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Improvements in the base Layer docstring.

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PiperOrigin-RevId: 303783601
Change-Id: I434c204c1ab799033366b948f1642580ba267e2d
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Francois Chollet 2020-03-30 11:26:22 -07:00 committed by TensorFlower Gardener
parent 39b77be0ed
commit ea70d5bcd3
1 changed files with 33 additions and 29 deletions
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62
tensorflow/python/keras/engine/base_layer.py
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@ -104,6 +104,38 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
Users will just instantiate a layer and then treat it as a callable. Users will just instantiate a layer and then treat it as a callable.
Arguments:
trainable: Boolean, whether the layer's variables should be trainable.
name: String name of the layer.
dtype: The dtype of the layer's computations and weights (default of
`None` means use `tf.keras.backend.floatx` in TensorFlow 2, or the type
of the first input in TensorFlow 1).
dynamic: Set this to `True` if your layer should only be run eagerly, and
should not be used to generate a static computation graph.
This would be the case for a Tree-RNN or a recursive network,
for example, or generally for any layer that manipulates tensors
using Python control flow. If `False`, we assume that the layer can
safely be used to generate a static computation graph.
Attributes:
name: The name of the layer (string).
dtype: The dtype of the layer's computations and weights. If mixed
precision is used with a `tf.keras.mixed_precision.experimental.Policy`,
this is instead just the dtype of the layer's weights, as the computations
are done in a different dtype.
losses: List of losses added to this layer (via `self.add_loss()`).
metrics: List of metrics added to this layer (via `self.add_metric()`)..
trainable_weights: List of variables to be included in backprop.
non_trainable_weights: List of variables that should not be
included in backprop.
weights: The concatenation of the lists trainable_weights and
non_trainable_weights (in this order).
trainable: Whether the layer should be trained (boolean), i.e. whether
its potentially-trainable weights should be returned as part of
`layer.trainable_weights`.
input_spec: Optional (list of) `InputSpec` object(s) specifying the
constraints on inputs that can be accepted by the layer.
We recommend that descendants of `Layer` implement the following methods: We recommend that descendants of `Layer` implement the following methods:
* `__init__()`: Defines custom layer attributes, and creates layer state * `__init__()`: Defines custom layer attributes, and creates layer state
@ -223,35 +255,7 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
[Writing custom layers and models with Keras]( [Writing custom layers and models with Keras](
https://www.tensorflow.org/guide/keras/custom_layers_and_models) https://www.tensorflow.org/guide/keras/custom_layers_and_models)
Arguments: About the layer's `dtype` attribute:
trainable: Boolean, whether the layer's variables should be trainable.
name: String name of the layer.
dtype: The dtype of the layer's computations and weights (default of
`None` means use `tf.keras.backend.floatx` in TensorFlow 2, or the type
of the first input in TensorFlow 1).
dynamic: Set this to `True` if your layer should only be run eagerly, and
should not be used to generate a static computation graph.
This would be the case for a Tree-RNN or a recursive network,
for example, or generally for any layer that manipulates tensors
using Python control flow. If `False`, we assume that the layer can
safely be used to generate a static computation graph.
Attributes:
name: The name of the layer (string).
dtype: The dtype of the layer's computations and weights. If mixed
precision is used with a `tf.keras.mixed_precision.experimental.Policy`,
this is instead just the dtype of the layer's weights, as the computations
are done in a different dtype.
updates: List of update ops of this layer.
losses: List of losses added by this layer.
trainable_weights: List of variables to be included in backprop.
non_trainable_weights: List of variables that should not be
included in backprop.
weights: The concatenation of the lists trainable_weights and
non_trainable_weights (in this order).
trainable: Whether the layer should be trained (boolean).
input_spec: Optional (list of) `InputSpec` object(s) specifying the
constraints on inputs that can be accepted by the layer.
Each layer has a dtype, which is typically the dtype of the layer's Each layer has a dtype, which is typically the dtype of the layer's
computations and variables. A layer's dtype can be queried via the computations and variables. A layer's dtype can be queried via the