garage.tf.models.lstm
¶
LSTM in TensorFlow.
-
lstm
(name, lstm_cell, all_input_var, step_input_var, step_hidden_var, step_cell_var, output_nonlinearity_layer, hidden_state_init=tf.zeros_initializer(), hidden_state_init_trainable=False, cell_state_init=tf.zeros_initializer(), cell_state_init_trainable=False)¶ Long Short-Term Memory (LSTM).
- Parameters
name (str) – Name of the variable scope.
lstm_cell (tf.keras.layers.Layer) – LSTM cell used to generate outputs.
all_input_var (tf.Tensor) – Place holder for entire time-seried inputs, with shape \((N, T, S^*)\).
step_input_var (tf.Tensor) – Place holder for step inputs, with shape \((N, S^*)\).
step_hidden_var (tf.Tensor) – Place holder for step hidden state, with shape \((N, H)\).
step_cell_var (tf.Tensor) – Place holder for cell state, with shape \((N, H)\).
output_nonlinearity_layer (callable) – Activation function for output dense layer. It should return a tf.Tensor. Set it to None to maintain a linear activation.
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.
- Returns
Entire time-seried outputs, with shape \((N, T, S^*)\). tf.Tensor: Step output, with shape \((N, S^*)\). tf.Tensor: Step hidden state, with shape \((N, H)\). tf.Tensor: Step cell state, with shape \((N, H)\). tf.Tensor: Initial hidden state, with shape \((H, )\). tf.Tensor: Initial cell state, with shape \((H, )\).
- Return type
tf.Tensor