Type tf.layers.experimental
Namespace tensorflow
Methods
Properties
Public static methods
IContextManager<T> keras_style_scope()
Use Keras-style variable management. 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:
The solution is to wrap the model construction and execution in a keras-style
scope:
Show 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)
object keras_style_scope_dyn()
Use Keras-style variable management. 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:
The solution is to wrap the model construction and execution in a keras-style
scope:
Show 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)
void set_keras_style()
Use Keras-style variable management. All tf.layers and tf RNN cells created after keras style ha been enabled
use Keras-style variable management. Creating such layers with a
scope= argument is disallowed, and reuse=True is disallowed. The purpose of this function is to allow users of existing layers to
slowly transition to Keras layers API without breaking existing
functionality. For more details, see the documentation for `keras_style_scope`. Note, once keras style has been set, it is set globally for the entire
program and cannot be unset. Example:
Show Example
set_keras_style() model_1 = RNNModel(name="model_1") model_2 = RNNModel(name="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)
object set_keras_style_dyn()
Use Keras-style variable management. All tf.layers and tf RNN cells created after keras style ha been enabled
use Keras-style variable management. Creating such layers with a
scope= argument is disallowed, and reuse=True is disallowed. The purpose of this function is to allow users of existing layers to
slowly transition to Keras layers API without breaking existing
functionality. For more details, see the documentation for `keras_style_scope`. Note, once keras style has been set, it is set globally for the entire
program and cannot be unset. Example:
Show Example
set_keras_style() model_1 = RNNModel(name="model_1") model_2 = RNNModel(name="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)