garage.tf.models.model¶
Base model classes.
- class BaseModel¶
Bases:
abc.ABC
Interface-only abstract class for models.
A Model contains the structure/configuration of a set of computation graphs, or can be understood as a set of networks. Using a model requires calling build() with given input placeholder, which can be either tf.compat.v1.placeholder, or the output from another model. This makes composition of complex models with simple models much easier.
Examples
model = SimpleModel(output_dim=2) # To use a model, first create a placeholder. # In the case of TensorFlow, we create a tf.compat.v1.placeholder. input_ph = tf.compat.v1.placeholder(tf.float32, shape=(None, 2))
# Building the model output = model.build(input_ph)
# We can also pass the output of a model to another model. # Here we pass the output from the above SimpleModel object. model_2 = ComplexModel(output_dim=2) output_2 = model_2.build(output)
- abstract property name¶
Name for this Model.
- abstract property parameters¶
Parameters of the Model.
The output of a model is determined by its parameter. It could be the weights of a neural network model or parameters of a loss function model.
- Returns
Parameters.
- Return type
list[tf.Tensor]
- build(*inputs, name=None)¶
Output of model with the given input placeholder(s).
This function is implemented by subclasses to create their computation graphs, which will be managed by Model. Generally, subclasses should implement build() directly.
- class Network¶
Network class For TensorFlow.
A Network contains connectivity information by inputs/outputs. When a Network is built, it appears as a subgraph in the computation graphs, scoped by the Network name. All Networks built with the same model share the same parameters, i.e same inputs yield to same outputs.
- property input¶
Tensor input of the Network.
- Returns
Input.
- Return type
tf.Tensor
- property output¶
Tensor output of the Network.
- Returns
Output.
- Return type
tf.Tensor
- class Model(name)¶
Bases:
BaseModel,garage.tf.models.module.Module
Model class for TensorFlow.
A TfModel only contains the structure/configuration of the underlying computation graphs. Connectivity information are all in Network class. A TfModel contains zero or more Network.
When a Network is created, it reuses the parameter from the model. If a Network is built without given a name, the name “default” will be used.
* Do not call tf.global_variable_initializers() after building a model as it will reassign random weights to the model. The parameters inside a model will be initialized when calling build(). *
Pickling is handled automatcailly. The target weights should be assigned to self._default_parameters before pickling, so that the newly created model can check if target weights exist or not. When unpickled, the unserialized model will load the weights from self._default_parameters.
The design is illustrated as the following:
- input_1 input_2
============== Model (TfModel)=================== | | | | | | Parameters | | | ============= / ============ | | | default | / | Network2 | | | | (Network) |/ |(Network) | | | ============= ============ | | | | | =================================================
- (outputs from ‘default’ networks) |
outputs from [‘Network2’] network
Examples are also available in tests/garage/tf/models/test_model.
- Parameters
name (str) – Name of the model. It will also become the variable scope of the model. Every model should have a unique name.
- property parameters¶
Parameters of the model.
- Returns
Parameters
- Return type
np.ndarray
- property name¶
Name (str) of the model.
This is also the variable scope of the model.
- Returns
Name of the model.
- Return type
- property input¶
Default input of the model.
When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the input of the network.
- Returns
Default input of the model.
- Return type
tf.Tensor
- property output¶
Default output of the model.
When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the output of the network.
- Returns
Default output of the model.
- Return type
tf.Tensor
- property inputs¶
Default inputs of the model.
When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the inputs of the network.
- Returns
Default inputs of the model.
- Return type
list[tf.Tensor]
- property outputs¶
Default outputs of the model.
When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the outputs of the network.
- Returns
Default outputs of the model.
- Return type
list[tf.Tensor]
- property state_info_specs¶
State info specification.
- Returns
- keys and shapes for the information related to the
module’s state when taking an action.
- Return type
List[str]
- property state_info_keys¶
State info keys.
- Returns
- keys for the information related to the module’s state
when taking an input.
- Return type
List[str]
- build(*inputs, name=None)¶
Build a Network with the given input(s).
* Do not call tf.global_variable_initializers() after building a model as it will reassign random weights to the model. The parameters inside a model will be initialized when calling build(). *
It uses the same, fixed variable scope for all Networks, to ensure parameter sharing. Different Networks must have an unique name.
- Parameters
- Raises
ValueError – When a Network with the same name is already built.
- Returns
- Output tensors of the model with the given
inputs.
- Return type
list[tf.Tensor]
- network_input_spec()¶
Network input spec.
- network_output_spec()¶
Network output spec.
- reset(do_resets=None)¶
Reset the module.
This is effective only to recurrent modules. do_resets is effective only to vectoried modules.
For a vectorized modules, do_resets is an array of boolean indicating which internal states to be reset. The length of do_resets should be equal to the length of inputs.
- Parameters
do_resets (numpy.ndarray) – Bool array indicating which states to be reset.
- terminate()¶
Clean up operation.
- get_trainable_vars()¶
Get trainable variables.
- Returns
- A list of trainable variables in the current
variable scope.
- Return type
List[tf.Variable]
- get_global_vars()¶
Get global variables.
- Returns
- A list of global variables in the current
variable scope.
- Return type
List[tf.Variable]
- get_regularizable_vars()¶
Get all network weight variables in the current scope.
- Returns
- A list of network weight variables in the
current variable scope.
- Return type
List[tf.Variable]
- get_params()¶
Get the trainable variables.
- Returns
- A list of trainable variables in the current
variable scope.
- Return type
List[tf.Variable]
- get_param_shapes()¶
Get parameter shapes.
- Returns
A list of variable shapes.
- Return type
List[tuple]
- get_param_values()¶
Get param values.
- Returns
- Values of the parameters evaluated in
the current session
- Return type
np.ndarray
- set_param_values(param_values)¶
Set param values.
- Parameters
param_values (np.ndarray) – A numpy array of parameter values.
- flat_to_params(flattened_params)¶
Unflatten tensors according to their respective shapes.
- Parameters
flattened_params (np.ndarray) – A numpy array of flattened params.
- Returns
- A list of parameters reshaped to the
shapes specified.
- Return type
List[np.ndarray]