garage.tf.models.categorical_lstm_model

Categorical LSTM Model.

A model represented by a Categorical distribution which is parameterized by a Long short-term memory (LSTM).

class CategoricalLSTMModel(output_dim, hidden_dim, name=None, hidden_nonlinearity=tf.nn.tanh, hidden_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), hidden_b_init=tf.zeros_initializer(), recurrent_nonlinearity=tf.nn.sigmoid, recurrent_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), output_nonlinearity=tf.nn.softmax, output_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), output_b_init=tf.zeros_initializer(), hidden_state_init=tf.zeros_initializer(), hidden_state_init_trainable=False, cell_state_init=tf.zeros_initializer(), cell_state_init_trainable=False, forget_bias=True, layer_normalization=False)

Bases: garage.tf.models.lstm_model.LSTMModel

Inheritance diagram of garage.tf.models.categorical_lstm_model.CategoricalLSTMModel

Categorical LSTM Model.

A model represented by a Categorical distribution which is parameterized by a Long short-term memory (LSTM).

Parameters
  • output_dim (int) – Dimension of the network output.

  • hidden_dim (int) – Hidden dimension for LSTM cell.

  • name (str) – Policy 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.

  • recurrent_nonlinearity (callable) – Activation function for recurrent layers. It should return a tf.Tensor. Set it to None to maintain a linear activation.

  • recurrent_w_init (callable) – Initializer function for the weight of recurrent 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.

  • hidden_state_init (callable) – Initializer function for the initial hidden state. The functino should return a tf.Tensor.

  • hidden_state_init_trainable (bool) – Bool for whether the initial hidden state is trainable.

  • cell_state_init (callable) – Initializer function for the initial cell state. The functino should return a tf.Tensor.

  • cell_state_init_trainable (bool) – Bool for whether the initial cell state is trainable.

  • forget_bias (bool) – If True, add 1 to the bias of the forget gate at initialization. It’s used to reduce the scale of forgetting at the beginning of the training.

  • layer_normalization (bool) – Bool for using layer normalization or not.

network_output_spec(self)

Network output spec.

Returns

Name of the model outputs, in order.

Return type

list[str]

network_input_spec(self)

Network input spec.

Returns

List of key(str) for the network outputs.

Return type

list[str]

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
  • inputs (list[tf.Tensor]) – Tensor input(s), recommended to be positional arguments, for example, def build(self, state_input, action_input, name=None).

  • name (str) – Name of the model, which is also the name scope of the model.

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]

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

str

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]