garage.tf.models.parameter
¶
Parameter layer in TensorFlow.
-
parameter
(input_var, length, initializer=tf.zeros_initializer(), dtype=tf.float32, trainable=True, name='parameter')¶ Parameter layer.
Used as layer that could be broadcast to a certain shape to match with input variable during training.
For recurrent usage, use garage.tf.models.recurrent_parameter().
Example: A trainable parameter variable with shape (2,), it needs to be broadcasted to (32, 2) when applied to a batch with size 32.
- Parameters
input_var (tf.Tensor) – Input tf.Tensor.
length (int) – Integer dimension of the variable.
initializer (callable) – Initializer of the variable. The function should return a tf.Tensor.
dtype – Data type of the variable (default is tf.float32).
trainable (bool) – Whether the variable is trainable.
name (str) – Variable scope of the variable.
- Returns
A tensor of the broadcasted variables.
-
recurrent_parameter
(input_var, step_input_var, length, initializer=tf.zeros_initializer(), dtype=tf.float32, trainable=True, name='recurrent_parameter')¶ Parameter layer for recurrent networks.
Used as layer that could be broadcast to a certain shape to match with input variable during training.
Example: A trainable parameter variable with shape (2,), it needs to be broadcasted to (32, 4, 2) when applied to a batch with size 32 and time-length 4.
- Parameters
input_var (tf.Tensor) – Input tf.Tensor for full time-series inputs.
step_input_var (tf.Tensor) – Input tf.Tensor for step inputs.
length (int) – Integer dimension of the variable.
initializer (callable) – Initializer of the variable. The function should return a tf.Tensor.
dtype – Data type of the variable (default is tf.float32).
trainable (bool) – Whether the variable is trainable.
name (str) – Variable scope of the variable.
- Returns
- one for full time-series
inputs, one for step inputs.
- Return type
A tensor of the two broadcasted variables