garage.tf.models.parameter module¶
Parameter layer in TensorFlow.
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parameter
(input_var, length, initializer=<tensorflow.python.ops.init_ops.Zeros object>, dtype=tf.float32, trainable=True, name='parameter')[source]¶ 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.
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recurrent_parameter
(input_var, step_input_var, length, initializer=<tensorflow.python.ops.init_ops.Zeros object>, dtype=tf.float32, trainable=True, name='recurrent_parameter')[source]¶ 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