code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
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def create_tf_record_dataset(
filenames: MutableSequence[Text],
batch_size: int,
shuffle_buffer_size_per_record: int = 100,
shuffle_buffer_size: int = 100,
load_buffer_size: int = 100000000,
num_shards: int = 50,
cycle_length: int = tf.data.experimental.AUTOTUNE,
block_length: int = 10,
... | Create a TF dataset from a list of filenames.
A dataset is created for each record file and these are interleaved together
to create the final dataset.
Args:
filenames: List of filenames of a TFRecord dataset containing TF Examples.
batch_size: The batch size of tensors in the returned dataset.
shuf... | create_tf_record_dataset | python | tensorflow/agents | tf_agents/examples/cql_sac/kumar20/data_utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/examples/cql_sac/kumar20/data_utils.py | Apache-2.0 |
def create_collect_data_spec(
dataset_dict: EpisodeDictType, use_trajectories: bool = True
) -> Union[trajectory.Transition, trajectory.Trajectory]:
"""Create a spec that describes the data collected by agent.collect_policy."""
reward = dataset_dict['rewards'][0]
discount = dataset_dict['discounts'][0]
obse... | Create a spec that describes the data collected by agent.collect_policy. | create_collect_data_spec | python | tensorflow/agents | tf_agents/examples/cql_sac/kumar20/dataset/dataset_utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/examples/cql_sac/kumar20/dataset/dataset_utils.py | Apache-2.0 |
def create_trajectory(
state: types.Array,
action: types.Array,
discount: types.Array,
reward: types.Array,
step_type: types.Array,
next_step_type: types.Array,
) -> trajectory.Trajectory:
"""Creates a Trajectory from current and next state information."""
return trajectory.Trajectory(
... | Creates a Trajectory from current and next state information. | create_trajectory | python | tensorflow/agents | tf_agents/examples/cql_sac/kumar20/dataset/file_utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/examples/cql_sac/kumar20/dataset/file_utils.py | Apache-2.0 |
def create_transition(
state: types.Array,
action: types.Array,
next_state: types.Array,
discount: types.Array,
reward: types.Array,
step_type: types.Array,
next_step_type: types.Array,
) -> trajectory.Transition:
"""Creates a Transition from current and next state information."""
tfagen... | Creates a Transition from current and next state information. | create_transition | python | tensorflow/agents | tf_agents/examples/cql_sac/kumar20/dataset/file_utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/examples/cql_sac/kumar20/dataset/file_utils.py | Apache-2.0 |
def write_samples_to_tfrecord(
dataset_dict: Dict[str, types.Array],
collect_data_spec: trajectory.Transition,
dataset_path: str,
start_episode: int,
end_episode: int,
use_trajectories: bool = True,
) -> None:
"""Creates and writes samples to a TFRecord file."""
tfrecord_observer = example_e... | Creates and writes samples to a TFRecord file. | write_samples_to_tfrecord | python | tensorflow/agents | tf_agents/examples/cql_sac/kumar20/dataset/file_utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/examples/cql_sac/kumar20/dataset/file_utils.py | Apache-2.0 |
def q_lstm_network(num_actions):
"""Create the RNN based on layer parameters."""
lstm_cell = tf.keras.layers.LSTM( # pylint: disable=g-complex-comprehension
20,
implementation=KERAS_LSTM_FUSED,
return_state=True,
return_sequences=True,
)
return sequential.Sequential(
[dense(50), ... | Create the RNN based on layer parameters. | q_lstm_network | python | tensorflow/agents | tf_agents/examples/dqn/dqn_train_eval_rnn.py | https://github.com/tensorflow/agents/blob/master/tf_agents/examples/dqn/dqn_train_eval_rnn.py | Apache-2.0 |
def create_q_network(num_actions):
"""Create a Q network following the architecture from Minh 15."""
kernel_initializer = tf.keras.initializers.VarianceScaling(scale=2.0)
conv2d = functools.partial(
tf.keras.layers.Conv2D,
activation=tf.keras.activations.relu,
kernel_initializer=kernel_initiali... | Create a Q network following the architecture from Minh 15. | create_q_network | python | tensorflow/agents | tf_agents/examples/dqn/mnih15/dqn_train_eval_atari.py | https://github.com/tensorflow/agents/blob/master/tf_agents/examples/dqn/mnih15/dqn_train_eval_atari.py | Apache-2.0 |
def ppo_clip_train_eval(
root_dir, num_iterations, reverb_port=None, eval_interval=0
):
"""Executes train and eval for ppo_clip.
gin is used to configure parameters related to the agent and environment.
Arguments related to the execution, e.g. number of iterations and how often to
eval, are set directly by... | Executes train and eval for ppo_clip.
gin is used to configure parameters related to the agent and environment.
Arguments related to the execution, e.g. number of iterations and how often to
eval, are set directly by this method. This keeps the gin config focused on
the agent and execution level arguments quic... | ppo_clip_train_eval | python | tensorflow/agents | tf_agents/examples/ppo/schulman17/ppo_clip_train_eval.py | https://github.com/tensorflow/agents/blob/master/tf_agents/examples/ppo/schulman17/ppo_clip_train_eval.py | Apache-2.0 |
def __call__(self, trajectory):
"""Writes the trajectory into the underlying replay buffer.
Allows trajectory to be a flattened trajectory. No batch dimension allowed.
Args:
trajectory: The trajectory to be written which could be (possibly nested)
trajectory object or a flattened version of ... | Writes the trajectory into the underlying replay buffer.
Allows trajectory to be a flattened trajectory. No batch dimension allowed.
Args:
trajectory: The trajectory to be written which could be (possibly nested)
trajectory object or a flattened version of a trajectory. It assumes
there ... | __call__ | python | tensorflow/agents | tf_agents/examples/ppo/schulman17/train_eval_lib.py | https://github.com/tensorflow/agents/blob/master/tf_agents/examples/ppo/schulman17/train_eval_lib.py | Apache-2.0 |
def train_eval(
root_dir,
env_name='HalfCheetah-v2',
# Training params
num_iterations=1600,
actor_fc_layers=(64, 64),
value_fc_layers=(64, 64),
learning_rate=3e-4,
collect_sequence_length=2048,
minibatch_size=64,
num_epochs=10,
# Agent params
importance_ratio_clipping=0.2... | Trains and evaluates PPO (Importance Ratio Clipping).
Args:
root_dir: Main directory path where checkpoints, saved_models, and summaries
will be written to.
env_name: Name for the Mujoco environment to load.
num_iterations: The number of iterations to perform collection and training.
actor_fc_l... | train_eval | python | tensorflow/agents | tf_agents/examples/ppo/schulman17/train_eval_lib.py | https://github.com/tensorflow/agents/blob/master/tf_agents/examples/ppo/schulman17/train_eval_lib.py | Apache-2.0 |
def __init__(
self, server_address: Text, table_names: Iterable[Text] = (DEFAULT_TABLE,)
):
"""Initializes the class.
Args:
server_address: The address of the Reverb server.
table_names: Table names. By default, it is assumed that only a single
table is used with the name `variables... | Initializes the class.
Args:
server_address: The address of the Reverb server.
table_names: Table names. By default, it is assumed that only a single
table is used with the name `variables`. Each table assumed to exist in
the server, has signature defined, and set the capacity to 1.
... | __init__ | python | tensorflow/agents | tf_agents/experimental/distributed/reverb_variable_container.py | https://github.com/tensorflow/agents/blob/master/tf_agents/experimental/distributed/reverb_variable_container.py | Apache-2.0 |
def push(
self, values: types.NestedTensor, table: Text = DEFAULT_TABLE
) -> None:
"""Pushes values into a Reverb table.
Args:
values: Nested structure of tensors.
table: The name of the table.
Raises:
KeyError: If the table name is not provided during construction time.
tf... | Pushes values into a Reverb table.
Args:
values: Nested structure of tensors.
table: The name of the table.
Raises:
KeyError: If the table name is not provided during construction time.
tf.errors.InvalidArgumentError: If the nested structure of the variable
does not match the s... | push | python | tensorflow/agents | tf_agents/experimental/distributed/reverb_variable_container.py | https://github.com/tensorflow/agents/blob/master/tf_agents/experimental/distributed/reverb_variable_container.py | Apache-2.0 |
def pull(self, table: Text = DEFAULT_TABLE) -> types.NestedTensor:
"""Pulls values from a Reverb table and returns them as nested tensors."""
sample = self._tf_client.sample(table, data_dtypes=[self._dtypes[table]])
# The data is received in the form of a sequence. In the case of variable
# container th... | Pulls values from a Reverb table and returns them as nested tensors. | pull | python | tensorflow/agents | tf_agents/experimental/distributed/reverb_variable_container.py | https://github.com/tensorflow/agents/blob/master/tf_agents/experimental/distributed/reverb_variable_container.py | Apache-2.0 |
def update(
self, variables: types.NestedVariable, table: Text = DEFAULT_TABLE
) -> None:
"""Updates variables using values pulled from a Reverb table.
Args:
variables: Nested structure of variables.
table: The name of the table.
Raises:
KeyError: If the table name is not provide... | Updates variables using values pulled from a Reverb table.
Args:
variables: Nested structure of variables.
table: The name of the table.
Raises:
KeyError: If the table name is not provided during construction time.
ValueError: If the nested structure of the variable does not match the
... | update | python | tensorflow/agents | tf_agents/experimental/distributed/reverb_variable_container.py | https://github.com/tensorflow/agents/blob/master/tf_agents/experimental/distributed/reverb_variable_container.py | Apache-2.0 |
def _assign(
self,
variables: types.NestedVariable,
values: types.NestedTensor,
check_types: bool = False,
) -> None:
"""Assigns the nested values to variables."""
nest_utils.assert_same_structure(variables, values, check_types=check_types)
for variable, value in zip(
tf.ne... | Assigns the nested values to variables. | _assign | python | tensorflow/agents | tf_agents/experimental/distributed/reverb_variable_container.py | https://github.com/tensorflow/agents/blob/master/tf_agents/experimental/distributed/reverb_variable_container.py | Apache-2.0 |
def evaluate(
summary_dir: Text,
environment_name: Text,
policy: py_tf_eager_policy.PyTFEagerPolicyBase,
variable_container: reverb_variable_container.ReverbVariableContainer,
suite_load_fn: Callable[
[Text], py_environment.PyEnvironment
] = suite_mujoco.load,
additional_metrics: Opt... | Evaluates a policy iteratively fetching weights from variable container.
Args:
summary_dir: Directory which is used to store the summaries.
environment_name: Name of the environment used to evaluate the policy.
policy: The policy being evaluated. The weights of this policy are fetched
from the vari... | evaluate | python | tensorflow/agents | tf_agents/experimental/distributed/examples/eval_job.py | https://github.com/tensorflow/agents/blob/master/tf_agents/experimental/distributed/examples/eval_job.py | Apache-2.0 |
def run_eval(
root_dir: Text,
# TODO(b/178225158): Deprecate in favor of the reporting libray when ready.
return_reporting_fn: Optional[Callable[[int, float], None]] = None,
) -> None:
"""Load the policy and evaluate it.
Args:
root_dir: the root directory for this experiment.
return_reporting_f... | Load the policy and evaluate it.
Args:
root_dir: the root directory for this experiment.
return_reporting_fn: Optional callback function of the form `fn(train_step,
average_return)` which reports the average return to a custom destination.
| run_eval | python | tensorflow/agents | tf_agents/experimental/distributed/examples/eval_job.py | https://github.com/tensorflow/agents/blob/master/tf_agents/experimental/distributed/examples/eval_job.py | Apache-2.0 |
def test_eval_job(self):
"""Tests the eval job doing an eval every 5 steps for 10 train steps."""
summary_dir = self.create_tempdir().full_path
environment = test_envs.CountingEnv(steps_per_episode=4)
action_tensor_spec = tensor_spec.from_spec(environment.action_spec())
time_step_tensor_spec = tenso... | Tests the eval job doing an eval every 5 steps for 10 train steps. | test_eval_job | python | tensorflow/agents | tf_agents/experimental/distributed/examples/eval_job_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/experimental/distributed/examples/eval_job_test.py | Apache-2.0 |
def test_eval_job_constant_eval(self):
"""Tests eval every step for 2 steps.
This test's `variable_container` passes the same train step twice to test
that `is_train_step_the_same_or_behind` is working as expected. If were not
working, the number of train steps processed will be incorrect (2x higher).
... | Tests eval every step for 2 steps.
This test's `variable_container` passes the same train step twice to test
that `is_train_step_the_same_or_behind` is working as expected. If were not
working, the number of train steps processed will be incorrect (2x higher).
| test_eval_job_constant_eval | python | tensorflow/agents | tf_agents/experimental/distributed/examples/eval_job_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/experimental/distributed/examples/eval_job_test.py | Apache-2.0 |
def count_summary_scalar_tags_in_call_list(self, mock_summary_scalar, tag):
"""Returns the number of time the tag is found in `mock_summary_scalar`.
This is used because `assert_has_calls` uses a list for verification that
is cumbersome and produces confusing error messages on unit test failure.
Exampl... | Returns the number of time the tag is found in `mock_summary_scalar`.
This is used because `assert_has_calls` uses a list for verification that
is cumbersome and produces confusing error messages on unit test failure.
Example: Index out of bounds if more values exist than expected. This is not
intutive... | count_summary_scalar_tags_in_call_list | python | tensorflow/agents | tf_agents/experimental/distributed/examples/eval_job_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/experimental/distributed/examples/eval_job_test.py | Apache-2.0 |
def collect(
summary_dir: Text,
environment_name: Text,
collect_policy: py_tf_eager_policy.PyTFEagerPolicyBase,
replay_buffer_server_address: Text,
variable_container_server_address: Text,
suite_load_fn: Callable[
[Text], py_environment.PyEnvironment
] = suite_mujoco.load,
initia... | Collects experience using a policy updated after every episode. | collect | python | tensorflow/agents | tf_agents/experimental/distributed/examples/sac/sac_collect.py | https://github.com/tensorflow/agents/blob/master/tf_agents/experimental/distributed/examples/sac/sac_collect.py | Apache-2.0 |
def train_eval(
root_dir,
env_name='HalfCheetah-v2',
# Training params
num_iterations=1600,
actor_fc_layers=(64, 64),
value_fc_layers=(64, 64),
learning_rate=3e-4,
collect_sequence_length=2048,
minibatch_size=64,
num_epochs=10,
# Agent params
importance_ratio_clipping=0.2... | Trains and evaluates PPO (Importance Ratio Clipping).
Args:
root_dir: Main directory path where checkpoints, saved_models, and summaries
will be written to.
env_name: Name for the Mujoco environment to load.
num_iterations: The number of iterations to perform collection and training.
actor_fc_l... | train_eval | python | tensorflow/agents | tf_agents/experimental/examples/ppo/train_eval_lib.py | https://github.com/tensorflow/agents/blob/master/tf_agents/experimental/examples/ppo/train_eval_lib.py | Apache-2.0 |
def _infer_state_dtype(explicit_dtype, state):
"""Infer the dtype of an RNN state.
Args:
explicit_dtype: explicitly declared dtype or None.
state: RNN's hidden state. Must be a Tensor or a nested iterable containing
Tensors.
Returns:
dtype: inferred dtype of hidden state.
Raises:
ValueE... | Infer the dtype of an RNN state.
Args:
explicit_dtype: explicitly declared dtype or None.
state: RNN's hidden state. Must be a Tensor or a nested iterable containing
Tensors.
Returns:
dtype: inferred dtype of hidden state.
Raises:
ValueError: if `state` has heterogeneous dtypes or is empt... | _infer_state_dtype | python | tensorflow/agents | tf_agents/keras_layers/dynamic_unroll_layer.py | https://github.com/tensorflow/agents/blob/master/tf_agents/keras_layers/dynamic_unroll_layer.py | Apache-2.0 |
def _best_effort_input_batch_size(flat_input):
"""Get static input batch size if available, with fallback to the dynamic one.
Args:
flat_input: An iterable of time major input Tensors of shape `[max_time,
batch_size, ...]`. All inputs should have compatible batch sizes.
Returns:
The batch size in ... | Get static input batch size if available, with fallback to the dynamic one.
Args:
flat_input: An iterable of time major input Tensors of shape `[max_time,
batch_size, ...]`. All inputs should have compatible batch sizes.
Returns:
The batch size in Python integer if available, or a scalar Tensor othe... | _best_effort_input_batch_size | python | tensorflow/agents | tf_agents/keras_layers/dynamic_unroll_layer.py | https://github.com/tensorflow/agents/blob/master/tf_agents/keras_layers/dynamic_unroll_layer.py | Apache-2.0 |
def __init__(self, cell, parallel_iterations=20, swap_memory=None, **kwargs):
"""Create a `DynamicUnroll` layer.
Args:
cell: A `tf.nn.rnn_cell.RNNCell` or Keras `RNNCell` (e.g. `LSTMCell`)
whose `call()` method has the signature `call(input, state, ...)`. Each
tensor in the tuple is shape... | Create a `DynamicUnroll` layer.
Args:
cell: A `tf.nn.rnn_cell.RNNCell` or Keras `RNNCell` (e.g. `LSTMCell`)
whose `call()` method has the signature `call(input, state, ...)`. Each
tensor in the tuple is shaped `[batch_size, ...]`.
parallel_iterations: Parallel iterations to pass to `tf.... | __init__ | python | tensorflow/agents | tf_agents/keras_layers/dynamic_unroll_layer.py | https://github.com/tensorflow/agents/blob/master/tf_agents/keras_layers/dynamic_unroll_layer.py | Apache-2.0 |
def call(self, inputs, initial_state=None, reset_mask=None, training=False):
"""Perform the computation.
Args:
inputs: A tuple containing tensors in batch-major format, each shaped
`[batch_size, n, ...]`. If none of the inputs has rank greater than 2
(i.e., all inputs are shaped `[batch_... | Perform the computation.
Args:
inputs: A tuple containing tensors in batch-major format, each shaped
`[batch_size, n, ...]`. If none of the inputs has rank greater than 2
(i.e., all inputs are shaped `[batch_size, d]` or `[batch_size]`) then
it is assumed that a single frame is being... | call | python | tensorflow/agents | tf_agents/keras_layers/dynamic_unroll_layer.py | https://github.com/tensorflow/agents/blob/master/tf_agents/keras_layers/dynamic_unroll_layer.py | Apache-2.0 |
def _static_unroll_single_step(
cell, inputs, reset_mask, state, zero_state, training
):
"""Helper for dynamic_unroll which runs a single step."""
def _squeeze(t):
if not isinstance(t, tf.TensorArray) and t.shape.rank > 0:
return tf.squeeze(t, [0])
else:
return t
# Remove time dimension.... | Helper for dynamic_unroll which runs a single step. | _static_unroll_single_step | python | tensorflow/agents | tf_agents/keras_layers/dynamic_unroll_layer.py | https://github.com/tensorflow/agents/blob/master/tf_agents/keras_layers/dynamic_unroll_layer.py | Apache-2.0 |
def body(time, state, output_tas):
"""Internal while_loop body.
Args:
time: time
state: rnn state @ time
output_tas: output tensorarrays
Returns:
- time + 1
- state: rnn state @ time + 1
- output_tas: output tensorarrays with values written @ time
- masks_ta: opti... | Internal while_loop body.
Args:
time: time
state: rnn state @ time
output_tas: output tensorarrays
Returns:
- time + 1
- state: rnn state @ time + 1
- output_tas: output tensorarrays with values written @ time
- masks_ta: optional mask tensorarray with mask written @ ... | body | python | tensorflow/agents | tf_agents/keras_layers/dynamic_unroll_layer.py | https://github.com/tensorflow/agents/blob/master/tf_agents/keras_layers/dynamic_unroll_layer.py | Apache-2.0 |
def InnerReshape(
current_shape: types.Shape, # pylint: disable=invalid-name
new_shape: types.Shape,
**kwargs
) -> tf.keras.layers.Layer:
"""Returns a Keras layer that reshapes the inner dimensions of tensors.
Each tensor passed to an instance of `InnerReshape`, will be reshaped to:
```python
sha... | Returns a Keras layer that reshapes the inner dimensions of tensors.
Each tensor passed to an instance of `InnerReshape`, will be reshaped to:
```python
shape(tensor)[:-len(current_shape)] + new_shape
```
(after its inner shape is validated against `current_shape`). Note:
The `current_shape` may contain... | InnerReshape | python | tensorflow/agents | tf_agents/keras_layers/inner_reshape.py | https://github.com/tensorflow/agents/blob/master/tf_agents/keras_layers/inner_reshape.py | Apache-2.0 |
def _reshape_inner_dims(
tensor: tf.Tensor, shape: tf.TensorShape, new_shape: tf.TensorShape
) -> tf.Tensor:
"""Reshapes tensor to: shape(tensor)[:-len(shape)] + new_shape."""
tensor_shape = tf.shape(tensor)
ndims = shape.rank
tensor.shape[-ndims:].assert_is_compatible_with(shape)
new_shape_inner_tensor =... | Reshapes tensor to: shape(tensor)[:-len(shape)] + new_shape. | _reshape_inner_dims | python | tensorflow/agents | tf_agents/keras_layers/inner_reshape.py | https://github.com/tensorflow/agents/blob/master/tf_agents/keras_layers/inner_reshape.py | Apache-2.0 |
def __init__(self, layer: tf.keras.layers.RNN, **kwargs):
"""Create a `RNNWrapper`.
Args:
layer: An instance of `tf.keras.layers.RNN` or subclasses (including
`tf.keras.layers.{LSTM,GRU,...}`.
**kwargs: Extra args to `Layer` parent class.
Raises:
TypeError: If `layer` is not a su... | Create a `RNNWrapper`.
Args:
layer: An instance of `tf.keras.layers.RNN` or subclasses (including
`tf.keras.layers.{LSTM,GRU,...}`.
**kwargs: Extra args to `Layer` parent class.
Raises:
TypeError: If `layer` is not a subclass of `tf.keras.layers.RNN`.
NotImplementedError: If `l... | __init__ | python | tensorflow/agents | tf_agents/keras_layers/rnn_wrapper.py | https://github.com/tensorflow/agents/blob/master/tf_agents/keras_layers/rnn_wrapper.py | Apache-2.0 |
def call(self, inputs, initial_state=None, mask=None, training=False):
"""Perform the computation.
Args:
inputs: A tuple containing tensors in batch-major format, each shaped
`[batch_size, n, ...]`.
initial_state: (Optional) An initial state for the wrapped layer. If not
provided, `... | Perform the computation.
Args:
inputs: A tuple containing tensors in batch-major format, each shaped
`[batch_size, n, ...]`.
initial_state: (Optional) An initial state for the wrapped layer. If not
provided, `get_initial_state()` is used instead.
mask: The mask to pass down to the... | call | python | tensorflow/agents | tf_agents/keras_layers/rnn_wrapper.py | https://github.com/tensorflow/agents/blob/master/tf_agents/keras_layers/rnn_wrapper.py | Apache-2.0 |
def __init__(
self,
wrapped: tf.keras.layers.Layer,
inner_rank: int,
**kwargs: Mapping[Text, Any]
):
"""Initialize `SquashedOuterWrapper`.
Args:
wrapped: The keras layer to wrap.
inner_rank: The inner rank of inputs that will be passed to the layer.
This value allo... | Initialize `SquashedOuterWrapper`.
Args:
wrapped: The keras layer to wrap.
inner_rank: The inner rank of inputs that will be passed to the layer.
This value allows us to infer the outer batch dimension regardless of
the input shape to `build` or `call`.
**kwargs: Additional argume... | __init__ | python | tensorflow/agents | tf_agents/keras_layers/squashed_outer_wrapper.py | https://github.com/tensorflow/agents/blob/master/tf_agents/keras_layers/squashed_outer_wrapper.py | Apache-2.0 |
def call(self, batched_trajectory: traj.Trajectory):
"""Processes the batched_trajectory to update the metric.
Args:
batched_trajectory: A Trajectory containing batches of experience.
Raises:
ValueError: If the batch size is an unexpected value.
"""
trajectories = nest_utils.unstack_ne... | Processes the batched_trajectory to update the metric.
Args:
batched_trajectory: A Trajectory containing batches of experience.
Raises:
ValueError: If the batch size is an unexpected value.
| call | python | tensorflow/agents | tf_agents/metrics/batched_py_metric.py | https://github.com/tensorflow/agents/blob/master/tf_agents/metrics/batched_py_metric.py | Apache-2.0 |
def reset(self):
"""Resets internal stat gathering variables used to compute the metric."""
if self._built:
for metric in self._metrics:
metric.reset() | Resets internal stat gathering variables used to compute the metric. | reset | python | tensorflow/agents | tf_agents/metrics/batched_py_metric.py | https://github.com/tensorflow/agents/blob/master/tf_agents/metrics/batched_py_metric.py | Apache-2.0 |
def result(self) -> Any:
"""Evaluates the current value of the metric."""
if self._built:
return self._metric_class.aggregate(self._metrics)
else:
return np.array(0.0, dtype=self._dtype) | Evaluates the current value of the metric. | result | python | tensorflow/agents | tf_agents/metrics/batched_py_metric.py | https://github.com/tensorflow/agents/blob/master/tf_agents/metrics/batched_py_metric.py | Apache-2.0 |
def export_metrics(step, metrics, loss_info=None):
"""Exports the metrics and loss information to logging.info.
Args:
step: Integer denoting the round at which we log the metrics.
metrics: List of `TF metrics` to log.
loss_info: An optional instance of `LossInfo` whose value is logged.
"""
def log... | Exports the metrics and loss information to logging.info.
Args:
step: Integer denoting the round at which we log the metrics.
metrics: List of `TF metrics` to log.
loss_info: An optional instance of `LossInfo` whose value is logged.
| export_metrics | python | tensorflow/agents | tf_agents/metrics/export_utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/metrics/export_utils.py | Apache-2.0 |
def run_summaries(
metrics: Sequence[PyMetricType],
session: Optional[tf.compat.v1.Session] = None,
):
"""Execute summary ops for py_metrics.
Args:
metrics: A list of py_metric.Base objects.
session: A TensorFlow session-like object. If it is not provided, it will
use the current TensorFlow s... | Execute summary ops for py_metrics.
Args:
metrics: A list of py_metric.Base objects.
session: A TensorFlow session-like object. If it is not provided, it will
use the current TensorFlow session context manager.
Raises:
RuntimeError: If .tf_summaries() was not previously called on any of the
... | run_summaries | python | tensorflow/agents | tf_agents/metrics/py_metric.py | https://github.com/tensorflow/agents/blob/master/tf_agents/metrics/py_metric.py | Apache-2.0 |
def tf_summaries(
self,
train_step: types.Int = None,
step_metrics: Sequence[MetricType] = (),
) -> tf.Operation:
"""Build TF summary op and placeholder for this metric.
To execute the op, call py_metric.run_summaries.
Args:
train_step: Step counter for training iterations. If No... | Build TF summary op and placeholder for this metric.
To execute the op, call py_metric.run_summaries.
Args:
train_step: Step counter for training iterations. If None, no metric is
generated against the global step.
step_metrics: Step values to plot as X axis in addition to global_step.
... | tf_summaries | python | tensorflow/agents | tf_agents/metrics/py_metric.py | https://github.com/tensorflow/agents/blob/master/tf_agents/metrics/py_metric.py | Apache-2.0 |
def summary_placeholder(self) -> tf.compat.v1.placeholder:
"""TF placeholder to be used for the result of this metric."""
if self._summary_placeholder is None:
result = self.result()
if not isinstance(result, (np.ndarray, np.generic)):
result = np.array(result)
dtype = tf.as_dtype(resu... | TF placeholder to be used for the result of this metric. | summary_placeholder | python | tensorflow/agents | tf_agents/metrics/py_metric.py | https://github.com/tensorflow/agents/blob/master/tf_agents/metrics/py_metric.py | Apache-2.0 |
def call(self, trajectory: traj.Trajectory):
"""Processes a trajectory to update the metric.
Args:
trajectory: A trajectory.Trajectory.
""" | Processes a trajectory to update the metric.
Args:
trajectory: A trajectory.Trajectory.
| call | python | tensorflow/agents | tf_agents/metrics/py_metric.py | https://github.com/tensorflow/agents/blob/master/tf_agents/metrics/py_metric.py | Apache-2.0 |
def result(self) -> np.float32:
"""Returns the value of this metric."""
if self._buffer:
return self._buffer.mean(dtype=np.float32)
return np.array(0.0, dtype=np.float32) | Returns the value of this metric. | result | python | tensorflow/agents | tf_agents/metrics/py_metrics.py | https://github.com/tensorflow/agents/blob/master/tf_agents/metrics/py_metrics.py | Apache-2.0 |
def _batched_call(self, trajectory):
"""Processes the trajectory to update the metric.
Args:
trajectory: a tf_agents.trajectory.Trajectory.
"""
episode_return = self._np_state.episode_return
is_first = np.where(trajectory.is_first())
episode_return[is_first] = 0
episode_return += tr... | Processes the trajectory to update the metric.
Args:
trajectory: a tf_agents.trajectory.Trajectory.
| _batched_call | python | tensorflow/agents | tf_agents/metrics/py_metrics.py | https://github.com/tensorflow/agents/blob/master/tf_agents/metrics/py_metrics.py | Apache-2.0 |
def _batched_call(self, trajectory):
"""Processes the trajectory to update the metric.
Args:
trajectory: a tf_agents.trajectory.Trajectory.
"""
episode_steps = self._np_state.episode_steps
# Each non-boundary trajectory (first, mid or last) represents a step.
episode_steps[np.where(~traj... | Processes the trajectory to update the metric.
Args:
trajectory: a tf_agents.trajectory.Trajectory.
| _batched_call | python | tensorflow/agents | tf_agents/metrics/py_metrics.py | https://github.com/tensorflow/agents/blob/master/tf_agents/metrics/py_metrics.py | Apache-2.0 |
def init_variables(self):
"""Initializes this Metric's variables.
Should be called after variables are created in the first execution
of `__call__()`. If using graph execution, the return value should be
`run()` in a session before running the op returned by `__call__()`.
(See example above.)
... | Initializes this Metric's variables.
Should be called after variables are created in the first execution
of `__call__()`. If using graph execution, the return value should be
`run()` in a session before running the op returned by `__call__()`.
(See example above.)
Returns:
If using graph exe... | init_variables | python | tensorflow/agents | tf_agents/metrics/tf_metric.py | https://github.com/tensorflow/agents/blob/master/tf_agents/metrics/tf_metric.py | Apache-2.0 |
def tf_summaries(self, train_step=None, step_metrics=()):
"""Generates summaries against train_step and all step_metrics.
Args:
train_step: (Optional) Step counter for training iterations. If None, no
metric is generated against the global step.
step_metrics: (Optional) Iterable of step met... | Generates summaries against train_step and all step_metrics.
Args:
train_step: (Optional) Step counter for training iterations. If None, no
metric is generated against the global step.
step_metrics: (Optional) Iterable of step metrics to generate summaries
against.
Returns:
A... | tf_summaries | python | tensorflow/agents | tf_agents/metrics/tf_metric.py | https://github.com/tensorflow/agents/blob/master/tf_agents/metrics/tf_metric.py | Apache-2.0 |
def tf_summaries(self, train_step=None, step_metrics=()):
"""Generates histogram summaries against train_step and all step_metrics.
Args:
train_step: (Optional) Step counter for training iterations. If None, no
metric is generated against the global step.
step_metrics: (Optional) Iterable o... | Generates histogram summaries against train_step and all step_metrics.
Args:
train_step: (Optional) Step counter for training iterations. If None, no
metric is generated against the global step.
step_metrics: (Optional) Iterable of step metrics to generate summaries
against.
Return... | tf_summaries | python | tensorflow/agents | tf_agents/metrics/tf_metric.py | https://github.com/tensorflow/agents/blob/master/tf_agents/metrics/tf_metric.py | Apache-2.0 |
def tf_summaries(self, train_step=None, step_metrics=()):
"""Generates per-metric summaries against `train_step` and `step_metrics`.
Args:
train_step: (Optional) Step counter for training iterations. If None, no
metric is generated against the global step.
step_metrics: (Optional) Iterable ... | Generates per-metric summaries against `train_step` and `step_metrics`.
Args:
train_step: (Optional) Step counter for training iterations. If None, no
metric is generated against the global step.
step_metrics: (Optional) Iterable of step metrics to generate summaries
against.
Retur... | tf_summaries | python | tensorflow/agents | tf_agents/metrics/tf_metric.py | https://github.com/tensorflow/agents/blob/master/tf_agents/metrics/tf_metric.py | Apache-2.0 |
def call(self, trajectory):
"""Increase the number of environment_steps according to trajectory.
Step count is not increased on trajectory.boundary() since that step
is not part of any episode.
Args:
trajectory: A tf_agents.trajectory.Trajectory
Returns:
The arguments, for easy chaini... | Increase the number of environment_steps according to trajectory.
Step count is not increased on trajectory.boundary() since that step
is not part of any episode.
Args:
trajectory: A tf_agents.trajectory.Trajectory
Returns:
The arguments, for easy chaining.
| call | python | tensorflow/agents | tf_agents/metrics/tf_metrics.py | https://github.com/tensorflow/agents/blob/master/tf_agents/metrics/tf_metrics.py | Apache-2.0 |
def call(self, trajectory):
"""Increase the number of number_episodes according to trajectory.
It would increase for all trajectory.is_last().
Args:
trajectory: A tf_agents.trajectory.Trajectory
Returns:
The arguments, for easy chaining.
"""
# The __call__ will execute this.
n... | Increase the number of number_episodes according to trajectory.
It would increase for all trajectory.is_last().
Args:
trajectory: A tf_agents.trajectory.Trajectory
Returns:
The arguments, for easy chaining.
| call | python | tensorflow/agents | tf_agents/metrics/tf_metrics.py | https://github.com/tensorflow/agents/blob/master/tf_agents/metrics/tf_metrics.py | Apache-2.0 |
def _check_not_called_concurrently(lock):
"""Checks the returned context is not executed concurrently with any other."""
if not lock.acquire(False): # Non-blocking.
raise RuntimeError('Detected concurrent execution of TFPyMetric ops.')
try:
yield
finally:
lock.release() | Checks the returned context is not executed concurrently with any other. | _check_not_called_concurrently | python | tensorflow/agents | tf_agents/metrics/tf_py_metric.py | https://github.com/tensorflow/agents/blob/master/tf_agents/metrics/tf_py_metric.py | Apache-2.0 |
def __init__(self, py_metric, name=None, dtype=tf.float32):
"""Creates a TF metric given a py metric to wrap.
Args:
py_metric: A batched python metric to wrap.
name: Name of the metric.
dtype: Data type of the metric.
"""
name = name or py_metric.name
super(TFPyMetric, self).__ini... | Creates a TF metric given a py metric to wrap.
Args:
py_metric: A batched python metric to wrap.
name: Name of the metric.
dtype: Data type of the metric.
| __init__ | python | tensorflow/agents | tf_agents/metrics/tf_py_metric.py | https://github.com/tensorflow/agents/blob/master/tf_agents/metrics/tf_py_metric.py | Apache-2.0 |
def call(self, trajectory):
"""Update the value of the metric using trajectory.
The trajectory can be either batched or un-batched depending on
the expected inputs for the py_metric being wrapped.
Args:
trajectory: A tf_agents.trajectory.Trajectory.
Returns:
The arguments, for easy ch... | Update the value of the metric using trajectory.
The trajectory can be either batched or un-batched depending on
the expected inputs for the py_metric being wrapped.
Args:
trajectory: A tf_agents.trajectory.Trajectory.
Returns:
The arguments, for easy chaining.
| call | python | tensorflow/agents | tf_agents/metrics/tf_py_metric.py | https://github.com/tensorflow/agents/blob/master/tf_agents/metrics/tf_py_metric.py | Apache-2.0 |
def __init__(
self,
input_tensor_spec,
output_tensor_spec,
preprocessing_layers=None,
preprocessing_combiner=None,
conv_layer_params=None,
fc_layer_params=(200, 100),
dropout_layer_params=None,
activation_fn=tf.keras.activations.relu,
kernel_initializer=None,
... | Creates an instance of `ActorDistributionNetwork`.
Args:
input_tensor_spec: A nest of `tensor_spec.TensorSpec` representing the
input.
output_tensor_spec: A nest of `tensor_spec.BoundedTensorSpec` representing
the output.
preprocessing_layers: (Optional.) A nest of `tf.keras.layer... | __init__ | python | tensorflow/agents | tf_agents/networks/actor_distribution_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/actor_distribution_network.py | Apache-2.0 |
def __init__(
self,
input_tensor_spec,
output_tensor_spec,
preprocessing_layers=None,
preprocessing_combiner=None,
conv_layer_params=None,
input_fc_layer_params=(200, 100),
input_dropout_layer_params=None,
lstm_size=None,
output_fc_layer_params=(200, 100),
... | Creates an instance of `ActorDistributionRnnNetwork`.
Args:
input_tensor_spec: A nest of `tensor_spec.TensorSpec` representing the
input.
output_tensor_spec: A nest of `tensor_spec.BoundedTensorSpec` representing
the output.
preprocessing_layers: (Optional.) A nest of `tf.keras.la... | __init__ | python | tensorflow/agents | tf_agents/networks/actor_distribution_rnn_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/actor_distribution_rnn_network.py | Apache-2.0 |
def __init__(
self,
sample_spec,
logits_init_output_factor=0.1,
name='CategoricalProjectionNetwork',
):
"""Creates an instance of CategoricalProjectionNetwork.
Args:
sample_spec: A `tensor_spec.BoundedTensorSpec` detailing the shape and
dtypes of samples pulled from the ... | Creates an instance of CategoricalProjectionNetwork.
Args:
sample_spec: A `tensor_spec.BoundedTensorSpec` detailing the shape and
dtypes of samples pulled from the output distribution.
logits_init_output_factor: Output factor for initializing kernel logits
weights.
name: A string ... | __init__ | python | tensorflow/agents | tf_agents/networks/categorical_projection_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/categorical_projection_network.py | Apache-2.0 |
def __init__(
self,
input_tensor_spec,
action_spec,
num_atoms=51,
preprocessing_layers=None,
preprocessing_combiner=None,
conv_layer_params=None,
fc_layer_params=None,
activation_fn=tf.nn.relu,
name='CategoricalQNetwork',
):
"""Creates an instance of `Ca... | Creates an instance of `CategoricalQNetwork`.
The logits output by __call__ will ultimately have a shape of
`[batch_size, num_actions, num_atoms]`, where `num_actions` is computed as
`action_spec.maximum - action_spec.minimum + 1`. Each value is a logit for
a particular action at a particular atom (see... | __init__ | python | tensorflow/agents | tf_agents/networks/categorical_q_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/categorical_q_network.py | Apache-2.0 |
def call(self, observation, step_type=None, network_state=(), training=False):
"""Runs the given observation through the network.
Args:
observation: The observation to provide to the network.
step_type: The step type for the given observation. See `StepType` in
time_step.py.
network_s... | Runs the given observation through the network.
Args:
observation: The observation to provide to the network.
step_type: The step type for the given observation. See `StepType` in
time_step.py.
network_state: A state tuple to pass to the network, mainly used by RNNs.
training: Wheth... | call | python | tensorflow/agents | tf_agents/networks/categorical_q_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/categorical_q_network.py | Apache-2.0 |
def __init__(
self,
input_tensor_spec,
action_spec,
preprocessing_layers=None,
preprocessing_combiner=None,
conv_layer_params=None,
fc_layer_params=(75, 40),
dropout_layer_params=None,
activation_fn=tf.keras.activations.relu,
kernel_initializer=None,
bat... | Creates an instance of `DuelingQNetwork` as a subclass of QNetwork.
Args:
input_tensor_spec: A nest of `tensor_spec.TensorSpec` representing the
input observations.
action_spec: A nest of `tensor_spec.BoundedTensorSpec` representing the
actions.
preprocessing_layers: (Optional.) A... | __init__ | python | tensorflow/agents | tf_agents/networks/dueling_q_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/dueling_q_network.py | Apache-2.0 |
def call(self, observation, step_type=None, network_state=(), training=False):
"""Runs the given observation through the network.
Args:
observation: The observation to provide to the network.
step_type: The step type for the given observation. See `StepType` in
time_step.py.
network_s... | Runs the given observation through the network.
Args:
observation: The observation to provide to the network.
step_type: The step type for the given observation. See `StepType` in
time_step.py.
network_state: A state tuple to pass to the network, mainly used by RNNs.
training: Wheth... | call | python | tensorflow/agents | tf_agents/networks/dueling_q_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/dueling_q_network.py | Apache-2.0 |
def _copy_layer(layer):
"""Create a copy of a Keras layer with identical parameters.
The new layer will not share weights with the old one.
Args:
layer: An instance of `tf.keras.layers.Layer`.
Returns:
A new keras layer.
Raises:
TypeError: If `layer` is not a keras layer.
ValueError: If `l... | Create a copy of a Keras layer with identical parameters.
The new layer will not share weights with the old one.
Args:
layer: An instance of `tf.keras.layers.Layer`.
Returns:
A new keras layer.
Raises:
TypeError: If `layer` is not a keras layer.
ValueError: If `layer` cannot be correctly clo... | _copy_layer | python | tensorflow/agents | tf_agents/networks/encoding_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/encoding_network.py | Apache-2.0 |
def __init__(
self,
input_tensor_spec,
preprocessing_layers=None,
preprocessing_combiner=None,
conv_layer_params=None,
fc_layer_params=None,
dropout_layer_params=None,
activation_fn=tf.keras.activations.relu,
weight_decay_params=None,
kernel_initializer=None,
... | Creates an instance of `EncodingNetwork`.
Network supports calls with shape outer_rank + input_tensor_spec.shape. Note
outer_rank must be at least 1.
For example an input tensor spec with shape `(2, 3)` will require
inputs with at least a batch size, the input shape is `(?, 2, 3)`.
Input preproce... | __init__ | python | tensorflow/agents | tf_agents/networks/encoding_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/encoding_network.py | Apache-2.0 |
def _count_params(weights):
"""Count the total number of scalars composing the weights.
Args:
weights: An iterable containing the weights on which to compute params
Returns:
The total number of scalars composing the weights
"""
unique_weights = {id(w): w for w in weights}.values()
# Ignore Tra... | Count the total number of scalars composing the weights.
Args:
weights: An iterable containing the weights on which to compute params
Returns:
The total number of scalars composing the weights
| _count_params | python | tensorflow/agents | tf_agents/networks/layer_utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/layer_utils.py | Apache-2.0 |
def _weight_memory_size(weights):
"""Calculate the memory footprint for weights based on their dtypes.
Args:
weights: An iterable contains the weights to compute weight size.
Returns:
The total memory size (in Bytes) of the weights.
"""
unique_weights = {id(w): w for w in weights}.values()
to... | Calculate the memory footprint for weights based on their dtypes.
Args:
weights: An iterable contains the weights to compute weight size.
Returns:
The total memory size (in Bytes) of the weights.
| _weight_memory_size | python | tensorflow/agents | tf_agents/networks/layer_utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/layer_utils.py | Apache-2.0 |
def _get_layer_index_bound_by_layer_name(model, layer_range=None):
"""Get the layer indexes from the model based on layer names.
The layer indexes can be used to slice the model into sub models for
display.
Args:
model: `tf.keras.Model` instance.
layer_range: a list or tuple of 2 strings, the star... | Get the layer indexes from the model based on layer names.
The layer indexes can be used to slice the model into sub models for
display.
Args:
model: `tf.keras.Model` instance.
layer_range: a list or tuple of 2 strings, the starting layer name and
ending layer name (both inclusive) for the r... | _get_layer_index_bound_by_layer_name | python | tensorflow/agents | tf_agents/networks/layer_utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/layer_utils.py | Apache-2.0 |
def _readable_memory_size(weight_memory_size):
"""Convert the weight memory size (Bytes) to a readable string."""
units = ["Byte", "KB", "MB", "GB", "TB", "PB"]
scale = 1024
for unit in units:
if weight_memory_size / scale < 1:
return "{:.2f} {}".format(weight_memory_size, unit)
else:
weight... | Convert the weight memory size (Bytes) to a readable string. | _readable_memory_size | python | tensorflow/agents | tf_agents/networks/layer_utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/layer_utils.py | Apache-2.0 |
def _dtensor_variable_summary(weights):
"""Group and calculate DTensor based weights memory size.
Since DTensor weights can be sharded across multiple device, the result
will be grouped by the layout/sharding spec for the variables, so that
the accurate per-device memory size can be calculated.
Args:
... | Group and calculate DTensor based weights memory size.
Since DTensor weights can be sharded across multiple device, the result
will be grouped by the layout/sharding spec for the variables, so that
the accurate per-device memory size can be calculated.
Args:
weights: An iterable contains the weights to ... | _dtensor_variable_summary | python | tensorflow/agents | tf_agents/networks/layer_utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/layer_utils.py | Apache-2.0 |
def print_layer_summary(layer, nested_level=0):
"""Prints a summary for a single layer.
Args:
layer: target layer.
nested_level: level of nesting of the layer inside its parent layer
(e.g. 0 for a top-level layer, 1 for a nested layer).
"""
try:
output_shape = layer.outp... | Prints a summary for a single layer.
Args:
layer: target layer.
nested_level: level of nesting of the layer inside its parent layer
(e.g. 0 for a top-level layer, 1 for a nested layer).
| print_layer_summary | python | tensorflow/agents | tf_agents/networks/layer_utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/layer_utils.py | Apache-2.0 |
def print_layer_summary_with_connections(layer, nested_level=0):
"""Prints a summary for a single layer (including its connections).
Args:
layer: target layer.
nested_level: level of nesting of the layer inside its parent layer
(e.g. 0 for a top-level layer, 1 for a nested layer).
... | Prints a summary for a single layer (including its connections).
Args:
layer: target layer.
nested_level: level of nesting of the layer inside its parent layer
(e.g. 0 for a top-level layer, 1 for a nested layer).
| print_layer_summary_with_connections | python | tensorflow/agents | tf_agents/networks/layer_utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/layer_utils.py | Apache-2.0 |
def __init__(
self,
input_tensor_spec,
preprocessing_layers=None,
preprocessing_combiner=None,
conv_layer_params=None,
input_fc_layer_params=(75, 40),
lstm_size=None,
output_fc_layer_params=(75, 40),
activation_fn=tf.keras.activations.relu,
rnn_construction_fn... | Creates an instance of `LSTMEncodingNetwork`.
Input preprocessing is possible via `preprocessing_layers` and
`preprocessing_combiner` Layers. If the `preprocessing_layers` nest is
shallower than `input_tensor_spec`, then the layers will get the subnests.
For example, if:
```python
input_tenso... | __init__ | python | tensorflow/agents | tf_agents/networks/lstm_encoding_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/lstm_encoding_network.py | Apache-2.0 |
def call(self, observation, step_type, network_state=(), training=False):
"""Apply the network.
Args:
observation: A tuple of tensors matching `input_tensor_spec`.
step_type: A tensor of `StepType.
network_state: (optional.) The network state.
training: Whether the output is being used ... | Apply the network.
Args:
observation: A tuple of tensors matching `input_tensor_spec`.
step_type: A tensor of `StepType.
network_state: (optional.) The network state.
training: Whether the output is being used for training.
Returns:
`(outputs, network_state)` - the network output... | call | python | tensorflow/agents | tf_agents/networks/lstm_encoding_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/lstm_encoding_network.py | Apache-2.0 |
def __init__(
self,
splitter_fn: types.Splitter,
wrapped_network: network.Network,
passthrough_mask: bool = False,
input_tensor_spec: Optional[types.NestedTensorSpec] = None,
name: Text = 'MaskSplitterNetwork',
):
"""Initializes an instance of `MaskSplitterNetwork`.
Args:
... | Initializes an instance of `MaskSplitterNetwork`.
Args:
splitter_fn: A function used to process observations with action
constraints (i.e. mask). *Note*: The input spec of the wrapped network
must be compatible with the network-specific half of the output of the
`splitter_fn` on the i... | __init__ | python | tensorflow/agents | tf_agents/networks/mask_splitter_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/mask_splitter_network.py | Apache-2.0 |
def __init__(
self,
nested_layers: types.NestedLayer,
input_spec: typing.Optional[types.NestedTensorSpec] = None,
name: typing.Optional[typing.Text] = None,
):
"""Create a Sequential Network.
Args:
nested_layers: A nest of layers and/or networks. These will be used to
p... | Create a Sequential Network.
Args:
nested_layers: A nest of layers and/or networks. These will be used to
process the inputs (input nest structure will have to match this
structure). Any layers that are subclasses of
`tf.keras.layers.{RNN,LSTM,GRU,...}` are wrapped in
`tf_ag... | __init__ | python | tensorflow/agents | tf_agents/networks/nest_map.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/nest_map.py | Apache-2.0 |
def copy(self, **kwargs) -> 'NestMap':
"""Make a copy of a `NestMap` instance.
**NOTE** A copy of a `NestMap` instance always performs a deep copy
of the underlying layers, so the new instance will not share weights
with the original - but it will start with the same weights.
Args:
**kwargs:... | Make a copy of a `NestMap` instance.
**NOTE** A copy of a `NestMap` instance always performs a deep copy
of the underlying layers, so the new instance will not share weights
with the original - but it will start with the same weights.
Args:
**kwargs: Args to override when recreating this network... | copy | python | tensorflow/agents | tf_agents/networks/nest_map.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/nest_map.py | Apache-2.0 |
def __new__(mcs, classname, baseclasses, attrs):
"""Control the creation of subclasses of the Network class.
Args:
classname: The name of the subclass being created.
baseclasses: A tuple of parent classes.
attrs: A dict mapping new attributes to their values.
Returns:
The class obj... | Control the creation of subclasses of the Network class.
Args:
classname: The name of the subclass being created.
baseclasses: A tuple of parent classes.
attrs: A dict mapping new attributes to their values.
Returns:
The class object.
Raises:
RuntimeError: if the class __ini... | __new__ | python | tensorflow/agents | tf_agents/networks/network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/network.py | Apache-2.0 |
def _capture_init(self, *args, **kwargs):
"""Captures init args and kwargs and stores them into `_saved_kwargs`."""
if len(args) > len(arg_spec.args) + 1:
# Error case: more inputs than args. Call init so that the appropriate
# error can be raised to the user.
init(self, *args, **kw... | Captures init args and kwargs and stores them into `_saved_kwargs`. | _capture_init | python | tensorflow/agents | tf_agents/networks/network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/network.py | Apache-2.0 |
def __init__(self, input_tensor_spec=None, state_spec=(), name=None):
"""Creates an instance of `Network`.
Args:
input_tensor_spec: A nest of `tf.TypeSpec` representing the input
observations. Optional. If not provided, `create_variables()` will
fail unless a spec is provided.
sta... | Creates an instance of `Network`.
Args:
input_tensor_spec: A nest of `tf.TypeSpec` representing the input
observations. Optional. If not provided, `create_variables()` will
fail unless a spec is provided.
state_spec: A nest of `tensor_spec.TensorSpec` representing the state
ne... | __init__ | python | tensorflow/agents | tf_agents/networks/network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/network.py | Apache-2.0 |
def create_variables(self, input_tensor_spec=None, **kwargs):
"""Force creation of the network's variables.
Return output specs.
Args:
input_tensor_spec: (Optional). Override or provide an input tensor spec
when creating variables.
**kwargs: Other arguments to `network.call()`, e.g. `... | Force creation of the network's variables.
Return output specs.
Args:
input_tensor_spec: (Optional). Override or provide an input tensor spec
when creating variables.
**kwargs: Other arguments to `network.call()`, e.g. `training=True`.
Returns:
Output specs - a nested spec calc... | create_variables | python | tensorflow/agents | tf_agents/networks/network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/network.py | Apache-2.0 |
def _calc_unbatched_spec(x):
"""Build Network output spec by removing previously added batch dimension.
Args:
x: tfp.distributions.Distribution or Tensor.
Returns:
Specs without batch dimension representing x.
"""
if isinstance(x, tfp.distributions.Distribution):
... | Build Network output spec by removing previously added batch dimension.
Args:
x: tfp.distributions.Distribution or Tensor.
Returns:
Specs without batch dimension representing x.
| _calc_unbatched_spec | python | tensorflow/agents | tf_agents/networks/network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/network.py | Apache-2.0 |
def get_layer(self, name=None, index=None):
"""Retrieves a layer based on either its name (unique) or index.
If `name` and `index` are both provided, `index` will take precedence.
Indices are based on order of horizontal graph traversal (bottom-up).
Args:
name: String, name of layer.
i... | Retrieves a layer based on either its name (unique) or index.
If `name` and `index` are both provided, `index` will take precedence.
Indices are based on order of horizontal graph traversal (bottom-up).
Args:
name: String, name of layer.
index: Integer, index of layer.
Returns:
... | get_layer | python | tensorflow/agents | tf_agents/networks/network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/network.py | Apache-2.0 |
def summary(self, line_length=None, positions=None, print_fn=None):
"""Prints a string summary of the network.
Args:
line_length: Total length of printed lines (e.g. set this to adapt the
display to different terminal window sizes).
positions: Relative or absolute positions of log ele... | Prints a string summary of the network.
Args:
line_length: Total length of printed lines (e.g. set this to adapt the
display to different terminal window sizes).
positions: Relative or absolute positions of log elements in each line.
If not provided, defaults to `[.33, .55, .67,... | summary | python | tensorflow/agents | tf_agents/networks/network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/network.py | Apache-2.0 |
def _get_initial_state(self, batch_size):
"""Returns the initial state of the policy network.
Args:
batch_size: A constant or Tensor holding the batch size. Can be None, in
which case the state will not have a batch dimension added.
Returns:
A nest of zero tensors matching the spec of ... | Returns the initial state of the policy network.
Args:
batch_size: A constant or Tensor holding the batch size. Can be None, in
which case the state will not have a batch dimension added.
Returns:
A nest of zero tensors matching the spec of the policy network state.
| _get_initial_state | python | tensorflow/agents | tf_agents/networks/network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/network.py | Apache-2.0 |
def _is_layer(obj):
"""Implicit check for Layer-like objects."""
# TODO(b/110718070): Replace with isinstance(obj, tf.keras.layers.Layer).
return hasattr(obj, "_is_layer") and not isinstance(obj, type) | Implicit check for Layer-like objects. | _is_layer | python | tensorflow/agents | tf_agents/networks/network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/network.py | Apache-2.0 |
def _convert_to_spec_and_remove_singleton_batch_dim(
parameters: distribution_utils.Params, outer_ndim: int
) -> distribution_utils.Params:
"""Convert a `Params` object of tensors to one containing unbatched specs.
Note: The `Params` provided to this function are typically contain tensors
generated by Layers... | Convert a `Params` object of tensors to one containing unbatched specs.
Note: The `Params` provided to this function are typically contain tensors
generated by Layers and therefore containing an outer singleton dimension.
Since TF-Agents specs exclude batch and time prefixes, here we need to
remove the single... | _convert_to_spec_and_remove_singleton_batch_dim | python | tensorflow/agents | tf_agents/networks/network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/network.py | Apache-2.0 |
def create_variables(
module: typing.Union[Network, tf.keras.layers.Layer],
input_spec: typing.Optional[types.NestedTensorSpec] = None,
**kwargs: typing.Any,
) -> types.NestedTensorSpec:
"""Create variables in `module` given `input_spec`; return `output_spec`.
Here `module` can be a `tf_agents.networks... | Create variables in `module` given `input_spec`; return `output_spec`.
Here `module` can be a `tf_agents.networks.Network` or `Keras` layer.
Args:
module: The instance we would like to create layers on.
input_spec: The input spec (excluding batch dimensions).
**kwargs: Extra arguments to `module.__cal... | create_variables | python | tensorflow/agents | tf_agents/networks/network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/network.py | Apache-2.0 |
def _get_input_outer_ndim(
layer: tf.keras.layers.Layer, input_spec: types.NestedTensorSpec
) -> int:
"""Calculate or guess the number of batch (outer) ndims in `layer`."""
if isinstance(layer, tf.keras.layers.RNN):
raise TypeError(
"Saw a tf.keras.layers.RNN layer nested inside e.g. a keras Sequent... | Calculate or guess the number of batch (outer) ndims in `layer`. | _get_input_outer_ndim | python | tensorflow/agents | tf_agents/networks/network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/network.py | Apache-2.0 |
def get_state_spec(layer: tf.keras.layers.Layer) -> types.NestedTensorSpec:
"""Extracts the state spec from a layer.
Args:
layer: The layer to extract from; can be a `Network`.
Returns:
The state spec.
Raises:
TypeError: If `layer` is a subclass of `tf.keras.layers.RNN` (it must
be wrapped ... | Extracts the state spec from a layer.
Args:
layer: The layer to extract from; can be a `Network`.
Returns:
The state spec.
Raises:
TypeError: If `layer` is a subclass of `tf.keras.layers.RNN` (it must
be wrapped by an `RNNWrapper` object).
ValueError: If `layer` is a Keras layer and `crea... | get_state_spec | python | tensorflow/agents | tf_agents/networks/network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/network.py | Apache-2.0 |
def test_summary_no_exception(self):
"""Tests that Network.summary() does not throw an exception."""
observation_spec = specs.TensorSpec([1], tf.float32, 'observation')
action_spec = specs.TensorSpec([2], tf.float32, 'action')
net = MockNetwork(observation_spec, action_spec)
net.create_variables()
... | Tests that Network.summary() does not throw an exception. | test_summary_no_exception | python | tensorflow/agents | tf_agents/networks/network_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/network_test.py | Apache-2.0 |
def tanh_squash_to_spec(inputs, spec):
"""Maps inputs with arbitrary range to range defined by spec using `tanh`."""
means = (spec.maximum + spec.minimum) / 2.0
magnitudes = (spec.maximum - spec.minimum) / 2.0
return means + magnitudes * tf.tanh(inputs) | Maps inputs with arbitrary range to range defined by spec using `tanh`. | tanh_squash_to_spec | python | tensorflow/agents | tf_agents/networks/normal_projection_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/normal_projection_network.py | Apache-2.0 |
def __init__(
self,
sample_spec,
activation_fn=None,
init_means_output_factor=0.1,
std_bias_initializer_value=0.0,
mean_transform=tanh_squash_to_spec,
std_transform=tf.nn.softplus,
state_dependent_std=False,
scale_distribution=False,
seed=None,
seed_stre... | Creates an instance of NormalProjectionNetwork.
Args:
sample_spec: A `tensor_spec.BoundedTensorSpec` detailing the shape and
dtypes of samples pulled from the output distribution.
activation_fn: Activation function to use in dense layer.
init_means_output_factor: Output factor for initial... | __init__ | python | tensorflow/agents | tf_agents/networks/normal_projection_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/normal_projection_network.py | Apache-2.0 |
def validate_specs(action_spec, observation_spec):
"""Validates the spec contains a single action."""
del observation_spec # not currently validated
flat_action_spec = tf.nest.flatten(action_spec)
if len(flat_action_spec) > 1:
raise ValueError('Network only supports action_specs with a single action.')
... | Validates the spec contains a single action. | validate_specs | python | tensorflow/agents | tf_agents/networks/q_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/q_network.py | Apache-2.0 |
def __init__(
self,
input_tensor_spec,
action_spec,
preprocessing_layers=None,
preprocessing_combiner=None,
conv_layer_params=None,
fc_layer_params=(75, 40),
dropout_layer_params=None,
activation_fn=tf.keras.activations.relu,
kernel_initializer=None,
bat... | Creates an instance of `QNetwork`.
Args:
input_tensor_spec: A nest of `tensor_spec.TensorSpec` representing the
input observations.
action_spec: A nest of `tensor_spec.BoundedTensorSpec` representing the
actions.
preprocessing_layers: (Optional.) A nest of `tf.keras.layers.Layer`
... | __init__ | python | tensorflow/agents | tf_agents/networks/q_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/q_network.py | Apache-2.0 |
def call(self, observation, step_type=None, network_state=(), training=False):
"""Runs the given observation through the network.
Args:
observation: The observation to provide to the network.
step_type: The step type for the given observation. See `StepType` in
time_step.py.
network_s... | Runs the given observation through the network.
Args:
observation: The observation to provide to the network.
step_type: The step type for the given observation. See `StepType` in
time_step.py.
network_state: A state tuple to pass to the network, mainly used by RNNs.
training: Wheth... | call | python | tensorflow/agents | tf_agents/networks/q_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/q_network.py | Apache-2.0 |
def __init__(
self,
input_tensor_spec,
action_spec,
preprocessing_layers=None,
preprocessing_combiner=None,
conv_layer_params=None,
input_fc_layer_params=(75, 40),
lstm_size=None,
output_fc_layer_params=(75, 40),
activation_fn=tf.keras.activations.relu,
... | Creates an instance of `QRnnNetwork`.
Args:
input_tensor_spec: A nest of `tensor_spec.TensorSpec` representing the
input observations.
action_spec: A nest of `tensor_spec.BoundedTensorSpec` representing the
actions.
preprocessing_layers: (Optional.) A nest of `tf.keras.layers.Laye... | __init__ | python | tensorflow/agents | tf_agents/networks/q_rnn_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/q_rnn_network.py | Apache-2.0 |
def _infer_state_specs(
layers: Sequence[tf.keras.layers.Layer],
) -> Tuple[types.NestedTensorSpec, List[bool]]:
"""Infer the state spec of a sequence of keras Layers and Networks.
Args:
layers: A list of Keras layers and Network.
Returns:
A tuple with `state_spec`, a tuple of the state specs of len... | Infer the state spec of a sequence of keras Layers and Networks.
Args:
layers: A list of Keras layers and Network.
Returns:
A tuple with `state_spec`, a tuple of the state specs of length
`len(layers)` and a list of bools indicating if the corresponding layer
has lists in it's state.
| _infer_state_specs | python | tensorflow/agents | tf_agents/networks/sequential.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/sequential.py | Apache-2.0 |
def __init__(
self,
layers: Sequence[tf.keras.layers.Layer],
input_spec: Optional[types.NestedTensorSpec] = None,
name: Optional[Text] = None,
):
"""Create a Sequential Network.
Args:
layers: A list or tuple of layers to compose. Any layers that are
subclasses of `tf.ke... | Create a Sequential Network.
Args:
layers: A list or tuple of layers to compose. Any layers that are
subclasses of `tf.keras.layers.{RNN,LSTM,GRU,...}` are wrapped in
`tf_agents.keras_layers.RNNWrapper`.
input_spec: (Optional.) A nest of `tf.TypeSpec` representing the input
obs... | __init__ | python | tensorflow/agents | tf_agents/networks/sequential.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/sequential.py | Apache-2.0 |
def copy(self, **kwargs) -> 'Sequential':
"""Make a copy of a `Sequential` instance.
**NOTE** A copy of a `Sequential` instance always performs a deep copy
of the underlying layers, so the new instance will not share weights
with the original - but it will start with the same weights.
Args:
... | Make a copy of a `Sequential` instance.
**NOTE** A copy of a `Sequential` instance always performs a deep copy
of the underlying layers, so the new instance will not share weights
with the original - but it will start with the same weights.
Args:
**kwargs: Args to override when recreating this n... | copy | python | tensorflow/agents | tf_agents/networks/sequential.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/sequential.py | Apache-2.0 |
def __init__(self, batch_dims):
"""Create two tied ops to flatten and unflatten the front dimensions.
Args:
batch_dims: Number of batch dimensions the flatten/unflatten ops should
handle.
Raises:
ValueError: if batch dims is negative.
"""
if batch_dims < 0:
raise ValueErr... | Create two tied ops to flatten and unflatten the front dimensions.
Args:
batch_dims: Number of batch dimensions the flatten/unflatten ops should
handle.
Raises:
ValueError: if batch dims is negative.
| __init__ | python | tensorflow/agents | tf_agents/networks/utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/utils.py | Apache-2.0 |
def unflatten(self, tensor):
"""Unflattens the tensor's batch_dims using the cached shape."""
with tf.name_scope('batch_unflatten'):
if self._batch_dims == 1:
return tensor
if self._original_tensor_shape is None:
raise ValueError('Please call flatten before unflatten.')
# pyf... | Unflattens the tensor's batch_dims using the cached shape. | unflatten | python | tensorflow/agents | tf_agents/networks/utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/utils.py | Apache-2.0 |
def mlp_layers(
conv_layer_params=None,
fc_layer_params=None,
dropout_layer_params=None,
activation_fn=tf.keras.activations.relu,
kernel_initializer=None,
weight_decay_params=None,
name=None,
):
"""Generates conv and fc layers to encode into a hidden state.
Args:
conv_layer_params: ... | Generates conv and fc layers to encode into a hidden state.
Args:
conv_layer_params: Optional list of convolution layers parameters, where
each item is a length-three tuple indicating (filters, kernel_size,
stride).
fc_layer_params: Optional list of fully_connected parameters, where each
it... | mlp_layers | python | tensorflow/agents | tf_agents/networks/utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/networks/utils.py | Apache-2.0 |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.