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tf.layers.experimental.keras_style_scope

Use Keras-style variable management.

Aliases:

  • tf.compat.v1.layers.experimental.keras_style_scope
  • tf.layers.experimental.keras_style_scope
tf.layers.experimental.keras_style_scope()

Defined in python/layers/base.py.

All tf.layers and tf RNN cells created in this scope use Keras-style variable management. Creating such layers with a scope= argument is disallowed, and reuse=True is disallowed.

The purpose of this scope is to allow users of existing layers to slowly transition to a Keras layers API without breaking existing functionality.

One example of this is when using TensorFlow's RNN classes with Keras Models or Networks. Because Keras models do not properly set variable scopes, users of RNNs may either accidentally share scopes between two different models, or get errors about variables that already exist.

Example:

class RNNModel(tf.keras.Model):

  def __init__(self, name):
    super(RNNModel, self.).__init__(name=name)
    self.rnn = tf.compat.v1.nn.rnn_cell.MultiRNNCell(
      [tf.compat.v1.nn.rnn_cell.LSTMCell(64) for _ in range(2)])

  def call(self, input, state):
    return self.rnn(input, state)

model_1 = RNNModel("model_1")
model_2 = RNNModel("model_2")

# OK
output_1, next_state_1 = model_1(input, state)
# Raises an error about trying to create an already existing variable.
output_2, next_state_2 = model_2(input, state)

The solution is to wrap the model construction and execution in a keras-style scope:

with keras_style_scope():
  model_1 = RNNModel("model_1")
  model_2 = RNNModel("model_2")

  # model_1 and model_2 are guaranteed to create their own variables.
  output_1, next_state_1 = model_1(input, state)
  output_2, next_state_2 = model_2(input, state)

  assert len(model_1.weights) > 0
  assert len(model_2.weights) > 0
  assert(model_1.weights != model_2.weights)

Yields:

A keras layer style scope.