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tf.compat.v1.keras.utils.track_tf1_style_variables

Wrap layer & module methods in this decorator to capture tf1-style weights.

Used in the notebooks

Used in the guide

Decorating a tf.keras.Layer's or tf.Module's methods with this decorator will cause the layer/module to track weights created/used via tf.compat.v1.get_variable (and by extension tf.compat.v1.layers) inside the decorated method.

In addition to tracking the weights themselves under the standard layer.variable/module.variable/etc. properties, if the method belongs to a tf.keras.Layer then any regularization losses specified via the get_variable or tf.compat.v1.layers regularizer arguments will get tracked by the layer under the standard layer.losses property.

This tracking enables using large classes of TF1-style model-forward-pass code inside of Keras layers or tf.Modules in TF2 with TF2 behaviors enabled.

Example of capturing tf.compat.v1.layer-based modeling code as a Keras layer:

class WrappedDoubleDenseLayer(tf.keras.layers.Layer):

  def __init__(self, units, *args, **kwargs):
    super().__init__(*args, **kwargs)
    self.units = units

  @tf.compat.v1.keras.utils.track_tf1_style_variables
  def call(self, inputs):
    with tf.compat.v1.variable_scope("double_dense_layer"):
      out = tf.compat.v1.layers.dense(
          inputs, self.units, name="dense_one",
          kernel_initializer=tf.compat.v1.random_normal_initializer,
          kernel_regularizer="l2")
      out = tf.compat.v1.layers.dense(
          out, self.units, name="dense_two",
          kernel_initializer=tf.compat.v1.random_normal_initializer(),
          kernel_regularizer="l2")
    return out

# Create a layer that can be used as a standard keras layer
layer = WrappedDoubleDenseLayer(10)

# call the layer on inputs
layer(...)

# Variables created/used within the scope will be tracked by the layer
layer.weights
layer.trainable_variables

# Regularization losses will be captured in layer.losses after a call,
# just like any other Keras layer
reg_losses = layer.losses

Example of capturing tf.compat.v1.get_variable-based modeling code as a Keras layer:

class WrappedDoubleDenseLayer(tf.keras.layers.Layer):

  def __init__(self, units, *args, **kwargs):
    super().__init__(*args, **kwargs)
    self.units = units

  @tf.compat.v1.keras.utils.track_tf1_style_variables
  def call(self, inputs):
    out = inputs
    with tf.compat.v1.variable_scope("double_dense_layer"):
      with tf.compat.v1.variable_scope("dense_one"):
        # The weights are created with a `regularizer`,
        # so the layer should track their regularization losses
        kernel = tf.compat.v1.get_variable(
            shape=[out.shape[-1], self.units],
            regularizer=regularizers.L2(),
            initializer=init_ops.ones_initializer(),
            name="kernel")
        bias = tf.compat.v1.get_variable(
            shape=[self.units,],
            initializer=init_ops.zeros_initializer(),
            name="bias")
        out = tf.compat.v1.math.matmul(out, kernel)
        out = tf.compat.v1.nn.bias_add(out, bias)
      with tf.compat.v1.variable_scope("dense_two"):
        kernel = tf.compat.v1.get_variable(
            shape=[out.shape[-1], self.units],
            regularizer=regularizers.L2(),
            initializer=init_ops.ones_initializer(),
            name="kernel")
        bias = tf.compat.v1.get_variable(
            shape=[self.units,],
            initializer=init_ops.zeros_initializer(),
            name="bias")
        out = tf.compat.v1.math.matmul(out, kernel)
        out = tf.compat.v1.nn.bias_add(out, bias)
    return out

# Create a layer that can be used as a standard keras layer
layer = WrappedDoubleDenseLayer(10)

# call the layer on inputs
layer(...)

# Variables created/used within the scope will be tracked by the layer
layer.weights
layer.trainable_variables

# Regularization losses will be captured in layer.losses after a call,
# just like any other Keras layer
reg_losses = layer.losses

Regularization losses:

Any regularizers specified in the get_variable calls or compat.v1.layer creations will get captured if they occur in your decorated method and the method belongs to a tf.keras.Layer/tf.keras.Module. Regularization losses are accessible in layer.losses after a call just like in a standard Keras layer, and will be captured by any model that includes this layer. Regularization losses attached to Keras layers/models set as attributes of your layer will also get captured in the standard Keras regularization loss tracking.

(While Modules have no losses property, no-arg callables to compute the regularization losses may be tracked as dict values in a private module._tf1_style_var_store._regularizers property, but only for tf.compat.v1.layers and get_variable weights and not for any other nested Keras layers/tf.Modules)

Variable scope / variable reuse: variable-scope based reuse in your decorated method will be respected, and work like variable-scope based reuse in TF1.

Variable Names/Pre-trained checkpoint loading: Variable naming from get_variable and compat.v1.layer layers will match the TF1 names, so you should be able to re-use your old name-based checkpoints. Variable naming for Keras layers/models or for variables created by tf.Variable may change when going to eager execution.

Training Arg if you decorate layer.call: Keras will pass a training arg to this layer if call contains a training arg or a **kwargs varargs in its call signature, similarly to how keras passes training to other layers in TF2 that have similar signatures in their call implementations. See more details in the docs on tf.keras.layers.Layer to understand what will be passed and when. Note: tf.compat.v1.layers are usually not called with training=None, so the training arg to forward_pass might not feed through to them unless you pass it to their calls explicitly.

Caveats:

  • TF2 will not prune unused variable updates (or unused outputs). You may need to adjust your forward pass code to avoid computations or variable updates that you don't intend to use.
  • Avoid Nesting variable creation in tf.function inside of methods decorated with track_tf1_style_variables While the method may safely be used from inside a tf.function, using a function inside of a decorated method may break the variable scoping.
  • This decorator only adds implicit tracking for legacy tf1-style get_variable / compat.v1.layers usage. If you would like to use nested Keras layers/models inside the decorated method, you need to assign them as attributes of your layer so that Keras/Module's standard object-oriented weights (and loss tracking for layers) will kick in. See the intro to modules, layers, and models guide for more info

method The method to decorate. This should belong to a custom tf.Module, tf.keras.layers.Layer, or tf.keras.Model.

The decorated method.