def _rnn_step(time, sequence_length, min_sequence_length, max_sequence_length,
zero_output, state, call_cell):
""" Calculate one step of a dynamic RNN minibatch.
Returns an (output, state) pair conditioned on the sequence_lengths.
The pseudocode is something like:
if t >= max_sequence_length:
return (zero_output, state)
if t < min_sequence_length:
return call_cell()
# Selectively output zeros or output, old state or new state depending
# on if we've finished calculating each row.
new_output, new_state = call_cell()
final_output = np.vstack([
zero_output if time >= sequence_lengths[r] else new_output_r
for r, new_output_r in enumerate(new_output)
])
final_state = np.vstack([
state[r] if time >= sequence_lengths[r] else new_state_r
for r, new_state_r in enumerate(new_state)
])
return (final_output, final_state)
Arguments:
time: Python int, the current time step
sequence_length: int32 `Tensor` vector of size [batch_size]
min_sequence_length: int32 `Tensor` scalar, min of sequence_length
max_sequence_length: int32 `Tensor` scalar, max of sequence_length
zero_output: `Tensor` vector of shape [output_size]
state: `Tensor` matrix of shape [batch_size, state_size]
call_cell: lambda returning tuple of (new_output, new_state) where
new_output is a `Tensor` matrix of shape [batch_size, output_size]
new_state is a `Tensor` matrix of shape [batch_size, state_size]
Returns:
A tuple of (final_output, final_state) as given by the pseudocode above:
final_output is a `Tensor` matrix of shape [batch_size, output_size]
final_state is a `Tensor` matrix of shape [batch_size, state_size]
"""
# Step 1: determine whether we need to call_cell or not
empty_update = lambda: (zero_output, state)
state_shape = state.get_shape()
output, new_state = control_flow_ops.cond(
time < max_sequence_length, call_cell, empty_update)
# Step 2: determine whether we need to copy through state and/or outputs
existing_output_state = lambda: (output, new_state)
def copy_through():
# Use broadcasting select to determine which values should get
# the previous state & zero output, and which values should get
# a calculated state & output.
copy_cond = (time >= sequence_length)
return (math_ops.select(copy_cond, zero_output, output),
math_ops.select(copy_cond, state, new_state))
(output, state) = control_flow_ops.cond(
time < min_sequence_length, existing_output_state, copy_through)
output.set_shape(zero_output.get_shape())
state.set_shape(state_shape)
return (output, state)
def random_flip_left_right(image, bboxes, seed=None):
"""Random flip left-right of an image and its bounding boxes.
"""
def flip_bboxes(bboxes):
"""Flip bounding boxes coordinates.
"""
bboxes = tf.stack([bboxes[:, 0], 1 - bboxes[:, 3],
bboxes[:, 2], 1 - bboxes[:, 1]], axis=-1)
return bboxes
# Random flip. Tensorflow implementation.
with tf.name_scope('random_flip_left_right'):
image = ops.convert_to_tensor(image, name='image')
_Check3DImage(image, require_static=False)
uniform_random = random_ops.random_uniform([], 0, 1.0, seed=seed)
mirror_cond = math_ops.less(uniform_random, .5)
# Flip image.
result = control_flow_ops.cond(mirror_cond,
lambda: array_ops.reverse_v2(image, [1]),
lambda: image)
# Flip bboxes.
bboxes = control_flow_ops.cond(mirror_cond,
lambda: flip_bboxes(bboxes),
lambda: bboxes)
return fix_image_flip_shape(image, result), bboxes
def dense_make_stats_update(is_active, are_buckets_ready, float_column,
quantile_buckets, example_partition_ids, gradients,
hessians, weights, empty_gradients, empty_hessians):
"""Updates the state for dense split handler."""
empty_float = constant_op.constant_v1([], dtype=dtypes.float32)
quantile_values, quantile_weights = control_flow_ops.cond(
is_active[1], # For the next layer, this handler is inactive.
lambda: (float_column, weights),
lambda: (empty_float, empty_float))
def ready_inputs_fn():
"""Branch to execute when quantiles are ready."""
quantized_feature = quantile_ops.quantiles([float_column], [],
[quantile_buckets], [], [])
quantized_feature = math_ops.cast(quantized_feature[0], dtypes.int64)
quantized_feature = array_ops.squeeze(quantized_feature, axis=0)
return (example_partition_ids, quantized_feature, gradients, hessians)
def not_ready_inputs_fn():
return (constant_op.constant_v1([], dtype=dtypes.int32),
constant_op.constant_v1([[]], dtype=dtypes.int64, shape=[1, 2]),
empty_gradients, empty_hessians)
example_partition_ids, feature_ids, gradients, hessians = (
control_flow_ops.cond(
math_ops.logical_and(
math_ops.logical_and(are_buckets_ready,
array_ops.size(quantile_buckets) > 0),
is_active[0]), ready_inputs_fn, not_ready_inputs_fn))
return (quantile_values, quantile_weights, example_partition_ids, feature_ids,
gradients, hessians)
def testCondNested(self):
with context.graph_mode(), self.test_session():
v = resource_variable_ops.ResourceVariable(1.0)
variables.global_variables_initializer().run()
p = array_ops.placeholder(dtype=dtypes.bool)
q = array_ops.placeholder(dtype=dtypes.bool)
with function.AutomaticControlDependencies() as c:
def true_fn():
v.assign(v + 1, name='true')
return 1.0
def false_fn():
def inner_true_fn():
v.assign(v * 2, name='false_true')
return 2.0
def inner_false_fn():
v.assign(v * 3, name='false_false')
return 3.0
control_flow_ops.cond(q, inner_true_fn, inner_false_fn)
return 1.0
control_flow_ops.cond(p, true_fn, false_fn)
with ops.name_scope('final'):
val = v.read_value()
val = c.mark_as_return(val)
self.assertAllEqual(val.eval(feed_dict={p: False, q: False}), 3.0)
self.assertAllEqual(val.eval(feed_dict={p: False, q: True}), 6.0)
self.assertAllEqual(val.eval(feed_dict={p: True, q: True}), 7.0)
self.assertAllEqual(val.eval(feed_dict={p: True, q: False}), 8.0)
def remove_squeezable_dimensions(
labels, predictions, expected_rank_diff=0, name=None):
"""Squeeze last dim if ranks differ from expected by exactly 1.
In the common case where we expect shapes to match, `expected_rank_diff`
defaults to 0, and we squeeze the last dimension of the larger rank if they
differ by 1.
But, for example, if `labels` contains class IDs and `predictions` contains 1
probability per class, we expect `predictions` to have 1 more dimension than
`labels`, so `expected_rank_diff` would be 1. In this case, we'd squeeze
`labels` if `rank(predictions) - rank(labels) == 0`, and
`predictions` if `rank(predictions) - rank(labels) == 2`.
This will use static shape if available. Otherwise, it will add graph
operations, which could result in a performance hit.
Args:
labels: Label values, a `Tensor` whose dimensions match `predictions`.
predictions: Predicted values, a `Tensor` of arbitrary dimensions.
expected_rank_diff: Expected result of `rank(predictions) - rank(labels)`.
name: Name of the op.
Returns:
Tuple of `labels` and `predictions`, possibly with last dim squeezed.
"""
with ops.name_scope(name, 'remove_squeezable_dimensions',
[labels, predictions]):
predictions = ops.convert_to_tensor(predictions)
labels = ops.convert_to_tensor(labels)
predictions_shape = predictions.get_shape()
predictions_rank = predictions_shape.ndims
labels_shape = labels.get_shape()
labels_rank = labels_shape.ndims
if (labels_rank is not None) and (predictions_rank is not None):
# Use static rank.
rank_diff = predictions_rank - labels_rank
if rank_diff == expected_rank_diff + 1:
predictions = array_ops.squeeze(predictions, [-1])
elif rank_diff == expected_rank_diff - 1:
labels = array_ops.squeeze(labels, [-1])
return labels, predictions
# Use dynamic rank.
rank_diff = array_ops.rank(predictions) - array_ops.rank(labels)
if (predictions_rank is None) or (
predictions_shape.dims[-1].is_compatible_with(1)):
predictions = control_flow_ops.cond(
math_ops.equal(expected_rank_diff + 1, rank_diff),
lambda: array_ops.squeeze(predictions, [-1]),
lambda: predictions)
if (labels_rank is None) or (
labels_shape.dims[-1].is_compatible_with(1)):
labels = control_flow_ops.cond(
math_ops.equal(expected_rank_diff - 1, rank_diff),
lambda: array_ops.squeeze(labels, [-1]),
lambda: labels)
return labels, predictions
def _get_chol_and_x_compatible_shape(self, x):
"""Return self.chol and x, (possibly) broadcast to compatible shape."""
# x and chol are "compatible" if their shape matches except for the last two
# dimensions of chol are [k, k], and the last two of x are [k, 1].
# E.g. x.shape = [A, B, k, 1], and chol.shape = [A, B, k, k]
# This is required for the batch_triangular_solve, which does not broadcast.
# TODO(langmore) This broadcast replicates matrices unnecesarily! In the
# case where
# x.shape = [M1,...,Mr, N1,...,Nb, k], and chol.shape = [N1,...,Nb, k, k]
# (which is common if x was sampled), the front dimensions of x can be
# "flipped" to the end, making
# x_flipped.shape = [N1,...,Nb, k, M1*...*Mr],
# and this can be handled by the linear solvers. This is preferred, because
# it does not replicate the matrix, or create any new data.
# We assume x starts without the trailing singleton dimension, e.g.
# x.shape = [B, k].
chol = self._chol
with ops.op_scope([x] + self.inputs, 'get_chol_and_x_compatible_shape'):
# If we determine statically that shapes match, we're done.
if x.get_shape() == chol.get_shape()[:-1]:
x_expanded = array_ops.expand_dims(x, -1)
return chol, x_expanded
# Dynamic check if shapes match or not.
vector_shape = self.vector_shape() # Shape of chol minus last dim.
are_same_rank = math_ops.equal(
array_ops.rank(x), array_ops.rank(vector_shape))
def shapes_match_if_same_rank():
return math_ops.reduce_all(math_ops.equal(
array_ops.shape(x), vector_shape))
shapes_match = control_flow_ops.cond(are_same_rank,
shapes_match_if_same_rank,
lambda: ops.convert_to_tensor(False))
# Make tensors (never instantiated) holding the broadcast shape.
# matrix_broadcast_dummy is the shape we will broadcast chol to.
matrix_bcast_dummy = chol + array_ops.expand_dims(x, -1)
# vector_bcast_dummy is the shape we will bcast x to, before we expand it.
chol_minus_last_dim = math_ops.reduce_sum(chol, reduction_indices=[-1])
vector_bcast_dummy = x + chol_minus_last_dim
chol_bcast = chol + array_ops.zeros_like(matrix_bcast_dummy)
x_bcast = x + array_ops.zeros_like(vector_bcast_dummy)
chol_result = control_flow_ops.cond(shapes_match, lambda: chol,
lambda: chol_bcast)
chol_result.set_shape(matrix_bcast_dummy.get_shape())
x_result = control_flow_ops.cond(shapes_match, lambda: x, lambda: x_bcast)
x_result.set_shape(vector_bcast_dummy.get_shape())
x_expanded = array_ops.expand_dims(x_result, -1)
return chol_result, x_expanded
def body(i, prev_c, prev_h, actions, log_probs):
# pylint: disable=g-long-lambda
signal = control_flow_ops.cond(
math_ops.equal(i, 0),
lambda: array_ops.tile(device_go_embedding,
[self.hparams.num_children, 1]),
lambda: embedding_ops.embedding_lookup(device_embeddings,
actions.read(i - 1))
)
if self.hparams.keep_prob is not None:
signal = nn_ops.dropout(signal, self.hparams.keep_prob)
next_c, next_h = lstm(signal, prev_c, prev_h, w_lstm, forget_bias)
query = math_ops.matmul(next_h, attn_w_2)
query = array_ops.reshape(
query, [self.hparams.num_children, 1, self.hparams.hidden_size])
query = math_ops.tanh(query + attn_mem)
query = array_ops.reshape(query, [
self.hparams.num_children * self.num_groups, self.hparams.hidden_size
])
query = math_ops.matmul(query, attn_v)
query = array_ops.reshape(query,
[self.hparams.num_children, self.num_groups])
query = nn_ops.softmax(query)
query = array_ops.reshape(query,
[self.hparams.num_children, self.num_groups, 1])
query = math_ops.reduce_sum(attn_mem * query, axis=1)
query = array_ops.concat([next_h, query], axis=1)
logits = math_ops.matmul(query, device_softmax)
logits /= self.hparams.temperature
if self.hparams.tanh_constant > 0:
logits = math_ops.tanh(logits) * self.hparams.tanh_constant
if self.hparams.logits_std_noise > 0:
num_in_logits = math_ops.cast(
array_ops.size(logits), dtype=dtypes.float32)
avg_norm = math_ops.divide(
linalg_ops.norm(logits), math_ops.sqrt(num_in_logits))
logits_noise = random_ops.random_normal(
array_ops.shape(logits),
stddev=self.hparams.logits_std_noise * avg_norm)
logits = control_flow_ops.cond(
self.global_step > self.hparams.stop_noise_step, lambda: logits,
lambda: logits + logits_noise)
if mode == "sample":
next_y = random_ops.multinomial(logits, 1, seed=self.hparams.seed)
elif mode == "greedy":
next_y = math_ops.argmax(logits, 1)
elif mode == "target":
next_y = array_ops.slice(y, [0, i], [-1, 1])
else:
raise NotImplementedError
next_y = math_ops.to_int32(next_y)
next_y = array_ops.reshape(next_y, [self.hparams.num_children])
actions = actions.write(i, next_y)
log_probs += nn_ops.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=next_y)
return i + 1, next_c, next_h, actions, log_probs
def test_none(self):
fn_none = lambda: None
fn_tensor = lambda: constant_op.constant(1)
with self.assertRaises(ValueError):
control_flow_ops.cond(constant_op.constant(True), fn_none, fn_tensor)
with self.assertRaises(ValueError):
control_flow_ops.cond(constant_op.constant(True), fn_tensor, fn_none)
def testCondContext(self):
with self.test_session() as sess:
x = constant_op.constant(2)
y = constant_op.constant(5)
control_flow_ops.cond(math_ops.less(x, y), lambda: math_ops.multiply(x, 17), lambda: math_ops.add(y, 23))
for op in sess.graph.get_operations():
c = op._get_control_flow_context()
if c:
compare.ProtoEq(c.to_proto(), control_flow_ops.CondContext.from_proto(c.to_proto()).to_proto())
def testCond_3(self):
with self.test_session():
x = tf.constant(10)
pred = tf.less(1, 2)
fn1 = lambda: tf.add(x, 1)
fn2 = lambda: tf.sub(x, 1)
fn3 = lambda: tf.add(control_flow_ops.cond(pred, fn1, fn2), 1)
r = control_flow_ops.cond(pred, fn3, fn2)
result = r.eval()
self.assertTrue(check_op_order(x.graph))
self.assertAllEqual(12, result)
def false_fn():
def inner_true_fn():
v.assign(v * 2, name='false_true')
return 2.0
def inner_false_fn():
v.assign(v * 3, name='false_false')
return 3.0
control_flow_ops.cond(q, inner_true_fn, inner_false_fn)
return 1.0
def testRaisesOutputStructuresMismatch(self):
x = constant_op.constant(1.0, name="x")
y = constant_op.constant(3.0, name="y")
def true_fn():
return x * y, y
def false_fn():
return ((x,), y * 3.0)
with self.assertRaisesRegexp(
TypeError, "true_fn and false_fn arguments to tf.cond must have the "
"same number, type, and overall structure of return values."):
control_flow_ops.cond(constant_op.constant(False), true_fn, false_fn)
def _initialize_variables(self, data, initial_means=None):
"""Initializes variables.
Args:
data: a list of Tensors with data, each row is a new example.
initial_means: a Tensor with a matrix of means.
"""
first_shard = data[0]
# Initialize means: num_classes X 1 X dimensions.
if initial_means is not None:
means = array_ops.expand_dims(initial_means, 1)
else:
# Sample data randomly
means = array_ops.expand_dims(
_init_clusters_random(data, self._num_classes, self._random_seed), 1)
# Initialize covariances.
if self._covariance_type == FULL_COVARIANCE:
cov = _covariance(first_shard, False) + self._min_var
# A matrix per class, num_classes X dimensions X dimensions
covs = array_ops.tile(
array_ops.expand_dims(cov, 0), [self._num_classes, 1, 1])
elif self._covariance_type == DIAG_COVARIANCE:
cov = _covariance(first_shard, True) + self._min_var
# A diagonal per row, num_classes X dimensions.
covs = array_ops.tile(
array_ops.expand_dims(array_ops.diag_part(cov), 0),
[self._num_classes, 1])
with ops.colocate_with(self._cluster_centers_initialized):
initialized = control_flow_ops.with_dependencies(
[means, covs],
array_ops.identity(self._cluster_centers_initialized))
self._init_ops = []
with ops.colocate_with(self._means):
init_means = state_ops.assign(self._means, means, validate_shape=False)
init_means = control_flow_ops.with_dependencies(
[init_means],
state_ops.assign(self._cluster_centers_initialized, True))
self._init_ops.append(control_flow_ops.cond(initialized,
control_flow_ops.no_op,
lambda: init_means).op)
with ops.colocate_with(self._covs):
init_covs = state_ops.assign(self._covs, covs, validate_shape=False)
init_covs = control_flow_ops.with_dependencies(
[init_covs],
state_ops.assign(self._cluster_centers_initialized, True))
self._init_ops.append(control_flow_ops.cond(initialized,
control_flow_ops.no_op,
lambda: init_covs).op)
def testRaisesOutputStructuresMismatch(self):
x = constant_op.constant(1.0, name="x")
y = constant_op.constant(3.0, name="y")
def true_fn():
return x * y, y
def false_fn():
return ((x,), y * 3.0)
with self.assertRaisesRegexp(
ValueError, "Outputs of true_fn and false_fn must"
" have the same structure"):
control_flow_ops.cond(constant_op.constant(False), true_fn, false_fn)
def batch_norm(x, deterministic, alpha=0.9, shift=True, scope='bn'):
with vs.variable_scope(scope):
dtype = x.dtype
input_shape = x.get_shape().as_list()
feat_dim = input_shape[-1]
axes = range(len(input_shape)-1)
if shift:
beta = vs.get_variable(
scope+"_beta", shape=[feat_dim],
initializer=init_ops.zeros_initializer, dtype=dtype)
else:
beta = vs.get_variable(
scope+"_beta", shape=[feat_dim],
initializer=init_ops.zeros_initializer,
dtype=dtype, trainable=False)
gamma = vs.get_variable(
scope+"_gamma", shape=[feat_dim],
initializer=init_ops.constant_initializer(0.1), dtype=dtype)
mean = vs.get_variable(scope+"_mean", shape=[feat_dim],
initializer=init_ops.zeros_initializer,
dtype=dtype, trainable=False)
var = vs.get_variable(scope+"_var", shape=[feat_dim],
initializer=init_ops.ones_initializer,
dtype=dtype, trainable=False)
counter = vs.get_variable(scope+"_counter", shape=[],
initializer=init_ops.constant_initializer(0),
dtype=tf.int64, trainable=False)
zero_cnt = vs.get_variable(scope+"_zero_cnt", shape=[],
initializer=init_ops.constant_initializer(0),
dtype=tf.int64, trainable=False)
batch_mean, batch_var = moments(x, axes, name=scope+'_moments')
mean, var = cond(math_ops.equal(counter, zero_cnt), lambda: (batch_mean, batch_var),
lambda: (mean, var))
mean, var, counter = cond(deterministic, lambda: (mean, var, counter),
lambda: ((1-alpha) * batch_mean + alpha * mean,
(1-alpha) * batch_var + alpha * var,
counter + 1))
normed = batch_normalization(x, mean, var, beta, gamma, 1e-8)
return normed
def next_inputs(self, time, outputs, state, sample_ids, name=None):
with ops.name_scope(name, "ScheduledEmbeddingTrainingHelperSample",
[time, outputs, state, sample_ids]):
(finished, base_next_inputs, state) = (
super(ScheduledEmbeddingTrainingHelper, self).next_inputs(
time=time,
outputs=outputs,
state=state,
sample_ids=sample_ids,
name=name))
def maybe_sample():
"""Perform scheduled sampling."""
where_sampling = math_ops.cast(
array_ops.where(sample_ids > -1), dtypes.int32)
where_not_sampling = math_ops.cast(
array_ops.where(sample_ids <= -1), dtypes.int32)
where_sampling_flat = array_ops.reshape(where_sampling, [-1])
where_not_sampling_flat = array_ops.reshape(where_not_sampling, [-1])
sample_ids_sampling = array_ops.gather(sample_ids, where_sampling_flat)
inputs_not_sampling = array_ops.gather(
base_next_inputs, where_not_sampling_flat)
sampled_next_inputs = self._embedding_fn(sample_ids_sampling)
base_shape = array_ops.shape(base_next_inputs)
return (array_ops.scatter_nd(indices=where_sampling,
updates=sampled_next_inputs,
shape=base_shape)
+ array_ops.scatter_nd(indices=where_not_sampling,
updates=inputs_not_sampling,
shape=base_shape))
all_finished = math_ops.reduce_all(finished)
next_inputs = control_flow_ops.cond(
all_finished, lambda: base_next_inputs, maybe_sample)
return (finished, next_inputs, state)
def dropout(inputs,
keep_prob=0.5,
noise_shape=None,
is_training=True,
outputs_collections=None,
scope=None):
"""Returns a dropout op applied to the input.
With probability `keep_prob`, outputs the input element scaled up by
`1 / keep_prob`, otherwise outputs `0`. The scaling is so that the expected
sum is unchanged.
Args:
inputs: the tensor to pass to the nn.dropout op.
keep_prob: A scalar `Tensor` with the same type as x. The probability
that each element is kept.
noise_shape: A 1-D `Tensor` of type `int32`, representing the
shape for randomly generated keep/drop flags.
is_training: A bool `Tensor` indicating whether or not the model
is in training mode. If so, dropout is applied and values scaled.
Otherwise, inputs is returned.
outputs_collections: collection to add the outputs.
scope: Optional scope for op_scope.
Returns:
a tensor representing the output of the operation.
"""
with ops.op_scope([inputs], scope, 'Dropout') as sc:
is_training = ops.convert_to_tensor(is_training)
outputs = control_flow_ops.cond(
is_training,
lambda: nn.dropout(inputs, keep_prob, noise_shape),
lambda: inputs)
return utils.collect_named_outputs(outputs_collections, sc, outputs)
def __call__(self, step):
with ops.name_scope(
self.name, "PolynomialDecay",
[self.initial_learning_rate, step, self.decay_steps,
self.end_learning_rate, self.power]
) as name:
initial_learning_rate = ops.convert_to_tensor(
self.initial_learning_rate, name="initial_learning_rate")
dtype = initial_learning_rate.dtype
end_learning_rate = math_ops.cast(self.end_learning_rate, dtype)
power = math_ops.cast(self.power, dtype)
global_step_recomp = math_ops.cast(step, dtype)
decay_steps_recomp = math_ops.cast(self.decay_steps, dtype)
if self.cycle:
# Find the first multiple of decay_steps that is bigger than
# global_step. If global_step is zero set the multiplier to 1
multiplier = control_flow_ops.cond(
math_ops.equal(global_step_recomp, 0), lambda: 1.0,
lambda: math_ops.ceil(global_step_recomp / self.decay_steps))
decay_steps_recomp = math_ops.multiply(decay_steps_recomp, multiplier)
else:
# Make sure that the global_step used is not bigger than decay_steps.
global_step_recomp = math_ops.minimum(global_step_recomp,
self.decay_steps)
p = math_ops.div(global_step_recomp, decay_steps_recomp)
return math_ops.add(
math_ops.multiply(initial_learning_rate - end_learning_rate,
math_ops.pow(1 - p, power)),
end_learning_rate,
name=name)
def AddCrossEntropy(batch_size, n):
"""Adds a cross entropy cost function."""
cross_entropies = []
def _Pass():
return tf.constant(0, dtype=tf.float32, shape=[1])
for beam_id in range(batch_size):
beam_gold_slot = tf.reshape(
tf.strided_slice(n['gold_slot'], [beam_id], [beam_id + 1]), [1])
def _ComputeCrossEntropy():
"""Adds ops to compute cross entropy of the gold path in a beam."""
# Requires a cast so that UnsortedSegmentSum, in the gradient,
# is happy with the type of its input 'segment_ids', which
# must be int32.
idx = tf.cast(
tf.reshape(
tf.where(tf.equal(n['beam_ids'], beam_id)), [-1]), tf.int32)
beam_scores = tf.reshape(tf.gather(n['all_path_scores'], idx), [1, -1])
num = tf.shape(idx)
return tf.nn.softmax_cross_entropy_with_logits(
labels=tf.expand_dims(
tf.sparse_to_dense(beam_gold_slot, num, [1.], 0.), 0),
logits=beam_scores)
# The conditional here is needed to deal with the last few batches of the
# corpus which can contain -1 in beam_gold_slot for empty batch slots.
cross_entropies.append(cf.cond(
beam_gold_slot[0] >= 0, _ComputeCrossEntropy, _Pass))
return {'cross_entropy': tf.div(tf.add_n(cross_entropies), batch_size)}
def _fn():
x = constant_op.constant(points)
if batch_size == num_points:
return input_lib.limit_epochs(x, num_epochs=num_epochs), None
if randomize:
indices = random_ops.random_uniform(
constant_op.constant([batch_size]),
minval=0,
maxval=num_points - 1,
dtype=dtypes.int32,
seed=10)
else:
# We need to cycle through the indices sequentially. We create a queue
# to maintain the list of indices.
q = data_flow_ops.FIFOQueue(num_points, dtypes.int32, ())
# Conditionally initialize the Queue.
def _init_q():
with ops.control_dependencies(
[q.enqueue_many(math_ops.range(num_points))]):
return control_flow_ops.no_op()
init_q = control_flow_ops.cond(q.size() <= 0, _init_q,
control_flow_ops.no_op)
with ops.control_dependencies([init_q]):
offsets = q.dequeue_many(batch_size)
with ops.control_dependencies([q.enqueue_many(offsets)]):
indices = array_ops.identity(offsets)
batch = array_ops.gather(x, indices)
return (input_lib.limit_epochs(batch, num_epochs=num_epochs), None)
def average_impurity(self):
"""Constructs a TF graph for evaluating the average leaf impurity of a tree.
If in regression mode, this is the leaf variance. If in classification mode,
this is the gini impurity.
Returns:
The last op in the graph.
"""
children = array_ops.squeeze(array_ops.slice(
self.variables.tree, [0, 0], [-1, 1]), squeeze_dims=[1])
is_leaf = math_ops.equal(constants.LEAF_NODE, children)
leaves = math_ops.to_int32(array_ops.squeeze(array_ops.where(is_leaf),
squeeze_dims=[1]))
counts = array_ops.gather(self.variables.node_sums, leaves)
gini = self._weighted_gini(counts)
# Guard against step 1, when there often are no leaves yet.
def impurity():
return gini
# Since average impurity can be used for loss, when there's no data just
# return a big number so that loss always decreases.
def big():
return array_ops.ones_like(gini, dtype=dtypes.float32) * 10000000.
return control_flow_ops.cond(math_ops.greater(
array_ops.shape(leaves)[0], 0), impurity, big)
def same_dynamic_shape(a, b):
"""Returns whether a and b have the same dynamic shape.
Args:
a: `Tensor`
b: `Tensor`
Returns:
`Boolean` `Tensor` representing if both tensors have the same shape.
"""
a = ops.convert_to_tensor(a, name="a")
b = ops.convert_to_tensor(b, name="b")
# Here we can't just do math_ops.equal(a.shape, b.shape), since
# static shape inference may break the equality comparison between
# shape(a) and shape(b) in math_ops.equal.
def all_shapes_equal():
return math_ops.reduce_all(math_ops.equal(
array_ops.concat([array_ops.shape(a), array_ops.shape(b)], 0),
array_ops.concat([array_ops.shape(b), array_ops.shape(a)], 0)))
# One of the shapes isn't fully defined, so we need to use the dynamic
# shape.
return control_flow_ops.cond(
math_ops.equal(array_ops.rank(a), array_ops.rank(b)),
all_shapes_equal,
lambda: constant_op.constant(False))
def testControlFlow(self):
with self.test_session() as sess:
v0 = tf.get_variable("v0", [], initializer=tf.constant_initializer(0))
var_dict = {}
# Call get_variable in each of the cond clauses.
def var_in_then_clause():
v1 = tf.get_variable("v1", [1], initializer=tf.constant_initializer(1))
var_dict["v1"] = v1
return v1 + v0
def var_in_else_clause():
v2 = tf.get_variable("v2", [1], initializer=tf.constant_initializer(2))
var_dict["v2"] = v2
return v2 + v0
add = control_flow_ops.cond(tf.less(v0, 10),
var_in_then_clause,
var_in_else_clause)
v1 = var_dict["v1"]
v2 = var_dict["v2"]
# We should be able to initialize and run v1 and v2 without initializing
# v0, even if the variable was created with a control dep on v0.
sess.run(v1.initializer)
self.assertEqual([1], sess.run(v1))
sess.run(v2.initializer)
self.assertEqual([2], sess.run(v2))
# v0 should still be uninitialized.
with self.assertRaisesRegexp(tf.OpError, "uninitialized"):
sess.run(v0)
# We should not be able to run 'add' yet.
with self.assertRaisesRegexp(tf.OpError, "uninitialized"):
sess.run(add)
# If we initialize v0 we should be able to run 'add'.
sess.run(v0.initializer)
sess.run(add)
def testCond(self):
"""Tests that compilation handles switch operators."""
with self.session(config=NoRewriteSessionConfig()) as session:
x = array_ops.placeholder(dtypes.float32)
y = array_ops.placeholder(dtypes.float32)
c = array_ops.placeholder(dtypes.bool)
with jit_scope():
z = x + 1.0
w = control_flow_ops.cond(c, lambda: z, lambda: y)
t = math_ops.add(z, w)
# If JIT compilation chooses to cluster z and t, then execution will
# deadlock.
run_metadata = config_pb2.RunMetadata()
result = test_utils.RunWithWarmup(
session,
t, {
x: np.float32(2),
y: np.float32(4),
c: True
},
run_metadata=run_metadata,
options=config_pb2.RunOptions(
trace_level=config_pb2.RunOptions.FULL_TRACE))
self.assert_(MetadataHasXlaRunOp(run_metadata))
self.assertAllClose(result, np.float32(6), rtol=1e-1)
def bn(x, c):
x_shape = x.get_shape()
params_shape = x_shape[-1:]
if c["use_bias"]:
bias = _get_variable("bias", params_shape, initializer=tf.zeros_initializer)
return x + bias
axis = list(range(len(x_shape) - 1))
beta = _get_variable("beta", params_shape, initializer=tf.zeros_initializer)
gamma = _get_variable("gamma", params_shape, initializer=tf.ones_initializer)
moving_mean = _get_variable("moving_mean", params_shape, initializer=tf.zeros_initializer, trainable=False)
moving_variance = _get_variable("moving_variance", params_shape, initializer=tf.ones_initializer, trainable=False)
# These ops will only be preformed when training.
mean, variance = tf.nn.moments(x, axis)
update_moving_mean = moving_averages.assign_moving_average(moving_mean, mean, BN_DECAY)
update_moving_variance = moving_averages.assign_moving_average(moving_variance, variance, BN_DECAY)
tf.add_to_collection(UPDATE_OPS_COLLECTION, update_moving_mean)
tf.add_to_collection(UPDATE_OPS_COLLECTION, update_moving_variance)
mean, variance = control_flow_ops.cond(
c["is_training"], lambda: (mean, variance), lambda: (moving_mean, moving_variance)
)
x = tf.nn.batch_normalization(x, mean, variance, beta, gamma, BN_EPSILON)
# x.set_shape(inputs.get_shape()) ??
return x
def batch_norm(self, input_, name, over=[0]):
with tf.variable_scope(name):
batch_mean, batch_var = tf.nn.moments(
input_,
over,
name='moments',
)
batch_inv_std = 1/(tf.sqrt(batch_var) + 1e-6)
ema = tf.train.ExponentialMovingAverage(decay=0.9)
ema_op = ema.apply([batch_mean, batch_inv_std])
ema_mean = ema.average(batch_mean)
ema_inv_std = ema.average(batch_inv_std)
def with_updates():
with tf.control_dependencies([ema_op]):
return tf.identity(batch_mean), tf.identity(batch_inv_std)
def without_updates():
return (ema_mean, ema_inv_std)
mean, inv_std = control_flow_ops.cond(
self.use_precomputed_means,
without_updates,
with_updates,
)
#tf.scalar_summary('ema/mean', ema_mean)
#tf.scalar_summary('ema/inv_std', ema_inv_std)
return (input_ - mean) * inv_std
def resample_at_rate(inputs, rates, scope=None, seed=None, back_prop=False):
"""Given `inputs` tensors, stochastically resamples each at a given rate.
For example, if the inputs are `[[a1, a2], [b1, b2]]` and the rates
tensor contains `[3, 1]`, then the return value may look like `[[a1,
a2, a1, a1], [b1, b2, b1, b1]]`. However, many other outputs are
possible, since this is stochastic -- averaged over many repeated
calls, each set of inputs should appear in the output `rate` times
the number of invocations.
Args:
inputs: A list of tensors, each of which has a shape of `[batch_size, ...]`
rates: A tensor of shape `[batch_size]` contiaining the resampling rates
for each input.
scope: Scope for the op.
seed: Random seed to use.
back_prop: Whether to allow back-propagation through this op.
Returns:
Selections from the input tensors.
"""
with ops.name_scope(scope, default_name='resample_at_rate',
values=list(inputs) + [rates]):
rates = ops.convert_to_tensor(rates, name='rates')
# random_poisson does not support rates of size 0 (b/36076216)
sample_counts = math_ops.cast(control_flow_ops.cond(
array_ops.shape(rates)[0] > 0,
lambda: random_ops.random_poisson(rates, (), rates.dtype, seed=seed),
lambda: array_ops.zeros(shape=[0], dtype=rates.dtype)), dtypes.int32)
sample_indices = _repeat_range(sample_counts)
if not back_prop:
sample_indices = array_ops.stop_gradient(sample_indices)
return [array_ops.gather(x, sample_indices) for x in inputs]
def testControlFlow(self):
with self.cached_session() as sess:
v0 = variables.Variable(0, name="v0")
var_dict = {}
# Call get_variable in each of the cond clauses.
def var_in_then_clause():
v1 = variables.Variable(1, name="v1")
var_dict["v1"] = v1
return v1 + v0
def var_in_else_clause():
v2 = variables.Variable(2, name="v2")
var_dict["v2"] = v2
return v2 + v0
add = control_flow_ops.cond(
math_ops.less(v0, 10), var_in_then_clause, var_in_else_clause)
v1 = var_dict["v1"]
v2 = var_dict["v2"]
# We should be able to initialize and run v1 and v2 without initializing
# v0, even if the variable was created with a control dep on v0.
self.evaluate(v1.initializer)
self.assertEqual([1], self.evaluate(v1))
self.evaluate(v2.initializer)
self.assertEqual([2], self.evaluate(v2))
# v0 should still be uninitialized.
with self.assertRaisesRegexp(errors_impl.OpError, "uninitialized"):
self.evaluate(v0)
# We should not be able to run 'add' yet.
with self.assertRaisesRegexp(errors_impl.OpError, "uninitialized"):
self.evaluate(add)
# If we initialize v0 we should be able to run 'add'.
self.evaluate(v0.initializer)
self.evaluate(add)
def _repeat_range(counts, name=None):
"""Repeat integers given by range(len(counts)) each the given number of times.
Example behavior:
[0, 1, 2, 3] -> [1, 2, 2, 3, 3, 3]
Args:
counts: 1D tensor with dtype=int32.
name: optional name for operation.
Returns:
1D tensor with dtype=int32 and dynamic length giving the repeated integers.
"""
with ops.name_scope(name, 'repeat_range', [counts]) as scope:
counts = ops.convert_to_tensor(counts, name='counts')
def cond(unused_output, i):
return i < size
def body(output, i):
value = array_ops.fill(counts[i:i+1], i)
return (output.write(i, value), i + 1)
size = array_ops.shape(counts)[0]
init_output_array = tensor_array_ops.TensorArray(
dtype=dtypes.int32, size=size, infer_shape=False)
output_array, num_writes = control_flow_ops.while_loop(
cond, body, loop_vars=[init_output_array, 0])
return control_flow_ops.cond(
num_writes > 0,
output_array.concat,
lambda: array_ops.zeros(shape=[0], dtype=dtypes.int32),
name=scope)
def batch_norm(x, phase_train):
"""
Batch normalization on convolutional maps.
Args:
x: Tensor, 4D BHWD input maps
n_out: integer, depth of input maps
phase_train: boolean tf.Variable, true indicates training phase
scope: string, variable scope
affn: whether to affn-transform outputs
Return:
normed: batch-normalized maps
Ref: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow/33950177
"""
name = 'batch_norm'
with tf.variable_scope(name):
phase_train = tf.convert_to_tensor(phase_train, dtype=tf.bool)
n_out = int(x.get_shape()[3])
beta = tf.Variable(tf.constant(0.0, shape=[n_out], dtype=x.dtype),
name=name+'/beta', trainable=True, dtype=x.dtype)
gamma = tf.Variable(tf.constant(1.0, shape=[n_out], dtype=x.dtype),
name=name+'/gamma', trainable=True, dtype=x.dtype)
batch_mean, batch_var = tf.nn.moments(x, [0,1,2], name='moments')
ema = tf.train.ExponentialMovingAverage(decay=0.9)
def mean_var_with_update():
ema_apply_op = ema.apply([batch_mean, batch_var])
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean), tf.identity(batch_var)
mean, var = control_flow_ops.cond(phase_train,
mean_var_with_update,
lambda: (ema.average(batch_mean), ema.average(batch_var)))
normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, 1e-3)
return normed
def polynomial_decay(learning_rate,
global_step,
decay_steps,
end_learning_rate=0.0001,
power=1.0,
cycle=False,
name=None):
"""Applies a polynomial decay to the learning rate.
It is commonly observed that a monotonically decreasing learning rate, whose
degree of change is carefully chosen, results in a better performing model.
This function applies a polynomial decay function to a provided initial
`learning_rate` to reach an `end_learning_rate` in the given `decay_steps`.
It requires a `global_step` value to compute the decayed learning rate. You
can just pass a TensorFlow variable that you increment at each training step.
The function returns the decayed learning rate. It is computed as:
```python
global_step = min(global_step, decay_steps)
decayed_learning_rate = (learning_rate - end_learning_rate) *
(1 - global_step / decay_steps) ^ (power) +
end_learning_rate
```
If `cycle` is True then a multiple of `decay_steps` is used, the first one
that is bigger than `global_steps`.
```python
decay_steps = decay_steps * ceil(global_step / decay_steps)
decayed_learning_rate = (learning_rate - end_learning_rate) *
(1 - global_step / decay_steps) ^ (power) +
end_learning_rate
```
Example: decay from 0.1 to 0.01 in 10000 steps using sqrt (i.e. power=0.5):
```python
...
global_step = tf.Variable(0, trainable=False)
starter_learning_rate = 0.1
end_learning_rate = 0.01
decay_steps = 10000
learning_rate = tf.train.polynomial_decay(starter_learning_rate, global_step,
decay_steps, end_learning_rate,
power=0.5)
# Passing global_step to minimize() will increment it at each step.
learning_step = (
tf.train.GradientDescentOptimizer(learning_rate)
.minimize(...my loss..., global_step=global_step)
)
```
Args:
learning_rate: A scalar `float32` or `float64` `Tensor` or a
Python number. The initial learning rate.
global_step: A scalar `int32` or `int64` `Tensor` or a Python number.
Global step to use for the decay computation. Must not be negative.
decay_steps: A scalar `int32` or `int64` `Tensor` or a Python number.
Must be positive. See the decay computation above.
end_learning_rate: A scalar `float32` or `float64` `Tensor` or a
Python number. The minimal end learning rate.
power: A scalar `float32` or `float64` `Tensor` or a
Python number. The power of the polynomial. Defaults to linear, 1.0.
cycle: A boolean, whether or not it should cycle beyond decay_steps.
name: String. Optional name of the operation. Defaults to
'PolynomialDecay'.
Returns:
A scalar `Tensor` of the same type as `learning_rate`. The decayed
learning rate.
Raises:
ValueError: if `global_step` is not supplied.
"""
if global_step is None:
raise ValueError("global_step is required for polynomial_decay.")
with ops.name_scope(
name, "PolynomialDecay",
[learning_rate, global_step, decay_steps, end_learning_rate, power
]) as name:
learning_rate = ops.convert_to_tensor(learning_rate,
name="learning_rate")
dtype = learning_rate.dtype
global_step = math_ops.cast(global_step, dtype)
decay_steps = math_ops.cast(decay_steps, dtype)
end_learning_rate = math_ops.cast(end_learning_rate, dtype)
power = math_ops.cast(power, dtype)
if cycle:
# Find the first multiple of decay_steps that is bigger than global_step.
# If global_step is zero set the multiplier to 1
multiplier = control_flow_ops.cond(
math_ops.equal(global_step, 0), lambda: 1.0,
lambda: math_ops.ceil(global_step / decay_steps))
decay_steps = math_ops.multiply(decay_steps, multiplier)
else:
# Make sure that the global_step used is not bigger than decay_steps.
global_step = math_ops.minimum(global_step, decay_steps)
p = math_ops.div(global_step, decay_steps)
return math_ops.add(math_ops.multiply(
learning_rate - end_learning_rate, math_ops.pow(1 - p, power)),
end_learning_rate,
name=name)
def defaults_two():
return control_flow_ops.cond(math_ops.equal(
math_ops.mod(x, 2), 0),
multiply,
divide,
name="cond_mult")
def squeeze_or_expand_dimensions(y_pred, y_true=None, sample_weight=None):
"""Squeeze or expand last dimension if needed.
1. Squeezes last dim of `y_pred` or `y_true` if their rank differs by 1
(using `remove_squeezable_dimensions`).
2. Squeezes or expands last dim of `sample_weight` if its rank differs by 1
from the new rank of `y_pred`.
If `sample_weight` is scalar, it is kept scalar.
This will use static shape if available. Otherwise, it will add graph
operations, which could result in a performance hit.
Args:
y_pred: Predicted values, a `Tensor` of arbitrary dimensions.
y_true: Optional label `Tensor` whose dimensions match `y_pred`.
sample_weight: Optional weight scalar or `Tensor` whose dimensions match
`y_pred`.
Returns:
Tuple of `y_pred`, `y_true` and `sample_weight`. Each of them possibly has
the last dimension squeezed,
`sample_weight` could be extended by one dimension.
If `sample_weight` is None, (y_pred, y_true) is returned.
"""
y_pred_shape = y_pred.shape
y_pred_rank = y_pred_shape.ndims
if y_true is not None:
# If sparse matrix is provided as `y_true`, the last dimension in `y_pred`
# may be > 1. Eg: y_true = [0, 1, 2] (shape=(3,)),
# y_pred = [[.9, .05, .05], [.5, .89, .6], [.05, .01, .94]] (shape=(3, 3))
# In this case, we should not try to remove squeezable dimension.
y_true_shape = y_true.shape
y_true_rank = y_true_shape.ndims
if (y_true_rank is not None) and (y_pred_rank is not None):
# Use static rank for `y_true` and `y_pred`.
if (y_pred_rank - y_true_rank != 1) or y_pred_shape[-1] == 1:
y_true, y_pred = remove_squeezable_dimensions(y_true, y_pred)
else:
# Use dynamic rank.
rank_diff = array_ops.rank(y_pred) - array_ops.rank(y_true)
squeeze_dims = lambda: remove_squeezable_dimensions( # pylint: disable=g-long-lambda
y_true, y_pred)
is_last_dim_1 = math_ops.equal(1, array_ops.shape(y_pred)[-1])
maybe_squeeze_dims = lambda: control_flow_ops.cond( # pylint: disable=g-long-lambda
is_last_dim_1, squeeze_dims, lambda: (y_true, y_pred))
y_true, y_pred = control_flow_ops.cond(
math_ops.equal(1, rank_diff), maybe_squeeze_dims, squeeze_dims)
if sample_weight is None:
return y_pred, y_true
weights_shape = sample_weight.shape
weights_rank = weights_shape.ndims
if weights_rank == 0: # If weights is scalar, do nothing.
return y_pred, y_true, sample_weight
if (y_pred_rank is not None) and (weights_rank is not None):
# Use static rank.
if weights_rank - y_pred_rank == 1:
sample_weight = array_ops.squeeze(sample_weight, [-1])
elif y_pred_rank - weights_rank == 1:
sample_weight = array_ops.expand_dims(sample_weight, [-1])
return y_pred, y_true, sample_weight
# Use dynamic rank.
weights_rank_tensor = array_ops.rank(sample_weight)
rank_diff = weights_rank_tensor - array_ops.rank(y_pred)
maybe_squeeze_weights = lambda: array_ops.squeeze(sample_weight, [-1])
def _maybe_expand_weights():
expand_weights = lambda: array_ops.expand_dims(sample_weight, [-1])
return control_flow_ops.cond(math_ops.equal(rank_diff, -1),
expand_weights, lambda: sample_weight)
def _maybe_adjust_weights():
return control_flow_ops.cond(math_ops.equal(rank_diff,
1), maybe_squeeze_weights,
_maybe_expand_weights)
# squeeze or expand last dim of `sample_weight` if its rank differs by 1
# from the new rank of `y_pred`.
sample_weight = control_flow_ops.cond(
math_ops.equal(weights_rank_tensor, 0), lambda: sample_weight,
_maybe_adjust_weights)
return y_pred, y_true, sample_weight
def _cond(pred, true_fn, false_fn, name):
if _is_old_cond():
return control_flow_ops.cond(pred, true_fn, false_fn, name=name)
else:
return cond_v2.cond_v2(pred, true_fn, false_fn, name=name)
def __init__(
self, # pylint: disable=super-init-not-called
initial_value=None,
trainable=True,
caching_device=None,
name=None,
dtype=None,
constraint=None,
**unused_kwargs):
"""Creates a variable.
Args:
initial_value: A `Tensor`, or Python object convertible to a `Tensor`,
which is the initial value for the Variable. The initial value must have
a shape specified unless `validate_shape` is set to False. Can also be a
callable with no argument that returns the initial value when called.
(Note that initializer functions from init_ops.py must first be bound
to a shape before being used here.)
trainable: If `True`, GradientTapes automatically watch uses of this
Variable.
caching_device: Optional device string or function describing where the
Variable should be cached for reading. Defaults to the Variable's
device. If not `None`, caches on another device. Typical use is to
cache on the device where the Ops using the Variable reside, to
deduplicate copying through `Switch` and other conditional statements.
name: Optional name for the variable. Defaults to `'Variable'` and gets
uniquified automatically.
dtype: If set, initial_value will be converted to the given type.
If None, either the datatype will be kept (if initial_value is
a Tensor) or float32 will be used (if it is a Python object convertible
to a Tensor).
constraint: An optional projection function to be applied to the variable
after being updated by an `Optimizer` (e.g. used to implement norm
constraints or value constraints for layer weights). The function must
take as input the unprojected Tensor representing the value of the
variable and return the Tensor for the projected value
(which must have the same shape). Constraints are not safe to
use when doing asynchronous distributed training.
Raises:
ValueError: If the initial value is not specified, or does not have a
shape and `validate_shape` is `True`.
RuntimeError: If called outside of a function definition.
"""
if context.executing_eagerly():
raise RuntimeError(
"UnliftedInitializerVariable should not be created "
"outside of functions.")
with ops.init_scope():
if not context.executing_eagerly():
raise RuntimeError(
"UnliftedInitializerVariable does not support legacy graph mode."
)
self._in_graph_mode = False
if initial_value is None:
raise ValueError("initial_value must be specified.")
init_from_fn = callable(initial_value)
if constraint is not None and not callable(constraint):
raise ValueError("The `constraint` argument must be a callable.")
if isinstance(initial_value, checkpointable.CheckpointInitialValue):
self._maybe_initialize_checkpointable()
self._update_uid = initial_value.checkpoint_position.restore_uid
initial_value = initial_value.wrapped_value
self._trainable = trainable
self._save_slice_info = None
self._initial_value = None
self._initializer_op = None
self._is_initialized_op = None
self._graph_element = None
self._cached_value = None
# Store the graph key so optimizers know how to only retrieve variables from
# this graph. Guaranteed to be the same as the eager graph_key.
self._graph_key = ops.get_default_graph()._graph_key # pylint: disable=protected-access
with ops.name_scope(name, "Variable",
[] if init_from_fn else [initial_value]) as name:
# pylint: disable=protected-access
with ops.init_scope():
assert context.executing_eagerly()
shared_name = ops._name_from_scope_name(name)
shared_name = "%s_%d" % (shared_name, ops.uid())
# Use attr_scope and device(None) to simulate the behavior of
# colocate_with when the variable we want to colocate with doesn't
# yet exist.
with ops.name_scope("Initializer"), ops.device(None):
initial_value = ops.convert_to_tensor(
initial_value() if init_from_fn else initial_value,
name="initial_value",
dtype=dtype)
with ops.init_scope():
self._handle = resource_variable_ops.eager_safe_variable_handle(
shape=initial_value.get_shape(),
dtype=initial_value.dtype.base_dtype,
shared_name=shared_name,
name=name,
graph_mode=False)
self._shape = initial_value.shape
self._unique_id = shared_name
self._handle_name = shared_name + ":0"
self._dtype = initial_value.dtype.base_dtype
self._constraint = constraint
assert initial_value is not None
def assign_fn():
with ops.name_scope("Assign") as n, ops.colocate_with(
self._handle):
resource_variable_ops.assign_variable_op(self._handle,
initial_value,
name=n)
# Returning values to keep tf.cond happy.
return ops.convert_to_tensor(1)
def not_assign_fn():
return ops.convert_to_tensor(0)
# Note: this cond is always guaranteed to run because we're inside a defun
# which will insert automatic control dependencies.
control_flow_ops.cond(
resource_variable_ops.var_is_initialized_op(self._handle),
not_assign_fn, assign_fn)
# After the handle has been created, set up a way to clean it up when
# executing eagerly. We'll hold the only reference to the deleter, so that
# when this object is garbage collected the deleter will be too. This
# means ResourceVariables can be part of reference cycles without those
# cycles being uncollectable.
self._handle_deleter = resource_variable_ops.EagerResourceDeleter(
handle=self._handle, handle_device=self._handle.device)
self._cached_shape_as_list = None
def _get_queue_ops_stale(self, var_update_op: ops.Operation,
source_op: ops.Operation, is_chief: bool,
is_trainable: bool) -> List[ops.Operation]:
"""
Get queue operations for staleness synchronous parameter update.
Maintain a list of queues of size equal to <staleness>. At the beginning of each call of this function
(either by the chief worker or other workers), it checks whether each queue is not full. If yes, it pushes
a token to each queue. If not, it does nothing (a no_op).
Then, for the current worker that calls this function, it dequeues a token from its corresponding queue
(indexed by its worker id).
The potential enqueue operations and definite dequeue operation are grouped together, and have to be
finished before the model moves on to the next step.
As at each invocation of this function, a row of empty space in the list of queues will be filled. Thus
<staleness> number of consecutive dequeue operations can be done by a worker without blocking, achieving
stale synchronous parameter update with maximum <staleness> steps difference.
Args:
var_update_op: The op
Returns:
A list of queue operations.
"""
var_op = var_update_op.inputs[UPDATE_OP_VAR_POS].op
var_update_sync_queues = \
[data_flow_ops.FIFOQueue(self._staleness, [dtypes.bool], shapes=None,
name='%s_update_sync_queue_%d' % (var_op.name, i),
shared_name='%s_update_sync_queue_%d' % (var_op.name, i))
for i in range(self.num_workers)]
# Enqueue one token to every queue if all queues are not full.
def _enqueue_row_op():
enqueue_ops = []
for q in var_update_sync_queues:
enqueue_ops.append(q.enqueue(False))
enqueue_a_row_ops = control_flow_ops.group(*enqueue_ops)
return enqueue_a_row_ops
def _no_op():
return gen_control_flow_ops.no_op()
switch_cond = gen_array_ops.identity(True)
for q in var_update_sync_queues:
switch_cond = gen_math_ops.logical_and(
switch_cond,
gen_math_ops.less(q.size(),
gen_array_ops.identity(self._staleness)))
enqueue_a_row_ops = control_flow_ops.cond(switch_cond, _enqueue_row_op,
_no_op)
queue_ops = [enqueue_a_row_ops]
if is_chief:
if is_trainable:
var_update_deps = [
self._var_op_to_accum_apply_op[var_op], source_op
]
else:
var_update_deps = [var_update_op]
with ops.control_dependencies(var_update_deps):
dequeue = var_update_sync_queues[self.worker_id].dequeue()
else:
# wait for execution of var_update_op
if is_trainable:
with ops.control_dependencies(
[self._var_op_to_accum_apply_op[var_op]]):
dequeue = var_update_sync_queues[self.worker_id].dequeue()
else:
dequeue = var_update_sync_queues[self.worker_id].dequeue()
queue_ops.append(dequeue)
return queue_ops
def _MatrixSolveLsGrad(op, grad):
"""Gradients for MatrixSolveLs."""
# TODO(rmlarsen): The implementation could be more efficient:
# a) Output the Cholesky factorization from forward op instead of
# recomputing it here.
# b) Implement a symmetric rank-k update op instead of computing
# x*z + transpose(x*z). This pattern occurs other places in TensorFlow.
def _Overdetermined(op, grad):
"""Gradients for the overdetermined case of MatrixSolveLs.
This is the backprop for the solution to the normal equations of the first
kind:
X = F(A, B) = (A^T * A + lambda * I)^{-1} * A^T * B
which solve the least squares problem
min ||A * X - B||_F^2 + lambda ||X||_F^2.
"""
a = op.inputs[0]
b = op.inputs[1]
x = op.outputs[0]
l2_regularizer = math_ops.cast(op.inputs[2], a.dtype.base_dtype)
# pylint: disable=protected-access
chol = linalg_ops._RegularizedGramianCholesky(
a, l2_regularizer=l2_regularizer, first_kind=True)
# pylint: enable=protected-access
# Temporary z = (A^T * A + lambda * I)^{-1} * grad.
z = linalg_ops.cholesky_solve(chol, grad)
xzt = math_ops.matmul(x, z, adjoint_b=True)
zx_sym = xzt + array_ops.matrix_transpose(xzt)
grad_a = -math_ops.matmul(a, zx_sym) + math_ops.matmul(
b, z, adjoint_b=True)
grad_b = math_ops.matmul(a, z)
return (grad_a, grad_b, None)
def _Underdetermined(op, grad):
"""Gradients for the underdetermined case of MatrixSolveLs.
This is the backprop for the solution to the normal equations of the second
kind:
X = F(A, B) = A * (A*A^T + lambda*I)^{-1} * B
that (for lambda=0) solve the least squares problem
min ||X||_F subject to A*X = B.
"""
a = op.inputs[0]
b = op.inputs[1]
l2_regularizer = math_ops.cast(op.inputs[2], a.dtype.base_dtype)
# pylint: disable=protected-access
chol = linalg_ops._RegularizedGramianCholesky(
a, l2_regularizer=l2_regularizer, first_kind=False)
# pylint: enable=protected-access
grad_b = linalg_ops.cholesky_solve(chol, math_ops.matmul(a, grad))
# Temporary tmp = (A * A^T + lambda * I)^{-1} * B.
tmp = linalg_ops.cholesky_solve(chol, b)
a1 = math_ops.matmul(tmp, a, adjoint_a=True)
a1 = -math_ops.matmul(grad_b, a1)
a2 = grad - math_ops.matmul(a, grad_b, adjoint_a=True)
a2 = math_ops.matmul(tmp, a2, adjoint_b=True)
grad_a = a1 + a2
return (grad_a, grad_b, None)
fast = op.get_attr("fast")
if fast is False:
raise ValueError("Gradient not defined for fast=False")
matrix_shape = op.inputs[0].get_shape()[-2:]
if matrix_shape.is_fully_defined():
if matrix_shape[-2] >= matrix_shape[-1]:
return _Overdetermined(op, grad)
else:
return _Underdetermined(op, grad)
else:
# We have to defer determining the shape to runtime and use
# conditional execution of the appropriate graph.
matrix_shape = array_ops.shape(op.inputs[0])[-2:]
return control_flow_ops.cond(matrix_shape[-2] >= matrix_shape[-1],
lambda: _Overdetermined(op, grad),
lambda: _Underdetermined(op, grad))
def dynamic_simple_hard_distraction_seq2seq(encoder_inputs,
decoder_inputs,
query_inputs,
cell_encoder_fw,
cell_encoder_bw,
distraction_cell,
num_encoder_symbols,
num_decoder_symbols,
embedding_size,
initial_embedding=None,
num_heads=1,
output_projection=None,
feed_previous=False,
embedding_trainable=False,
dtype=None,
scope=None,
initial_state_attention=False):
"""Embedding sequence-to-sequence model with attention.
This model first embeds encoder_inputs by a newly created embedding (of shape
[num_encoder_symbols x input_size]). Then it runs an RNN to encode
embedded encoder_inputs into a state vector. It keeps the outputs of this
RNN at every step to use for attention later. Next, it embeds decoder_inputs
by another newly created embedding (of shape [num_decoder_symbols x
input_size]). Then it runs attention decoder, initialized with the last
encoder state, on embedded decoder_inputs and attending to encoder outputs.
Args:
encoder_inputs: A list of 1D int32 Tensors of shape [batch_size].
decoder_inputs: A list of 1D int32 Tensors of shape [batch_size].
cell: rnn_cell.RNNCell defining the cell function and size.
num_encoder_symbols: Integer; number of symbols on the encoder side.
num_decoder_symbols: Integer; number of symbols on the decoder side.
embedding_size: Integer, the length of the embedding vector for each symbol.
num_heads: Number of attention heads that read from attention_states.
output_projection: None or a pair (W, B) of output projection weights and
biases; W has shape [output_size x num_decoder_symbols] and B has
shape [num_decoder_symbols]; if provided and feed_previous=True, each
fed previous output will first be multiplied by W and added B.
feed_previous: Boolean or scalar Boolean Tensor; if True, only the first
of decoder_inputs will be used (the "GO" symbol), and all other decoder
inputs will be taken from previous outputs (as in embedding_rnn_decoder).
If False, decoder_inputs are used as given (the standard decoder case).
dtype: The dtype of the initial RNN state (default: tf.float32).
scope: VariableScope for the created subgraph; defaults to
"embedding_attention_seq2seq".
initial_state_attention: If False (default), initial attentions are zero.
If True, initialize the attentions from the initial state and attention
states.
Returns:
A tuple of the form (outputs, state), where:
outputs: A list of the same length as decoder_inputs of 2D Tensors with
shape [batch_size x num_decoder_symbols] containing the generated
outputs.
state: The state of each decoder cell at the final time-step.
It is a 2D Tensor of shape [batch_size x cell.state_size].
"""
with variable_scope.variable_scope(
scope or "dynamic_simple_hard_distraction_seq2seq",
dtype=dtype) as scope:
dtype = scope.dtype
# Encoder.
"""encoder_cell = rnn_cell.EmbeddingWrapper(
cell, embedding_classes=num_encoder_symbols,
embedding_size=embedding_size)
"""
if initial_embedding is not None:
embedding = variable_scope.get_variable(
'embedding',
initializer=initial_embedding,
trainable=embedding_trainable)
else:
embedding = variable_scope.get_variable(
'embedding', [num_encoder_symbols, embedding_size],
trainable=embedding_trainable)
embedded_inputs = embedding_ops.embedding_lookup(
embedding, encoder_inputs)
embedded_inputs = array_ops.unpack(embedded_inputs)
query_embeddings = embedding_ops.embedding_lookup(
embedding, query_inputs)
query_embeddings = array_ops.unpack(query_embeddings)
print("Embedded Inputs length:", len(embedded_inputs))
print("Shape in embedded inputs:", embedded_inputs[0].get_shape())
with variable_scope.variable_scope("Encoder_Cell"):
encoder_outputs, encoder_state_fw, encoder_state_bw = rnn.bidirectional_rnn(
cell_encoder_fw, cell_encoder_bw, embedded_inputs, dtype=dtype)
with variable_scope.variable_scope("Query_Cell"):
query_outputs, query_state_fw, query_state_bw = rnn.bidirectional_rnn(
cell_encoder_fw,
cell_encoder_bw,
query_embeddings,
dtype=dtype)
# First calculate a concatenation of encoder outputs to put attention on.
encoder_state = array_ops.concat(1,
[encoder_state_fw, encoder_state_bw])
query_state = array_ops.concat(1, [query_state_fw, query_state_bw])
top_states_encoder = [
array_ops.reshape(e, [-1, 1, 2 * cell_encoder_fw.output_size])
for e in encoder_outputs
]
attention_states_encoder = array_ops.concat(1, top_states_encoder)
top_states_query = [
array_ops.reshape(e, [-1, 1, 2 * cell_encoder_fw.output_size])
for e in query_outputs
]
attention_states_query = array_ops.concat(1, top_states_query)
# Decoder.
output_size = None
if output_projection is None:
cell_encoder_fw = rnn_cell.OutputProjectionWrapper(
cell_encoder_fw, num_decoder_symbols)
output_size = num_decoder_symbols
if isinstance(feed_previous, bool):
return dynamic_simple_hard_distraction_decoder_wrapper(
decoder_inputs,
initial_state=encoder_state,
attention_state=attention_states_encoder,
attention_states_query=attention_states_query,
cell_encoder=cell_encoder_fw,
num_symbols=num_decoder_symbols,
embedding_size=embedding_size,
distract_initial_state=encoder_state,
num_heads=num_heads,
output_size=output_size,
output_projection=output_projection,
feed_previous=feed_previous,
embedding_scope=scope,
initial_state_attention=initial_state_attention)
# If feed_previous is a Tensor, we construct 2 graphs and use cond.
def decoder(feed_previous_bool):
reuse = None if feed_previous_bool else True
with variable_scope.variable_scope(
variable_scope.get_variable_scope(), reuse=reuse) as scope:
outputs, state = dynamic_simple_hard_distraction_decoder_wrapper(
decoder_inputs,
initial_state=encoder_state,
attention_states=attention_states_encoder,
attention_states_query=attention_states_query,
cell_encoder=cell_encoder_fw,
num_symbols=num_decoder_symbols,
embedding_size=embedding_size,
distract_initial_state=encoder_state,
distraction_cell=distraction_cell,
num_heads=num_heads,
output_size=output_size,
output_projection=output_projection,
feed_previous=feed_previous_bool,
embedding_scope=scope,
update_embedding_for_previous=False,
initial_state_attention=initial_state_attention)
state_list = [state]
if nest.is_sequence(state):
state_list = nest.flatten(state)
return outputs + state_list
outputs_and_state = control_flow_ops.cond(feed_previous,
lambda: decoder(True),
lambda: decoder(False))
outputs_len = len(
decoder_inputs) # Outputs length same as decoder inputs.
state_list = outputs_and_state[outputs_len:]
state = state_list[0]
if nest.is_sequence(encoder_state):
state = nest.pack_sequence_as(structure=encoder_state,
flat_sequence=state_list)
return outputs_and_state[:outputs_len], state
def _pfor_impl(loop_fn,
iters,
fallback_to_while_loop,
parallel_iterations=None,
pfor_config=None):
"""Implementation of pfor."""
assert not context.executing_eagerly()
loop_fn_has_config = _loop_fn_has_config(loop_fn)
existing_ops = set(ops.get_default_graph().get_operations())
iters_value = tensor_util.constant_value(iters)
# Run the loop body
with ops.name_scope("loop_body"):
loop_var = array_ops.placeholder_with_default(0, shape=[])
if loop_fn_has_config:
if pfor_config is None:
pfor_config = PForConfig()
pfor_config._set_iters(iters) # pylint: disable=protected-access
loop_fn_outputs = loop_fn(loop_var, **{PFOR_CONFIG_ARG: pfor_config})
else:
assert pfor_config is None
loop_fn_outputs = loop_fn(loop_var)
loop_fn_output_tensors = nest.map_structure(_composite_to_tensors,
loop_fn_outputs)
# Convert outputs to Tensor if needed.
tmp_loop_fn_outputs = []
for loop_fn_output in nest.flatten(loop_fn_output_tensors):
if (loop_fn_output is not None and not isinstance(
loop_fn_output,
(ops.Operation, ops.Tensor, sparse_tensor.SparseTensor))):
if isinstance(loop_fn_output, indexed_slices.IndexedSlices):
logging.warn("Converting %s to a dense representation may make it slow."
" Alternatively, output the indices and values of the"
" IndexedSlices separately, and handle the vectorized"
" outputs directly." % loop_fn_output)
loop_fn_output = ops.convert_to_tensor(loop_fn_output)
else:
loop_fn_output = ops.convert_to_tensor(loop_fn_output)
tmp_loop_fn_outputs.append(loop_fn_output)
loop_fn_output_tensors = nest.pack_sequence_as(loop_fn_output_tensors,
tmp_loop_fn_outputs)
new_ops = set(ops.get_default_graph().get_operations()) - existing_ops
iters = ops.convert_to_tensor(iters)
if parallel_iterations is not None:
if parallel_iterations < 1:
raise ValueError("parallel_iterations must be None or a positive integer")
if parallel_iterations == 1:
raise ValueError("Found parallel_iterations == 1. Use for_loop instead.")
if iters_value is not None and iters_value < parallel_iterations:
parallel_iterations = None
if parallel_iterations is None:
with ops.name_scope("pfor"):
converter = PFor(loop_var, iters, new_ops,
fallback_to_while_loop=fallback_to_while_loop,
pfor_config=pfor_config)
flattened_output_tensors = []
for loop_fn_output in nest.flatten(loop_fn_output_tensors):
output = converter.convert(loop_fn_output)
flattened_output_tensors.append(output)
else:
if pfor_config is not None and pfor_config._has_reductions(): # pylint: disable=protected-access
raise ValueError("Setting parallel_iterations currently unsupported if"
" reductions across iterations are performed.")
num_tiled_iterations = iters // parallel_iterations
num_remaining_iterations = iters % parallel_iterations
# TODO(agarwal): Avoid calling loop_fn twice. Generate the loop body inside
# a tf.function and extract the graph from there to vectorize it.
with ops.name_scope("pfor_untiled"):
converter = PFor(loop_var, num_remaining_iterations, new_ops,
fallback_to_while_loop=fallback_to_while_loop,
pfor_config=pfor_config)
remaining_output_tensors = []
flattened_output_tensors = nest.flatten(loop_fn_output_tensors)
for loop_fn_output in flattened_output_tensors:
output = converter.convert(loop_fn_output)
remaining_output_tensors.append(output)
with ops.name_scope("pfor_tiled"):
loop_fn_dtypes = [ops.convert_to_tensor(x).dtype
for x in flattened_output_tensors]
def tiled_loop_body(j):
offset = j * parallel_iterations + num_remaining_iterations
def tiled_loop_fn(i, pfor_config=None):
if loop_fn_has_config:
loop_fn_outputs = loop_fn(i + offset, pfor_config=pfor_config)
else:
loop_fn_outputs = loop_fn(i + offset)
return nest.flatten(
# Stacking across iterations requires explicit Tensors.
nest.map_structure(_composite_to_tensors, loop_fn_outputs))
return _pfor_impl(
tiled_loop_fn,
parallel_iterations,
fallback_to_while_loop=fallback_to_while_loop,
pfor_config=pfor_config)
tiled_output_tensors = for_loop(
tiled_loop_body, loop_fn_dtypes,
num_tiled_iterations, parallel_iterations=1)
tiled_output_tensors = [
_flatten_first_two_dims(y) for y in tiled_output_tensors]
with ops.name_scope("pfor"):
if iters_value is None or iters_value % parallel_iterations:
output_tensors = control_flow_ops.cond(
math_ops.equal(num_remaining_iterations, 0),
lambda: tiled_output_tensors,
lambda: [array_ops.concat([x, y], axis=0) # pylint: disable=g-long-lambda
for x, y in zip(remaining_output_tensors,
tiled_output_tensors)])
else:
output_tensors = tiled_output_tensors
flattened_output_tensors = nest.flatten(output_tensors)
for output, original_output in zip(flattened_output_tensors,
nest.flatten(loop_fn_output_tensors)):
# Restore any shape information lost from tiling.
# TODO(b/174254748): this may not be correct for stacked `variant`s.
output.set_shape(
tensor_shape.TensorShape([iters_value]).concatenate(
original_output.shape))
return nest.map_structure_up_to(
loop_fn_outputs,
functools.partial(_composite_from_tensors, batch_size=iters_value),
nest.pack_sequence_as(loop_fn_output_tensors,
flattened_output_tensors),
loop_fn_outputs)
def decoder(self,
decoder_inputs,
utterance_output,
attention_states,
num_heads,
initial_state_attention=False):
#decoder_inputs is of dimension max_len * batch_size
#utterance_output is of dimension cell_size * batch_size
with tf.variable_scope(self.dec_scope_text) as scope:
batch_size = array_ops.shape(self.encoder_text_inputs[0][0])[0]
attn_length = attention_states.get_shape()[1].value
attn_size = attention_states.get_shape()[2].value
hidden = array_ops.reshape(attention_states,
[-1, attn_length, 1, attn_size])
hidden_features = []
v = []
attention_vec_size = attn_size
for a in xrange(num_heads):
k = variable_scope.get_variable(
"AttnW_%d" % a, [1, 1, attn_size, attention_vec_size])
hidden_features.append(
nn_ops.conv2d(hidden, k, [1, 1, 1, 1], "SAME"))
v.append(
variable_scope.get_variable("AttnV_%d" % a,
[attention_vec_size]))
init_state = self.dec_cells_text.zero_state(
self.batch_size, tf.float32)
max_val = np.sqrt(6. / (self.decoder_words + self.cell_size))
weights = tf.get_variable(
"dec_weights", [self.cell_size, self.decoder_words],
initializer=tf.random_uniform_initializer(
-1. * max_val, max_val)
) #For projecting decoder output which is of self.batch_size*self.cell_size to self.batch_size*self.vocab_size.
biases = tf.get_variable("dec_biases", [self.decoder_words],
initializer=tf.constant_initializer(0.0))
def feed_previous_decode(feed_previous_bool):
dec_embed, loop_fn = seq2seq.get_decoder_embedding(
decoder_inputs,
self.decoder_words,
self.text_embedding_size,
output_projection=(weights, biases),
feed_previous=feed_previous_bool)
#dec_embed is of dimension max_len * batch_size * self.text_embedding_size
#utterance_output is of dimension batch_size * cell_size
concatenated_input = self.get_dec_concat_ip(
dec_embed, utterance_output)
dec_output, _ = self.decode(
concatenated_input, loop_fn, self.dec_cells_text,
init_state, utterance_output, scope, attention_states,
batch_size, attn_size, attn_length, num_heads, v, hidden,
hidden_features, initial_state_attention)
#dec_output is a max_len sized list of tensors of dimension batch_size * cell_size
return dec_output
dec_output = control_flow_ops.cond(
self.feed_previous, lambda: feed_previous_decode(True),
lambda: feed_previous_decode(False))
output_projection = (weights, biases)
#weights is a tensor of dimension cell_size * decoder_words
#bias is a tensor of dimension decoder_words
for i in range(len(dec_output)):
dec_output[i] = tf.matmul(
dec_output[i], output_projection[0]) + output_projection[1]
if self.output_activation is not None:
dec_output[i] = self.output_activation(dec_output[i])
#before the linear transformation, dec_output[i] is a tensor of dimension batch_size * cell_size
#weights is a tensor of dimension cell_size * decoder_words
#after the linear transformation, dec_output[i] is a tensor of dimension batch_size * decoder_words
#dec_output is a max_len sized list of 2D tensors of dimension batch_size * decoder_words
return dec_output
def _pfor_impl(loop_fn, iters, parallel_iterations=None, pfor_config=None):
"""Implementation of pfor."""
loop_fn_has_config = _loop_fn_has_config(loop_fn)
existing_ops = set(ops.get_default_graph().get_operations())
# Run the loop body
with ops.name_scope("loop_body"):
loop_var = array_ops.placeholder(dtypes.int32, shape=[])
if loop_fn_has_config:
if pfor_config is None:
pfor_config = PForConfig()
pfor_config._set_iters(iters) # pylint: disable=protected-access
loop_fn_outputs = loop_fn(loop_var,
**{PFOR_CONFIG_ARG: pfor_config})
else:
assert pfor_config is None
loop_fn_outputs = loop_fn(loop_var)
# Convert outputs to Tensor if needed.
tmp_loop_fn_outputs = []
for loop_fn_output in nest.flatten(loop_fn_outputs):
if (loop_fn_output is not None and not isinstance(
loop_fn_output,
(ops.Operation, ops.Tensor, sparse_tensor.SparseTensor))):
if isinstance(loop_fn_output, indexed_slices.IndexedSlices):
logging.warn(
"Converting %s to a dense representation may make it slow."
" Alternatively, output the indices and values of the"
" IndexedSlices separately, and handle the vectorized"
" outputs directly." % loop_fn_output)
loop_fn_output = ops.convert_to_tensor(loop_fn_output)
tmp_loop_fn_outputs.append(loop_fn_output)
loop_fn_outputs = nest.pack_sequence_as(loop_fn_outputs,
tmp_loop_fn_outputs)
new_ops = set(ops.get_default_graph().get_operations()) - existing_ops
iters = ops.convert_to_tensor(iters)
if parallel_iterations is not None:
if parallel_iterations < 1:
raise ValueError(
"parallel_iterations must be None or a positive integer")
if parallel_iterations == 1:
raise ValueError(
"Found parallel_iterations == 1. Use for_loop instead.")
iters_value = tensor_util.constant_value(iters)
if iters_value is not None and iters_value < parallel_iterations:
parallel_iterations = None
if parallel_iterations is None:
with ops.name_scope("pfor"):
converter = PFor(loop_var, iters, new_ops, pfor_config=pfor_config)
outputs = []
for loop_fn_output in nest.flatten(loop_fn_outputs):
outputs.append(converter.convert(loop_fn_output))
return nest.pack_sequence_as(loop_fn_outputs, outputs)
else:
if pfor_config is not None and pfor_config._has_reductions(): # pylint: disable=protected-access
raise ValueError(
"Setting parallel_iterations currently unsupported if"
" reductions across iterations are performed.")
num_tiled_iterations = iters // parallel_iterations
num_remaining_iterations = iters % parallel_iterations
# TODO(agarwal): Avoid calling loop_fn twice. Generate the loop body inside
# a tf.function and extract the graph from there to vectorize it.
with ops.name_scope("pfor_untiled"):
converter = PFor(loop_var,
num_remaining_iterations,
new_ops,
pfor_config=pfor_config)
remaining_outputs = []
flattened_loop_fn_outputs = nest.flatten(loop_fn_outputs)
for loop_fn_output in flattened_loop_fn_outputs:
remaining_outputs.append(converter.convert(loop_fn_output))
with ops.name_scope("pfor_tiled"):
loop_fn_dtypes = [
ops.convert_to_tensor(x).dtype
for x in flattened_loop_fn_outputs
]
def tiled_loop_body(j):
offset = j * parallel_iterations + num_remaining_iterations
def tiled_loop_fn(i, pfor_config=None):
if loop_fn_has_config:
return nest.flatten(
loop_fn(i + offset, pfor_config=pfor_config))
else:
return nest.flatten(loop_fn(i + offset))
return _pfor_impl(tiled_loop_fn,
parallel_iterations,
pfor_config=pfor_config)
tiled_outputs = for_loop(tiled_loop_body,
loop_fn_dtypes,
num_tiled_iterations,
parallel_iterations=1)
tiled_outputs = [_flatten_first_two_dims(y) for y in tiled_outputs]
with ops.name_scope("pfor"):
iters_value = tensor_util.constant_value(iters)
if iters_value is None or iters_value % parallel_iterations:
outputs = control_flow_ops.cond(
math_ops.equal(num_remaining_iterations, 0),
lambda: tiled_outputs, lambda: [
array_ops.concat([x, y], axis=0)
for x, y in zip(remaining_outputs, tiled_outputs)
])
else:
outputs = tiled_outputs
return nest.pack_sequence_as(loop_fn_outputs,
nest.flatten(outputs))
def _testScopedExport(self, test_dir, exported_filenames):
graph = ops.Graph()
with graph.as_default():
# Creates an inference graph.
# Hidden 1
colocate_constraint = constant_op.constant(1.2, name="constraint")
images = constant_op.constant(1.2,
dtypes.float32,
shape=[100, 28],
name="images")
with ops.name_scope("hidden1"):
with graph.colocate_with(colocate_constraint.op):
weights1 = variables.Variable(random_ops.truncated_normal(
[28, 128], stddev=1.0 / math.sqrt(float(28))),
name="weights")
# The use of control_flow_ops.cond here is purely for adding test
# coverage the save and restore of control flow context (which doesn't
# make any sense here from a machine learning perspective). The typical
# biases is a simple Variable without the conditions.
biases1 = variables.Variable(control_flow_ops.cond(
math_ops.less(random.random(),
0.5), lambda: array_ops.ones([128]),
lambda: array_ops.zeros([128])),
name="biases")
hidden1 = nn_ops.relu(
math_ops.matmul(images, weights1) + biases1)
# Hidden 2
with ops.name_scope("hidden2"):
weights2 = variables.Variable(random_ops.truncated_normal(
[128, 32], stddev=1.0 / math.sqrt(float(128))),
name="weights")
# The use of control_flow_ops.while_loop here is purely for adding test
# coverage the save and restore of control flow context (which doesn't
# make any sense here from a machine learning perspective). The typical
# biases is a simple Variable without the conditions.
def loop_cond(it, _):
return it < 2
def loop_body(it, biases2):
biases2 += constant_op.constant(0.1, shape=[32])
return it + 1, biases2
_, biases2 = control_flow_ops.while_loop(
loop_cond, loop_body, [
constant_op.constant(0),
variables.Variable(array_ops.zeros([32]),
name="biases")
])
hidden2 = nn_ops.relu(
math_ops.matmul(hidden1, weights2) + biases2)
# Linear
with ops.name_scope("softmax_linear"):
weights3 = variables.Variable(random_ops.truncated_normal(
[32, 10], stddev=1.0 / math.sqrt(float(32))),
name="weights")
biases3 = variables.Variable(array_ops.zeros([10]),
name="biases")
logits = math_ops.matmul(hidden2, weights3) + biases3
ops.add_to_collection("logits", logits)
# Exports each sub-graph.
# Exports the first one with unbound_inputs_col_name set to default.
orig_meta_graph1, var_list = meta_graph.export_scoped_meta_graph(
filename=os.path.join(test_dir, exported_filenames[0]),
graph=ops.get_default_graph(),
export_scope="hidden1")
self.assertEqual(["biases:0", "weights:0"],
sorted(var_list.keys()))
var_names = [v.name for _, v in var_list.items()]
self.assertEqual(["hidden1/biases:0", "hidden1/weights:0"],
sorted(var_names))
# Exports the rest with no unbound_inputs_col_name.
orig_meta_graph2, _ = meta_graph.export_scoped_meta_graph(
filename=os.path.join(test_dir, exported_filenames[1]),
graph=ops.get_default_graph(),
export_scope="hidden2",
unbound_inputs_col_name=None)
orig_meta_graph3, _ = meta_graph.export_scoped_meta_graph(
filename=os.path.join(test_dir, exported_filenames[2]),
graph=ops.get_default_graph(),
export_scope="softmax_linear",
unbound_inputs_col_name=None)
return [orig_meta_graph1, orig_meta_graph2, orig_meta_graph3]
def call(self, inputs):
inputs = self._preprocess(inputs)
# If we're not doing any output processing, return right away.
if self._output_mode is None:
return inputs
# The table lookup ops don't natively support ragged tensors, so if we have
# a RT we need to use map_flat_values to look up every element.
if ragged_tensor.is_ragged(inputs):
indexed_data = ragged_functional_ops.map_flat_values(
self._table.lookup, inputs)
else:
indexed_data = self._table.lookup(inputs)
if self._output_mode == INT:
# Once we have the dense tensor, we can return it if we weren't given a
# fixed output sequence length. If we were, though, we have to dynamically
# choose whether to pad or trim it based on each tensor.
# We need to convert to dense if we have a ragged tensor.
if ragged_tensor.is_ragged(indexed_data):
dense_data = indexed_data.to_tensor(default_value=0)
else:
dense_data = indexed_data
if self._output_sequence_length is None:
return dense_data
else:
sequence_len = K.shape(dense_data)[1]
pad_amt = self._output_sequence_length - sequence_len
pad_fn = lambda: array_ops.pad(dense_data, [[0, 0],
[0, pad_amt]])
slice_fn = lambda: dense_data[:, :self._output_sequence_length]
return control_flow_ops.cond(
sequence_len < self._output_sequence_length,
true_fn=pad_fn,
false_fn=slice_fn)
out_depth = self._max_tokens if self._pad_to_max else math_ops.cast(
(self._get_table_size() + self._reserved_values), dtypes.int32)
if self._output_mode == BINARY:
bool_one_hot_data = array_ops.one_hot(indexed_data,
depth=out_depth,
on_value=True,
off_value=False)
reduced_bool_data = math_ops.reduce_any(bool_one_hot_data, axis=1)
binary_data = math_ops.cast(reduced_bool_data, dtypes.int64)
return binary_data
one_hot_data = array_ops.one_hot(indexed_data, depth=out_depth)
counts = math_ops.reduce_sum(one_hot_data, axis=1)
if self._output_mode == COUNT:
return math_ops.cast(counts, dtypes.int64)
tf_idf_data = math_ops.multiply(counts, self._tf_idf_weights)
if self._output_mode == TFIDF:
return tf_idf_data
# We can only get here if we didn't recognize the passed mode.
raise ValueError("Unknown output mode %s" % self._output_mode)
def _rnn_step(
time, sequence_length, min_sequence_length, max_sequence_length,
zero_output, state, call_cell, state_size, skip_conditionals=False):
"""Calculate one step of a dynamic RNN minibatch.
Returns an (output, state) pair conditioned on the sequence_lengths.
When skip_conditionals=False, the pseudocode is something like:
if t >= max_sequence_length:
return (zero_output, state)
if t < min_sequence_length:
return call_cell()
# Selectively output zeros or output, old state or new state depending
# on if we've finished calculating each row.
new_output, new_state = call_cell()
final_output = np.vstack([
zero_output if time >= sequence_lengths[r] else new_output_r
for r, new_output_r in enumerate(new_output)
])
final_state = np.vstack([
state[r] if time >= sequence_lengths[r] else new_state_r
for r, new_state_r in enumerate(new_state)
])
return (final_output, final_state)
Args:
time: Python int, the current time step
sequence_length: int32 `Tensor` vector of size [batch_size]
min_sequence_length: int32 `Tensor` scalar, min of sequence_length
max_sequence_length: int32 `Tensor` scalar, max of sequence_length
zero_output: `Tensor` vector of shape [output_size]
state: Either a single `Tensor` matrix of shape `[batch_size, state_size]`,
or a list/tuple of such tensors.
call_cell: lambda returning tuple of (new_output, new_state) where
new_output is a `Tensor` matrix of shape `[batch_size, output_size]`.
new_state is a `Tensor` matrix of shape `[batch_size, state_size]`.
state_size: The `cell.state_size` associated with the state.
skip_conditionals: Python bool, whether to skip using the conditional
calculations. This is useful for `dynamic_rnn`, where the input tensor
matches `max_sequence_length`, and using conditionals just slows
everything down.
Returns:
A tuple of (`final_output`, `final_state`) as given by the pseudocode above:
final_output is a `Tensor` matrix of shape [batch_size, output_size]
final_state is either a single `Tensor` matrix, or a tuple of such
matrices (matching length and shapes of input `state`).
Raises:
ValueError: If the cell returns a state tuple whose length does not match
that returned by `state_size`.
"""
# Convert state to a list for ease of use
flat_state = nest.flatten(state)
flat_zero_output = nest.flatten(zero_output)
def _copy_one_through(output, new_output):
copy_cond = (time >= sequence_length)
return _on_device(
lambda: array_ops.where(copy_cond, output, new_output),
device=new_output.op.device)
def _copy_some_through(flat_new_output, flat_new_state):
# Use broadcasting select to determine which values should get
# the previous state & zero output, and which values should get
# a calculated state & output.
flat_new_output = [
_copy_one_through(zero_output, new_output)
for zero_output, new_output in zip(flat_zero_output, flat_new_output)]
flat_new_state = [
_copy_one_through(state, new_state)
for state, new_state in zip(flat_state, flat_new_state)]
return flat_new_output + flat_new_state
def _maybe_copy_some_through():
"""Run RNN step. Pass through either no or some past state."""
new_output, new_state = call_cell()
nest.assert_same_structure(state, new_state)
flat_new_state = nest.flatten(new_state)
flat_new_output = nest.flatten(new_output)
return control_flow_ops.cond(
# if t < min_seq_len: calculate and return everything
time < min_sequence_length, lambda: flat_new_output + flat_new_state,
# else copy some of it through
lambda: _copy_some_through(flat_new_output, flat_new_state))
# TODO(ebrevdo): skipping these conditionals may cause a slowdown,
# but benefits from removing cond() and its gradient. We should
# profile with and without this switch here.
if skip_conditionals:
# Instead of using conditionals, perform the selective copy at all time
# steps. This is faster when max_seq_len is equal to the number of unrolls
# (which is typical for dynamic_rnn).
new_output, new_state = call_cell()
nest.assert_same_structure(state, new_state)
new_state = nest.flatten(new_state)
new_output = nest.flatten(new_output)
final_output_and_state = _copy_some_through(new_output, new_state)
else:
empty_update = lambda: flat_zero_output + flat_state
final_output_and_state = control_flow_ops.cond(
# if t >= max_seq_len: copy all state through, output zeros
time >= max_sequence_length, empty_update,
# otherwise calculation is required: copy some or all of it through
_maybe_copy_some_through)
if len(final_output_and_state) != len(flat_zero_output) + len(flat_state):
raise ValueError("Internal error: state and output were not concatenated "
"correctly.")
final_output = final_output_and_state[:len(flat_zero_output)]
final_state = final_output_and_state[len(flat_zero_output):]
for output, flat_output in zip(final_output, flat_zero_output):
output.set_shape(flat_output.get_shape())
for substate, flat_substate in zip(final_state, flat_state):
substate.set_shape(flat_substate.get_shape())
final_output = nest.pack_sequence_as(
structure=zero_output, flat_sequence=final_output)
final_state = nest.pack_sequence_as(
structure=state, flat_sequence=final_state)
return final_output, final_state
def __init__(self,
source_vocab_size,
target_vocab_size,
buckets,
dummy_set,
size,
num_layers,
max_gradient_norm,
batch_size,
learning_rate,
learning_rate_decay_factor,
fixed_rate,
weibo_rate,
qa_rate,
use_lstm=False,
num_samples=512,
forward_only=False,
scope_name='seq2seq',
dtype=tf.float32):
self.scope_name = scope_name
with tf.variable_scope(self.scope_name):
self.source_vocab_size = source_vocab_size
self.target_vocab_size = target_vocab_size
self.buckets = buckets
self.batch_size = batch_size
self.fixed_rate = fixed_rate
self.weibo_rate = weibo_rate
self.qa_rate = qa_rate
self.learning_rate = tf.Variable(float(learning_rate),
trainable=False,
dtype=dtype)
self.learning_rate_decay_op = self.learning_rate.assign(
self.learning_rate * learning_rate_decay_factor)
self.learning_rate_set_op = self.learning_rate.assign(
self.learning_rate * 20)
self.global_step = tf.Variable(0, trainable=False)
self.dummy_dialogs = dummy_set
# If we use sampled softmax, we need an output projection.
output_projection = None
softmax_loss_function = None
# Sampled softmax only makes sense if we sample less than vocabulary size.
if num_samples > 0 and num_samples < self.target_vocab_size:
w_t = tf.get_variable("proj_w", [self.target_vocab_size, size],
dtype=dtype)
w = tf.transpose(w_t)
b = tf.get_variable("proj_b", [self.target_vocab_size],
dtype=dtype)
output_projection = (w, b)
def sampled_loss(inputs, labels):
labels = tf.reshape(labels, [-1, 1])
# We need to compute the sampled_softmax_loss using 32bit floats to
# avoid numerical instabilities.
local_w_t = tf.cast(w_t, tf.float32)
local_b = tf.cast(b, tf.float32)
local_inputs = tf.cast(inputs, tf.float32)
return tf.cast(
tf.nn.sampled_softmax_loss(local_w_t, local_b,
local_inputs, labels,
num_samples,
self.target_vocab_size),
dtype)
softmax_loss_function = sampled_loss
# Create the internal multi-layer cell for our RNN.
single_cell = tf.nn.rnn_cell.GRUCell(size)
if use_lstm:
single_cell = tf.nn.rnn_cell.BasicLSTMCell(size)
cell = single_cell
if num_layers > 1:
cell = tf.nn.rnn_cell.MultiRNNCell([single_cell] * num_layers)
# The seq2seq function: we use embedding for the input and attention.
def seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
return rl_seq2seq.embedding_attention_seq2seq(
encoder_inputs,
decoder_inputs,
cell,
num_encoder_symbols=source_vocab_size,
num_decoder_symbols=target_vocab_size,
embedding_size=size,
output_projection=output_projection,
feed_previous=do_decode,
dtype=dtype)
# Feeds for inputs.
self.encoder_inputs = []
self.decoder_inputs = []
self.target_weights = []
for i in xrange(buckets[-1][0]): # Last bucket is the biggest one.
self.encoder_inputs.append(
tf.placeholder(tf.int32,
shape=[None],
name="encoder{0}".format(i)))
for i in xrange(buckets[-1][1] + 1):
self.decoder_inputs.append(
tf.placeholder(tf.int32,
shape=[None],
name="decoder{0}".format(i)))
self.target_weights.append(
tf.placeholder(dtype,
shape=[None],
name="weight{0}".format(i)))
# Our targets are decoder inputs shifted by one.
targets = [
self.decoder_inputs[i + 1]
for i in xrange(len(self.decoder_inputs) - 1)
]
# for reinforcement learning
self.force_dec_input = tf.placeholder(tf.bool,
name="force_dec_input")
self.en_output_proj = tf.placeholder(tf.bool,
name="en_output_proj")
# Training outputs and losses.
#if forward_only:
self.outputs, self.losses, self.encoder_state = rl_seq2seq.model_with_buckets(
self.encoder_inputs,
self.decoder_inputs,
targets,
self.target_weights,
buckets,
lambda x, y: seq2seq_f(
x, y, tf.select(self.force_dec_input, False, True)),
softmax_loss_function=softmax_loss_function,
per_example_loss=True)
# If we use output projection, we need to project outputs for decoding.
#if output_projection is not None:
for b in xrange(len(buckets)):
self.outputs[b] = [
control_flow_ops.cond(
self.en_output_proj,
lambda: tf.matmul(output, output_projection[
0]) + output_projection[1], lambda: output)
for output in self.outputs[b]
]
# Gradients and SGD update operation for training the model.
self.tvars = tf.trainable_variables()
#if not forward_only:
self.gradient_norms = []
self.updates = []
self.advantage = [
tf.placeholder(tf.float32,
shape=[None],
name="advantage_%i" % i)
for i in range(len(buckets))
]
opt = tf.train.GradientDescentOptimizer(self.learning_rate)
#opt = tf.train.AdamOptimizer()
for b in xrange(len(buckets)):
self.adjusted_losses = tf.reduce_sum(
tf.matmul(tf.reshape(self.losses[b], [1, -1]),
tf.reshape(self.advantage[b], [-1, 1])))
gradients = tf.gradients(self.adjusted_losses, self.tvars)
clipped_gradients, norm = tf.clip_by_global_norm(
gradients, max_gradient_norm)
self.gradient_norms.append(norm)
self.updates.append(
opt.apply_gradients(zip(clipped_gradients, self.tvars),
global_step=self.global_step))
# self.saver = tf.train.Saver(tf.all_variables())
all_variables = [
k for k in tf.global_variables()
if k.name.startswith(self.scope_name)
]
self.saver = tf.train.Saver(all_variables)
def body(list_, m):
list_ = control_flow_ops.cond(
math_ops.equal(list_ops.tensor_list_length(list_), 0),
lambda: list_ops.empty_tensor_list(m.shape, m.dtype), lambda: list_)
list_ = list_ops.tensor_list_push_back(list_, m)
return list_, m
def _maybe_adjust_weights():
return control_flow_ops.cond(math_ops.equal(rank_diff,
1), maybe_squeeze_weights,
_maybe_expand_weights)
def remove_squeezable_dimensions(labels,
predictions,
expected_rank_diff=0,
name=None):
"""Squeeze last dim if ranks differ from expected by exactly 1.
In the common case where we expect shapes to match, `expected_rank_diff`
defaults to 0, and we squeeze the last dimension of the larger rank if they
differ by 1.
But, for example, if `labels` contains class IDs and `predictions` contains 1
probability per class, we expect `predictions` to have 1 more dimension than
`labels`, so `expected_rank_diff` would be 1. In this case, we'd squeeze
`labels` if `rank(predictions) - rank(labels) == 0`, and
`predictions` if `rank(predictions) - rank(labels) == 2`.
This will use static shape if available. Otherwise, it will add graph
operations, which could result in a performance hit.
Args:
labels: Label values, a `Tensor` whose dimensions match `predictions`.
predictions: Predicted values, a `Tensor` of arbitrary dimensions.
expected_rank_diff: Expected result of `rank(predictions) - rank(labels)`.
name: Name of the op.
Returns:
Tuple of `labels` and `predictions`, possibly with last dim squeezed.
"""
with K.name_scope(name or 'remove_squeezable_dimensions'):
if not isinstance(predictions, ragged_tensor.RaggedTensor):
predictions = ops.convert_to_tensor_v2_with_dispatch(predictions)
if not isinstance(labels, ragged_tensor.RaggedTensor):
labels = ops.convert_to_tensor_v2_with_dispatch(labels)
predictions_shape = predictions.shape
predictions_rank = predictions_shape.ndims
labels_shape = labels.shape
labels_rank = labels_shape.ndims
if (labels_rank is not None) and (predictions_rank is not None):
# Use static rank.
rank_diff = predictions_rank - labels_rank
if (rank_diff == expected_rank_diff + 1
and predictions_shape.dims[-1].is_compatible_with(1)):
predictions = array_ops.squeeze(predictions, [-1])
elif (rank_diff == expected_rank_diff - 1
and labels_shape.dims[-1].is_compatible_with(1)):
labels = array_ops.squeeze(labels, [-1])
return labels, predictions
# Use dynamic rank.
rank_diff = array_ops.rank(predictions) - array_ops.rank(labels)
if (predictions_rank is
None) or (predictions_shape.dims[-1].is_compatible_with(1)):
predictions = control_flow_ops.cond(
math_ops.equal(expected_rank_diff + 1, rank_diff),
lambda: array_ops.squeeze(predictions, [-1]),
lambda: predictions)
if (labels_rank is
None) or (labels_shape.dims[-1].is_compatible_with(1)):
labels = control_flow_ops.cond(
math_ops.equal(expected_rank_diff - 1, rank_diff),
lambda: array_ops.squeeze(labels, [-1]), lambda: labels)
return labels, predictions
def resample_at_rate(inputs, rates, scope=None, seed=None, back_prop=False):
"""Given `inputs` tensors, stochastically resamples each at a given rate.
For example, if the inputs are `[[a1, a2], [b1, b2]]` and the rates
tensor contains `[3, 1]`, then the return value may look like `[[a1,
a2, a1, a1], [b1, b2, b1, b1]]`. However, many other outputs are
possible, since this is stochastic -- averaged over many repeated
calls, each set of inputs should appear in the output `rate` times
the number of invocations.
Uses Knuth's method to generate samples from the poisson
distribution (but instead of just incrementing a count, actually
emits the input); this is described at
https://en.wikipedia.org/wiki/Poisson_distribution in the section on
generating Poisson-distributed random variables.
Note that this method is not appropriate for large rate values: with
float16 it will stop performing correctly for rates above 9.17;
float32, 87; and float64, 708. (These are the base-e versions of the
minimum representable exponent for each type.)
Args:
inputs: A list of tensors, each of which has a shape of `[batch_size, ...]`
rates: A tensor of shape `[batch_size]` contiaining the resampling rates
for each input.
scope: Scope for the op.
seed: Random seed to use.
back_prop: Whether to allow back-propagation through this op.
Returns:
Selections from the input tensors.
"""
# TODO(shoutis): Refactor, splitting this up into a poisson draw and a repeat.
# What this implementation does is loop, simulating the intervals
# between events by drawing from the exponential distribution
# (`-log(random_uniform)/rate`), and emitting another copy of the
# corresponding input so long as sum(intervals) < 1. However, that
# condition can be transformed into the easier-to-compute condition
# `product(random_uniforms) > e^-rate`.
with ops.name_scope(scope, default_name='resample_at_rate', values=inputs):
floor_vals = math_ops.exp(-rates)
def _body(chosen_inputs, running_products, idx, output_count):
"""Body of the resampling loop."""
# Update the running product
next_running_products = running_products * random_ops.random_uniform(
shape=array_ops.shape(running_products), seed=seed)
# Append inputs which still pass the condition:
indexes = array_ops.reshape(
array_ops.where(next_running_products > floor_vals), [-1])
next_output_count = output_count + array_ops.shape(indexes)[0]
next_chosen_inputs = [
chosen_inputs[i].write(idx,
array_ops.gather(inputs[i], indexes))
for i in range(len(inputs))
]
return [
next_chosen_inputs, next_running_products, idx + 1,
next_output_count
]
def _cond(unused_chosen_inputs, running_products, unused_idx,
unused_count):
"""Resampling loop exit condition."""
return math_ops.reduce_any(running_products > floor_vals)
initial_chosen_inputs = [
tensor_array_ops.TensorArray(dtype=x.dtype,
size=0,
dynamic_size=True) for x in inputs
]
resampled_inputs, _, unused_idx, count = control_flow_ops.while_loop(
_cond,
_body,
loop_vars=[
initial_chosen_inputs,
array_ops.ones_like(rates), # initial running_products
0, # initial idx
0
], # initial count
back_prop=back_prop)
# Work around TensorArray "Currently only static shapes are supported when
# concatenating zero-size TensorArrays" limitation:
def _empty_tensor_like(t):
result = array_ops.zeros(shape=(array_ops.concat(
0, [[0], array_ops.shape(t)[1:]])),
dtype=t.dtype)
if t.get_shape().ndims is not None:
# preserve known shapes
result.set_shape([0] + t.get_shape()[1:].as_list())
return result
return control_flow_ops.cond(
count > 0,
lambda: [tensor_array.concat() for tensor_array in resampled_inputs],
lambda: [_empty_tensor_like(t) for t in inputs])
def _maybe_expand_weights():
expand_weights = lambda: array_ops.expand_dims(sample_weight, [-1])
return control_flow_ops.cond(math_ops.equal(rank_diff, -1),
expand_weights, lambda: sample_weight)
def embedding_rnn_seq2seq(self,
encoder_inputs,
embeddings,
cell,
output_projection=None,
feed_previous=False,
dtype=dtypes.float32,
scope=None):
"""Embedding RNN sequence-to-sequence model.
"""
with variable_scope.variable_scope(scope or "embedding_rnn_seq2seq"):
embeddings = tf.concat(axis=0, values=[embeddings, self.PAD_embed])
encoder_embed = []
def abstract(input_data, index):
reuse = None if index < 1 else True
with variable_scope.variable_scope(
variable_scope.get_variable_scope(), reuse=reuse):
output_data = input_data
output_sizes = [
int((self.embed_size + self.expand_embed_size) / 2),
self.expand_embed_size
]
current_size = self.embed_size
for i in xrange(2):
expand_W = variable_scope.get_variable(
"expand_W_%d" % i, [current_size, output_sizes[i]])
expand_b = variable_scope.get_variable(
"expand_b_%d" % i, [output_sizes[i]])
output_data = tf.nn.bias_add(
tf.matmul(output_data, expand_W), expand_b)
output_data = tf.nn.elu(output_data)
current_size = output_sizes[i]
return output_data
for i in xrange(self.rank_list_size):
encoder_embed.append(
embedding_ops.embedding_lookup(embeddings,
encoder_inputs[i]))
#expand encoder size
if self.expand_embed_size > 0:
encoder_embed[-1] = tf.concat(axis=1,
values=[
encoder_embed[-1],
abstract(
encoder_embed[-1], i)
])
enc_cell = copy.deepcopy(cell)
encoder_outputs, encoder_state = tf.nn.static_rnn(enc_cell,
encoder_embed,
dtype=dtype)
top_states = [
tf.reshape(e, [-1, 1, cell.output_size])
for e in encoder_outputs
]
#for e in encoder_embed]
attention_states = tf.concat(axis=1, values=top_states)
'''
# Concat encoder inputs with encoder outputs to form attention vector
input_states = [tf.reshape(e, [-1, 1, cell.output_size])
for e in encoder_embed]
input_att_states = tf.concat(1, input_states)
attention_states = tf.concat(2, [input_att_states, attention_states])
'''
'''
# Concat hidden states with encoder outputs to form attention vector
hidden_states = [tf.reshape(e, [-1, 1, cell.output_size])
for e in encoder_states]
hidden_att_states = tf.concat(1, hidden_states)
attention_states = tf.concat(2, [hidden_att_states, attention_states])
'''
# Decoder.
#GO = tf.matmul(batch_expansion_mat, self.GO_embed)
#decoder_embed_patched = [GO] + decoder_embed
if isinstance(feed_previous, bool):
return self.embedding_rnn_decoder(
encoder_state,
cell,
attention_states,
encoder_embed,
num_heads=self.hparams.num_heads,
output_projection=output_projection,
feed_previous=feed_previous)
# If feed_previous is a Tensor, we construct 2 graphs and use cond.
def decoder(feed_previous_bool):
reuse = None if feed_previous_bool else True
with variable_scope.variable_scope(
variable_scope.get_variable_scope(), reuse=reuse):
outputs, state = self.embedding_rnn_decoder(
encoder_state,
cell,
attention_states,
encoder_embed,
num_heads=self.hparams.num_heads,
output_projection=output_projection,
feed_previous=feed_previous_bool,
update_embedding_for_previous=False)
return outputs + [state]
outputs_and_state = control_flow_ops.cond(feed_previous,
lambda: decoder(True),
lambda: decoder(False))
return outputs_and_state[:-1], outputs_and_state[-1]
def _train_op_fn(loss):
"""Run one training iteration."""
if training_state_cache:
# Cache logits only after center_bias is complete, if it's in progress.
train_op.append(
control_flow_ops.cond(
center_bias_var, control_flow_ops.no_op,
lambda: training_state_cache.insert(
tree_ids, node_ids, logits)))
if closed_form_grad_and_hess_fn:
gradients, hessians = closed_form_grad_and_hess_fn(
logits, labels)
else:
gradients = gradients_impl.gradients(loss,
logits,
name='Gradients')[0]
hessians = gradients_impl.gradients(gradients,
logits,
name='Hessians')[0]
stats_summaries_list = []
for i, feature_ids in enumerate(feature_ids_list):
num_buckets = bucket_size_list[i]
summaries = [
array_ops.squeeze(boosted_trees_ops.make_stats_summary(
node_ids=node_ids,
gradients=gradients,
hessians=hessians,
bucketized_features_list=[input_feature_list[f]],
max_splits=max_splits,
num_buckets=num_buckets),
axis=0) for f in feature_ids
]
stats_summaries_list.append(summaries)
# ========= Helper methods for both in and not in memory. ==============
def grow_tree_from_stats_summaries(stats_summaries_list,
feature_ids_list):
"""Updates ensemble based on the best gains from stats summaries."""
node_ids_per_feature = []
gains_list = []
thresholds_list = []
left_node_contribs_list = []
right_node_contribs_list = []
all_feature_ids = []
assert len(stats_summaries_list) == len(feature_ids_list)
for i, feature_ids in enumerate(feature_ids_list):
(numeric_node_ids_per_feature, numeric_gains_list,
numeric_thresholds_list, numeric_left_node_contribs_list,
numeric_right_node_contribs_list) = (
boosted_trees_ops.calculate_best_gains_per_feature(
node_id_range=last_layer_nodes_range,
stats_summary_list=stats_summaries_list[i],
l1=tree_hparams.l1,
l2=tree_hparams.l2,
tree_complexity=tree_hparams.tree_complexity,
min_node_weight=tree_hparams.min_node_weight,
max_splits=max_splits))
all_feature_ids += feature_ids
node_ids_per_feature += numeric_node_ids_per_feature
gains_list += numeric_gains_list
thresholds_list += numeric_thresholds_list
left_node_contribs_list += numeric_left_node_contribs_list
right_node_contribs_list += numeric_right_node_contribs_list
grow_op = boosted_trees_ops.update_ensemble(
# Confirm if local_tree_ensemble or tree_ensemble should be used.
tree_ensemble.resource_handle,
feature_ids=all_feature_ids,
node_ids=node_ids_per_feature,
gains=gains_list,
thresholds=thresholds_list,
left_node_contribs=left_node_contribs_list,
right_node_contribs=right_node_contribs_list,
learning_rate=tree_hparams.learning_rate,
max_depth=tree_hparams.max_depth,
pruning_mode=boosted_trees_ops.PruningMode.NO_PRUNING)
return grow_op
def _center_bias_fn(mean_gradients, mean_hessians):
"""Updates the ensembles and cache (if needed) with logits prior."""
continue_centering = boosted_trees_ops.center_bias(
tree_ensemble.resource_handle,
mean_gradients=mean_gradients,
mean_hessians=mean_hessians,
l1=tree_hparams.l1,
l2=tree_hparams.l2)
return center_bias_var.assign(continue_centering)
# ========= End of helper methods. ==============
if train_in_memory and is_single_machine:
train_op.append(distribute_lib.increment_var(global_step))
mean_gradients = array_ops.expand_dims(
math_ops.reduce_mean(gradients, 0), 0)
mean_heassians = array_ops.expand_dims(
math_ops.reduce_mean(hessians, 0), 0)
train_op.append(
control_flow_ops.cond(
center_bias_var, lambda: _center_bias_fn(
mean_gradients, mean_heassians),
functools.partial(grow_tree_from_stats_summaries,
stats_summaries_list,
feature_ids_list)))
else:
def center_bias_not_in_mem():
"""Accumulates the data and updates the logits bias, when ready."""
bias_dependencies = []
bias_accumulator = data_flow_ops.ConditionalAccumulator(
dtype=dtypes.float32,
# The stats consist of grads and hessians means only.
# TODO(nponomareva): this will change for a multiclass
shape=[2, 1],
shared_name='bias_accumulator')
grads_and_hess = array_ops.stack([gradients, hessians],
axis=0)
grads_and_hess = math_ops.reduce_mean(grads_and_hess,
axis=1)
apply_grad = bias_accumulator.apply_grad(
grads_and_hess, stamp_token)
bias_dependencies.append(apply_grad)
def center_bias_from_accumulator():
accumulated = array_ops.unstack(
bias_accumulator.take_grad(1), axis=0)
return _center_bias_fn(
array_ops.expand_dims(accumulated[0], 0),
array_ops.expand_dims(accumulated[1], 0))
with ops.control_dependencies(bias_dependencies):
if config.is_chief:
center_bias_op = control_flow_ops.cond(
math_ops.greater_equal(
bias_accumulator.num_accumulated(),
n_batches_per_layer),
center_bias_from_accumulator,
control_flow_ops.no_op,
name='wait_until_n_batches_for_bias_accumulated'
)
return center_bias_op
else:
return control_flow_ops.no_op()
def grow_not_in_mem():
"""Accumulates the data and grows a layer when ready."""
accumulators = []
dependencies = []
for i, feature_ids in enumerate(feature_ids_list):
stats_summaries = stats_summaries_list[i]
accumulator = data_flow_ops.ConditionalAccumulator(
dtype=dtypes.float32,
# The stats consist of grads and hessians (the last dimension).
shape=[
len(feature_ids), max_splits,
bucket_size_list[i], 2
],
shared_name='numeric_stats_summary_accumulator_' +
str(i))
accumulators.append(accumulator)
apply_grad = accumulator.apply_grad(
array_ops.stack(stats_summaries, axis=0),
stamp_token)
dependencies.append(apply_grad)
def grow_tree_from_accumulated_summaries_fn():
"""Updates tree with the best layer from accumulated summaries."""
# Take out the accumulated summaries from the accumulator and grow.
stats_summaries_list = []
stats_summaries_list = [
array_ops.unstack(accumulator.take_grad(1), axis=0)
for accumulator in accumulators
]
grow_op = grow_tree_from_stats_summaries(
stats_summaries_list, feature_ids_list)
return grow_op
with ops.control_dependencies(dependencies):
if config.is_chief:
min_accumulated = math_ops.reduce_min(
array_ops.stack([
acc.num_accumulated()
for acc in accumulators
]))
grow_model = control_flow_ops.cond(
math_ops.greater_equal(min_accumulated,
n_batches_per_layer),
grow_tree_from_accumulated_summaries_fn,
control_flow_ops.no_op,
name='wait_until_n_batches_accumulated')
return grow_model
else:
return control_flow_ops.no_op()
update_model = control_flow_ops.cond(center_bias_var,
center_bias_not_in_mem,
grow_not_in_mem)
train_op.append(update_model)
with ops.control_dependencies([update_model]):
increment_global = distribute_lib.increment_var(
global_step)
train_op.append(increment_global)
return control_flow_ops.group(train_op, name='train_op')
def stratified_sample(tensors,
labels,
target_probs,
batch_size,
init_probs=None,
enqueue_many=False,
queue_capacity=16,
threads_per_queue=1,
name=None):
"""Stochastically creates batches based on per-class probabilities.
This method discards examples. Internally, it creates one queue to amortize
the cost of disk reads, and one queue to hold the properly-proportioned
batch. See `stratified_sample_unknown_dist` for a function that performs
stratified sampling with one queue per class and doesn't require knowing the
class data-distribution ahead of time.
Args:
tensors: List of tensors for data. All tensors are either one item or a
batch, according to enqueue_many.
labels: Tensor for label of data. Label is a single integer or a batch,
depending on enqueue_many. It is not a one-hot vector.
target_probs: Target class proportions in batch. An object whose type has a
registered Tensor conversion function.
batch_size: Size of batch to be returned.
init_probs: Class proportions in the data. An object whose type has a
registered Tensor conversion function, or `None` for estimating the
initial distribution.
enqueue_many: Bool. If true, interpret input tensors as having a batch
dimension.
queue_capacity: Capacity of the large queue that holds input examples.
threads_per_queue: Number of threads for the large queue that holds input
examples and for the final queue with the proper class proportions.
name: Optional prefix for ops created by this function.
Raises:
ValueError: enqueue_many is True and labels doesn't have a batch
dimension, or if enqueue_many is False and labels isn't a scalar.
ValueError: enqueue_many is True, and batch dimension on data and labels
don't match.
ValueError: if probs don't sum to one.
ValueError: if a zero initial probability class has a nonzero target
probability.
TFAssertion: if labels aren't integers in [0, num classes).
Returns:
(data_batch, label_batch), where data_batch is a list of tensors of the same
length as `tensors`
Example:
# Get tensor for a single data and label example.
data, label = data_provider.Get(['data', 'label'])
# Get stratified batch according to per-class probabilities.
target_probs = [...distribution you want...]
[data_batch], labels = tf.contrib.framework.sampling_ops.stratified_sample(
[data], label, target_probs)
# Run batch through network.
...
"""
with ops.name_scope(name, 'stratified_sample', tensors + [labels]):
tensor_list = ops.convert_n_to_tensor_or_indexed_slices(tensors)
labels = ops.convert_to_tensor(labels)
target_probs = ops.convert_to_tensor(target_probs,
dtype=dtypes.float32)
# Reduce the case of a single example to that of a batch of size 1.
if not enqueue_many:
tensor_list = [
array_ops.expand_dims(tensor, 0) for tensor in tensor_list
]
labels = array_ops.expand_dims(labels, 0)
# If `init_probs` is `None`, set up online estimation of data distribution.
if init_probs is None:
# We use `target_probs` to get the number of classes, so its shape must be
# fully defined at graph construction time.
target_probs.get_shape().assert_is_fully_defined()
init_probs = _estimate_data_distribution(
labels,
target_probs.get_shape().num_elements())
else:
init_probs = ops.convert_to_tensor(init_probs,
dtype=dtypes.float32)
# Validate that input is consistent.
tensor_list, labels, [init_probs, target_probs
] = _verify_input(tensor_list, labels,
[init_probs, target_probs])
# Check that all zero initial probabilities also have zero target
# probabilities.
assert_op = logging_ops.Assert(
math_ops.reduce_all(
math_ops.logical_or(math_ops.not_equal(init_probs, 0),
math_ops.equal(target_probs, 0))),
[
'All classes with zero initial probability must also have zero target '
'probability: ', init_probs, target_probs
])
init_probs = control_flow_ops.with_dependencies([assert_op],
init_probs)
# Calculate acceptance sampling probabilities.
accept_probs = _calculate_acceptance_probabilities(
init_probs, target_probs)
proportion_rejected = math_ops.reduce_sum(
(1 - accept_probs) * init_probs)
accept_probs = control_flow_ops.cond(
math_ops.less(proportion_rejected, .5),
lambda: accept_probs,
lambda: logging_ops.Print( # pylint: disable=g-long-lambda
accept_probs, [accept_probs],
message='Proportion of examples rejected by sampler is high.',
first_n=10))
# Make a single queue to hold input examples. Reshape output so examples
# don't have singleton batch dimension.
batched = input_ops.batch(tensor_list + [labels],
batch_size=1,
num_threads=threads_per_queue,
capacity=queue_capacity,
enqueue_many=True)
val_list = [array_ops.squeeze(x, [0]) for x in batched[:-1]]
label = array_ops.squeeze(batched[-1], [0])
# Set up second queue containing batches that have the desired class
# proportions.
cur_prob = array_ops.gather(accept_probs, label)
batched = _conditional_batch(val_list + [label], cur_prob, batch_size,
threads_per_queue)
return batched[:-1], batched[-1]
def embedding_attention_seq2seq(encoder_inputs,
decoder_inputs,
cell,
num_encoder_symbols,
num_decoder_symbols,
num_heads=1,
output_projection=None,
feed_previous=False,
dtype=tf.float32,
scope=None,
initial_state_attention=False):
"""Embedding sequence-to-sequence model with attention.
This model first embeds encoder_inputs by a newly created embedding (of shape
[num_encoder_symbols x cell.input_size]). Then it runs an RNN to encode
embedded encoder_inputs into a state vector. It keeps the outputs of this
RNN at every step to use for attention later. Next, it embeds decoder_inputs
by another newly created embedding (of shape [num_decoder_symbols x
cell.input_size]). Then it runs attention decoder, initialized with the last
encoder state, on embedded decoder_inputs and attending to encoder outputs.
Args:
encoder_inputs: a list of 1D int32 Tensors of shape [batch_size].
decoder_inputs: a list of 1D int32 Tensors of shape [batch_size].
cell: rnn_cell.RNNCell defining the cell function and size.
num_encoder_symbols: integer; number of symbols on the encoder side.
num_decoder_symbols: integer; number of symbols on the decoder side.
num_heads: number of attention heads that read from attention_states.
output_projection: None or a pair (W, B) of output projection weights and
biases; W has shape [cell.output_size x num_decoder_symbols] and B has
shape [num_decoder_symbols]; if provided and feed_previous=True, each
fed previous output will first be multiplied by W and added B.
feed_previous: Boolean or scalar Boolean Tensor; if True, only the first
of decoder_inputs will be used (the "GO" symbol), and all other decoder
inputs will be taken from previous outputs (as in embedding_rnn_decoder).
If False, decoder_inputs are used as given (the standard decoder case).
dtype: The dtype of the initial RNN state (default: tf.float32).
scope: VariableScope for the created subgraph; defaults to
"embedding_attention_seq2seq".
initial_state_attention: If False (default), initial attentions are zero.
If True, initialize the attentions from the initial state and attention
states.
Returns:
outputs: A list of the same length as decoder_inputs of 2D Tensors with
shape [batch_size x num_decoder_symbols] containing the generated outputs.
states: The state of each decoder cell in each time-step. This is a list
with length len(decoder_inputs) -- one item for each time-step.
Each item is a 2D Tensor of shape [batch_size x cell.state_size].
"""
with tf.variable_scope(scope or "embedding_attention_seq2seq"):
# Encoder.
encoder_cell = rnn_cell.EmbeddingWrapper(cell, num_encoder_symbols)
encoder_outputs, encoder_states = rnn.rnn(encoder_cell,
encoder_inputs,
dtype=dtype)
# First calculate a concatenation of encoder outputs to put attention on.
top_states = [
tf.reshape(e, [-1, 1, cell.output_size]) for e in encoder_outputs
]
attention_states = tf.concat(1, top_states)
# Decoder.
output_size = None
if output_projection is None:
cell = rnn_cell.OutputProjectionWrapper(cell, num_decoder_symbols)
output_size = num_decoder_symbols
if isinstance(feed_previous, bool):
return embedding_attention_decoder(
decoder_inputs,
encoder_states[-1],
attention_states,
cell,
num_decoder_symbols,
num_heads,
output_size,
output_projection,
feed_previous,
initial_state_attention=initial_state_attention)
else: # If feed_previous is a Tensor, we construct 2 graphs and use cond.
outputs1, states1 = embedding_attention_decoder(
decoder_inputs,
encoder_states[-1],
attention_states,
cell,
num_decoder_symbols,
num_heads,
output_size,
output_projection,
True,
initial_state_attention=initial_state_attention)
tf.get_variable_scope().reuse_variables()
outputs2, states2 = embedding_attention_decoder(
decoder_inputs,
encoder_states[-1],
attention_states,
cell,
num_decoder_symbols,
num_heads,
output_size,
output_projection,
False,
initial_state_attention=initial_state_attention)
outputs = control_flow_ops.cond(feed_previous, lambda: outputs1,
lambda: outputs2)
states = control_flow_ops.cond(feed_previous, lambda: states1,
lambda: states2)
return outputs, states
def inference_nn4_max_pool_96(images,
pool_type,
use_lrn,
keep_probability,
phase_train=True):
""" Define an inference network for face recognition based
on inception modules using batch normalization
Args:
images: The images to run inference on, dimensions batch_size x height x width x channels
phase_train: True if batch normalization should operate in training mode
"""
conv1 = _conv(images,
3,
64,
7,
7,
2,
2,
'SAME',
'conv1_7x7',
phase_train=phase_train,
use_batch_norm=True)
pool1 = _mpool(conv1, 3, 3, 2, 2, 'SAME')
if use_lrn:
lrn1 = tf.nn.local_response_normalization(pool1,
depth_radius=5,
bias=1.0,
alpha=1e-4,
beta=0.75)
else:
lrn1 = pool1
conv2 = _conv(lrn1,
64,
64,
1,
1,
1,
1,
'SAME',
'conv2_1x1',
phase_train=phase_train,
use_batch_norm=True)
conv3 = _conv(conv2,
64,
192,
3,
3,
1,
1,
'SAME',
'conv3_3x3',
phase_train=phase_train,
use_batch_norm=True)
if use_lrn:
lrn2 = tf.nn.local_response_normalization(conv3,
depth_radius=5,
bias=1.0,
alpha=1e-4,
beta=0.75)
else:
lrn2 = conv3
pool3 = _mpool(lrn2, 3, 3, 2, 2, 'SAME')
incept3a = _inception(pool3,
192,
1,
64,
96,
128,
16,
32,
3,
32,
1,
'MAX',
'incept3a',
phase_train=phase_train,
use_batch_norm=True)
incept3b = _inception(incept3a,
256,
1,
64,
96,
128,
32,
64,
3,
64,
1,
pool_type,
'incept3b',
phase_train=phase_train,
use_batch_norm=True)
incept3c = _inception(incept3b,
320,
2,
0,
128,
256,
32,
64,
3,
0,
2,
'MAX',
'incept3c',
phase_train=phase_train,
use_batch_norm=True)
incept4a = _inception(incept3c,
640,
1,
256,
96,
192,
32,
64,
3,
128,
1,
pool_type,
'incept4a',
phase_train=phase_train,
use_batch_norm=True)
incept4b = _inception(incept4a,
640,
1,
224,
112,
224,
32,
64,
3,
128,
1,
pool_type,
'incept4b',
phase_train=phase_train,
use_batch_norm=True)
incept4c = _inception(incept4b,
640,
1,
192,
128,
256,
32,
64,
3,
128,
1,
pool_type,
'incept4c',
phase_train=phase_train,
use_batch_norm=True)
incept4d = _inception(incept4c,
640,
1,
160,
144,
288,
32,
64,
3,
128,
1,
pool_type,
'incept4d',
phase_train=phase_train,
use_batch_norm=True)
incept4e = _inception(incept4d,
640,
2,
0,
160,
256,
64,
128,
3,
0,
2,
'MAX',
'incept4e',
phase_train=phase_train,
use_batch_norm=True)
incept5a = _inception(incept4e,
1024,
1,
384,
192,
384,
0,
0,
3,
128,
1,
pool_type,
'incept5a',
phase_train=phase_train,
use_batch_norm=True)
incept5b = _inception(incept5a,
896,
1,
384,
192,
384,
0,
0,
3,
128,
1,
'MAX',
'incept5b',
phase_train=phase_train,
use_batch_norm=True)
pool6 = _apool(incept5b, 3, 3, 1, 1, 'VALID')
resh1 = tf.reshape(pool6, [-1, 896])
affn1 = _affine(resh1, 896, 128)
if keep_probability < 1.0:
affn1 = control_flow_ops.cond(
phase_train, lambda: tf.nn.dropout(affn1, keep_probability),
lambda: affn1)
norm = tf.nn.l2_normalize(affn1, 1, 1e-10, name='embeddings')
return norm
def __init__(
self, # pylint: disable=super-init-not-called
initial_value=None,
trainable=None,
caching_device=None,
name=None,
dtype=None,
constraint=None,
add_initializers_to=None,
lifted_initializer_graph=None,
**unused_kwargs):
"""Creates a variable.
Args:
initial_value: A `Tensor`, or Python object convertible to a `Tensor`,
which is the initial value for the Variable. The initial value must have
a shape specified unless `validate_shape` is set to False. Can also be a
callable with no argument that returns the initial value when called.
(Note that initializer functions from init_ops.py must first be bound
to a shape before being used here.)
trainable: If `True`, GradientTapes automatically watch uses of this
Variable.
caching_device: Optional device string or function describing where the
Variable should be cached for reading. Defaults to the Variable's
device. If not `None`, caches on another device. Typical use is to
cache on the device where the Ops using the Variable reside, to
deduplicate copying through `Switch` and other conditional statements.
name: Optional name for the variable. Defaults to `'Variable'` and gets
uniquified automatically.
dtype: If set, initial_value will be converted to the given type.
If None, either the datatype will be kept (if initial_value is
a Tensor) or float32 will be used (if it is a Python object convertible
to a Tensor).
constraint: An optional projection function to be applied to the variable
after being updated by an `Optimizer` (e.g. used to implement norm
constraints or value constraints for layer weights). The function must
take as input the unprojected Tensor representing the value of the
variable and return the Tensor for the projected value
(which must have the same shape). Constraints are not safe to
use when doing asynchronous distributed training.
add_initializers_to: if not None and not in legacy graph mode, the
initializer tensor will be added to this map in addition to adding the
assignment to the function.
lifted_initializer_graph: FuncGraph to try to lift initializers to.
Raises:
ValueError: If the initial value is not specified, or does not have a
shape and `validate_shape` is `True`.
RuntimeError: If called outside of a function definition.
"""
if not ops.inside_function():
# If we've been init_scope()d out of the function definition nothing to do
# here; we can't really do the capturing or conditional logic.
resource_variable_ops.ResourceVariable.__init__(
self,
initial_value=initial_value,
trainable=trainable,
caching_device=caching_device,
name=name,
dtype=dtype,
constraint=constraint)
return
with ops.init_scope():
self._in_graph_mode = not context.executing_eagerly()
if initial_value is None:
raise ValueError("initial_value must be specified.")
init_from_fn = callable(initial_value)
if constraint is not None and not callable(constraint):
raise ValueError("The `constraint` argument must be a callable.")
if isinstance(initial_value, trackable.CheckpointInitialValue):
self._maybe_initialize_trackable()
self._update_uid = initial_value.checkpoint_position.restore_uid
initial_value = initial_value.wrapped_value
if trainable is None:
trainable = True
self._trainable = trainable
self._save_slice_info = None
self._initial_value = None
self._initializer_op = None
self._is_initialized_op = None
self._graph_element = None
self._cached_value = None
# Store the graph key so optimizers know how to only retrieve variables from
# this graph. Guaranteed to be the same as the eager graph_key.
self._graph_key = ops.get_default_graph()._graph_key # pylint: disable=protected-access
with ops.name_scope(name, "Variable",
[] if init_from_fn else [initial_value]) as name:
# pylint: disable=protected-access
with ops.init_scope():
handle_name = ops._name_from_scope_name(name)
unique_id = "%s_%d" % (handle_name, ops.uid())
shared_name = context.shared_name(unique_id)
with ops.name_scope("Initializer"), ops.device(None):
initial_value = ops.convert_to_tensor(
initial_value() if init_from_fn else initial_value,
name="initial_value",
dtype=dtype)
with ops.init_scope():
self._handle = resource_variable_ops.eager_safe_variable_handle(
initial_value=initial_value,
shared_name=shared_name,
name=name,
graph_mode=self._in_graph_mode)
self._shape = initial_value.shape
self._unique_id = unique_id
self._handle_name = handle_name + ":0"
self._dtype = initial_value.dtype.base_dtype
self._constraint = constraint
assert initial_value is not None
if self._in_graph_mode:
with ops.init_scope():
outer_graph = ops.get_default_graph()
func_graph = ops.get_default_graph()
function_placeholders = (func_graph.inputs +
func_graph.internal_captures)
placeholder_ops = set(
[tensor.op for tensor in function_placeholders])
lifted_initializer = lift_to_graph.lift_to_graph(
[initial_value],
outer_graph,
disallowed_placeholders=placeholder_ops)[initial_value]
with ops.init_scope():
self._initial_value = lifted_initializer
with ops.name_scope("IsInitialized"):
self._is_initialized_op = (
resource_variable_ops.var_is_initialized_op(
self._handle))
if initial_value is not None:
with ops.name_scope("Assign") as n, ops.colocate_with(
self._handle):
self._initializer_op = resource_variable_ops.assign_variable_op(
self._handle, lifted_initializer, name=n)
with ops.name_scope("Read"), ops.colocate_with(
self._handle):
# Manually assign reads to the handle's device to avoid log
# messages.
with ops.device(self._handle.device):
value = self._read_variable_op()
self._graph_element = value
ops.add_to_collection(ops.GraphKeys.GLOBAL_VARIABLES, self)
else:
if add_initializers_to is not None:
add_initializers_to[self] = initial_value
def assign_fn():
with ops.name_scope("Assign") as n, ops.colocate_with(
self._handle):
resource_variable_ops.assign_variable_op(self._handle,
initial_value,
name=n)
# Returning values to keep tf.cond happy.
return ops.convert_to_tensor(1)
def not_assign_fn():
return ops.convert_to_tensor(0)
# Note: this cond is always guaranteed to run because we're inside a
# defun which will insert automatic control dependencies.
control_flow_ops.cond(
resource_variable_ops.var_is_initialized_op(self._handle),
not_assign_fn, assign_fn)
# After the handle has been created, set up a way to clean it up when
# executing eagerly. We'll hold the only reference to the deleter, so that
# when this object is garbage collected the deleter will be too. This
# means ResourceVariables can be part of reference cycles without those
# cycles being uncollectable.
if not self._in_graph_mode:
self._handle_deleter = resource_variable_ops.EagerResourceDeleter(
handle=self._handle, handle_device=self._handle.device)
self._cached_shape_as_list = None
def decoder_fn(time, cell_state, cell_input, cell_output, context_state):
"""Decoder function used in the `dynamic_rnn_decoder` for inference.
The main difference between this decoder function and the `decoder_fn` in
`attention_decoder_fn_train` is how `next_cell_input` is calculated. In
decoder function we calculate the next input by applying an argmax across
the feature dimension of the output from the decoder. This is a
greedy-search approach. (Bahdanau et al., 2014) & (Sutskever et al., 2014)
use beam-search instead.
Args:
time: positive integer constant reflecting the current timestep.
cell_state: state of RNNCell.
cell_input: input provided by `dynamic_rnn_decoder`.
cell_output: output of RNNCell.
context_state: context state provided by `dynamic_rnn_decoder`.
Returns:
A tuple (done, next state, next input, emit output, next context state)
where:
done: A boolean vector to indicate which sentences has reached a
`end_of_sequence_id`. This is used for early stopping by the
`dynamic_rnn_decoder`. When `time>=maximum_length` a boolean vector with
all elements as `true` is returned.
next state: `cell_state`, this decoder function does not modify the
given state.
next input: The embedding from argmax of the `cell_output` is used as
`next_input`.
emit output: If `output_fn is None` the supplied `cell_output` is
returned, else the `output_fn` is used to update the `cell_output`
before calculating `next_input` and returning `cell_output`.
next context state: `context_state`, this decoder function does not
modify the given context state. The context state could be modified when
applying e.g. beam search.
Raises:
ValueError: if cell_input is not None.
"""
with ops.name_scope(
name, "attention_decoder_fn_inference",
[time, cell_state, cell_input, cell_output, context_state]):
if cell_input is not None:
raise ValueError("Expected cell_input to be None, but saw: %s" %
cell_input)
if cell_output is None:
# invariant that this is time == 0
next_input_id = array_ops.ones(
[batch_size,], dtype=dtype) * (start_of_sequence_id)
done = array_ops.zeros([batch_size,], dtype=dtypes.bool)
cell_state = encoder_state
cell_output = array_ops.zeros(
[num_decoder_symbols], dtype=dtypes.float32)
cell_input = array_ops.gather(embeddings, next_input_id)
# init attention
attention = _init_attention(encoder_state)
else:
# construct attention
attention = attention_construct_fn(cell_output, attention_keys,
attention_values)
cell_output = attention
# argmax decoder
cell_output = output_fn(cell_output) # logits
next_input_id = math_ops.cast(
math_ops.argmax(cell_output, 1), dtype=dtype)
done = math_ops.equal(next_input_id, end_of_sequence_id)
cell_input = array_ops.gather(embeddings, next_input_id)
# combine cell_input and attention
next_input = array_ops.concat(1, [cell_input, attention])
# if time > maxlen, return all true vector
done = control_flow_ops.cond(
math_ops.greater(time, maximum_length),
lambda: array_ops.ones([batch_size,], dtype=dtypes.bool),
lambda: done)
return (done, cell_state, next_input, cell_output, context_state)
def body(i):
return control_flow_ops.cond(
alive,
lambda: [tf.less(i, 3), tf.add(count, 1)],
lambda: [alive, count])
def norm(tensor,
ord='euclidean',
axis=None,
keepdims=None,
name=None,
keep_dims=None):
r"""Computes the norm of vectors, matrices, and tensors.
This function can compute several different vector norms (the 1-norm, the
Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0) and
matrix norms (Frobenius, 1-norm, 2-norm and inf-norm).
Args:
tensor: `Tensor` of types `float32`, `float64`, `complex64`, `complex128`
ord: Order of the norm. Supported values are 'fro', 'euclidean',
`1`, `2`, `np.inf` and any positive real number yielding the corresponding
p-norm. Default is 'euclidean' which is equivalent to Frobenius norm if
`tensor` is a matrix and equivalent to 2-norm for vectors.
Some restrictions apply:
a) The Frobenius norm `fro` is not defined for vectors,
b) If axis is a 2-tuple (matrix norm), only 'euclidean', 'fro', `1`,
`2`, `np.inf` are supported.
See the description of `axis` on how to compute norms for a batch of
vectors or matrices stored in a tensor.
axis: If `axis` is `None` (the default), the input is considered a vector
and a single vector norm is computed over the entire set of values in the
tensor, i.e. `norm(tensor, ord=ord)` is equivalent to
`norm(reshape(tensor, [-1]), ord=ord)`.
If `axis` is a Python integer, the input is considered a batch of vectors,
and `axis` determines the axis in `tensor` over which to compute vector
norms.
If `axis` is a 2-tuple of Python integers it is considered a batch of
matrices and `axis` determines the axes in `tensor` over which to compute
a matrix norm.
Negative indices are supported. Example: If you are passing a tensor that
can be either a matrix or a batch of matrices at runtime, pass
`axis=[-2,-1]` instead of `axis=None` to make sure that matrix norms are
computed.
keepdims: If True, the axis indicated in `axis` are kept with size 1.
Otherwise, the dimensions in `axis` are removed from the output shape.
name: The name of the op.
keep_dims: Deprecated alias for `keepdims`.
Returns:
output: A `Tensor` of the same type as tensor, containing the vector or
matrix norms. If `keepdims` is True then the rank of output is equal to
the rank of `tensor`. Otherwise, if `axis` is none the output is a scalar,
if `axis` is an integer, the rank of `output` is one less than the rank
of `tensor`, if `axis` is a 2-tuple the rank of `output` is two less
than the rank of `tensor`.
Raises:
ValueError: If `ord` or `axis` is invalid.
@compatibility(numpy)
Mostly equivalent to numpy.linalg.norm.
Not supported: ord <= 0, 2-norm for matrices, nuclear norm.
Other differences:
a) If axis is `None`, treats the flattened `tensor` as a vector
regardless of rank.
b) Explicitly supports 'euclidean' norm as the default, including for
higher order tensors.
@end_compatibility
"""
keepdims = deprecation.deprecated_argument_lookup('keepdims', keepdims,
'keep_dims', keep_dims)
if keepdims is None:
keepdims = False
is_matrix_norm = ((isinstance(axis, tuple) or isinstance(axis, list))
and len(axis) == 2)
if is_matrix_norm:
axis = tuple(axis)
if (not isinstance(axis[0], int) or not isinstance(axis[1], int)
or axis[0] == axis[1]):
raise ValueError(
"'axis' must be None, an integer, or a tuple of 2 unique integers"
)
supported_matrix_norms = ['euclidean', 'fro', 1, 2, np.inf]
if ord not in supported_matrix_norms:
raise ValueError(
"'ord' must be a supported matrix norm in %s, got %s" %
(supported_matrix_norms, ord))
else:
if not (isinstance(axis, int) or axis is None):
raise ValueError(
"'axis' must be None, an integer, or a tuple of 2 unique integers"
)
supported_vector_norms = ['euclidean', 1, 2, np.inf]
if (not np.isreal(ord)
or ord <= 0) and ord not in supported_vector_norms:
raise ValueError("'ord' must be a supported vector norm, got %s" %
ord)
if axis is not None:
axis = (axis, )
with ops.name_scope(name, 'norm', [tensor]):
tensor = ops.convert_to_tensor(tensor)
if ord in ['fro', 'euclidean', 2, 2.0]:
if is_matrix_norm and ord in [2, 2.0]:
rank = array_ops.rank(tensor)
positive_axis = map_fn.map_fn(
lambda i: control_flow_ops.cond(i >= 0, lambda: i, lambda:
i + rank),
ops.convert_to_tensor(axis))
axes = math_ops.range(rank)
perm_before = array_ops.concat([
array_ops.setdiff1d(axes, positive_axis)[0], positive_axis
],
axis=0)
perm_after = map_fn.map_fn(
lambda i: math_ops.cast(array_ops.squeeze(
array_ops.where_v2(math_ops.equal(perm_before, i))),
dtype=dtypes.int32), axes)
permed = array_ops.transpose(tensor, perm=perm_before)
matrix_2_norm = array_ops.expand_dims(math_ops.reduce_max(
math_ops.abs(
gen_linalg_ops.svd(permed, compute_uv=False)[0]),
axis=-1,
keepdims=True),
axis=-1)
result = array_ops.transpose(matrix_2_norm, perm=perm_after)
else:
result = math_ops.sqrt(
math_ops.reduce_sum(tensor * math_ops.conj(tensor),
axis,
keepdims=True))
# TODO(rmlarsen): Replace with the following, once gradients are defined
# result = math_ops.reduce_euclidean_norm(tensor, axis, keepdims=True)
else:
result = math_ops.abs(tensor)
if ord == 1:
sum_axis = None if axis is None else axis[0]
result = math_ops.reduce_sum(result, sum_axis, keepdims=True)
if is_matrix_norm:
result = math_ops.reduce_max(result,
axis[-1],
keepdims=True)
elif ord == np.inf:
if is_matrix_norm:
result = math_ops.reduce_sum(result,
axis[1],
keepdims=True)
max_axis = None if axis is None else axis[0]
result = math_ops.reduce_max(result, max_axis, keepdims=True)
else:
# General p-norms (positive p only)
result = math_ops.pow(
math_ops.reduce_sum(math_ops.pow(result, ord),
axis,
keepdims=True), 1.0 / ord)
if not keepdims:
result = array_ops.squeeze(result, axis)
return result
def f(pred, value): fn1 = lambda: math_ops.add(value, 1.0) fn2 = lambda: math_ops.subtract(value, 1.0) return control_flow_ops.cond(pred, fn1, fn2)