 2020 -0700
committer TensorFlower Gardener <gardener@tensorflow.org> Sun Apr 12 16:04:20 2020 -0700
tree f6dfafe6b2b4f174589b6c7f7dfd31efc9adc59e
parent dd519b931ad259076a5c1a4bde589b7b60de2143

Unifies the behaviors of "regular Sequential" (starts with an Input, wraps a Functional model) and "deferred Sequential" models (only gets built when it sees its input data for the first time).

Before, the following two models had the following behavior differences:

```
regular_sequential = Sequential([
  Dense(2, activation='relu', input_shape=(3,)),
  Dense(4),
]
deferred_sequential = Sequential([
  Dense(2, activation='relu'),
  Dense(4),
])
deferred_sequential(tf.zeros((1, 3)))  # Builds the deferred sequential
```

1) The regular sequential is inspectable: its `.summary()` displays intermediate output shapes. The deferred sequential does not display output shapes.
2) The regular sequential has `inputs` and `outputs` attributes, and its intermediate layers can be used to do feature extraction (see example below), etc. The deferred sequential can't do this.

Feature extraction example:

```
model = Sequential(...)
extractor = keras.Model(inputs=model.inputs,
                        outputs=[layer.output for layer in model.layers])
features = extractor(data)
```

After this CL, the two models behave exactly the same once the deferred sequential has been built (whether by `__call__`ing it, by calling `fit`/`evaluate`/`predict`, or by calling `build` directly). The input shape used is the most restrictive shape compatible with all shapes previously seen by the model (i.e. the set of invariants among all shapes).

The behavior unification is not applied for TF V1, since we don't want to disrupt legacy behaviors and don't want to add new features in V1.

Note that the deferred Sequential remain different in the following cases:
- When a deferred Sequential is called with inputs of different ranks. This is impossible to express in the Functional API (of which "regular Sequential" is a wrapper). However, this is almost certainly not something that anyone is doing.
- When a deferred Sequential starts with a layer that takes multiple inputs. At this time this is something that regular Sequential models do not support. This is an invalid use case of Sequential (which should be single-input and single-output), which unfortunately some users have come to rely on. We may choose to enable it in the future (since it can be expressed with the Functional API). When we enable it we could unify the deferred Sequential behavior in this case.
- When a deferred Sequential contains a non-autographable layer that isn't marked as dynamic, or that is marked as dynamic but does not support shape inference. In that case no Functional model can be built.

PiperOrigin-RevId: 306151882
Change-Id: I4aa881af254ee845f771e375933deae664c80354