def _graph_fn_get_probabilities_log_probs(self, logits):
"""
Creates properties/parameters and log-probs from some reshaped output.
Args:
logits (SingleDataOp): The output of some layer that is already reshaped
according to our action Space.
Returns:
tuple (2x SingleDataOp):
parameters (DataOp): The parameters, ready to be passed to a Distribution object's
get_distribution API-method (usually some probabilities or loc/scale pairs).
log_probs (DataOp): Simply the log(parameters).
"""
if get_backend() == "tf":
if isinstance(self.action_space, IntBox):
# Discrete actions.
parameters = tf.maximum(x=tf.nn.softmax(logits=logits,
axis=-1),
y=SMALL_NUMBER)
parameters._batch_rank = 0
# Log probs.
log_probs = tf.log(x=parameters)
log_probs._batch_rank = 0
elif isinstance(self.action_space, FloatBox):
# Continuous actions.
mean, log_sd = tf.split(value=logits,
num_or_size_splits=2,
axis=1)
# Remove moments rank.
mean = tf.squeeze(input=mean, axis=1)
log_sd = tf.squeeze(input=log_sd, axis=1)
# Clip log_sd. log(SMALL_NUMBER) is negative.
log_sd = tf.clip_by_value(t=log_sd,
clip_value_min=log(SMALL_NUMBER),
clip_value_max=-log(SMALL_NUMBER))
# Turn log sd into sd.
sd = tf.exp(x=log_sd)
parameters = DataOpTuple(mean, sd)
log_probs = DataOpTuple(tf.log(x=mean), log_sd)
else:
raise NotImplementedError
return parameters, log_probs
elif get_backend() == "pytorch":
if isinstance(self.action_space, IntBox):
# Discrete actions.
softmax_logits = torch.softmax(logits, dim=-1)
parameters = torch.max(softmax_logits, SMALL_NUMBER_TORCH)
# Log probs.
log_probs = torch.log(parameters)
elif isinstance(self.action_space, FloatBox):
# Continuous actions.
mean, log_sd = torch.split(logits,
split_size_or_sections=2,
dim=1)
# Remove moments rank.
mean = torch.squeeze(mean, dim=1)
log_sd = torch.squeeze(log_sd, dim=1)
# Clip log_sd. log(SMALL_NUMBER) is negative.
log_sd = torch.clamp(log_sd,
min=LOG_SMALL_NUMBER,
max=-LOG_SMALL_NUMBER)
# Turn log sd into sd.
sd = torch.exp(log_sd)
parameters = DataOpTuple(mean, sd)
log_probs = DataOpTuple(torch.log(mean), log_sd)
else:
raise NotImplementedError
return parameters, log_probs
def _graph_fn_call(self,
inputs,
initial_c_and_h_states=None,
sequence_length=None):
"""
Args:
inputs (SingleDataOp): The data to pass through the layer (batch of n items, m timesteps).
Position of batch- and time-ranks in the input depend on `self.time_major` setting.
initial_c_and_h_states (DataOpTuple): The initial cell- and hidden-states to use.
None for the default behavior (all zeros).
The cell-state in an LSTM is passed between cells from step to step and only affected by element-wise
operations. The hidden state is identical to the output of the LSTM on the previous time step.
sequence_length (Optional[SingleDataOp]): An int tensor mapping each batch item to a sequence length
such that the remaining time slots for each batch item are filled with zeros.
Returns:
tuple:
- The outputs over all timesteps of the LSTM.
- DataOpTuple: The final cell- and hidden-states.
"""
if get_backend() == "tf":
# Convert to tf's LSTMStateTuple from DataOpTuple.
if initial_c_and_h_states is not None:
initial_c_and_h_states = tf.nn.rnn_cell.LSTMStateTuple(
initial_c_and_h_states[0], initial_c_and_h_states[1])
# We are running the LSTM as a dynamic while-loop.
if self.static_loop is False:
lstm_out, lstm_state_tuple = tf.nn.dynamic_rnn(
cell=self.lstm,
inputs=inputs,
sequence_length=sequence_length,
initial_state=initial_c_and_h_states,
parallel_iterations=self.parallel_iterations,
swap_memory=self.swap_memory,
time_major=self.in_space.time_major,
dtype="float")
# We are running with a fixed number of time steps (static unroll).
else:
# Set to zeros as tf lstm object does not handle None.
if initial_c_and_h_states is None:
shape = (tf.shape(
inputs)[0 if self.in_space.time_major is False else 1],
self.units)
initial_c_and_h_states = tf.nn.rnn_cell.LSTMStateTuple(
tf.zeros(shape=shape, dtype=tf.float32),
tf.zeros(shape=shape, dtype=tf.float32))
output_list = list()
lstm_state_tuple = initial_c_and_h_states
# TODO: Add option to reset the internal state in the middle of this loop iff some reset signal
# TODO: (e.g. terminal) is True during the loop.
inputs.set_shape([self.static_loop] +
inputs.shape.as_list()[1:])
#for input_, terminal in zip(tf.unstack(inputs), tf.unstack(terminals)):
for input_ in tf.unstack(inputs):
# If the episode ended, the core state should be reset before the next.
#core_state = nest.map_structure(functools.partial(tf.where, d),
# initial_core_state, core_state)
output, lstm_state_tuple = self.lstm(
input_, lstm_state_tuple)
output_list.append(output)
lstm_out = tf.stack(output_list)
# Only return last value.
if self.return_sequences is False:
if self.in_space.time_major is True:
lstm_out = lstm_out[-1]
else:
lstm_out = lstm_out[:, -1]
lstm_out._batch_rank = 0
# Return entire sequence.
else:
lstm_out._batch_rank = 0 if self.in_space.time_major is False else 1
lstm_out._time_rank = 0 if self.in_space.time_major is True else 1
# Returns: Unrolled-outputs (time series of all encountered h-states), final c- and h-states.
return lstm_out, DataOpTuple(lstm_state_tuple)
elif get_backend() == "pytorch":
# TODO init hidden state has to be available at create variable time to use.
inputs = torch.cat(inputs).view(len(inputs), 1, -1)
# TODO: support `self.return_sequences` = False
out, self.hidden_state = self.lstm(inputs, self.hidden_state)
return out, DataOpTuple(self.hidden_state)
def dtype(self):
return DataOpTuple([c.dtype for c in self])
def _graph_fn_update_alpha(self, alpha_loss, alpha_loss_per_item):
alpha_step_op, _, _ = self.alpha_optimizer.step(
DataOpTuple([self.log_alpha]), alpha_loss, alpha_loss_per_item)
return alpha_step_op