Make python code in Sequential docs consistent
@@ -46,44 +46,44 @@ class Sequential(functional.Functional):
Examples:
- >>> # Optionally, the first layer can receive an `input_shape` argument:
- >>> model = tf.keras.Sequential()
- >>> model.add(tf.keras.layers.Dense(8, input_shape=(16,)))
- >>> # Afterwards, we do automatic shape inference:
- >>> model.add(tf.keras.layers.Dense(4))
-
- >>> # This is identical to the following:
- >>> model = tf.keras.Sequential()
- >>> model.add(tf.keras.Input(shape=(16,)))
- >>> model.add(tf.keras.layers.Dense(8))
-
- >>> # Note that you can also omit the `input_shape` argument.
- >>> # In that case the model doesn't have any weights until the first call
- >>> # to a training/evaluation method (since it isn't yet built):
- >>> model = tf.keras.Sequential()
- >>> model.add(tf.keras.layers.Dense(8))
- >>> model.add(tf.keras.layers.Dense(4))
- >>> # model.weights not created yet
-
- >>> # Whereas if you specify the input shape, the model gets built
- >>> # continuously as you are adding layers:
- >>> model = tf.keras.Sequential()
- >>> model.add(tf.keras.layers.Dense(8, input_shape=(16,)))
- >>> model.add(tf.keras.layers.Dense(4))
- >>> len(model.weights)
+ ```python
+ # Optionally, the first layer can receive an `input_shape` argument:
+ model = tf.keras.Sequential()
+ model.add(tf.keras.layers.Dense(8, input_shape=(16,)))
+ # Afterwards, we do automatic shape inference:
+ model.add(tf.keras.layers.Dense(4))
+
+ # This is identical to the following:
+ model = tf.keras.Sequential()
+ model.add(tf.keras.Input(shape=(16,)))
+ model.add(tf.keras.layers.Dense(8))
+
+ # Note that you can also omit the `input_shape` argument.
+ # In that case the model doesn't have any weights until the first call
+ # to a training/evaluation method (since it isn't yet built):
+ model = tf.keras.Sequential()
+ model.add(tf.keras.layers.Dense(8))
+ model.add(tf.keras.layers.Dense(4))
+ # model.weights not created yet
+
+ # Whereas if you specify the input shape, the model gets built
+ # continuously as you are adding layers:
+ model = tf.keras.Sequential()
+ model.add(tf.keras.layers.Dense(8, input_shape=(16,)))
+ model.add(tf.keras.layers.Dense(4))
+ len(model.weights)
4
- >>> # When using the delayed-build pattern (no input shape specified), you can
- >>> # choose to manually build your model by calling
- >>> # `build(batch_input_shape)`:
- >>> model = tf.keras.Sequential()
- >>> model.add(tf.keras.layers.Dense(8))
- >>> model.add(tf.keras.layers.Dense(4))
- >>> model.build((None, 16))
- >>> len(model.weights)
+ # When using the delayed-build pattern (no input shape specified), you can
+ # choose to manually build your model by calling
+ # `build(batch_input_shape)`:
+ model = tf.keras.Sequential()
+ model.add(tf.keras.layers.Dense(8))
+ model.add(tf.keras.layers.Dense(4))
+ model.build((None, 16))
+ len(model.weights)
4
- ```python
# Note that when using the delayed-build pattern (no input shape specified),
# the model gets built the first time you call `fit`, `eval`, or `predict`,
# or the first time you call the model on some input data.
