def _build(self, incoming, *args, **kwargs):
"""
Args:
incoming : A `Tensor`. The incoming tensor.
"""
inference = incoming
def apply_dropout():
if isinstance(self.keep_prob, float):
_keep_prob = tf.get_variable(
name='keep_prob', shape=[],
initializer=tf.constant_initializer(self.keep_prob), trainable=False)
tf.add_to_collection(tf.GraphKeys.DROPOUTS, _keep_prob)
if type(inference) in [list, np.array]:
for x in inference:
tf.nn.dropout(x=x, keep_prob=_keep_prob,
noise_shape=self.noise_shape, seed=self.seed)
return inference
else:
return tf.nn.dropout(x=inference, keep_prob=_keep_prob,
noise_shape=self.noise_shape, seed=self.seed)
if Modes.is_train(self.mode):
inference = apply_dropout()
track(inference, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return inference
def get_summary(stype, name, value=None, collect=True, **kwargs):
"""Creates or retrieves a summary.
It keep tracks of all graph summaries through SUMMARIES_BY_NAMES collection.
If a summary tags already exists, it will return that summary tensor.
Args:
stype: `str`. Summary type: 'histogram', 'scalar' or 'image'.
name: `str`. The summary tag (name).
value: `Tensor`. The summary initialization value. Default: None.
collect: `boolean`. Adds the summary to the summaries collection.
Returns:
The summary `Tensor`.
"""
summary = _summary_for_name(name)
if summary is None:
summary = SummaryTypes.summarize(stype, name, value, **kwargs)
track({name: summary}, collection=tf.GraphKeys.SUMMARIES_BY_NAMES)
if collect:
track(summary, collection=tf.GraphKeys.TRAIN_SUMMARIES)
return summary
def _build(self, features, labels=None, params=None, config=None):
# Pre-process features and labels
features, labels = self._preprocess(features, labels)
results = self._call_graph_fn(inputs=features)
if not isinstance(results, BridgeSpec):
raise ValueError('`bridge_fn` should return a BridgeSpec.')
loss = None
train_op = None
eval_metrics = None
if Modes.is_infer(self.mode):
predictions = self._build_predictions(
results=results.results, features=features, labels=labels)
else:
losses, loss = self._build_loss(results, features, features)
eval_metrics = self._build_eval_metrics(results.results, features, features)
if Modes.is_train(self.mode):
train_op = self._build_train_op(loss)
self._build_summary_op(results=results.results, features=features, labels=labels)
predictions = self._build_predictions(
results=results.results, features=features, labels=labels)
# We add 'useful' tensors to the graph collection so that we
# can easly find them in our hooks/monitors.
track(predictions, tf.GraphKeys.PREDICTIONS)
return EstimatorSpec(mode=self.mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metrics)
def _build(self, incoming, *args, **kwargs):
"""
Args:
2-D Tensor [samples, ids].
Returns:
3-D Tensor [samples, embedded_ids, features].
"""
input_shape = get_shape(incoming)
assert len(input_shape) == 2, 'Incoming Tensor shape must be 2-D'
weights_init = getters.get_initializer(self.weights_init)
self._w = variable('w',
shape=[self.input_dim, self.output_dim],
initializer=weights_init,
trainable=self.trainable,
restore=self.restore)
track(self._w, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
inference = tf.cast(x=incoming, dtype=tf.int32)
inference = tf.nn.embedding_lookup(
params=self._w,
ids=inference,
validate_indices=self.validate_indices)
track(inference, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return inference
def decay(exploration_rate=0.15, decay_type='polynomial_decay', start_decay_at=0, stop_decay_at=1e9,
decay_rate=0., staircase=False, decay_steps=100000, min_exploration_rate=0):
"""Builds a decaying exploration.
Decay epsilon based on number of states and the decay_type.
Args:
exploration_rate: `float` or `list` of `float`. The initial value of the exploration rate.
decay_type: A decay function name defined in `exploration_decay`
possible Values: exponential_decay, inverse_time_decay, natural_exp_decay,
piecewise_constant, polynomial_decay.
start_decay_at: `int`. When to start the decay.
stop_decay_at: `int`. When to stop the decay.
decay_rate: A Python number. The decay rate.
staircase: Whether to apply decay in a discrete staircase,
as opposed to continuous, fashion.
decay_steps: How often to apply decay.
min_exploration_rate: `float`. Don't decay below this number.
Returns:
`function` the exploration logic operation.
"""
exploration_rate = _decay_fn(timestep=get_global_timestep(),
exploration_rate=exploration_rate,
decay_type=decay_type,
start_decay_at=start_decay_at,
stop_decay_at=stop_decay_at,
decay_rate=decay_rate,
staircase=staircase,
decay_steps=decay_steps,
min_exploration_rate=min_exploration_rate)
track(exploration_rate, tf.GraphKeys.EXPLORATION_RATE)
return exploration_rate
def _build(self, features, labels, params=None, config=None):
"""Build the different operation of the model."""
# Pre-process features and labels
features, labels = self._preprocess(features, labels)
results = self._call_graph_fn(features=features, labels=labels)
loss = None
train_op = None
eval_metrics = None
if Modes.is_infer(self.mode):
predictions = self._build_predictions(results=results, features=features, labels=labels)
extra_ops = self._build_extra_ops(results=results, features=features, labels=labels)
else:
losses, loss = self._build_loss(results, features, labels)
eval_metrics = self._build_eval_metrics(results, features, labels)
if Modes.is_train(self.mode):
train_op = self._build_train_op(loss)
self._build_summary_op(results=results, features=features, labels=labels)
predictions = self._build_predictions(results=results, features=features, labels=labels)
extra_ops = self._build_extra_ops(results=results, features=features, labels=labels)
track(predictions, tf.GraphKeys.PREDICTIONS)
return EstimatorSpec(mode=self.mode,
predictions=predictions,
loss=loss,
extra_ops=extra_ops,
train_op=train_op,
eval_metric_ops=eval_metrics)
def decay(exploration_rate=0.15, decay_type='polynomial_decay', start_decay_at=0, stop_decay_at=1e9,
decay_rate=0., staircase=False, decay_steps=100000, min_exploration_rate=0):
"""Builds a decaying exploration.
Decay epsilon based on number of states and the decay_type.
Args:
exploration_rate: `float` or `list` of `float`. The initial value of the exploration rate.
decay_type: A decay function name defined in `exploration_decay`
possible Values: exponential_decay, inverse_time_decay, natural_exp_decay,
piecewise_constant, polynomial_decay.
start_decay_at: `int`. When to start the decay.
stop_decay_at: `int`. When to stop the decay.
decay_rate: A Python number. The decay rate.
staircase: Whether to apply decay in a discrete staircase,
as opposed to continuous, fashion.
decay_steps: How often to apply decay.
min_exploration_rate: `float`. Don't decay below this number.
Returns:
`function` the exploration logic operation.
"""
exploration_rate = _decay_fn(timestep=get_global_timestep(),
exploration_rate=exploration_rate,
decay_type=decay_type,
start_decay_at=start_decay_at,
stop_decay_at=stop_decay_at,
decay_rate=decay_rate,
staircase=staircase,
decay_steps=decay_steps,
min_exploration_rate=min_exploration_rate)
track(exploration_rate, tf.GraphKeys.EXPLORATION_RATE)
return exploration_rate
def _build(self, incoming, *args, **kwargs):
"""
Args:
2-D Tensor [samples, ids].
Returns:
3-D Tensor [samples, embedded_ids, features].
"""
input_shape = get_shape(incoming)
assert len(input_shape) == 2, 'Incoming Tensor shape must be 2-D'
weights_init = getters.get_initializer(self.weights_init)
self._w = variable('w',
shape=[self.input_dim, self.output_dim],
initializer=weights_init,
trainable=self.trainable,
restore=self.restore)
track(self._w, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
inference = tf.cast(x=incoming, dtype=tf.int32)
inference = tf.nn.embedding_lookup(
params=self._w,
ids=inference,
validate_indices=self.validate_indices)
# Embedding doesn't support masking, so we save sequence length prior to the lookup.
# Expand dim to 3d.
shape = [-1] + inference.get_shape().as_list()[1:3] + [1]
inference.seq_length = retrieve_seq_length_op(
tf.reshape(incoming, shape))
track(inference, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return inference
def _build(self, features, labels=None, params=None, config=None):
# Pre-process features and labels
features, labels = self._preprocess(features, labels)
results = self._call_graph_fn(features=features, labels=labels)
if not isinstance(results, BridgeSpec):
raise ValueError('`bridge_fn` should return a BridgeSpec.')
loss = None
train_op = None
eval_metrics = None
if Modes.is_infer(self.mode):
predictions = self._build_predictions(
results=results.results, features=features, labels=labels)
else:
losses, loss = self._build_loss(results, features, features)
eval_metrics = self._build_eval_metrics(results.results, features, features)
if Modes.is_train(self.mode):
train_op = self._build_train_op(loss)
self._build_summary_op(results=results.results, features=features, labels=labels)
predictions = self._build_predictions(
results=results.results, features=features, labels=labels)
track(predictions, tf.GraphKeys.PREDICTIONS)
return EstimatorSpec(mode=self.mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metrics)
def _build(self, # pylint: disable=arguments-differ
features, labels, params=None, config=None):
"""Build the different operation of the model."""
# Pre-process features and labels
features, labels = self._preprocess(features, labels)
results = self._call_graph_fn(features=features, labels=labels)
loss = None
train_op = None
eval_metrics = None
if Modes.is_infer(self.mode):
predictions = self._build_predictions(results=results, features=features, labels=labels)
extra_ops = self._build_extra_ops(results=results, features=features, labels=labels)
else:
_, loss = self._build_loss(results, features, labels)
eval_metrics = self._build_eval_metrics(results, features, labels)
if Modes.is_train(self.mode):
train_op = self._build_train_op(loss)
self._build_summary_op(results=results, features=features, labels=labels)
predictions = self._build_predictions(results=results, features=features, labels=labels)
extra_ops = self._build_extra_ops(results=results, features=features, labels=labels)
track(predictions, tf.GraphKeys.PREDICTIONS)
return EstimatorSpec(mode=self.mode,
predictions=predictions,
loss=loss,
extra_ops=extra_ops,
train_op=train_op,
eval_metric_ops=eval_metrics)
def _build(self, dependencies, *args, **kwargs):
"""
Args:
dependencies: List of Tensors [_shape_].
Returns:
Concatenated Tensors [nb_tensors, _shape_].
"""
x = tf.concat(axis=1, values=dependencies)
track(x, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return x
def _build(self, incoming, *args, **kwargs):
"""
Args:
incoming: List of Tensors [_shape_].
Returns:
Concatenated Tensors [nb_tensors, _shape_].
"""
x = tf.slice(incoming, begin=self.being, size=self.size)
track(x, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return x
def built_activation(x, collect):
"""Builds the metric function.
Args:
x: activated tensor.
collect: whether to collect this metric under the metric collection.
"""
if collect:
track(x, tf.GraphKeys.ACTIVATIONS)
return x
def _build(self, incoming, *args, **kwargs):
"""
Args:
4-D Tensor Layer.
Returns:
4-D Tensor Layer. (Same dimension as input).
"""
incoming = tf.nn.lrn( input=incoming, depth_radius=self.depth_radius, bias=self.bias,
alpha=self.alpha, beta=self.beta, name=self.name)
track(incoming, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return incoming
def _build(self, incoming, *args, **kwargs):
"""
Args:
incoming: List of Tensors.
Returns:
Merged Tensors.
"""
self._built_modules = [
module(incoming, *args, **kwargs) for module in self._modules
]
if self.merge_mode == self.MergeMode.CONCAT:
x = tf.concat(axis=self.axis, values=self._built_modules)
elif self.merge_mode == self.MergeMode.ELEMENTWISE_SUM:
x = self._built_modules[0]
for i in xrange(1, len(self._built_modules)):
x = tf.add(x, self._built_modules[i])
elif self.merge_mode == self.MergeMode.ELEMENTWISE_MUL:
x = self._built_modules[0]
for i in xrange(1, len(self._built_modules)):
x = tf.multiply(x, self._built_modules[i])
elif self.merge_mode == self.MergeMode.SUM:
x = tf.reduce_sum(tf.concat(axis=self.axis,
values=self._built_modules),
axis=self.axis)
elif self.merge_mode == self.MergeMode.MEAN:
x = tf.reduce_mean(tf.concat(axis=self.axis,
values=self._built_modules),
axis=self.axis)
elif self.merge_mode == self.MergeMode.PROD:
x = tf.reduce_prod(tf.concat(axis=self.axis,
values=self._built_modules),
axis=self.axis)
elif self.merge_mode == self.MergeMode.MAX:
x = tf.reduce_max(tf.concat(axis=self.axis,
values=self._built_modules),
axis=self.axis)
elif self.merge_mode == self.MergeMode.MIN:
x = tf.reduce_min(tf.concat(axis=self.axis,
values=self._built_modules),
axis=self.axis)
elif self.merge_mode == self.MergeMode.AND:
x = tf.reduce_all(tf.concat(axis=self.axis,
values=self._built_modules),
axis=self.axis)
elif self.merge_mode == self.MergeMode.OR:
x = tf.reduce_any(tf.concat(axis=self.axis,
values=self._built_modules),
axis=self.axis)
else:
raise Exception('Unknown merge mode', str(self.merge_mode))
track(x, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return x
def _build(self, incoming, *args, **kwargs):
"""
Args:
incoming: A `Tensor`. The incoming tensor.
"""
inference = incoming
if isinstance(inference, list):
inference = tf.concat(axis=inference, values=0)
inference = tf.cast(x=inference, dtype=tf.float32)
inference = tf.reshape(tensor=inference, shape=self.new_shape)
track(inference, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return inference
def _build(self, incoming, *args, **kwargs):
"""
Args:
incoming: 1-D tensor
Returns:
A `Tensor` with the same shape as `x`.
"""
incoming = tf.convert_to_tensor(value=incoming, name='x')
square_sum = tf.reduce_sum(input_tensor=tf.square(x=incoming), axis=[self.dim],
keep_dims=True)
x_inv_norm = tf.rsqrt(x=tf.maximum(x=square_sum, y=self.epsilon))
incoming = tf.multiply(x=incoming, y=x_inv_norm, name=self.module_name)
track(incoming, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return incoming
def _build(self, incoming, *args, **kwargs):
"""
Args:
incoming: (2+)-D `Tensor`.
Returns:
2-D `Tensor` [batch, flatten_dims].
"""
input_shape = get_shape(incoming)
assert len(input_shape) > 1, 'Incoming Tensor shape must be at least 2-D'
dims = total_tensor_depth(tensor_shape=input_shape)
x = tf.reshape(tensor=incoming, shape=[-1, dims])
track(x, tf.GraphKeys.LAYER_TENSOR, self.name)
return x
def _build(self, incoming, *args, **kwargs):
"""
Args:
incoming: The Labels Placeholder.
Returns:
2-D Tensor, The encoded labels.
"""
if incoming.dtype != dtypes.int64:
incoming = standard_ops.to_int64(incoming)
incoming = standard_ops.one_hot(indices=incoming, depth=self.n_classes,
on_value=self.on_value, off_value=self.off_value)
track(incoming, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return incoming
def _build(self, incoming, *args, **kwargs):
"""
Args:
incoming: `Tensor`. 3-D Tensor [samples, timesteps, input dim].
"""
self._declare_dependencies()
sequence_length = None
if self.dynamic:
sequence_length = retrieve_seq_length_op(incoming if isinstance(
incoming, tf.Tensor) else tf.stack(incoming))
input_shape = get_shape(incoming)
inference = incoming
# If a tensor given, convert it to a per timestep list
if type(inference) not in [list, np.array]:
ndim = len(input_shape)
assert ndim >= 3, 'Input dim should be at least 3.'
axes = [1, 0] + list(range(2, ndim))
inference = tf.transpose(inference, (axes))
inference = tf.unstack(value=inference)
if self.dynamic:
outputs, state = tf.nn.dynamic_rnn(
cell=self._cell,
inputs=inference,
dtype=tf.float32,
initial_state=self.initial_state,
sequence_length=sequence_length,
scope=self.module_name)
else:
outputs, state = rnn.static_rnn(cell=self._cell,
inputs=inference,
dtype=tf.float32,
initial_state=self.initial_state,
sequence_length=sequence_length,
scope=self.module_name)
for v in [self._cell.w, self._cell.b]:
if hasattr(v, '__len__'):
for var in v:
track(var, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
else:
track(v, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
track(outputs[-1], tf.GraphKeys.ACTIVATIONS, self.module_name)
if self.dynamic:
if self.return_seq:
o = outputs
else:
outputs = tf.transpose(tf.stack(outputs), [1, 0, 2])
o = advanced_indexing_op(outputs, sequence_length)
else:
o = outputs if self.return_seq else outputs[-1]
track(o, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return (o, state) if self.return_state else o
def _build(self, incoming, *args, **kwargs):
"""
Args:
4-D Tensor Layer.
Returns:
4-D Tensor Layer. (Same dimension as input).
"""
incoming = tf.nn.lrn(input=incoming,
depth_radius=self.depth_radius,
bias=self.bias,
alpha=self.alpha,
beta=self.beta,
name=self.name)
track(incoming, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return incoming
def _build(self, incoming, *args, **kwargs):
"""
Args:
incoming: 1-D tensor
Returns:
A `Tensor` with the same shape as `x`.
"""
incoming = tf.convert_to_tensor(value=incoming, name='x')
square_sum = tf.reduce_sum(input_tensor=tf.square(x=incoming),
axis=[self.dim],
keep_dims=True)
x_inv_norm = tf.rsqrt(x=tf.maximum(x=square_sum, y=self.epsilon))
incoming = tf.multiply(x=incoming, y=x_inv_norm, name=self.module_name)
track(incoming, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return incoming
def metric(y_pred, y_true):
"""
Args:
y_pred: `Tensor`
y_true: `Tensor`
Returns:
`Float`. The calculated metric.
"""
check_metric_data(y_pred, y_true)
with get_name_scope(name, scope):
x = fct(y_pred, y_true)
if collect:
track(x, tf.GraphKeys.METRICS)
return x
def metric(y_pred, y_true):
"""
Args:
y_pred: `Tensor`
y_true: `Tensor`
Returns:
`Float`. The calculated metric.
"""
check_metric_data(y_pred, y_true)
with get_name_scope(name, scope):
x = fct(y_pred, y_true)
if collect:
track(x, tf.GraphKeys.METRICS)
return x
def _build(self, incoming, *args, **kwargs):
input_shape = get_shape(incoming)
input_ndim = len(input_shape)
gamma_init = tf.random_normal_initializer(mean=self.gamma, stddev=self.stddev)
self._beta = variable(name='beta', shape=[input_shape[-1]],
initializer=tf.constant_initializer(self.beta),
trainable=self.trainable, restore=self.restore)
self._gamma = variable(name='gamma', shape=[input_shape[-1]],
initializer=gamma_init, trainable=self.trainable,
restore=self.restore)
track(self._beta, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
track(self._gamma, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
if not self.restore:
track(tf.GraphKeys.EXCL_RESTORE_VARIABLES, self._beta)
track(tf.GraphKeys.EXCL_RESTORE_VARIABLES, self._gamma)
axis = list(xrange(input_ndim - 1))
moving_mean = variable(name='moving_mean', shape=input_shape[-1:],
initializer=tf.zeros_initializer(), trainable=False,
restore=self.restore)
moving_variance = variable(name='moving_variance', shape=input_shape[-1:],
initializer=tf.constant_initializer(1.), trainable=False,
restore=self.restore)
def update_mean_var():
mean, variance = tf.nn.moments(x=incoming, axes=axis)
update_moving_mean = moving_averages.assign_moving_average(
variable=moving_mean, value=mean, decay=self.decay, zero_debias=False)
update_moving_variance = moving_averages.assign_moving_average(
variable=moving_variance, value=variance, decay=self.decay, zero_debias=False)
with tf.control_dependencies([update_moving_mean, update_moving_variance]):
return tf.identity(mean), tf.identity(variance)
# Retrieve variable managing training mode
if Modes.is_train(self.mode):
mean, var = update_mean_var()
else:
mean, var = moving_mean, moving_variance
incoming = tf.nn.batch_normalization(x=incoming, mean=mean, variance=var, offset=self._beta,
scale=self._gamma, variance_epsilon=self.epsilon)
incoming.set_shape(input_shape)
track(incoming, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return incoming
def _build(self, incoming, *args, **kwargs):
"""
Args:
incoming: `Tensor`. 3-D Tensor [samples, timesteps, input dim].
"""
self._declare_dependencies()
sequence_length = kwargs.get('sequence_length')
if self.dynamic and sequence_length is None:
sequence_length = retrieve_seq_length_op(
incoming if isinstance(incoming, tf.Tensor) else tf.stack(incoming))
input_shape = get_shape(incoming)
inference = incoming
# If a static rnn and tensor given, convert it to a per timestep list
if type(inference) not in [list, np.array] and not self.dynamic:
ndim = len(input_shape)
assert ndim >= 3, 'Input dim should be at least 3.'
axes = [1, 0] + list(xrange(2, ndim))
inference = tf.transpose(inference, axes)
inference = tf.unstack(value=inference)
if self.dynamic:
outputs, state = tf.nn.dynamic_rnn(
cell=self._cell, inputs=inference, dtype=tf.float32,
initial_state=self.initial_state, sequence_length=sequence_length,
scope=self.module_name)
else:
outputs, state = rnn.static_rnn(
cell=self._cell, inputs=inference, dtype=tf.float32,
initial_state=self.initial_state, sequence_length=sequence_length,
scope=self.module_name)
for v in [self._cell.w, self._cell.b]:
if hasattr(v, '__len__'):
for var in v:
track(var, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
else:
track(v, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
track(outputs[-1], tf.GraphKeys.ACTIVATIONS, self.module_name)
if self.dynamic:
if self.return_seq:
o = outputs
else:
o = get_sequence_relevant_output(outputs, sequence_length)
else:
o = outputs if self.return_seq else outputs[-1]
track(o, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return (o, state) if self.return_state else o
def _build(self, features, labels, params=None, config=None):
"""Build the different operation of the model."""
# Pre-process features and labels
features, labels = self._preprocess(features, labels)
results = self._call_graph_fn(inputs=features)
loss = None
train_op = None
eval_metrics = None
if Modes.is_infer(self.mode):
predictions = self._build_predictions(results=results,
features=features,
labels=labels)
extra_ops = self._build_extra_ops(results=results,
features=features,
labels=labels)
else:
losses, loss = self._build_loss(results, features, labels)
eval_metrics = self._build_eval_metrics(results, features, labels)
if Modes.is_train(self.mode):
train_op = self._build_train_op(loss)
self._build_summary_op(results=results,
features=features,
labels=labels)
predictions = self._build_predictions(results=results,
features=features,
labels=labels)
extra_ops = self._build_extra_ops(results=results,
features=features,
labels=labels)
# We add 'useful' tensors to the graph collection so that we
# can easily find them in our hooks/monitors.
track(predictions, tf.GraphKeys.PREDICTIONS)
return EstimatorSpec(mode=self.mode,
predictions=predictions,
loss=loss,
extra_ops=extra_ops,
train_op=train_op,
eval_metric_ops=eval_metrics)
def random_decay(num_actions=None, decay_type='polynomial_decay', start_decay_at=0,
stop_decay_at=1e9, decay_rate=0., staircase=False, decay_steps=10000,
min_exploration_rate=0):
"""Builds a random decaying exploration.
Decay a random value based on number of states and the decay_type.
Args:
num_actions: `int` or None. If discrete num_action must be None.
decay_type: A decay function name defined in `exploration_decay`
possible Values: exponential_decay, inverse_time_decay, natural_exp_decay,
piecewise_constant, polynomial_decay.
start_decay_at: `int`. When to start the decay.
stop_decay_at: `int`. When to stop the decay.
decay_rate: A Python number. The decay rate.
staircase: Whether to apply decay in a discrete staircase,
as opposed to continuous, fashion.
decay_steps: How often to apply decay.
min_exploration_rate: `float`. Don't decay below this number.
Returns:
`function` the exploration logic operation.
"""
if num_actions is None:
exploration_rate = partial(np.random.randn, 1)
else:
exploration_rate = partial(np.random.randn, num_actions)
exploration_rate = _decay_fn(timestep=get_global_timestep(),
exploration_rate=exploration_rate,
decay_type=decay_type,
start_decay_at=start_decay_at,
stop_decay_at=stop_decay_at,
decay_rate=decay_rate,
staircase=staircase,
decay_steps=decay_steps,
min_exploration_rate=min_exploration_rate)
track(exploration_rate, tf.GraphKeys.EXPLORATION_RATE)
return exploration_rate
def random_decay(num_actions=None, decay_type='polynomial_decay', start_decay_at=0,
stop_decay_at=1e9, decay_rate=0., staircase=False, decay_steps=10000,
min_exploration_rate=0):
"""Builds a random decaying exploration.
Decay a random value based on number of states and the decay_type.
Args:
num_actions: `int` or None. If discrete num_action must be None.
decay_type: A decay function name defined in `exploration_decay`
possible Values: exponential_decay, inverse_time_decay, natural_exp_decay,
piecewise_constant, polynomial_decay.
start_decay_at: `int`. When to start the decay.
stop_decay_at: `int`. When to stop the decay.
decay_rate: A Python number. The decay rate.
staircase: Whether to apply decay in a discrete staircase,
as opposed to continuous, fashion.
decay_steps: How often to apply decay.
min_exploration_rate: `float`. Don't decay below this number.
Returns:
`function` the exploration logic operation.
"""
if num_actions is None:
exploration_rate = partial(np.random.randn, 1)
else:
exploration_rate = partial(np.random.randn, num_actions)
exploration_rate = _decay_fn(timestep=get_global_timestep(),
exploration_rate=exploration_rate,
decay_type=decay_type,
start_decay_at=start_decay_at,
stop_decay_at=stop_decay_at,
decay_rate=decay_rate,
staircase=staircase,
decay_steps=decay_steps,
min_exploration_rate=min_exploration_rate)
track(exploration_rate, tf.GraphKeys.EXPLORATION_RATE)
return exploration_rate
def _build(self, incoming, *args, **kwargs):
"""
Args:
incoming: `Tensor`. 3-D Tensor Layer [samples, timesteps, input dim].
"""
assert (self.rnncell_fw.output_size ==
self.rnncell_bw.output_size), "RNN Cells number of units must match!"
input_shape = get_shape(incoming)
# TODO: DropoutWrapper
inference = incoming
# If a tensor given, convert it to a per timestep list
if type(inference) not in [list, np.array]:
ndim = len(input_shape)
assert ndim >= 3, 'Input dim should be at least 3.'
axes = [1, 0] + list(xrange(2, ndim))
inference = tf.transpose(inference, (axes,))
inference = tf.unstack(inference)
sequence_length = None
if self.dynamic:
sequence_length = retrieve_seq_length_op(
incoming if isinstance(incoming, tf.Tensor) else tf.stack(incoming))
outputs, states_fw, states_bw = tf.nn.bidirectional_dynamic_rnn(
cell_fw=self.rnncell_fw, cell_bw=self.rnncell_bw, inputs=inference,
initial_state_fw=self.initial_state_fw,
initial_state_bw=self.initial_state_bw,
sequence_length=sequence_length,
dtype=tf.float32)
else:
outputs, states_fw, states_bw = rnn.static_bidirectional_rnn(
cell_fw=self.rnncell_fw, cell_bw=self.rnncell_bw, inputs=inference,
initial_state_fw=self.initial_state_fw,
initial_state_bw=self.initial_state_bw,
dtype=tf.float32)
for v in [self.rnncell_fw.w, self.rnncell_fw.b, self.rnncell_bw.w, self.rnncell_bw.b]:
if hasattr(v, '__len__'):
for var in v:
track(var, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
else:
track(v, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
tf.add_to_collection(tf.GraphKeys.ACTIVATIONS, outputs[-1])
if self.dynamic:
if self.return_seq:
o = outputs
else:
outputs = tf.transpose(tf.stack(outputs), [1, 0, 2])
o = advanced_indexing_op(outputs, sequence_length)
else:
o = outputs if self.return_seq else outputs[-1]
track(o, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return (o, states_fw, states_bw) if self.return_states else o
def _build(self, incoming, *args, **kwargs):
"""
Args:
2-D Tensor [samples, ids].
Returns:
3-D Tensor [samples, embedded_ids, features].
"""
input_shape = get_shape(incoming)
assert len(input_shape) == 2, 'Incoming Tensor shape must be 2-D'
weights_init = getters.get_initializer(self.weights_init)
self._w = variable('w', shape=[self.input_dim, self.output_dim],
initializer=weights_init,
trainable=self.trainable, restore=self.restore)
track(self._w, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
inference = tf.cast(x=incoming, dtype=tf.int32)
inference = tf.nn.embedding_lookup(params=self._w, ids=inference,
validate_indices=self.validate_indices)
track(inference, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return inference
def _build(self, features, labels, params, config):
"""Subclasses should implement this method. See the `model_fn` documentation
in tf.contrib.learn.Estimator class for a more detailed explanation.
"""
# Pre-process features and labels
features, labels = self._preprocess(features, labels)
results = self._graph_fn(mode=self.mode, inputs=features['source_ids'])
loss = None
train_op = None
eval_metrics = None
if self.mode == ModeKeys.PREDICT:
predictions = self._build_predictions(results=results,
features=features,
labels=labels)
else:
losses, loss = self._build_loss(results, features, labels)
eval_metrics = self._build_eval_metrics(results, features, labels)
if self.mode == ModeKeys.TRAIN:
train_op = self._build_train_op(loss)
self._build_summary_op()
predictions = self._build_predictions(results=results,
features=features,
labels=labels,
losses=losses)
# We add 'useful' tensors to the graph collection so that we
# can easly find them in our hooks/monitors.
track(predictions, tf.GraphKeys.PREDICTIONS)
return EstimatorSpec(mode=self.mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metrics)
def _build(self, incoming, *args, **kwargs):
"""
Args:
incoming: List of Tensors.
Returns:
Merged Tensors.
"""
self._built_modules = [module(incoming, *args, **kwargs) for module in self._modules]
if self.merge_mode == self.MergeMode.CONCAT:
x = tf.concat(axis=self.axis, values=self._built_modules)
elif self.merge_mode == self.MergeMode.ELEMENTWISE_SUM:
x = self._built_modules[0]
for i in xrange(1, len(self._built_modules)):
x = tf.add(x, self._built_modules[i])
elif self.merge_mode == self.MergeMode.ELEMENTWISE_MUL:
x = self._built_modules[0]
for i in xrange(1, len(self._built_modules)):
x = tf.multiply(x, self._built_modules[i])
elif self.merge_mode == self.MergeMode.SUM:
x = tf.reduce_sum(tf.concat(axis=self.axis, values=self._built_modules), axis=self.axis)
elif self.merge_mode == self.MergeMode.MEAN:
x = tf.reduce_mean(tf.concat(axis=self.axis, values=self._built_modules), axis=self.axis)
elif self.merge_mode == self.MergeMode.PROD:
x = tf.reduce_prod(tf.concat(axis=self.axis, values=self._built_modules), axis=self.axis)
elif self.merge_mode == self.MergeMode.MAX:
x = tf.reduce_max(tf.concat(axis=self.axis, values=self._built_modules), axis=self.axis)
elif self.merge_mode == self.MergeMode.MIN:
x = tf.reduce_min(tf.concat(axis=self.axis, values=self._built_modules), axis=self.axis)
elif self.merge_mode == self.MergeMode.AND:
x = tf.reduce_all(tf.concat(axis=self.axis, values=self._built_modules), axis=self.axis)
elif self.merge_mode == self.MergeMode.OR:
x = tf.reduce_any(tf.concat(axis=self.axis, values=self._built_modules), axis=self.axis)
else:
raise Exception('Unknown merge mode', str(self.merge_mode))
track(x, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return x
def _build(self, features, labels=None, params=None, config=None):
# Pre-process features and labels
features, labels = self._preprocess(features, labels)
results = self._call_graph_fn(features=features, labels=labels)
if not isinstance(results, BridgeSpec):
raise ValueError('`bridge_fn` should return a BridgeSpec.')
loss = None
train_op = None
eval_metrics = None
if Modes.is_infer(self.mode):
predictions = self._build_predictions(results=results.results,
features=features,
labels=labels)
else:
_, loss = self._build_loss(results, features, features)
eval_metrics = self._build_eval_metrics(results.results, features,
features)
if Modes.is_train(self.mode):
train_op = self._build_train_op(loss)
self._build_summary_op(results=results.results,
features=features,
labels=labels)
predictions = self._build_predictions(results=results.results,
features=features,
labels=labels)
track(predictions, tf.GraphKeys.PREDICTIONS)
return EstimatorSpec(mode=self.mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metrics)
def _build(self, incoming, *args, **kwargs):
"""
Args:
incoming: (2+)-D Tensor [samples, input dim]. If not 2D, input will be flatten.
Returns:
2D Tensor [samples, num_units].
"""
self._declare_dependencies()
input_shape = get_shape(incoming)
incoming = validate_dtype(incoming)
assert len(
input_shape) > 1, 'Incoming Tensor shape must be at least 2-D'
n_inputs = total_tensor_depth(tensor_shape=input_shape)
regularizer = getters.get_regularizer(self.regularizer,
scale=self.scale,
collect=True)
self._w = variable(name='w',
shape=[n_inputs, self.num_units],
dtype=incoming.dtype,
regularizer=regularizer,
initializer=getters.get_initializer(
self.weights_init),
trainable=self.trainable,
restore=self.restore)
track(self._w, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
inference = incoming
# If input is not 2d, flatten it.
if len(input_shape) > 2:
inference = tf.reshape(tensor=inference, shape=[-1, n_inputs])
inference = tf.matmul(a=inference, b=self._w)
self._b = None
if self.bias:
self._b = variable(name='b',
shape=[self.num_units],
dtype=incoming.dtype,
initializer=getters.get_initializer(
self.bias_init),
trainable=self.trainable,
restore=self.restore)
track(self._b, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
inference = tf.nn.bias_add(value=inference, bias=self._b)
if self.activation:
inference = getters.get_activation(self.activation,
collect=True)(inference)
if self._dropout:
inference = self._dropout(inference)
track(inference, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return inference
def _build(self, incoming, *args, **kwargs):
"""
Args:
incoming: (2+)-D Tensor [samples, input dim]. If not 2D, input will be flatten.
Returns:
2D Tensor [samples, num_units].
"""
self._declare_dependencies()
input_shape = get_shape(incoming)
incoming = validate_dtype(incoming)
assert len(input_shape) > 1, 'Incoming Tensor shape must be at least 2-D'
n_inputs = total_tensor_depth(tensor_shape=input_shape)
regularizer = getters.get_regularizer(self.regularizer, scale=self.scale, collect=True)
self._w = variable(
name='w', shape=[n_inputs, self.num_units], dtype=incoming.dtype, regularizer=regularizer,
initializer=getters.get_initializer(self.weights_init), trainable=self.trainable,
restore=self.restore)
track(self._w, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
inference = incoming
# If input is not 2d, flatten it.
if len(input_shape) > 2:
inference = tf.reshape(tensor=inference, shape=[-1, n_inputs])
inference = tf.matmul(a=inference, b=self._w)
self._b = None
if self.bias:
self._b = variable(name='b', shape=[self.num_units], dtype=incoming.dtype,
initializer=getters.get_initializer(self.bias_init),
trainable=self.trainable, restore=self.restore)
track(self._b, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
inference = tf.nn.bias_add(value=inference, bias=self._b)
if self.activation:
inference = getters.get_activation(self.activation, collect=True)(inference)
if self._dropout:
inference = self._dropout(inference)
track(inference, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return inference
def _build(self, incoming, *args, **kwargs):
"""
Args:
incoming: 1-D Tensor [samples]. If not 2D, input will be flatten.
Returns:
1-D Tensor [samples].
"""
input_shape = get_shape(incoming)
n_inputs = int(np.prod(a=input_shape[1:]))
initializer = tf.constant_initializer(value=np.random.randn())
self._w = variable(name='w',
shape=[n_inputs],
dtype=incoming.dtype,
initializer=initializer,
trainable=self.trainable,
restore=self.restore)
track(self._w, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
inference = incoming
# If input is not 2d, flatten it.
if len(input_shape) > 1:
inference = tf.reshape(tensor=inference, shape=[-1])
inference = tf.multiply(x=inference, y=self._w)
self._b = None
if self.bias:
self._b = variable(name='b',
shape=[n_inputs],
dtype=incoming.dtype,
initializer=initializer,
trainable=self.trainable,
restore=self.restore)
inference = tf.add(inference, self._b)
track(self._b, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
if self.activation:
inference = getters.get_activation(self.activation,
collect=True)(inference)
track(inference, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return inference
def _build(self, incoming, *args, **kwargs):
"""
Args:
incoming: 1-D Tensor [samples]. If not 2D, input will be flatten.
Returns:
1-D Tensor [samples].
"""
input_shape = get_shape(incoming)
n_inputs = total_tensor_depth(tensor_shape=input_shape)
initializer = tf.constant_initializer(value=np.random.randn())
self._w = variable(name='w', shape=[n_inputs],
dtype=incoming.dtype, initializer=initializer,
trainable=self.trainable, restore=self.restore)
track(self._w, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
inference = incoming
# If input is not 2d, flatten it.
if len(input_shape) > 1:
inference = tf.reshape(tensor=inference, shape=[-1])
inference = tf.multiply(x=inference, y=self._w)
self._b = None
if self.bias:
self._b = variable(name='b', shape=[n_inputs],
dtype=incoming.dtype, initializer=initializer,
trainable=self.trainable, restore=self.restore)
inference = tf.add(inference, self._b)
track(self._b, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
if self.activation:
inference = getters.get_activation(self.activation, collect=True)(inference)
track(inference, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return inference
def create_learning_rate_decay_fn(learning_rate, decay_type, decay_steps, decay_rate,
start_decay_at=0, stop_decay_at=1e9, min_learning_rate=None,
staircase=False, global_step=None):
"""Creates a function that decays the learning rate.
Args:
learning_rate: A Tensor or a floating point value. The learning rate to use.
decay_steps: How often to apply decay.
decay_rate: A Python number. The decay rate.
start_decay_at: Don't decay before this step
stop_decay_at: Don't decay after this step
min_learning_rate: Don't decay below this number
decay_type: A decay function name defined in `tf.train`
possible Values: exponential_decay, inverse_time_decay, natural_exp_decay,
piecewise_constant, polynomial_decay.
staircase: Whether to apply decay in a discrete staircase,
as opposed to continuous, fashion.
global_step: Scalar int `Tensor`, step counter for each update. If not supplied,
it will be fetched from the default graph (see `tf.contrib.framework.get_global_step`
for details). If it's not been created, no step will be incremented with each weight
update. `learning_rate_decay_fn` requires `global_step`.
Returns:
A function that takes (learning_rate, global_step) as inputs
and returns the learning rate for the given step.
Returns `None` if decay_type is empty or None.
"""
if decay_type is None or decay_type == "":
return learning_rate
start_decay_at = tf.to_int32(start_decay_at)
stop_decay_at = tf.to_int32(stop_decay_at)
def decay_fn(learning_rate, global_step):
"""The computed learning rate decay function."""
global_step = tf.to_int32(global_step)
decay_type_fn = getattr(tf.train, decay_type)
kwargs = dict(
learning_rate=learning_rate,
global_step=tf.minimum(global_step, stop_decay_at) - start_decay_at,
decay_steps=decay_steps,
staircase=staircase,
name="decayed_learning_rate"
)
decay_fn_args = get_arguments(decay_type_fn)
if 'decay_rate' in decay_fn_args:
kwargs['decay_rate'] = decay_rate
if 'staircase' in decay_fn_args:
kwargs['staircase'] = staircase
decayed_learning_rate = decay_type_fn(**kwargs)
final_lr = tf.train.piecewise_constant(
x=global_step,
boundaries=[start_decay_at],
values=[learning_rate, decayed_learning_rate])
if min_learning_rate:
final_lr = tf.maximum(final_lr, min_learning_rate)
return final_lr
learning_rate = decay_fn(learning_rate, global_step or get_global_step())
track(learning_rate, tf.GraphKeys.LEARNING_RATE)
return learning_rate
def create_learning_rate_decay_fn(learning_rate,
decay_type,
decay_steps,
decay_rate,
start_decay_at=0,
stop_decay_at=1e9,
min_learning_rate=None,
staircase=False,
global_step=None):
"""Creates a function that decays the learning rate.
Args:
learning_rate: A Tensor or a floating point value. The learning rate to use.
decay_steps: How often to apply decay.
decay_rate: A Python number. The decay rate.
start_decay_at: Don't decay before this step
stop_decay_at: Don't decay after this step
min_learning_rate: Don't decay below this number
decay_type: A decay function name defined in `tf.train`
possible Values: exponential_decay, inverse_time_decay, natural_exp_decay,
piecewise_constant, polynomial_decay.
staircase: Whether to apply decay in a discrete staircase,
as opposed to continuous, fashion.
global_step: Scalar int `Tensor`, step counter for each update. If not supplied,
it will be fetched from the default graph (see `tf.contrib.framework.get_global_step`
for details). If it's not been created, no step will be incremented with each weight
update. `learning_rate_decay_fn` requires `global_step`.
Returns:
A function that takes (learning_rate, global_step) as inputs
and returns the learning rate for the given step.
Returns `None` if decay_type is empty or None.
"""
if decay_type is None or decay_type == "":
return learning_rate
start_decay_at = tf.to_int32(start_decay_at)
stop_decay_at = tf.to_int32(stop_decay_at)
def decay_fn(learning_rate, global_step):
"""The computed learning rate decay function."""
global_step = tf.to_int32(global_step)
decay_type_fn = getattr(tf.train, decay_type)
decayed_learning_rate = decay_type_fn(
learning_rate=learning_rate,
global_step=tf.minimum(global_step, stop_decay_at) -
start_decay_at,
decay_steps=decay_steps,
decay_rate=decay_rate,
staircase=staircase,
name="decayed_learning_rate")
final_lr = tf.train.piecewise_constant(
x=global_step,
boundaries=[start_decay_at],
values=[learning_rate, decayed_learning_rate])
if min_learning_rate:
final_lr = tf.maximum(final_lr, min_learning_rate)
return final_lr
learning_rate = decay_fn(learning_rate, global_step or get_global_step())
track(learning_rate, tf.GraphKeys.LEARNING_RATE)
return learning_rate
def _build(self, incoming, *args, **kwargs):
"""
Args:
incoming: (2+)-D Tensor [samples, input dim]. If not 2D, input will be flatten.
Returns:
2D Tensor [samples, num_units].
"""
self._declare_dependencies()
input_shape = get_shape(incoming)
assert len(input_shape) > 1, 'Incoming Tensor shape must be at least 2-D'
n_inputs = total_tensor_depth(tensor_shape=input_shape)
regularizer = getters.get_regularizer(self.regularizer, scale=self.scale, collect=True)
initializer = getters.get_initializer(self.weights_init)
self._w = variable(name='w', shape=[n_inputs, self.num_units], regularizer=regularizer,
initializer=initializer, trainable=self.trainable,
restore=self.restore)
track(self._w, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
self._b = variable(name='b', shape=[self.num_units],
initializer=getters.get_initializer(self.bias_init),
trainable=self.trainable, restore=self.restore)
track(self._b, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
# Weight and bias for the transform gate
self._w_t = variable(name='w_t', shape=[n_inputs, self.num_units],
regularizer=None, initializer=initializer,
trainable=self.trainable, restore=self.restore)
track(self._w_t, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
self._b_t = variable(name='b_t', shape=[self.num_units],
initializer=tf.constant_initializer(-1),
trainable=self.trainable, restore=self.restore)
track(self._b_t, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
# If input is not 2d, flatten it.
if len(input_shape) > 2:
incoming = tf.reshape(tensor=incoming, shape=[-1, n_inputs])
H = getters.get_activation(self.activation)(tf.matmul(a=incoming, b=self._w) + self._b)
T = tf.sigmoid(tf.matmul(a=incoming, b=self._w_t) + self._b_t)
if self._transform_dropout:
T = self._transform_dropout(T)
C = tf.subtract(x=1.0, y=T)
inference = tf.add(x=tf.multiply(x=H, y=T), y=tf.multiply(x=incoming, y=C))
track(inference, tf.GraphKeys.ACTIVATIONS)
track(inference, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return inference
def _build(self, incoming, *args, **kwargs):
"""
Args:
incoming: `Tensor`. 3-D Tensor Layer [samples, timesteps, input dim].
"""
assert (self.rnncell_fw.output_size ==
self.rnncell_bw.output_size), "RNN Cells number of units must match!"
sequence_length = kwargs.get('sequence_length')
if self.dynamic and sequence_length is None:
sequence_length = retrieve_seq_length_op(
incoming if isinstance(incoming, tf.Tensor) else tf.stack(incoming))
input_shape = get_shape(incoming)
# TODO: DropoutWrapper
inference = incoming
# If a static rnn and tensor given, convert it to a per timestep list
if type(inference) not in [list, np.array] and not self.dynamic:
ndim = len(input_shape)
assert ndim >= 3, 'Input dim should be at least 3.'
axes = [1, 0] + list(xrange(2, ndim))
inference = tf.transpose(inference, axes)
inference = tf.unstack(value=inference)
if self.dynamic:
# outputs are a tuple of (fw, bw) outputs
outputs, (states_fw, states_bw) = tf.nn.bidirectional_dynamic_rnn(
cell_fw=self.rnncell_fw, cell_bw=self.rnncell_bw, inputs=inference,
initial_state_fw=self.initial_state_fw,
initial_state_bw=self.initial_state_bw,
sequence_length=sequence_length,
dtype=tf.float32)
else:
# outputs are a concatenation of both bw and fw outputs
outputs, states_fw, states_bw = rnn.static_bidirectional_rnn(
cell_fw=self.rnncell_fw, cell_bw=self.rnncell_bw, inputs=inference,
initial_state_fw=self.initial_state_fw,
initial_state_bw=self.initial_state_bw,
sequence_length=sequence_length,
dtype=tf.float32)
for v in [self.rnncell_fw.w, self.rnncell_fw.b, self.rnncell_bw.w, self.rnncell_bw.b]:
if hasattr(v, '__len__'):
for var in v:
track(var, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
else:
track(v, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
if self.dynamic:
tf.add_to_collection(tf.GraphKeys.ACTIVATIONS, outputs[0][-1])
else:
tf.add_to_collection(tf.GraphKeys.ACTIVATIONS, outputs[-1])
if self.dynamic:
if self.return_seq:
o = outputs
else:
# we are only interested in the fw pass here
o = get_sequence_relevant_output(outputs[0], sequence_length)
else:
o = outputs if self.return_seq else outputs[-1]
track(o, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return (o, states_fw, states_bw) if self.return_states else o
def decay(exploration_rate=0.15,
decay_type='polynomial_decay',
start_decay_at=0,
stop_decay_at=1e9,
decay_rate=0.,
staircase=False,
decay_steps=100000,
min_exploration_rate=0):
"""Builds a decaying exploration.
Decay epsilon based on number of states and the decay_type.
Args:
exploration_rate: `float` or `list` of `float`. The initial value of the exploration rate.
decay_type: A decay function name defined in `exploration_decay`
possible Values: exponential_decay, inverse_time_decay, natural_exp_decay,
piecewise_constant, polynomial_decay.
start_decay_at: `int`. When to start the decay.
stop_decay_at: `int`. When to stop the decay.
decay_rate: A Python number. The decay rate.
staircase: Whether to apply decay in a discrete staircase,
as opposed to continuous, fashion.
decay_steps: How often to apply decay.
min_exploration_rate: `float`. Don't decay below this number.
Returns:
`function` the exploration function logic.
"""
def decay_fn(timestep):
"""The computed decayed exploration rate.
Args:
timestep: the current timestep.
"""
timestep = tf.to_int32(timestep)
decay_type_fn = getattr(exploration_decay, decay_type)
kwargs = dict(
exploration_rate=exploration_rate,
timestep=tf.minimum(timestep, tf.to_int32(stop_decay_at)) -
tf.to_int32(start_decay_at),
decay_steps=decay_steps,
name="decayed_exploration_rate")
decay_fn_args = get_arguments(decay_type_fn)
if 'decay_rate' in decay_fn_args:
kwargs['decay_rate'] = decay_rate
if 'staircase' in decay_fn_args:
kwargs['staircase'] = staircase
decayed_exploration_rate = decay_type_fn(**kwargs)
final_exploration_rate = tf.train.piecewise_constant(
x=timestep,
boundaries=[start_decay_at],
values=[exploration_rate, decayed_exploration_rate])
if min_exploration_rate:
final_exploration_rate = tf.maximum(final_exploration_rate,
min_exploration_rate)
return final_exploration_rate
exploration_rate = decay_fn(get_global_timestep())
track(exploration_rate, tf.GraphKeys.EXPLORATION_RATE)
return exploration_rate
def regularizer(x):
x = fct(x)
if collect:
track(x, tf.GraphKeys.REGULARIZATION_LOSSES)
return x
def regularizer(x):
x = fct(x)
if collect:
track(x, tf.GraphKeys.REGULARIZATION_LOSSES)
return x
def _build(self, incoming, *args, **kwargs):
input_shape = get_shape(incoming)
input_ndim = len(input_shape)
gamma_init = tf.random_normal_initializer(mean=self.gamma,
stddev=self.stddev)
self._beta = variable(name='beta',
shape=[input_shape[-1]],
initializer=tf.constant_initializer(self.beta),
trainable=self.trainable,
restore=self.restore)
self._gamma = variable(name='gamma',
shape=[input_shape[-1]],
initializer=gamma_init,
trainable=self.trainable,
restore=self.restore)
track(self._beta, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
track(self._gamma, tf.GraphKeys.LAYER_VARIABLES, self.module_name)
if not self.restore:
track(tf.GraphKeys.EXCL_RESTORE_VARIABLES, self._beta)
track(tf.GraphKeys.EXCL_RESTORE_VARIABLES, self._gamma)
axis = list(xrange(input_ndim - 1))
moving_mean = variable(name='moving_mean',
shape=input_shape[-1:],
initializer=tf.zeros_initializer(),
trainable=False,
restore=self.restore)
moving_variance = variable(name='moving_variance',
shape=input_shape[-1:],
initializer=tf.constant_initializer(1.),
trainable=False,
restore=self.restore)
def update_mean_var():
mean, variance = tf.nn.moments(x=incoming, axes=axis)
update_moving_mean = moving_averages.assign_moving_average(
variable=moving_mean,
value=mean,
decay=self.decay,
zero_debias=False)
update_moving_variance = moving_averages.assign_moving_average(
variable=moving_variance,
value=variance,
decay=self.decay,
zero_debias=False)
with tf.control_dependencies(
[update_moving_mean, update_moving_variance]):
return tf.identity(mean), tf.identity(variance)
# Retrieve variable managing training mode
if Modes.is_train(self.mode):
mean, var = update_mean_var()
else:
mean, var = moving_mean, moving_variance
incoming = tf.nn.batch_normalization(x=incoming,
mean=mean,
variance=var,
offset=self._beta,
scale=self._gamma,
variance_epsilon=self.epsilon)
incoming.set_shape(input_shape)
track(incoming, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return incoming
def activation(x):
x = fct(x, name=name)
if collect:
track(x, tf.GraphKeys.ACTIVATIONS)
return x
