garage.tf.models.mlp_model
¶
MLP Model.
A model composed only of a multi-layer perceptron (MLP), which maps real-valued inputs to real-valued outputs.
-
class
MLPModel
(output_dim, name='MLPModel', hidden_sizes=32, 32, hidden_nonlinearity=tf.nn.relu, hidden_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=None, output_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), output_b_init=tf.zeros_initializer(), layer_normalization=False)¶ Bases:
garage.tf.models.model.Model
MLP Model.
- Parameters
output_dim (int) – Dimension of the network output.
hidden_sizes (list[int]) – Output dimension of dense layer(s). For example, (32, 32) means this MLP consists of two hidden layers, each with 32 hidden units.
name (str) – Model name, also the variable scope.
hidden_nonlinearity (callable) – Activation function for intermediate dense layer(s). It should return a tf.Tensor. Set it to None to maintain a linear activation.
hidden_w_init (callable) – Initializer function for the weight of intermediate dense layer(s). The function should return a tf.Tensor.
hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a tf.Tensor.
output_nonlinearity (callable) – Activation function for output dense layer. It should return a tf.Tensor. Set it to None to maintain a linear activation.
output_w_init (callable) – Initializer function for the weight of output dense layer(s). The function should return a tf.Tensor.
output_b_init (callable) – Initializer function for the bias of output dense layer(s). The function should return a tf.Tensor.
layer_normalization (bool) – Bool for using layer normalization or not.
-
build
(self, *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
(self)¶ Network input spec.
-
network_output_spec
(self)¶ Network output spec.
-
property
parameters
(self)¶ Parameters of the model.
- Returns
Parameters
- Return type
np.ndarray
-
property
name
(self)¶ Name (str) of the model.
This is also the variable scope of the model.
- Returns
Name of the model.
- Return type
-
property
input
(self)¶ 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
(self)¶ 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
(self)¶ 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
(self)¶ 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]
-
reset
(self, 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.
-
property
state_info_specs
(self)¶ 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
(self)¶ State info keys.
- Returns
- keys for the information related to the module’s state
when taking an input.
- Return type
List[str]
-
terminate
(self)¶ Clean up operation.
-
get_trainable_vars
(self)¶ Get trainable variables.
- Returns
- A list of trainable variables in the current
variable scope.
- Return type
List[tf.Variable]
-
get_global_vars
(self)¶ Get global variables.
- Returns
- A list of global variables in the current
variable scope.
- Return type
List[tf.Variable]
-
get_regularizable_vars
(self)¶ 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
(self)¶ Get the trainable variables.
- Returns
- A list of trainable variables in the current
variable scope.
- Return type
List[tf.Variable]
-
get_param_shapes
(self)¶ Get parameter shapes.
- Returns
A list of variable shapes.
- Return type
List[tuple]
-
get_param_values
(self)¶ Get param values.
- Returns
- Values of the parameters evaluated in
the current session
- Return type
np.ndarray
-
set_param_values
(self, param_values)¶ Set param values.
- Parameters
param_values (np.ndarray) – A numpy array of parameter values.
-
flat_to_params
(self, 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]