garage.tf.models.gaussian_lstm_model module

GaussianLSTMModel.

class GaussianLSTMModel(output_dim, hidden_dim=32, name=None, hidden_nonlinearity=<function tanh>, hidden_w_init=<tensorflow.python.ops.init_ops.GlorotUniform object>, hidden_b_init=<tensorflow.python.ops.init_ops.Zeros object>, recurrent_nonlinearity=<function sigmoid>, recurrent_w_init=<tensorflow.python.ops.init_ops.GlorotUniform object>, output_nonlinearity=None, output_w_init=<tensorflow.python.ops.init_ops.GlorotUniform object>, output_b_init=<tensorflow.python.ops.init_ops.Zeros object>, hidden_state_init=<tensorflow.python.ops.init_ops.Zeros object>, hidden_state_init_trainable=False, cell_state_init=<tensorflow.python.ops.init_ops.Zeros object>, cell_state_init_trainable=False, forget_bias=True, learn_std=True, init_std=1.0, std_share_network=False, layer_normalization=False)[source]

Bases: garage.tf.models.base.Model

GaussianLSTMModel.

Parameters:
  • output_dim (int) – Output dimension of the model.
  • hidden_dim (int) – Hidden dimension for LSTM cell for mean.
  • 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.
  • 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.
  • learn_std (bool) – Is std trainable.
  • init_std (float) – Initial value for std.
  • std_share_network (bool) – Boolean for whether mean and std share the same network.
  • layer_normalization (bool) – Bool for using layer normalization or not.
network_input_spec()[source]

Network input spec.

network_output_spec()[source]

Network output spec.