def replace(self, episodes, length, rows=None):
"""Replace full episodes.
Args:
episodes: Tuple of transition quantities with batch and time dimensions.
length: Batch of sequence lengths.
rows: Episodes to replace, defaults to all.
Returns:
Operation.
"""
rows = tf.range(self._capacity) if rows is None else rows
assert rows.shape.ndims == 1
assert_capacity = tf.assert_less(
rows, self._capacity, message='capacity exceeded')
with tf.control_dependencies([assert_capacity]):
assert_max_length = tf.assert_less_equal(
length, self._max_length, message='max length exceeded')
replace_ops = []
with tf.control_dependencies([assert_max_length]):
for buffer_, elements in zip(self._buffers, episodes):
replace_op = tf.scatter_update(buffer_, rows, elements)
replace_ops.append(replace_op)
with tf.control_dependencies(replace_ops):
return tf.scatter_update(self._length, rows, length)
def preprocess_for_inception(images):
"""Preprocess images for inception.
Args:
images: images minibatch. Shape [batch size, width, height,
channels]. Values are in [0..255].
Returns:
preprocessed_images
"""
# Images should have 3 channels.
assert images.shape[3].value == 3
# tfgan_eval.preprocess_image function takes values in [0, 1], so rescale.
with tf.control_dependencies([tf.assert_greater_equal(images, 0.0),
tf.assert_less_equal(images, 255.0)]):
images = tf.identity(images)
preprocessed_images = tf.map_fn(
fn=tfgan_eval.preprocess_image,
elems=images,
back_prop=False
)
return preprocessed_images
def new_mean_squared(grad_vec, decay, ms):
"""Calculates the new accumulated mean squared of the gradient.
Args:
grad_vec: the vector for the current gradient
decay: the decay term
ms: the previous mean_squared value
Returns:
the new mean_squared value
"""
decay_size = decay.get_shape().num_elements()
decay_check_ops = [
tf.assert_less_equal(decay, 1., summarize=decay_size),
tf.assert_greater_equal(decay, 0., summarize=decay_size)]
with tf.control_dependencies(decay_check_ops):
grad_squared = tf.square(grad_vec)
# If the previous mean_squared is the 0 vector, don't use the decay and just
# return the full grad_squared. This should only happen on the first timestep.
decay = tf.cond(tf.reduce_all(tf.equal(ms, 0.)),
lambda: tf.zeros_like(decay, dtype=tf.float32), lambda: decay)
# Update the running average of squared gradients.
epsilon = 1e-12
return (1. - decay) * (grad_squared + epsilon) + decay * ms
def calculate_reshape(original_shape, new_shape, validate=False, name=None):
"""Calculates the reshaped dimensions (replacing up to one -1 in reshape)."""
batch_shape_static = tensor_util.constant_value_as_shape(new_shape)
if batch_shape_static.is_fully_defined():
return np.int32(batch_shape_static.as_list()), batch_shape_static, []
with tf.name_scope(name, "calculate_reshape", [original_shape, new_shape]):
original_size = tf.reduce_prod(original_shape)
implicit_dim = tf.equal(new_shape, -1)
size_implicit_dim = (
original_size // tf.maximum(1, -tf.reduce_prod(new_shape)))
new_ndims = tf.shape(new_shape)
expanded_new_shape = tf.where( # Assumes exactly one `-1`.
implicit_dim, tf.fill(new_ndims, size_implicit_dim), new_shape)
validations = [] if not validate else [
tf.assert_rank(
original_shape, 1, message="Original shape must be a vector."),
tf.assert_rank(new_shape, 1, message="New shape must be a vector."),
tf.assert_less_equal(
tf.count_nonzero(implicit_dim, dtype=tf.int32),
1,
message="At most one dimension can be unknown."),
tf.assert_positive(
expanded_new_shape, message="Shape elements must be >=-1."),
tf.assert_equal(
tf.reduce_prod(expanded_new_shape),
original_size,
message="Shape sizes do not match."),
]
return expanded_new_shape, batch_shape_static, validations
def test_doesnt_raise_when_both_empty(self):
with self.test_session():
larry = tf.constant([])
curly = tf.constant([])
with tf.control_dependencies([tf.assert_less_equal(larry, curly)]):
out = tf.identity(larry)
out.eval()
def test_doesnt_raise_when_less_equal_and_broadcastable_shapes(self):
with self.test_session():
small = tf.constant([1], name="small")
big = tf.constant([3, 1], name="big")
with tf.control_dependencies([tf.assert_less_equal(small, big)]):
out = tf.identity(small)
out.eval()
def _maybe_check_valid_shape(self, shape, validate_args):
"""Check that a shape Tensor is int-type and otherwise sane."""
if not shape.dtype.is_integer:
raise TypeError('{} dtype ({}) should be `int`-like.'.format(
shape, shape.dtype.name))
assertions = []
ndims = tf.rank(shape)
ndims_ = tensor_util.constant_value(ndims)
if ndims_ is not None and ndims_ > 1:
raise ValueError('`{}` rank ({}) should be <= 1.'.format(
shape, ndims_))
elif validate_args:
assertions.append(
tf.assert_less_equal(
ndims, 1, message='`{}` rank should be <= 1.'.format(shape)))
# Note, we might be inclined to use tensor_util.constant_value_as_shape
# here, but that method coerces negative values into `None`s, rendering the
# checks we do below impossible.
shape_tensor_ = tensor_util.constant_value(shape)
if shape_tensor_ is not None:
es = np.int32(shape_tensor_)
if sum(es == -1) > 1:
raise ValueError(
'`{}` must have at most one `-1` (given {})'
.format(shape, es))
if np.any(es < -1):
raise ValueError(
'`{}` elements must be either positive integers or `-1`'
'(given {}).'
.format(shape, es))
elif validate_args:
assertions.extend([
tf.assert_less_equal(
tf.reduce_sum(tf.cast(tf.equal(shape, -1), tf.int32)),
1,
message='`{}` elements must have at most one `-1`.'
.format(shape)),
tf.assert_greater_equal(
shape,
-1,
message='`{}` elements must be either positive integers or `-1`.'
.format(shape)),
])
return assertions
def test_raises_when_greater(self):
with self.test_session():
small = tf.constant([1, 2], name="small")
big = tf.constant([3, 4], name="big")
with tf.control_dependencies([tf.assert_less_equal(big, small)]):
out = tf.identity(small)
with self.assertRaisesOpError("big.*small"):
out.eval()
def remidify(pitches):
"""Transforms [0, 88) to MIDI pitches [21, 108]."""
assertions = [
tf.assert_greater_equal(pitches, 0),
tf.assert_less_equal(pitches, 87)
]
with tf.control_dependencies(assertions):
return pitches + 21
def demidify(pitches):
"""Transforms MIDI pitches [21,108] to [0, 88)."""
assertions = [
tf.assert_greater_equal(pitches, 21),
tf.assert_less_equal(pitches, 108)
]
with tf.control_dependencies(assertions):
return pitches - 21
def _augment_data(self, inout, nchan=6):
"""Flip, crop and rotate samples randomly."""
with tf.name_scope('data_augmentation'):
if self.fliplr:
inout = tf.image.random_flip_left_right(inout, seed=1234)
if self.flipud:
inout = tf.image.random_flip_up_down(inout, seed=3456)
if self.rotate:
angle = tf.random_uniform((), minval=0, maxval=4, dtype=tf.int32, seed=4567)
inout = tf.case([(tf.equal(angle, 1), lambda: tf.image.rot90(inout, k=1)),
(tf.equal(angle, 2), lambda: tf.image.rot90(inout, k=2)),
(tf.equal(angle, 3), lambda: tf.image.rot90(inout, k=3))],
lambda: inout)
inout.set_shape([None, None, nchan])
with tf.name_scope('crop'):
shape = tf.shape(inout)
new_height = tf.to_int32(self.output_resolution[0])
new_width = tf.to_int32(self.output_resolution[1])
height_ok = tf.assert_less_equal(new_height, shape[0])
width_ok = tf.assert_less_equal(new_width, shape[1])
with tf.control_dependencies([height_ok, width_ok]):
if self.random_crop:
inout = tf.random_crop(
inout, tf.stack([new_height, new_width, nchan]))
else:
height_offset = tf.to_int32((shape[0]-new_height)/2)
width_offset = tf.to_int32((shape[1]-new_width)/2)
inout = tf.image.crop_to_bounding_box(
inout, height_offset, width_offset,
new_height, new_width)
inout.set_shape([None, None, nchan])
inout = tf.image.resize_images(
inout, [self.output_resolution[0], self.output_resolution[1]])
fullres = inout
with tf.name_scope('resize'):
new_size = 256
inout = tf.image.resize_images(
inout, [new_size, new_size],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
return fullres, inout
def test_raises_when_less_equal_but_non_broadcastable_shapes(self):
with self.test_session():
small = tf.constant([1, 1, 1], name="small")
big = tf.constant([3, 1], name="big")
with self.assertRaisesRegexp(ValueError, "broadcast"):
with tf.control_dependencies([tf.assert_less_equal(small, big)]):
out = tf.identity(small)
out.eval()
def scale_to_inception_range(image):
"""Scales an image in the range [0,1] to [-1,1] as expected by inception."""
# Assert that incoming images have been properly scaled to [0,1].
with tf.control_dependencies(
[tf.assert_less_equal(tf.reduce_max(image), 1.),
tf.assert_greater_equal(tf.reduce_min(image), 0.)]):
image = tf.subtract(image, 0.5)
image = tf.multiply(image, 2.0)
return image
def _maybe_assert_valid_y(self, y):
if not self.validate_args:
return y
is_positive = tf.assert_non_negative(
y, message="Inverse transformation input must be greater than 0.")
less_than_one = tf.assert_less_equal(
y,
tf.constant(1., y.dtype),
message="Inverse transformation input must be less than or equal to 1.")
return control_flow_ops.with_dependencies([is_positive, less_than_one], y)
def _maybe_assert_valid(self, x):
if not self.validate_args:
return x
return control_flow_ops.with_dependencies([
tf.assert_non_negative(x, message="sample must be non-negative"),
tf.assert_less_equal(
x,
tf.ones([], self.concentration0.dtype),
message="sample must be no larger than `1`."),
], x)
def _maybe_assert_valid_sample(self, counts):
"""Check counts for proper shape, values, then return tensor version."""
if not self.validate_args:
return counts
counts = distribution_util.embed_check_nonnegative_integer_form(counts)
return control_flow_ops.with_dependencies([
tf.assert_less_equal(
counts,
self.total_count,
message="counts are not less than or equal to n."),
], counts)
def _validate_correlationness(self, x):
if not self.validate_args:
return x
checks = [
tf.assert_less_equal(
tf.cast(-1., dtype=x.dtype.base_dtype),
x,
message='Correlations must be >= -1.'),
tf.assert_less_equal(
x,
tf.cast(1., x.dtype.base_dtype),
message='Correlations must be <= 1.'),
tf.assert_near(
tf.matrix_diag_part(x),
tf.cast(1., x.dtype.base_dtype),
message='Self-correlations must be = 1.'),
tf.assert_near(
x, tf.matrix_transpose(x),
message='Correlation matrices must be symmetric')
]
with tf.control_dependencies(checks):
return tf.identity(x)
def maybe_split_sequence_lengths(sequence_length, num_splits, total_length):
"""Validates and splits `sequence_length`, if necessary.
Returned value must be used in graph for all validations to be executed.
Args:
sequence_length: A batch of sequence lengths, either sized `[batch_size]`
and equal to either 0 or `total_length`, or sized
`[batch_size, num_splits]`.
num_splits: The scalar number of splits of the full sequences.
total_length: The scalar total sequence length (potentially padded).
Returns:
sequence_length: If input shape was `[batch_size, num_splits]`, returns the
same Tensor. Otherwise, returns a Tensor of that shape with each input
length in the batch divided by `num_splits`.
Raises:
ValueError: If `sequence_length` is not shaped `[batch_size]` or
`[batch_size, num_splits]`.
tf.errors.InvalidArgumentError: If `sequence_length` is shaped
`[batch_size]` and all values are not either 0 or `total_length`.
"""
if sequence_length.shape.ndims == 1:
if total_length % num_splits != 0:
raise ValueError(
'`total_length` must be evenly divisible by `num_splits`.')
with tf.control_dependencies(
[tf.Assert(
tf.reduce_all(
tf.logical_or(tf.equal(sequence_length, 0),
tf.equal(sequence_length, total_length))),
data=[sequence_length])]):
sequence_length = (
tf.tile(tf.expand_dims(sequence_length, axis=1), [1, num_splits]) //
num_splits)
elif sequence_length.shape.ndims == 2:
with tf.control_dependencies([
tf.assert_less_equal(
sequence_length,
tf.constant(total_length // num_splits, tf.int32),
message='Segment length cannot be more than '
'`total_length / num_splits`.')]):
sequence_length = tf.identity(sequence_length)
sequence_length.set_shape([sequence_length.shape[0], num_splits])
else:
raise ValueError(
'Sequence lengths must be given as a vector or a 2D Tensor whose '
'second dimension size matches its initial hierarchical split. Got '
'shape: %s' % sequence_length.shape.as_list())
return sequence_length
def _maximum_mean(samples, envelope, high, name=None):
"""Returns a stochastic upper bound on the mean of a scalar distribution.
The idea is that if the true CDF is within an `eps`-envelope of the
empirical CDF of the samples, and the support is bounded above, then
the mean is bounded above as well. In symbols,
```none
sup_x(|F_n(x) - F(x)|) < eps
```
The 0th dimension of `samples` is interpreted as independent and
identically distributed samples. The remaining dimensions are
broadcast together with `envelope` and `high`, and operated on
separately.
Args:
samples: Floating-point `Tensor` of samples from the distribution(s)
of interest. Entries are assumed IID across the 0th dimension.
The other dimensions must broadcast with `envelope` and `high`.
envelope: Floating-point `Tensor` of sizes of admissible CDF
envelopes (i.e., the `eps` above).
high: Floating-point `Tensor` of upper bounds on the distributions'
supports. `samples <= high`.
name: A name for this operation (optional).
Returns:
bound: Floating-point `Tensor` of upper bounds on the true means.
Raises:
InvalidArgumentError: If some `sample` is found to be larger than
the corresponding `high`.
"""
with tf.name_scope(name, "maximum_mean", [samples, envelope, high]):
dtype = dtype_util.common_dtype([samples, envelope, high], tf.float32)
samples = tf.convert_to_tensor(samples, name="samples", dtype=dtype)
envelope = tf.convert_to_tensor(envelope, name="envelope", dtype=dtype)
high = tf.convert_to_tensor(high, name="high", dtype=dtype)
xmax = tf.reduce_max(samples, axis=[0])
msg = "Given sample maximum value exceeds expectations"
check_op = tf.assert_less_equal(xmax, high, message=msg)
with tf.control_dependencies([check_op]):
return tf.identity(_do_maximum_mean(samples, envelope, high))
def _init_clusters_random(self):
"""Does random initialization of clusters.
Returns:
Tensor of randomly initialized clusters.
"""
num_data = tf.add_n([tf.shape(inp)[0] for inp in self._inputs])
# Note that for mini-batch k-means, we should ensure that the batch size of
# data used during initialization is sufficiently large to avoid duplicated
# clusters.
with tf.control_dependencies(
[tf.assert_less_equal(self._num_clusters, num_data)]):
indices = tf.random_uniform(tf.reshape(self._num_clusters, [-1]),
minval=0,
maxval=tf.cast(num_data, tf.int64),
seed=self._random_seed,
dtype=tf.int64)
clusters_init = embedding_lookup(self._inputs, indices,
partition_strategy='div')
return clusters_init
def _init_clusters_random(data, num_clusters, random_seed):
"""Does random initialization of clusters.
Args:
data: a list of Tensors with a matrix of data, each row is an example.
num_clusters: an integer with the number of clusters.
random_seed: Seed for PRNG used to initialize seeds.
Returns:
A Tensor with num_clusters random rows of data.
"""
assert isinstance(data, list)
num_data = tf.add_n([tf.shape(inp)[0] for inp in data])
with tf.control_dependencies([tf.assert_less_equal(num_clusters, num_data)]):
indices = tf.random_uniform([num_clusters],
minval=0,
maxval=tf.cast(num_data, tf.int64),
seed=random_seed,
dtype=tf.int64)
indices = tf.cast(indices, tf.int32) % num_data
clusters_init = embedding_lookup(data, indices, partition_strategy='div')
return clusters_init
def _make_runtime_assertions(
self, distribution, reinterpreted_batch_ndims, validate_args):
assertions = []
static_reinterpreted_batch_ndims = tf.contrib.util.constant_value(
reinterpreted_batch_ndims)
batch_ndims = distribution.batch_shape.ndims
if batch_ndims is not None and static_reinterpreted_batch_ndims is not None:
if static_reinterpreted_batch_ndims > batch_ndims:
raise ValueError("reinterpreted_batch_ndims({}) cannot exceed "
"distribution.batch_ndims({})".format(
static_reinterpreted_batch_ndims, batch_ndims))
elif validate_args:
batch_shape = distribution.batch_shape_tensor()
batch_ndims = (
batch_shape.shape[0].value
if batch_shape.shape.with_rank_at_least(1)[0].value is not None else
tf.shape(batch_shape)[0])
assertions.append(
tf.assert_less_equal(
reinterpreted_batch_ndims,
batch_ndims,
message=("reinterpreted_batch_ndims cannot exceed "
"distribution.batch_ndims")))
return assertions
def replace(self, episodes, length, rows=None):
"""Replace full episodes.
Args:
episodes: Tuple of transition quantities with batch and time dimensions.
length: Batch of sequence lengths.
rows: Episodes to replace, defaults to all.
Returns:
Operation.
"""
rows = tf.range(self._capacity) if rows is None else rows
assert rows.shape.ndims == 1
assert_capacity = tf.assert_less(
rows, self._capacity, message='capacity exceeded')
with tf.control_dependencies([assert_capacity]):
assert_max_length = tf.assert_less_equal(
length, self._max_length, message='max length exceeded')
with tf.control_dependencies([assert_max_length]):
replace_ops = tools.nested.map(
lambda var, val: tf.scatter_update(var, rows, val),
self._buffers, episodes, flatten=True)
with tf.control_dependencies(replace_ops):
return tf.scatter_update(self._length, rows, length)
def embedding_postprocessor(input_tensor,
use_token_type=False,
token_type_ids=None,
token_type_vocab_size=16,
token_type_embedding_name="token_type_embeddings",
use_position_embeddings=True,
position_embedding_name="position_embeddings",
initializer_range=0.02,
max_position_embeddings=512,
dropout_prob=0.1):
"""Performs various post-processing on a word embedding tensor.
Args:
input_tensor: float Tensor of shape [batch_size, seq_length,
embedding_size].
use_token_type: bool. Whether to add embeddings for `token_type_ids`.
token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
Must be specified if `use_token_type` is True.
token_type_vocab_size: int. The vocabulary size of `token_type_ids`.
token_type_embedding_name: string. The name of the embedding table variable
for token type ids.
use_position_embeddings: bool. Whether to add position embeddings for the
position of each token in the sequence.
position_embedding_name: string. The name of the embedding table variable
for positional embeddings.
initializer_range: float. Range of the weight initialization.
max_position_embeddings: int. Maximum sequence length that might ever be
used with this model. This can be longer than the sequence length of
input_tensor, but cannot be shorter.
dropout_prob: float. Dropout probability applied to the final output tensor.
Returns:
float tensor with same shape as `input_tensor`.
Raises:
ValueError: One of the tensor shapes or input values is invalid.
"""
input_shape = get_shape_list(input_tensor, expected_rank=3)
batch_size = input_shape[0]
seq_length = input_shape[1]
width = input_shape[2]
output = input_tensor
if use_token_type:
if token_type_ids is None:
raise ValueError("`token_type_ids` must be specified if"
"`use_token_type` is True.")
token_type_table = tf.get_variable(
name=token_type_embedding_name,
shape=[token_type_vocab_size, width],
initializer=create_initializer(initializer_range))
# This vocab will be small so we always do one-hot here, since it is always
# faster for a small vocabulary.
flat_token_type_ids = tf.reshape(token_type_ids, [-1])
one_hot_ids = tf.one_hot(flat_token_type_ids,
depth=token_type_vocab_size)
token_type_embeddings = tf.matmul(one_hot_ids, token_type_table)
token_type_embeddings = tf.reshape(token_type_embeddings,
[batch_size, seq_length, width])
output += token_type_embeddings
if use_position_embeddings:
assert_op = tf.assert_less_equal(seq_length, max_position_embeddings)
with tf.control_dependencies([assert_op]):
full_position_embeddings = tf.get_variable(
name=position_embedding_name,
shape=[max_position_embeddings, width],
initializer=create_initializer(initializer_range))
# Since the position embedding table is a learned variable, we create it
# using a (long) sequence length `max_position_embeddings`. The actual
# sequence length might be shorter than this, for faster training of
# tasks that do not have long sequences.
#
# So `full_position_embeddings` is effectively an embedding table
# for position [0, 1, 2, ..., max_position_embeddings-1], and the current
# sequence has positions [0, 1, 2, ... seq_length-1], so we can just
# perform a slice.
position_embeddings = tf.slice(full_position_embeddings, [0, 0],
[seq_length, -1])
num_dims = len(output.shape.as_list())
# Only the last two dimensions are relevant (`seq_length` and `width`), so
# we broadcast among the first dimensions, which is typically just
# the batch size.
position_broadcast_shape = []
for _ in range(num_dims - 2):
position_broadcast_shape.append(1)
position_broadcast_shape.extend([seq_length, width])
position_embeddings = tf.reshape(position_embeddings,
position_broadcast_shape)
output += position_embeddings
output = layer_norm_and_dropout(output, dropout_prob)
return output
def __init__(self,
df,
scale_operator,
input_output_cholesky=False,
validate_args=False,
allow_nan_stats=True,
name=None):
"""Construct Wishart distributions.
Args:
df: `float` or `double` tensor, the degrees of freedom of the
distribution(s). `df` must be greater than or equal to `k`.
scale_operator: `float` or `double` instance of `LinearOperator`.
input_output_cholesky: Python `bool`. If `True`, functions whose input or
output have the semantics of samples assume inputs are in Cholesky form
and return outputs in Cholesky form. In particular, if this flag is
`True`, input to `log_prob` is presumed of Cholesky form and output from
`sample`, `mean`, and `mode` are of Cholesky form. Setting this
argument to `True` is purely a computational optimization and does not
change the underlying distribution; for instance, `mean` returns the
Cholesky of the mean, not the mean of Cholesky factors. The `variance`
and `stddev` methods are unaffected by this flag.
Default value: `False` (i.e., input/output does not have Cholesky
semantics).
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
(e.g., mean, mode, variance) use the value "`NaN`" to indicate the
result is undefined. When `False`, an exception is raised if one or
more of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class.
Raises:
TypeError: if scale is not floating-type
TypeError: if scale.dtype != df.dtype
ValueError: if df < k, where scale operator event shape is
`(k, k)`
"""
parameters = dict(locals())
self._input_output_cholesky = input_output_cholesky
with tf.name_scope(name) as name:
with tf.name_scope("init", values=[df, scale_operator]):
if not scale_operator.dtype.is_floating:
raise TypeError(
"scale_operator.dtype=%s is not a floating-point type" %
scale_operator.dtype)
if not scale_operator.is_square:
print(scale_operator.to_dense().eval())
raise ValueError("scale_operator must be square.")
self._scale_operator = scale_operator
self._df = tf.convert_to_tensor(
df, dtype=scale_operator.dtype, name="df")
contrib_tensor_util.assert_same_float_dtype(
(self._df, self._scale_operator))
if (self._scale_operator.shape.ndims is None or
self._scale_operator.shape[-1].value is None):
self._dimension = tf.cast(
self._scale_operator.domain_dimension_tensor(),
dtype=self._scale_operator.dtype,
name="dimension")
else:
self._dimension = tf.convert_to_tensor(
self._scale_operator.shape[-1].value,
dtype=self._scale_operator.dtype,
name="dimension")
df_val = tensor_util.constant_value(self._df)
dim_val = tensor_util.constant_value(self._dimension)
if df_val is not None and dim_val is not None:
df_val = np.asarray(df_val)
if not df_val.shape:
df_val = [df_val]
if any(df_val < dim_val):
raise ValueError(
"Degrees of freedom (df = %s) cannot be less than "
"dimension of scale matrix (scale.dimension = %s)"
% (df_val, dim_val))
elif validate_args:
assertions = tf.assert_less_equal(
self._dimension,
self._df,
message=("Degrees of freedom (df = %s) cannot be "
"less than dimension of scale matrix "
"(scale.dimension = %s)" % (self._dimension, self._df)))
self._df = control_flow_ops.with_dependencies(
[assertions], self._df)
super(_WishartLinearOperator, self).__init__(
dtype=self._scale_operator.dtype,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
reparameterization_type=tf.distributions.FULLY_REPARAMETERIZED,
parameters=parameters,
graph_parents=(
[self._df, self._dimension] + self._scale_operator.graph_parents),
name=name)
def embedding_postprocessor(input_tensor,
use_token_type=False,
token_type_ids=None,
token_type_vocab_size=16,
token_type_embedding_name="token_type_embeddings",
use_position_embeddings=True,
position_embedding_name="position_embeddings",
initializer_range=0.02,
max_position_embeddings=512,
dropout_prob=0.1):
"""对词向量进行后处理
Args:
input_tensor: float张量，形状为[batch_size, seq_length,embedding_size]，词向量
use_token_type: bool. 是否添加token的类型向量
token_type_ids: (可选) int32张量，形状为[batch_size, seq_length]，use_token_type为True时必要有
token_type_vocab_size: int. token类型的数量
token_type_embedding_name: string. token的类型向量表的名字
use_position_embeddings: bool. 是否添加位置向量
position_embedding_name: string. 位置向量表的名字
initializer_range: float. 初始化的范围参数
max_position_embeddings: int. 位置向量的最大长度，只能比输入序列更长
dropout_prob: float. 最后输出的丢弃概率
Returns:
跟输入维度一致的float张量
Raises:
ValueError: 张量形状或者输入值无效
"""
# 获取输入张量维度，batch_size,seq_length,width(词向量的维度)
input_shape = get_shape_list(input_tensor, expected_rank=3)
batch_size = input_shape[0]
seq_length = input_shape[1]
width = input_shape[2]
output = input_tensor # 初始化输出张量
if use_token_type:
# 加上token类型向量
if token_type_ids is None:
raise ValueError("`token_type_ids` must be specified if"
"`use_token_type` is True.")
# token类型向量表变量
token_type_table = tf.get_variable(
name=token_type_embedding_name,
shape=[token_type_vocab_size, width],
initializer=create_initializer(initializer_range))
# 因为类型词表总是很小，所以直接使用one-hot的方式获取向量，因为这种方式在小词表时总是更快
flat_token_type_ids = tf.reshape(token_type_ids, [-1])
one_hot_ids = tf.one_hot(flat_token_type_ids,
depth=token_type_vocab_size)
token_type_embeddings = tf.matmul(one_hot_ids, token_type_table)
token_type_embeddings = tf.reshape(token_type_embeddings,
[batch_size, seq_length, width])
output += token_type_embeddings # 直接将token类型向量加到输出上
if use_position_embeddings:
# 加上位置向量
assert_op = tf.assert_less_equal(seq_length, max_position_embeddings)
with tf.control_dependencies([assert_op]):
# 位置向量表变量
full_position_embeddings = tf.get_variable(
name=position_embedding_name,
shape=[max_position_embeddings, width],
initializer=create_initializer(initializer_range))
# full_position_embeddings已经建立了0到max_position_embeddings-1位置上的向量，
# 为了获取0到seq_length-1位置上的向量，只要使用slice操作即可
position_embeddings = tf.slice(full_position_embeddings, [0, 0],
[seq_length, -1])
num_dims = len(output.shape.as_list())
# 只有最后两维是相关的(`seq_length` and `width`), 所以只要广播开始的维度，通常是batch_size的维度
position_broadcast_shape = []
for _ in range(num_dims - 2):
position_broadcast_shape.append(1)
position_broadcast_shape.extend(
[seq_length,
width]) # position_broadcast_shape=[1, seq_length, width]
position_embeddings = tf.reshape(
position_embeddings,
position_broadcast_shape) # [1, seq_length, width]
output += position_embeddings # 直接将位置向量加到输出上
# 先进行层标准化再dropout
output = layer_norm_and_dropout(output, dropout_prob)
return output
def transformer_model(input_tensor,
is_training,
attention_mask=None,
hidden_size=768,
num_hidden_layers=1,
num_attention_heads=12,
intermediate_size=3072,
intermediate_act_fn=gelu,
hidden_dropout_prob=0.2,
attention_probs_dropout_prob=0.2,
initializer_range=0.02,
do_return_all_layers=False,
use_position_embeddings=True,
max_position_embeddings=512):
"""Multi-headed, multi-layer Transformer from "Attention is All You Need".
This is almost an exact implementation of the original Transformer encoder.
See the original paper:
https://arxiv.org/abs/1706.03762
Also see:
https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py
Args:
input_tensor: float Tensor of shape [batch_size, seq_length, hidden_size].
attention_mask: (optional) int32 Tensor of shape [batch_size, seq_length,
seq_length], with 1 for positions that can be attended to and 0 in
positions that should not be.
hidden_size: int. Hidden size of the Transformer.
num_hidden_layers: int. Number of layers (blocks) in the Transformer.
num_attention_heads: int. Number of attention heads in the Transformer.
intermediate_size: int. The size of the "intermediate" (a.k.a., feed
forward) layer.
intermediate_act_fn: function. The non-linear activation function to apply
to the output of the intermediate/feed-forward layer.
hidden_dropout_prob: float. Dropout probability for the hidden layers.
attention_probs_dropout_prob: float. Dropout probability of the attention
probabilities.
initializer_range: float. Range of the initializer (stddev of truncated
normal).
do_return_all_layers: Whether to also return all layers or just the final
layer.
Returns:
float Tensor of shape [batch_size, seq_length, hidden_size], the final
hidden layer of the Transformer.
Raises:
ValueError: A Tensor shape or parameter is invalid.
"""
if hidden_size % num_attention_heads != 0:
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (hidden_size, num_attention_heads))
if not is_training:
hidden_dropout_prob = 0.0
attention_probs_dropout_prob = 0.0
attention_head_size = int(hidden_size / num_attention_heads)
input_shape = get_shape_list(input_tensor, expected_rank=3)
batch_size = input_shape[0]
seq_length = input_shape[1]
input_width = input_shape[2]
# The Transformer performs sum residuals on all layers so the input needs
# to be the same as the hidden size.
if input_width != hidden_size:
raise ValueError(
"The width of the input tensor (%d) != hidden size (%d)" %
(input_width, hidden_size))
# We keep the representation as a 2D tensor to avoid re-shaping it back and
# forth from a 3D tensor to a 2D tensor. Re-shapes are normally free on
# the GPU/CPU but may not be free on the TPU, so we want to minimize them to
# help the optimizer.
if use_position_embeddings:
assert_op = tf.assert_less_equal(seq_length, max_position_embeddings)
with tf.control_dependencies([assert_op]):
full_position_embeddings = tf.get_variable(
name='position_embeddings',
shape=[max_position_embeddings, input_width],
initializer=create_initializer(initializer_range))
# Since the position embedding table is a learned variable, we create it
# using a (long) sequence length `max_position_embeddings`. The actual
# sequence length might be shorter than this, for faster training of
# tasks that do not have long sequences.
#
# So `full_position_embeddings` is effectively an embedding table
# for position [0, 1, 2, ..., max_position_embeddings-1], and the current
# sequence has positions [0, 1, 2, ... seq_length-1], so we can just
# perform a slice.
position_embeddings = tf.slice(full_position_embeddings, [0, 0],
[seq_length, -1])
num_dims = len(input_tensor.shape.as_list())
# Only the last two dimensions are relevant (`seq_length` and `width`), so
# we broadcast among the first dimensions, which is typically just
# the batch size.
position_broadcast_shape = []
for _ in range(num_dims - 2):
position_broadcast_shape.append(1)
position_broadcast_shape.extend([seq_length, input_width])
position_embeddings = tf.reshape(position_embeddings,
position_broadcast_shape)
input_tensor += position_embeddings
prev_output = reshape_to_matrix(input_tensor)
all_layer_outputs = []
for layer_idx in range(num_hidden_layers):
with tf.variable_scope("layer_%d" % layer_idx):
layer_input = prev_output
with tf.variable_scope("attention"):
attention_heads = []
with tf.variable_scope("self"):
attention_head = attention_layer(
from_tensor=layer_input,
to_tensor=layer_input,
attention_mask=attention_mask,
num_attention_heads=num_attention_heads,
size_per_head=attention_head_size,
attention_probs_dropout_prob=
attention_probs_dropout_prob,
initializer_range=initializer_range,
do_return_2d_tensor=True,
batch_size=batch_size,
from_seq_length=seq_length,
to_seq_length=seq_length)
attention_heads.append(attention_head)
attention_output = None
if len(attention_heads) == 1:
attention_output = attention_heads[0]
else:
# In the case where we have other sequences, we just concatenate
# them to the self-attention head before the projection.
attention_output = tf.concat(attention_heads, axis=-1)
# Run a linear projection of `hidden_size` then add a residual
# with `layer_input`.
with tf.variable_scope("output"):
attention_output = tf.layers.dense(
attention_output,
hidden_size,
kernel_initializer=create_initializer(
initializer_range))
attention_output = dropout(attention_output,
hidden_dropout_prob)
attention_output = layer_norm(attention_output +
layer_input)
# The activation is only applied to the "intermediate" hidden layer.
with tf.variable_scope("intermediate"):
intermediate_output = tf.layers.dense(
attention_output,
intermediate_size,
activation=intermediate_act_fn,
kernel_initializer=create_initializer(initializer_range))
# Down-project back to `hidden_size` then add the residual.
with tf.variable_scope("output"):
layer_output = tf.layers.dense(
intermediate_output,
hidden_size,
kernel_initializer=create_initializer(initializer_range))
layer_output = dropout(layer_output, hidden_dropout_prob)
layer_output = layer_norm(layer_output + attention_output)
prev_output = layer_output
all_layer_outputs.append(layer_output)
if do_return_all_layers:
final_outputs = []
for layer_output in all_layer_outputs:
final_output = reshape_from_matrix(layer_output, input_shape)
final_outputs.append(final_output)
return final_outputs
else:
final_output = reshape_from_matrix(prev_output, input_shape)
first_token_tensor = tf.squeeze(final_output[:, 0:1, :], axis=1)
return first_token_tensor
def pgd_generate(x, model, eps=0.3,eps_iter=0.05, nb_iter=10, y=None, ord=np.inf, clip_min=None, clip_max=None, y_target=None,
rand_init= True, rand_init_eps= 0.3, clip_grad=False, sanity_checks=True):
"""
Generate symbolic graph for adversarial examples and return.
:param x: The model's symbolic inputs.
:param kwargs: See `parse_params`
"""
asserts = []
# If a data range was specified, check that the input was in that range
if clip_min is not None:
asserts.append(tf.assert_greater_equal(x,
tf.cast(clip_min,
x.dtype)))
if clip_max is not None:
asserts.append(tf.assert_less_equal(x,
tf.cast(clip_max,
x.dtype)))
# Initialize loop variables
if rand_init:
eta = random_lp_vector(tf.shape(x), ord,
tf.cast(rand_init_eps, x.dtype),
dtype=x.dtype)
else:
eta = tf.zeros(tf.shape(x))
# Clip eta
eta = clip_eta(eta, ord, eps)
adv_x = x + eta
if clip_min is not None or clip_max is not None:
adv_x = clip_by_value(adv_x, clip_min, clip_max)
if y_target is not None:
y = y_target
targeted = True
elif y is not None:
y = y
targeted = False
else:
model_preds = model(x)
preds_max = tf.reduce_max(model_preds, 1, keepdims=True)
y = tf.to_float(tf.equal(model_preds, preds_max))
y = tf.stop_gradient(y)
targeted = False
del model_preds
# def cond(i, _):
# """Iterate until requested number of iterations is completed"""
# return tf.less(i, nb_iter)
#
# def body(i, adv_x):
# """Do a projected gradient step"""
# adv_x = fgsm_generate(adv_x, model, y=y, eps=eps, ord=ord, clip_min=clip_min, clip_max=clip_max,
# clip_grad=clip_grad, targeted=targeted, sanity_checks=True)
#
# # Clipping perturbation eta to ord norm ball
# eta = adv_x - x
# eta = clip_eta(eta, ord, eps)
# adv_x = x + eta
#
# # Redo the clipping.
# # FGM already did it, but subtracting and re-adding eta can add some
# # small numerical error.
# if clip_min is not None or clip_max is not None:
# adv_x = utils_tf.clip_by_value(adv_x, clip_min, clip_max)
#
# return i + 1, adv_x
#
# _, adv_x = tf.while_loop(cond, body, (tf.zeros([]), adv_x), back_prop=True,
# maximum_iterations=nb_iter)
for i in range(nb_iter):
adv_x = fgsm_generate(adv_x, model, y=y, eps=eps_iter, ord=ord, clip_min=clip_min, clip_max=clip_max,
clip_grad=clip_grad, targeted=targeted, sanity_checks=True)
#Clipping perturbation eta to ord norm ball
eta = adv_x - x
eta = clip_eta(eta, ord, eps)
adv_x = x + eta
# Redo the clipping.
# FGM already did it, but subtracting and re-adding eta can add some
# small numerical error.
if clip_min is not None or clip_max is not None:
adv_x = clip_by_value(adv_x, clip_min, clip_max)
common_dtype = tf.float32
asserts.append(tf.assert_less_equal(tf.cast(eps_iter, dtype=common_dtype), tf.cast(eps, dtype=common_dtype)))
if ord == np.inf and clip_min is not None:
asserts.append(tf.assert_less_equal(tf.cast(eps, x.dtype), 1e-6 + tf.cast(clip_max, x.dtype) - tf.cast(clip_min, x.dtype)))
if sanity_checks:
with tf.control_dependencies(asserts):
adv_x = tf.identity(adv_x)
return adv_x
def model_fn(features, labels, mode, params):
is_training = mode == tf.estimator.ModeKeys.TRAIN
# Inputs
tokens = features[TEXT] # (N, L)
token_lengths = features[SENTENCE_LENGTH] # (N,)
sequence_mask = tf.sequence_mask(maxlen=tf.shape(tokens)[1],
lengths=token_lengths)
n = tf.shape(tokens)[0]
length = params.flat_length
with tf.control_dependencies([
tf.assert_greater_equal(
length,
token_lengths,
message="Tokens longer than flat_length"),
tf.assert_less_equal(tokens,
tf.cast(vocab_size - 1,
dtype=tokens.dtype),
message="Tokens larger than vocab"),
tf.assert_greater_equal(tokens,
tf.cast(0, dtype=tokens.dtype),
message="Tokens less than 0")
]):
tokens = tf.identity(tokens)
if params.l2 > 0:
weights_regularizer = slim.l2_regularizer(params.l2)
else:
weights_regularizer = None
with tf.variable_scope('autoencoder') as autoencoder_scope:
# Encoder
with tf.variable_scope('encoder'):
mu, logsigma = encoder_flat(
tokens=tokens,
token_lengths=token_lengths,
vocab_size=vocab_size,
params=params,
n=n,
weights_regularizer=weights_regularizer)
# Sampling
latent_sample, latent_prior_sample = sampling_flat(
mu=mu, logsigma=logsigma, params=params, n=n)
# Decoder
with tf.variable_scope('decoder', reuse=False) as decoder_scope:
logits = decoder_flat(latent=latent_sample,
vocab_size=vocab_size,
params=params,
weights_regularizer=weights_regularizer,
n=n)
if params.model_mode == ModelModes.AE:
glogits = None
else:
with tf.variable_scope(decoder_scope, reuse=True):
glogits = decoder_flat(
latent=latent_prior_sample,
vocab_size=vocab_size,
params=params,
weights_regularizer=weights_regularizer,
n=n)
if params.model_mode == ModelModes.AAE_RE or params.model_mode == ModelModes.AAE_STOCH:
with tf.variable_scope('discriminator') as discriminator_scope:
dis_inputs = tf.concat([latent_prior_sample, latent_sample],
axis=0)
dis_out = discriminator_output(
x=dis_inputs,
params=params,
weights_regularizer=weights_regularizer,
is_training=is_training)
dis_out = tf.squeeze(dis_out, -1)
print("Dis: {} -> {}".format(dis_inputs, dis_out))
build_gan_losses(params=params,
autoencoder_scope=autoencoder_scope.name,
discriminator_scope=discriminator_scope.name,
dis_out=dis_out,
n=n)
discriminator_hook = dis_train_hook(
discriminator_scope=discriminator_scope.name, params=params)
training_hooks = [discriminator_hook]
elif params.model_mode == ModelModes.VAE:
training_hooks = []
elif params.model_mode == ModelModes.AE:
training_hooks = []
else:
raise ValueError()
sequence_length_ctc = tf.tile([length], (n, ))
return ctc_estimator(tokens=tokens,
token_lengths=token_lengths,
logits=logits,
glogits=glogits,
sequence_mask=sequence_mask,
sequence_length_ctc=sequence_length_ctc,
vocab=vocab,
run_config=run_config,
params=params,
model_scope=autoencoder_scope.name,
training_hooks=training_hooks,
mode=mode)
def one_hots(offsets, name='one_hots'):
with tf.name_scope(name) as scope:
with tf.control_dependencies([tf.assert_less_equal(tf.abs(offsets), scale)]):
result = tf.expand_dims(tf.one_hot(scale - offsets, kernel_size), 1, name=scope)
assert_shape(result, [batch_size, 1, kernel_size])
return result
def bert_encoder(sequence, params):
# extract sequence mask information
seq_mask = 1. - tf.to_float(tf.equal(sequence, params.bert.vocab.pad))
# extract segment information
seg_pos = tf.to_float(tf.equal(sequence, params.bert.vocab.sep))
seg_ids = tf.cumsum(seg_pos, axis=1, reverse=True)
seg_num = tf.reduce_sum(seg_pos, axis=1, keepdims=True)
seg_ids = seg_num - seg_ids
seg_ids = tf.to_int32(seg_ids * seq_mask)
# sequence length information
seq_shp = util.shape_list(sequence)
batch_size, seq_length = seq_shp[:2]
def custom_getter(getter, name, *args, **kwargs):
kwargs['trainable'] = params.tune_bert
return getter(name, *args, **kwargs)
with tf.variable_scope("bert", custom_getter=custom_getter):
# handling sequence embeddings: token_embedding pls segment embedding pls positional embedding
embed_initializer = tf.truncated_normal_initializer(stddev=params.bert.initializer_range)
with tf.variable_scope("embeddings"):
word_embedding = tf.get_variable(
name="word_embeddings",
shape=[params.bert.vocab.size, params.bert.hidden_size],
initializer=embed_initializer
)
seq_embed = tf.nn.embedding_lookup(word_embedding, sequence)
segment_embedding = tf.get_variable(
name="token_type_embeddings",
shape=[2, params.bert.hidden_size],
initializer=embed_initializer
)
seg_embed = tf.nn.embedding_lookup(segment_embedding, seg_ids)
# word embedding + segment embedding
seq_embed = seq_embed + seg_embed
# add position embedding
assert_op = tf.assert_less_equal(seq_length, params.bert.max_position_embeddings)
with tf.control_dependencies([assert_op]):
position_embedding = tf.get_variable(
name="position_embeddings",
shape=[params.bert.max_position_embeddings, params.bert.hidden_size],
initializer=embed_initializer
)
pos_embed = position_embedding[:seq_length]
seq_embed = seq_embed + tf.expand_dims(pos_embed, 0)
# post-processing, layer norm and segmentation
seq_embed = tc.layers.layer_norm(
inputs=seq_embed, begin_norm_axis=-1, begin_params_axis=-1)
seq_embed = util.valid_apply_dropout(seq_embed, params.bert.hidden_dropout_prob)
bert_outputs = []
# handling sequence encoding with transformer encoder
with tf.variable_scope("encoder"):
attention_mask = encoder.create_attention_mask_from_input_mask(
sequence, seq_mask)
# Run the stacked transformer.
# `sequence_output` shape = [batch_size, seq_length, hidden_size].
all_encoder_layers = encoder.transformer_model(
input_tensor=seq_embed,
attention_mask=attention_mask,
hidden_size=params.bert.hidden_size,
num_hidden_layers=params.bert.num_hidden_layers,
num_attention_heads=params.bert.num_attention_heads,
intermediate_size=params.bert.intermediate_size,
intermediate_act_fn=encoder.get_activation(params.bert.hidden_act),
hidden_dropout_prob=params.bert.hidden_dropout_prob,
attention_probs_dropout_prob=params.bert.attention_probs_dropout_prob,
initializer_range=params.bert.initializer_range,
do_return_all_layers=True)
sequence_output = all_encoder_layers
bert_outputs.append(sequence_output)
if params.use_bert_single:
# The "pooler" converts the encoded sequence tensor of shape
# [batch_size, seq_length, hidden_size] to a tensor of shape
# [batch_size, hidden_size]. This is necessary for segment-level
# (or segment-pair-level) classification tasks where we need a fixed
# dimensional representation of the segment.
with tf.variable_scope("pooler"):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token. We assume that this has been pre-trained
first_token_tensor = tf.squeeze(sequence_output[-1][:, 0:1, :], axis=1)
pooled_output = tf.layers.dense(
first_token_tensor,
params.bert.hidden_size,
activation=tf.tanh,
kernel_initializer=embed_initializer)
bert_outputs.append(pooled_output)
return bert_outputs
def __init__(self,
mean_direction,
concentration,
validate_args=False,
allow_nan_stats=True,
name='VonMisesFisher'):
"""Creates a new `VonMisesFisher` instance.
Args:
mean_direction: Floating-point `Tensor` with shape [B1, ... Bn, D].
A unit vector indicating the mode of the distribution, or the
unit-normalized direction of the mean. (This is *not* in general the
mean of the distribution; the mean is not generally in the support of
the distribution.) NOTE: `D` is currently restricted to <= 5.
concentration: Floating-point `Tensor` having batch shape [B1, ... Bn]
broadcastable with `mean_direction`. The level of concentration of
samples around the `mean_direction`. `concentration=0` indicates a
uniform distribution over the unit hypersphere, and `concentration=+inf`
indicates a `Deterministic` distribution (delta function) at
`mean_direction`.
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
allow_nan_stats: Python `bool`, default `True`. When `True`,
statistics (e.g., mean, mode, variance) use the value "`NaN`" to
indicate the result is undefined. When `False`, an exception is raised
if one or more of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class.
Raises:
ValueError: For known-bad arguments, i.e. unsupported event dimension.
"""
parameters = dict(locals())
with tf.name_scope(name, values=[mean_direction,
concentration]) as name:
assertions = [
tf.assert_non_negative(
concentration,
message='`concentration` must be non-negative'),
tf.assert_greater(
tf.shape(mean_direction)[-1],
1,
message='`mean_direction` may not have scalar event shape'
),
tf.assert_near(1.,
tf.linalg.norm(mean_direction, axis=-1),
message='`mean_direction` must be unit-length')
] if validate_args else []
if mean_direction.shape.with_rank_at_least(
1)[-1].value is not None:
if mean_direction.shape.with_rank_at_least(1)[-1].value > 5:
raise ValueError(
'vMF ndims > 5 is not currently supported')
elif validate_args:
assertions += [
tf.assert_less_equal(
tf.shape(mean_direction)[-1],
5,
message='vMF ndims > 5 is not currently supported')
]
with tf.control_dependencies(assertions):
self._mean_direction = tf.convert_to_tensor(
mean_direction, name='mean_direction')
self._concentration = tf.convert_to_tensor(
concentration, name='concentration')
tf.assert_same_float_dtype(
[self._mean_direction, self._concentration])
# mean_direction is always reparameterized.
# concentration is only for event_dim==3, via an inversion sampler.
reparameterization_type = (
tf.distributions.FULLY_REPARAMETERIZED
if mean_direction.shape.with_rank_at_least(1)[-1].value == 3
else tf.distributions.NOT_REPARAMETERIZED)
super(VonMisesFisher, self).__init__(
dtype=self._concentration.dtype,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
reparameterization_type=reparameterization_type,
parameters=parameters,
graph_parents=[self._mean_direction, self._concentration],
name=name)
def embedding_postprocessor(input_tensor,
use_token_type=False,
token_type_ids=None,
token_type_vocab_size=16,
token_type_embedding_name="token_type_embeddings",
use_position_embeddings=True,
position_embedding_name="position_embeddings",
initializer_range=0.02,
max_position_embeddings=512,
dropout_prob=0.1):
"""Performs various post-processing on a word embedding tensor.
Args:
input_tensor: float Tensor of shape [batch_size, seq_length,
embedding_size].
use_token_type: bool. Whether to add embeddings for `token_type_ids`.
token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
Must be specified if `use_token_type` is True.
token_type_vocab_size: int. The vocabulary size of `token_type_ids`.
token_type_embedding_name: string. The name of the embedding table variable
for token type ids.
use_position_embeddings: bool. Whether to add position embeddings for the
position of each token in the sequence.
position_embedding_name: string. The name of the embedding table variable
for positional embeddings.
initializer_range: float. Range of the weight initialization.
max_position_embeddings: int. Maximum sequence length that might ever be
used with this model. This can be longer than the sequence length of
input_tensor, but cannot be shorter.
dropout_prob: float. Dropout probability applied to the final output tensor.
Returns:
float tensor with same shape as `input_tensor`.
Raises:
ValueError: One of the tensor shapes or input values is invalid.
"""
#此时input_tensor是三维，[batch_size, seq_length, embdding_size]
input_shape = get_shape_list(input_tensor, expected_rank=3)
batch_size = input_shape[0]
seq_length = input_shape[1]
width = input_shape[2]
output = input_tensor
#如果使用token_type，先创建token的embedding table, 维度是[token_type_vocab_size, width]
if use_token_type:
if token_type_ids is None:
raise ValueError("`token_type_ids` must be specified if"
"`use_token_type` is True.")
token_type_table = tf.get_variable(
name=token_type_embedding_name,
shape=[token_type_vocab_size, width],
initializer=create_initializer(initializer_range))
# This vocab will be small so we always do one-hot here, since it is always
# faster for a small vocabulary.
#token embdding直接使用时one-hot方式进行查找其对应的embedding向量，token是指一句话可以分成几段，在分类任务中,token type是0
#在句子对任务中, token type是2，默认token_type最大是16
flat_token_type_ids = tf.reshape(token_type_ids, [-1])
one_hot_ids = tf.one_hot(flat_token_type_ids,
depth=token_type_vocab_size)
token_type_embeddings = tf.matmul(one_hot_ids, token_type_table)
token_type_embeddings = tf.reshape(token_type_embeddings,
[batch_size, seq_length, width])
#将token embedding的结果加在原来的embedding结果中
output += token_type_embeddings
if use_position_embeddings:
#确保长度不超过最大长度
assert_op = tf.assert_less_equal(seq_length, max_position_embeddings)
#不理解tf.control_dependencies的作用
with tf.control_dependencies([assert_op]):
#创建位置编码的embedding table， [max_position_embeddings, width]
full_position_embeddings = tf.get_variable(
name=position_embedding_name,
shape=[max_position_embeddings, width],
initializer=create_initializer(initializer_range))
# Since the position embedding table is a learned variable, we create it
# using a (long) sequence length `max_position_embeddings`. The actual
# sequence length might be shorter than this, for faster training of
# tasks that do not have long sequences.
#
# So `full_position_embeddings` is effectively an embedding table
# for position [0, 1, 2, ..., max_position_embeddings-1], and the current
# sequence has positions [0, 1, 2, ... seq_length-1], so we can just
# perform a slice.
#虽然在上一步，创建了最大位置的embedding table，但在实际中，输入的最大序列长度不会超过bert润许的最大长度，为了高效计算，、
#我们从初始化的全位置词表中，将最大输入序列长度的位置取出来，即此时的position embedding维度是[seq_length, width]
position_embeddings = tf.slice(full_position_embeddings, [0, 0],
[seq_length, -1])
num_dims = len(output.shape.as_list())
# Only the last two dimensions are relevant (`seq_length` and `width`), so
# we broadcast among the first dimensions, which is typically just
# the batch size.
#在输入中，最后两维是seq_legth和width
position_broadcast_shape = []
for _ in range(num_dims - 2):
position_broadcast_shape.append(
1) #除了后两维，前面的维度现保存起来，此时position_broadcast_shape存的是1
position_broadcast_shape.extend([seq_length,
width]) #[1, seq_length, width]
position_embeddings = tf.reshape(position_embeddings,
position_broadcast_shape)
#batch_size中的每一个[seq_length, width]都加上positon_embedding，此时的position_embedding是同一个
output += position_embeddings
#运用layer norm和dropout
output = layer_norm_and_dropout(output, dropout_prob)
return output
def pix2pix_preprocess(images,
num_preprocessing_layers=0,
num_outputs=3,
encoder_base_num_filters=32,
is_training=True,
reuse=False,
is_chief=True,
num_rows=5,
batch_size=32,
verbose=True,
**kwargs):
"""Free-form transformation preprocessing.
Args:
x: 4D tensor of images
num_preprocessing_layers: I negative, number of layers in the pix2pix encoder.
num_outputs: Number of output channels for Pix2Pix
encoder_base_num_filters: Base number of filters in the pix2pix encoder
num_rows: required for summary (if is_chief is True)
batch_size: required for summary (if is_chief is True)
is_training: whether the model is in training mode
reuse: whether to reuse the model.
is_chief: determine whether to add summaries
verbose: verbosity level
kwargs: Unused keywords arguments
"""
assert num_preprocessing_layers <= 0
# No preprocessing
if num_preprocessing_layers == 0:
return images
# Pix2pix
else:
# Pix2Pix take images in [-1, 1]
with tf.control_dependencies([tf.assert_greater_equal(images, 0.)]):
with tf.control_dependencies([tf.assert_less_equal(images, 1.)]):
images = (images - 0.5) * 2
if is_chief:
input_images = viz_utils.image_grid(images,
num_rows=num_rows,
batch_size=batch_size)
# Pix2pix. Output in [-1, 1]
encoder_blocks = [
encoder_base_num_filters * (2**i)
for i in range(-num_preprocessing_layers)
]
images = net_utils.pix2pix(images,
encoder_blocks=encoder_blocks,
num_outputs=num_outputs,
is_training=is_training,
reuse=reuse,
is_chief=is_chief,
verbose=verbose,
**kwargs)
# Image summaries
if is_chief:
images = tf.identity(images, name='projected_images')
mode = ('train' if is_training else 'test')
output_images = viz_utils.image_grid(images,
num_rows=num_rows,
batch_size=batch_size)
# Tile if necessary
if num_outputs == 1 and input_images.get_shape()[-1] == 3:
output_images = tf.tile(output_images, (1, 1, 1, 3))
if input_images.get_shape()[-1] == 1 and num_outputs == 3:
input_images = tf.tile(input_images, (1, 1, 1, 3))
# Grid
summary_images = viz_utils.image_grid(tf.concat(
[input_images, output_images], axis=0),
num_rows=1,
num_cols=2,
batch_size=2)
summary_images = tf.identity(summary_images, name='in_out_images')
tf.summary.image('%s/in_out' % mode,
summary_images,
collections=[mode])
# Send output back to [0, 1]
images = images / 2. + 0.5
return images
def __init__(self,
learning_rate,
preconditioner_decay_rate=0.95,
num_pseudo_batches=1,
burnin=25,
diagonal_bias=1e-8,
name=None,
variable_scope=None):
default_name = 'StochasticGradientLangevinDynamics'
with tf.name_scope(name, default_name, [
learning_rate, preconditioner_decay_rate, num_pseudo_batches,
burnin, diagonal_bias
]):
if variable_scope is None:
var_scope_name = tf.get_default_graph().unique_name(
name or default_name)
with tf.variable_scope(var_scope_name) as scope:
self._variable_scope = scope
else:
self._variable_scope = variable_scope
self._preconditioner_decay_rate = tf.convert_to_tensor(
preconditioner_decay_rate, name='preconditioner_decay_rate')
self._num_pseudo_batches = tf.convert_to_tensor(
num_pseudo_batches, name='num_pseudo_batches')
self._burnin = tf.convert_to_tensor(burnin, name='burnin')
self._diagonal_bias = tf.convert_to_tensor(diagonal_bias,
name='diagonal_bias')
self._learning_rate = tf.convert_to_tensor(learning_rate,
name='learning_rate')
with tf.variable_scope(self._variable_scope):
self._counter = tf.get_variable('counter',
initializer=0,
trainable=False)
self._preconditioner_decay_rate = control_flow_ops.with_dependencies([
tf.assert_non_negative(
self._preconditioner_decay_rate,
message='`preconditioner_decay_rate` must be non-negative'
),
tf.assert_less_equal(
self._preconditioner_decay_rate,
1.,
message='`preconditioner_decay_rate` must be at most 1.'),
], self._preconditioner_decay_rate)
self._num_pseudo_batches = control_flow_ops.with_dependencies([
tf.assert_greater(
self._num_pseudo_batches,
0,
message='`num_pseudo_batches` must be greater than zero')
], self._num_pseudo_batches)
self._burnin = control_flow_ops.with_dependencies([
tf.assert_non_negative(
self._burnin, message='`burnin` must be non-negative'),
tf.assert_integer(self._burnin,
message='`burnin` must be an integer')
], self._burnin)
self._diagonal_bias = control_flow_ops.with_dependencies([
tf.assert_non_negative(
self._diagonal_bias,
message='`diagonal_bias` must be non-negative')
], self._diagonal_bias)
super(StochasticGradientLangevinDynamics,
self).__init__(use_locking=False, name=name or default_name)
def generate(self, x, **kwargs):
"""
Generate symbolic graph for adversarial examples and return.
:param x: The model's symbolic inputs.
:param eps: (optional float) maximum distortion of adversarial example
compared to original input
:param eps_iter: (optional float) step size for each attack iteration
:param nb_iter: (optional int) Number of attack iterations.
:param rand_init: (optional) Whether to use random initialization
:param y: (optional) A tensor with the true class labels
NOTE: do not use smoothed labels here
:param y_target: (optional) A tensor with the labels to target. Leave
y_target=None if y is also set. Labels should be
one-hot-encoded.
NOTE: do not use smoothed labels here
:param ord: (optional) Order of the norm (mimics Numpy).
Possible values: np.inf, 1 or 2.
:param clip_min: (optional float) Minimum input component value
:param clip_max: (optional float) Maximum input component value
"""
# Parse and save attack-specific parameters
assert self.parse_params(**kwargs)
# Initialize loop variables
if self.rand_init:
eta = tf.random_uniform(tf.shape(x),
-self.rand_minmax,
self.rand_minmax,
dtype=self.tf_dtype)
else:
eta = tf.zeros(tf.shape(x))
eta = clip_eta(eta, self.ord, self.eps)
# Fix labels to the first model predictions for loss computation
model_preds = self.model.get_output(x)
preds_max = reduce_max(model_preds, 1, keepdims=True)
if self.y_target is not None:
y = self.y_target
targeted = True
elif self.y is not None:
y = self.y
targeted = False
else:
y = tf.to_float(tf.equal(model_preds, preds_max))
y = tf.stop_gradient(y)
targeted = False
y_kwarg = 'y_target' if targeted else 'y'
fgm_params = {
'eps': self.eps_iter,
y_kwarg: y,
'ord': self.ord,
'clip_min': self.clip_min,
'clip_max': self.clip_max
}
# Use getattr() to avoid errors in eager execution attacks
FGM = self.FGM_CLASS(self.model,
sess=getattr(self, 'sess', None),
dtypestr=self.dtypestr)
def cond(i, _):
return tf.less(i, self.nb_iter)
def body(i, e):
adv_x = FGM.generate(x + e, **fgm_params)
# Clipping perturbation according to clip_min and clip_max
if self.clip_min is not None and self.clip_max is not None:
adv_x = tf.clip_by_value(adv_x, self.clip_min, self.clip_max)
# Clipping perturbation eta to self.ord norm ball
eta = adv_x - x
eta = clip_eta(eta, self.ord, self.eps)
return i + 1, eta
_, eta = tf.while_loop(cond, body, [tf.zeros([]), eta], back_prop=True)
# Define adversarial example (and clip if necessary)
adv_x = x + eta
if self.clip_min is not None or self.clip_max is not None:
assert self.clip_min is not None and self.clip_max is not None
adv_x = tf.clip_by_value(adv_x, self.clip_min, self.clip_max)
asserts = []
# Asserts run only on CPU.
# When multi-GPU eval code tries to force all PGD ops onto GPU, this
# can cause an error.
with tf.device("/CPU:0"):
asserts.append(tf.assert_less_equal(self.eps_iter, self.eps))
if self.ord == np.inf and self.clip_min is not None:
# The 1e-6 is needed to compensate for numerical error.
# Without the 1e-6 this fails when e.g. eps=.2, clip_min=.5, clip_max=.7
asserts.append(
tf.assert_less_equal(self.eps,
1e-6 + self.clip_max - self.clip_min))
if self.sanity_checks:
with tf.control_dependencies(asserts):
adv_x = tf.identity(adv_x)
return adv_x
def embedding_postprocessor(input_tensor,
use_token_type=False,
token_type_ids=None,
token_type_vocab_size=3,
token_type_embedding_name='token_type_embeddings',
use_positional_embeddings=True,
positional_embedding_type='normal',
pre_positional_embeddings=None,
positional_embedding_name='position_embeddings',
initializer_range=0.01,
max_positional_embeddings=512,
dropout_prob=0.01):
"""Performs some preprocessing on the word embeddings.
Args:
input_tensor: float Tensor of shape [batch_size, seq_length, embedding_size].
use_token_type: bool. Whether to add segment embeddings, very confused about the original comments
uses 'token' as name, as I realized, token_type_ids would be [[0, 0, 1], [0, 1, 0]], 0 refers to the segment 1,
and 1 refers to segment 2, the last 0 in the second array refers to the padding.
token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
token_type_vocab_size: the number of token types.
use_positional_embeddings: bool. Whether to add positional embeddings.
positional_embedding_type: ['normal', 'trigonometrical'].
pre_positional_embeddings: postional embeddings for the pre_positional_embeddings.
postional_embedding_name: string. The name of the embedding table variable.
initializer_range: float. Range of the weight initializer.
max_positional_embeddings: int. Maximum sequence length for each sentence, which should be equal to or longer than the sequence.
dropout_prob: float. Dropout probability applied to the final output tensor.
Returns:
float Tensor with the identical shape as 'input_tensor'.
"""
input_shape = get_shape_list(input_tensor, expected_rank=[2,3])
batch_size, seq_length, width = input_shape[0], input_shape[1], input_shape[2]
# create this variable in case of not use any pre-embeddings on the input_tensor
output = input_tensor
if use_token_type:
if token_type_ids is None:
_error('`token_type_ids` must be specified if `use_token_type` is True.')
raise ValueError
token_type_table = tf.get_variable(
name=token_type_embedding_name,
shape=[token_type_vocab_size, width],
initializer=create_initializer(initializer_range))
token_type_embeddings = tf.nn.embedding_lookup(token_type_table, token_type_ids)
output += token_type_embeddings
if use_positional_embeddings:
assert_op = tf.assert_less_equal(seq_length, max_positional_embeddings)
with tf.control_dependencies([assert_op]):
full_positional_embeddings = tf.get_variable(
name=positional_embedding_name,
shape=[max_positional_embeddings, width],
initializer=create_initializer(initializer_range))
# the full_positional_embeddings is created under the maximum sequence length,
# however, the actual length maybe less than the maximum length, so slicing is necessary.
positional_embeddings = tf.slice(full_positional_embeddings, [0, 0], [seq_length, -1])
output += positional_embeddings
output = layer_norm_and_dropout(output, dropout_prob)
return output
def embedding_postprocessor(
input_tensor,
use_token_type=False,
token_type_ids=None,
token_type_vocab_size=2,
token_type_embedding_name='token_type_embeddings',
use_position_embeddings=True,
position_embedding_name='position_embeddings',
initializer_range=0.02,
max_position_embeddings=512,
):
"""Performs various post-processing on a word embedding tensor.
Args:
input_tensor: float Tensor of shape [batch_size, seq_length,
embedding_size].
use_token_type: bool. Whether to add embeddings for `token_type_ids`.
token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
Must be specified if `use_token_type` is True.
token_type_vocab_size: int. The vocabulary size of `token_type_ids`.
token_type_embedding_name: string. The name of the embedding table variable
for token type ids.
use_position_embeddings: bool. Whether to add position embeddings for the
position of each token in the sequence.
position_embedding_name: string. The name of the embedding table variable
for positional embeddings.
initializer_range: float. Range of the weight initialization.
max_position_embeddings: int. Maximum sequence length that might ever be
used with this model. This can be longer than the sequence length of
input_tensor, but cannot be shorter.
dropout_prob: float. Dropout probability applied to the final output tensor.
Returns:
float tensor with same shape as `input_tensor`.
Raises:
ValueError: One of the tensor shapes or input values is invalid.
"""
input_shape = get_shape_list(input_tensor, expected_rank=3)
batch_size = input_shape[0]
seq_length = input_shape[1]
width = input_shape[2]
output = input_tensor
if use_token_type:
if token_type_ids is None:
raise ValueError(
'`token_type_ids` must be specified if'
'`use_token_type` is True.'
)
token_type_table = tf.get_variable(
name=token_type_embedding_name,
shape=[token_type_vocab_size, width],
initializer=create_initializer(initializer_range),
)
flat_token_type_ids = tf.reshape(token_type_ids, [-1])
one_hot_ids = tf.one_hot(
flat_token_type_ids, depth=token_type_vocab_size
)
token_type_embeddings = tf.matmul(one_hot_ids, token_type_table)
token_type_embeddings = tf.reshape(
token_type_embeddings, [batch_size, seq_length, width]
)
output += token_type_embeddings
if use_position_embeddings:
assert_op = tf.assert_less_equal(seq_length, max_position_embeddings)
with tf.control_dependencies([assert_op]):
full_position_embeddings = tf.get_variable(
name=position_embedding_name,
shape=[max_position_embeddings, width],
initializer=create_initializer(initializer_range),
)
position_embeddings = tf.slice(
full_position_embeddings, [0, 0], [seq_length, -1]
)
num_dims = len(output.shape.as_list())
position_broadcast_shape = []
for _ in range(num_dims - 2):
position_broadcast_shape.append(1)
position_broadcast_shape.extend([seq_length, width])
position_embeddings = tf.reshape(
position_embeddings, position_broadcast_shape
)
output += position_embeddings
return output
def _sample_n(self, n, seed=None):
seed = seed_stream.SeedStream(seed, salt='vom_mises_fisher')
# The sampling strategy relies on the fact that vMF variates are symmetric
# about the mean direction. Accordingly, if we have a sampling strategy for
# the away-from-mean angle, then we can uniformly sample the remaining
# dimensions on the S^{dim-2} sphere for , and rotate these samples from a
# (1, 0, 0, ..., 0)-mode distribution into the target orientation.
#
# This is easy to imagine on the 1-sphere (S^1; in 2-D space): sample a
# von-Mises distributed `x` value in [-1, 1], then uniformly select what
# amounts to a "up" or "down" additional degree of freedom after unit
# normalizing, followed by a final rotation to the desired mean direction
# from a basis of (1, 0).
#
# On S^2 (in 3-D), selecting a vMF `x` identifies a circle in `yz` on the
# unit sphere over which the distribution is uniform, in particular the
# circle where x = \hat{x} intersects the unit sphere. We pick a point on
# that circle, then rotate to the desired mean direction from a basis of
# (1, 0, 0).
event_dim = self.event_shape[0].value or self._event_shape_tensor()[0]
sample_batch_shape = tf.concat([[n], self._batch_shape_tensor()],
axis=0)
dim = tf.cast(event_dim - 1, self.dtype)
if event_dim == 3:
samples_dim0 = self._sample_3d(n, seed=seed)
else:
# Wood'94 provides a rejection algorithm to sample the x coordinate.
# Wood'94 definition of b:
# b = (-2 * kappa + tf.sqrt(4 * kappa**2 + dim**2)) / dim
# https://stats.stackexchange.com/questions/156729 suggests:
b = dim / (2 * self.concentration +
tf.sqrt(4 * self.concentration**2 + dim**2))
# TODO(bjp): Integrate any useful numerical tricks from hyperspherical VAE
# https://github.com/nicola-decao/s-vae-tf/
x = (1 - b) / (1 + b)
c = self.concentration * x + dim * tf.log1p(-x**2)
beta = tf.distributions.Beta(dim / 2, dim / 2)
def cond_fn(w, should_continue):
del w
return tf.reduce_any(should_continue)
def body_fn(w, should_continue):
z = beta.sample(sample_shape=sample_batch_shape, seed=seed())
w = tf.where(should_continue,
(1 - (1 + b) * z) / (1 - (1 - b) * z), w)
w = tf.check_numerics(w, 'w')
should_continue = tf.logical_and(
should_continue,
self.concentration * w + dim * tf.log1p(-x * w) - c <
tf.log(
tf.random_uniform(sample_batch_shape,
seed=seed(),
dtype=self.dtype)))
return w, should_continue
w = tf.zeros(sample_batch_shape, dtype=self.dtype)
should_continue = tf.ones(sample_batch_shape, dtype=tf.bool)
samples_dim0 = tf.while_loop(cond_fn, body_fn,
(w, should_continue))[0]
samples_dim0 = samples_dim0[..., tf.newaxis]
if not self._allow_nan_stats:
# Verify samples are w/in -1, 1, with useful error output tensors (top
# value rather than all values).
with tf.control_dependencies([
tf.assert_less_equal(
samples_dim0,
self.dtype.as_numpy_dtype(1.01),
data=[tf.nn.top_k(tf.reshape(samples_dim0, [-1]))[0]]),
tf.assert_greater_equal(
samples_dim0,
self.dtype.as_numpy_dtype(-1.01),
data=[
-tf.nn.top_k(tf.reshape(-samples_dim0, [-1]))[0]
])
]):
samples_dim0 = tf.identity(samples_dim0)
samples_otherdims_shape = tf.concat(
[sample_batch_shape, [event_dim - 1]], axis=0)
unit_otherdims = tf.nn.l2_normalize(tf.random_normal(
samples_otherdims_shape, seed=seed(), dtype=self.dtype),
axis=-1)
samples = tf.concat(
[
samples_dim0, # we must avoid sqrt(1 - (>1)**2)
tf.sqrt(tf.maximum(1 - samples_dim0**2, 0.)) * unit_otherdims
],
axis=-1)
samples = tf.nn.l2_normalize(samples, axis=-1)
if not self._allow_nan_stats:
samples = tf.check_numerics(samples, 'samples')
# Runtime assert that samples are unit length.
if not self._allow_nan_stats:
worst, idx = tf.nn.top_k(
tf.reshape(tf.abs(1 - tf.linalg.norm(samples, axis=-1)), [-1]))
with tf.control_dependencies([
tf.assert_near(self.dtype.as_numpy_dtype(0),
worst,
data=[
worst, idx,
tf.gather(
tf.reshape(samples,
[-1, event_dim]), idx)
],
atol=1e-4,
summarize=100)
]):
samples = tf.identity(samples)
# The samples generated are symmetric around a mode at (1, 0, 0, ...., 0).
# Now, we move the mode to `self.mean_direction` using a rotation matrix.
if not self._allow_nan_stats:
# Assert that the basis vector rotates to the mean direction, as expected.
basis = tf.cast(
tf.concat([[1.], tf.zeros([event_dim - 1])], axis=0),
self.dtype)
with tf.control_dependencies([
tf.assert_less(
tf.linalg.norm(self._rotate(basis) -
self.mean_direction,
axis=-1),
self.dtype.as_numpy_dtype(1e-5))
]):
return self._rotate(samples)
return self._rotate(samples)
def embedding_postprocessor(input_tensor,
use_token_type=False,
token_type_ids=None,
token_type_vocab_size=16,
token_type_embedding_name="token_type_embeddings",
use_position_embeddings=True,
position_embedding_name="position_embeddings",
initializer_range=0.02,
max_position_embeddings=512,
dropout_prob=0.1):
# 对一个word embedding执行各种后处理;
# Args:
# input_tensor: float类型的Tensor;
# use_token_type: bool类型, 是否对`token_type_ids`添加embeddings;
# token_type_ids: [可选] int32类型Tensor;
# token_type_vocab_size: int类型, `token_type_ids`词汇表的size;
# token_type_embedding_name: String. token-type-ids的embedding-table name;
# use_position_embeddings: bool, 是否为序列中每个token的位置添加位置embeddings;
# position_embedding_name: String, Position_Embedding name;
# initializer_range: float类型, 权重初始化的范围;
# max_position_embeddings: int类型, 最大序列长度, 可以比input_tensor长, 但不能比他短;
# dropout_prob: float, Dropout最终的大小
# Returns:
# 返回与"input_tensor"形状相同的Tensor;
input_shape = get_shape_list(input_tensor, expected_rank=3)
batch_size = input_shape[0]
seq_length = input_shape[1]
width = input_shape[2]
output = input_tensor
if use_token_type:
if token_type_ids is None:
raise ValueError("如果`use_token_type`是True, `token_type_ids`必须被赋值.")
token_type_table = tf.get_variable(
name=token_type_embedding_name,
shape=[token_type_vocab_size, width],
initializer=create_initializer(initializer_range))
# 由于这个vocab很小, 所以我们在这里使用one-hot, 对于小词汇量来说, one-hot更快;
flat_token_type_ids = tf.reshape(token_type_ids, [-1])
one_hot_ids = tf.one_hot(flat_token_type_ids,
depth=token_type_vocab_size)
token_type_embeddings = tf.matmul(one_hot_ids, token_type_table)
token_type_embeddings = tf.reshape(token_type_embeddings,
[batch_size, seq_length, width])
output += token_type_embeddings
if use_position_embeddings:
assert_op = tf.assert_less_equal(seq_length, max_position_embeddings)
with tf.control_dependencies([assert_op]):
full_position_embeddings = tf.get_variable(
name=position_embedding_name,
shape=[max_position_embeddings, width],
initializer=create_initializer(initializer_range))
# 由于Position embedding table 是一个学习变量,
# 我们使用(长)序列长度为"max_position_embeddings"创建它.
# 实际的序列长度可能比这个要短, 以便更快地训练序列不长的任务.
# 因此'full_position_embeddings'实际上是一个[0,1,2，…， max_position_embeddings-1],
# 当前序列为[0,1,2，…seq_length-1],因此我们可以执行一个切片.
position_embeddings = tf.slice(full_position_embeddings, [0, 0],
[seq_length, -1])
num_dims = len(output.shape.as_list())
# 只有最后两个维度和(`seq_length`和`width`相关.), 所以我们在第一个维度中进行了广播,
# 一般来说只是batch_size;
position_broadcast_shape = []
for _ in range(num_dims - 2):
position_broadcast_shape.append(1)
position_broadcast_shape.extend([seq_length, width])
position_embeddings = tf.reshape(position_embeddings,
position_broadcast_shape)
output += position_embeddings
output = layer_norm_and_dropout(output, dropout_prob)
return output
pass
def percentile(x,
q,
axis=None,
interpolation=None,
keep_dims=False,
validate_args=False,
name=None):
"""Compute the `q`-th percentile(s) of `x`.
Given a vector `x`, the `q`-th percentile of `x` is the value `q / 100` of the
way from the minimum to the maximum in a sorted copy of `x`.
The values and distances of the two nearest neighbors as well as the
`interpolation` parameter will determine the percentile if the normalized
ranking does not match the location of `q` exactly.
This function is the same as the median if `q = 50`, the same as the minimum
if `q = 0` and the same as the maximum if `q = 100`.
Multiple percentiles can be computed at once by using `1-D` vector `q`.
Dimension zero of the returned `Tensor` will index the different percentiles.
```python
# Get 30th percentile with default ('nearest') interpolation.
x = [1., 2., 3., 4.]
percentile(x, q=30.)
==> 2.0
# Get 30th and 70th percentiles with 'lower' interpolation
x = [1., 2., 3., 4.]
percentile(x, q=[30., 70.], interpolation='lower')
==> [1., 3.]
# Get 100th percentile (maximum). By default, this is computed over every dim
x = [[1., 2.]
[3., 4.]]
percentile(x, q=100.)
==> 4.
# Treat the leading dim as indexing samples, and find the 100th quantile (max)
# over all such samples.
x = [[1., 2.]
[3., 4.]]
percentile(x, q=100., axis=[0])
==> [3., 4.]
```
Compare to `numpy.percentile`.
Args:
x: Floating point `N-D` `Tensor` with `N > 0`. If `axis` is not `None`,
`x` must have statically known number of dimensions.
q: Scalar or vector `Tensor` with values in `[0, 100]`. The percentile(s).
axis: Optional `0-D` or `1-D` integer `Tensor` with constant values. The
axis that hold independent samples over which to return the desired
percentile. If `None` (the default), treat every dimension as a sample
dimension, returning a scalar.
interpolation : {'lower', 'higher', 'nearest'}. Default: 'nearest' This
optional parameter specifies the interpolation method to
use when the desired quantile lies between two data points `i < j`:
* lower: `i`.
* higher: `j`.
* nearest: `i` or `j`, whichever is nearest.
keep_dims: Python `bool`. If `True`, the last dimension is kept with size 1
If `False`, the last dimension is removed from the output shape.
validate_args: Whether to add runtime checks of argument validity. If
False, and arguments are incorrect, correct behavior is not guaranteed.
name: A Python string name to give this `Op`. Default is 'percentile'
Returns:
A `(rank(q) + N - len(axis))` dimensional `Tensor` of same dtype as `x`, or,
if `axis` is `None`, a `rank(q)` `Tensor`. The first `rank(q)` dimensions
index quantiles for different values of `q`.
Raises:
ValueError: If argument 'interpolation' is not an allowed type.
"""
name = name or 'percentile'
allowed_interpolations = {'lower', 'higher', 'nearest'}
if interpolation is None:
interpolation = 'nearest'
else:
if interpolation not in allowed_interpolations:
raise ValueError(
'Argument `interpolation` must be in %s. Found %s' %
(allowed_interpolations, interpolation))
with tf.name_scope(name, values=[x, q]):
x = tf.convert_to_tensor(x, name='x')
# Double is needed here and below, else we get the wrong index if the array
# is huge along axis.
q = tf.to_double(q, name='q')
_get_static_ndims(q, expect_ndims_no_more_than=1)
if validate_args:
q = control_flow_ops.with_dependencies([
tf.assert_rank_in(q, [0, 1]),
tf.assert_greater_equal(q, tf.to_double(0.)),
tf.assert_less_equal(q, tf.to_double(100.))
], q)
if axis is None:
y = tf.reshape(x, [-1])
else:
axis = tf.convert_to_tensor(axis, name='axis')
tf.assert_integer(axis)
axis_ndims = _get_static_ndims(axis,
expect_static=True,
expect_ndims_no_more_than=1)
axis_const = tensor_util.constant_value(axis)
if axis_const is None:
raise ValueError(
'Expected argument `axis` to be statically available. Found: %s'
% axis)
axis = axis_const
if axis_ndims == 0:
axis = [axis]
axis = [int(a) for a in axis]
x_ndims = _get_static_ndims(x,
expect_static=True,
expect_ndims_at_least=1)
axis = _make_static_axis_non_negative(axis, x_ndims)
# Move dims in axis to the end, since _sort_tensor, which calls top_k,
# only sorts the last dim.
y = _move_dims_to_flat_end(x, axis, x_ndims)
frac_at_q_or_above = 1. - q / 100.
d = tf.to_double(tf.shape(y)[-1])
if interpolation == 'lower':
indices = tf.ceil((d - 1) * frac_at_q_or_above)
elif interpolation == 'higher':
indices = tf.floor((d - 1) * frac_at_q_or_above)
elif interpolation == 'nearest':
indices = tf.round((d - 1) * frac_at_q_or_above)
# If d is gigantic, then we would have d == d - 1, even in double... So
# let's use max/min to avoid out of bounds errors.
d = tf.shape(y)[-1]
# d - 1 will be distinct from d in int32.
indices = tf.clip_by_value(tf.to_int32(indices), 0, d - 1)
# Sort everything, not just the top 'k' entries, which allows multiple calls
# to sort only once (under the hood) and use CSE.
sorted_y = _sort_tensor(y)
# Gather the indices along the sorted (last) dimension.
# If q is a vector, the last dim of gathered_y indexes different q[i].
gathered_y = tf.gather(sorted_y, indices, axis=-1)
if keep_dims:
if axis is None:
ones_vec = tf.ones(shape=[
_get_best_effort_ndims(x) + _get_best_effort_ndims(q)
],
dtype=tf.int32)
gathered_y *= tf.ones(ones_vec, dtype=x.dtype)
else:
gathered_y = _insert_back_keep_dims(gathered_y, axis)
# If q is a scalar, then result has the right shape.
# If q is a vector, then result has trailing dim of shape q.shape, which
# needs to be rotated to dim 0.
return util.rotate_transpose(gathered_y, tf.rank(q))
def check_range(tensor, low, high, message_prefix=''):
low = tf.assert_greater_equal(tensor, low, message=message_prefix + '>=')
high = tf.assert_less_equal(tensor, high, message=message_prefix + '<=')
with tf.control_dependencies([low, high]):
return tf.identity(tensor)
def embedding_postprocessor(input_tensor,
use_token_type=False,
token_type_ids=None,
token_type_vocab_size=16,
token_type_embedding_name="token_type_embeddings",
use_position_embeddings=True,
position_embedding_name="position_embeddings",
initializer_range=0.02,
max_position_embeddings=512,
dropout_prob=0.1):
"""Performs various post-processing on a word embedding tensor.
Args:
input_tensor: float Tensor of shape [batch_size, seq_length,
embedding_size].
use_token_type: bool. Whether to add embeddings for `token_type_ids`.
token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
Must be specified if `use_token_type` is True.
token_type_vocab_size: int. The vocabulary size of `token_type_ids`.
token_type_embedding_name: string. The name of the embedding table variable
for token type ids.
use_position_embeddings: bool. Whether to add position embeddings for the
position of each token in the sequence.
position_embedding_name: string. The name of the embedding table variable
for positional embeddings.
initializer_range: float. Range of the weight initialization.
max_position_embeddings: int. Maximum sequence length that might ever be
used with this model. This can be longer than the sequence length of
input_tensor, but cannot be shorter.
dropout_prob: float. Dropout probability applied to the final output tensor.
Returns:
float tensor with same shape as `input_tensor`.
Raises:
ValueError: One of the tensor shapes or input values is invalid.
"""
input_shape = get_shape_list(input_tensor, expected_rank=3)
batch_size = input_shape[0]
seq_length = input_shape[1]
width = input_shape[2]
output = input_tensor
if use_token_type:
if token_type_ids is None:
raise ValueError("`token_type_ids` must be specified if"
"`use_token_type` is True.")
token_type_table = tf.get_variable(
name=token_type_embedding_name,
shape=[token_type_vocab_size, width],
initializer=create_initializer(initializer_range))
# This vocab will be small so we always do one-hot here, since it is always
# faster for a small vocabulary.
flat_token_type_ids = tf.reshape(token_type_ids, [-1])
one_hot_ids = tf.one_hot(flat_token_type_ids, depth=token_type_vocab_size)
token_type_embeddings = tf.matmul(one_hot_ids, token_type_table)
token_type_embeddings = tf.reshape(token_type_embeddings,
[batch_size, seq_length, width])
output += token_type_embeddings
if use_position_embeddings:
assert_op = tf.assert_less_equal(seq_length, max_position_embeddings)
with tf.control_dependencies([assert_op]):
full_position_embeddings = tf.get_variable(
name=position_embedding_name,
shape=[max_position_embeddings, width],
initializer=create_initializer(initializer_range))
# Since the position embedding table is a learned variable, we create it
# using a (long) sequence length `max_position_embeddings`. The actual
# sequence length might be shorter than this, for faster training of
# tasks that do not have long sequences.
#
# So `full_position_embeddings` is effectively an embedding table
# for position [0, 1, 2, ..., max_position_embeddings-1], and the current
# sequence has positions [0, 1, 2, ... seq_length-1], so we can just
# perform a slice.
position_embeddings = tf.slice(full_position_embeddings, [0, 0],
[seq_length, -1])
num_dims = len(output.shape.as_list())
# Only the last two dimensions are relevant (`seq_length` and `width`), so
# we broadcast among the first dimensions, which is typically just
# the batch size.
position_broadcast_shape = []
for _ in range(num_dims - 2):
position_broadcast_shape.append(1)
position_broadcast_shape.extend([seq_length, width])
position_embeddings = tf.reshape(position_embeddings,
position_broadcast_shape)
output += position_embeddings
output = layer_norm_and_dropout(output, dropout_prob)
return output
def tower(inputs,
is_training,
dropout_probability,
input_noise,
normalize_input,
flip_horizontally,
translate,
num_logits,
is_initialization=False,
name=None):
with tf.name_scope(name, "tower"):
default_conv_args = dict(padding='SAME',
kernel_size=[3, 3],
activation_fn=nn.lrelu,
init=is_initialization)
training_mode_funcs = [
nn.random_translate, nn.flip_randomly, nn.gaussian_noise,
slim.dropout, wn.fully_connected, wn.conv2d
]
training_args = dict(is_training=is_training)
with \
slim.arg_scope([wn.conv2d], **default_conv_args), \
slim.arg_scope(training_mode_funcs, **training_args):
#pylint: disable=no-value-for-parameter
net = inputs
assert_shape(net, [None, 32, 32, 3])
net = tf.cond(
normalize_input, lambda: slim.layer_norm(
net, scale=False, center=False, scope='normalize_inputs'),
lambda: net)
assert_shape(net, [None, 32, 32, 3])
net = nn.flip_randomly(net,
horizontally=flip_horizontally,
vertically=False,
name='random_flip')
net = tf.cond(
translate, lambda: nn.random_translate(
net, scale=2, name='random_translate'), lambda: net)
net = nn.gaussian_noise(net,
scale=input_noise,
name='gaussian_noise')
net = wn.conv2d(net, 128, scope="conv_1_1")
net = wn.conv2d(net, 128, scope="conv_1_2")
net = wn.conv2d(net, 128, scope="conv_1_3")
net = slim.max_pool2d(net, [2, 2], scope='max_pool_1')
net = slim.dropout(net,
1 - dropout_probability,
scope='dropout_probability_1')
assert_shape(net, [None, 16, 16, 128])
net = wn.conv2d(net, 256, scope="conv_2_1")
net = wn.conv2d(net, 256, scope="conv_2_2")
net = wn.conv2d(net, 256, scope="conv_2_3")
net = slim.max_pool2d(net, [2, 2], scope='max_pool_2')
net = slim.dropout(net,
1 - dropout_probability,
scope='dropout_probability_2')
assert_shape(net, [None, 8, 8, 256])
net = wn.conv2d(net, 512, padding='VALID', scope="conv_3_1")
assert_shape(net, [None, 6, 6, 512])
net = wn.conv2d(net, 256, kernel_size=[1, 1], scope="conv_3_2")
net = wn.conv2d(net, 128, kernel_size=[1, 1], scope="conv_3_3")
net = slim.avg_pool2d(net, [6, 6], scope='avg_pool')
assert_shape(net, [None, 1, 1, 128])
net = slim.flatten(net)
assert_shape(net, [None, 128])
primary_logits = wn.fully_connected(net,
10,
init=is_initialization)
secondary_logits = wn.fully_connected(net,
10,
init=is_initialization)
with tf.control_dependencies([
tf.assert_greater_equal(num_logits, 1),
tf.assert_less_equal(num_logits, 2)
]):
secondary_logits = tf.case([
(tf.equal(num_logits, 1), lambda: primary_logits),
(tf.equal(num_logits, 2), lambda: secondary_logits),
],
exclusive=True,
default=lambda: primary_logits)
assert_shape(primary_logits, [None, 10])
assert_shape(secondary_logits, [None, 10])
return primary_logits, secondary_logits
def embedding_postprocessor(input_tensor,
use_position_embeddings=True,
position1_ids=None,
position2_ids=None,
position_embedding_name="position_embeddings",
initializer_range=0.02,
max_position_embeddings=512,
dropout_prob=0.1):
"""Performs various post-processing on a word embedding tensor.
Args:
input_tensor: float Tensor of shape [batch_size, seq_length,
embedding_size].
use_position_embeddings: bool. Whether to add position embeddings for the
position of each token in the sequence.
position_embedding_name: string. The name of the embedding table variable
for positional embeddings.
initializer_range: float. Range of the weight initialization.
max_position_embeddings: int. Maximum sequence length that might ever be
used with this model. This can be longer than the sequence length of
input_tensor, but cannot be shorter.
dropout_prob: float. Dropout probability applied to the final output tensor.
Returns:
float tensor with same shape as `input_tensor`.
Raises:
ValueError: One of the tensor shapes or input values is invalid.
"""
input_shape = get_shape_list(input_tensor, expected_rank=3)
batch_size = input_shape[0]
seq_length = input_shape[1]
embedding_size = input_shape[2]
output = input_tensor
if use_position_embeddings:
assert_op = tf.assert_less_equal(seq_length, max_position_embeddings)
with tf.control_dependencies([assert_op]):
full_position_embeddings = tf.get_variable(
name=position_embedding_name,
shape=[max_position_embeddings * 2, embedding_size],
initializer=create_initializer(initializer_range))
# Since the position embedding table is a learned variable, we create it
# using a (long) sequence length `max_position_embeddings`. The actual
# sequence length might be shorter than this, for faster training of
# tasks that do not have long sequences.
#
# So `full_position_embeddings` is effectively an embedding table
# for position [0, 1, 2, ..., max_position_embeddings-1], and the current
# sequence has positions [0, 1, 2, ... seq_length-1], so we can just
# perform a slice.
# position_embeddings = tf.slice(full_position_embeddings, [0, 0],
# [seq_length, -1])
if position1_ids == None or position2_ids == None:
ValueError('You need input the position information.')
flat_position1_ids = tf.reshape(position1_ids, shape=[-1])
flat_position2_ids = tf.reshape(position2_ids, shape=[-1])
position1_embeddings = tf.nn.embedding_lookup(
full_position_embeddings, flat_position1_ids)
position2_embeddings = tf.nn.embedding_lookup(
full_position_embeddings, flat_position2_ids)
# num_dims = len(output.shape.as_list())
position_embeddings = position1_embeddings + position2_embeddings
position_embeddings = tf.reshape(
position_embeddings,
shape=[batch_size, seq_length, embedding_size])
# Only the last two dimensions are relevant (`seq_length` and `width`), so
# we broadcast among the first dimensions, which is typically just
# the batch size.
# position_broadcast_shape = []
# for _ in range(num_dims - 2):
# position_broadcast_shape.append(1)
# position_broadcast_shape.extend([seq_length, embedding_size])
# position_embeddings = tf.reshape(position_embeddings,
# position_broadcast_shape)
output += position_embeddings
output = layer_norm_and_dropout(output, dropout_prob)
return output
def preprocess_data(sequence_id, sequence, audio, velocity_range, hparams,
is_training):
"""Compute spectral representation, labels, and length from sequence/audio.
Args:
sequence_id: id of the sequence.
sequence: String tensor containing serialized NoteSequence proto.
audio: String tensor containing containing WAV data.
velocity_range: String tensor containing max and min velocities of file as a
serialized VelocityRange.
hparams: HParams object specifying hyperparameters.
is_training: Whether or not this is a training run.
Returns:
An InputTensors tuple.
Raises:
ValueError: If hparams is contains an invalid spec_type.
"""
wav_jitter_amount_ms = label_jitter_amount_ms = 0
# if there is combined jitter, we must generate it once here
if is_training and hparams.jitter_amount_ms > 0:
wav_jitter_amount_ms = np.random.choice(hparams.jitter_amount_ms,
size=1)
label_jitter_amount_ms = wav_jitter_amount_ms
if label_jitter_amount_ms > 0:
sequence = jitter_label_op(sequence, label_jitter_amount_ms / 1000.)
# possibly shift the entire sequence backward for better forward only training
if hparams.backward_shift_amount_ms > 0:
sequence = jitter_label_op(sequence,
hparams.backward_shift_amount_ms / 1000.)
if is_training:
audio = transform_wav_data_op(audio,
hparams=hparams,
jitter_amount_sec=wav_jitter_amount_ms /
1000.)
spec = wav_to_spec_op(audio, hparams=hparams)
labels, label_weights, onsets, offsets, velocities = sequence_to_pianoroll_op(
sequence, velocity_range, hparams=hparams)
length = wav_to_num_frames_op(audio, hparams_frames_per_second(hparams))
asserts = []
if hparams.max_expected_train_example_len and is_training:
asserts.append(
tf.assert_less_equal(length,
hparams.max_expected_train_example_len))
with tf.control_dependencies(asserts):
return InputTensors(spec=spec,
labels=labels,
label_weights=label_weights,
length=length,
onsets=onsets,
offsets=offsets,
velocities=velocities,
sequence_id=sequence_id,
note_sequence=sequence)
def embedding_postprocessor(input_tensor,
use_token_type=False,
token_type_ids=None,
token_type_vocab_size=2,
token_type_embedding_name="token_type_embeddings",
use_position_embeddings=True,
position_embedding_name="position_embeddings",
initializer_range=0.02,
max_position_embeddings=512,
dropout_prob=0.1):
input_shape = get_shape_list(input_tensor, expected_rank=3)
batch_size = input_shape[0]
seq_length = input_shape[1]
width = input_shape[2]
output = input_tensor
if use_token_type:
if token_type_ids is None:
raise ValueError("`token_type_ids` must be specified if"
"`use_token_type` is True.")
token_type_table = tf.get_variable(
name=token_type_embedding_name,
shape=[token_type_vocab_size, width],
initializer=create_initializer(initializer_range))
# This vocab will be small so we always do one-hot here, since it is always
# faster for a small vocabulary.
flat_token_type_ids = tf.reshape(token_type_ids, [-1])
one_hot_ids = tf.one_hot(
flat_token_type_ids, depth=token_type_vocab_size)
token_type_embeddings = tf.matmul(one_hot_ids, token_type_table)
token_type_embeddings = tf.reshape(token_type_embeddings,
[batch_size, seq_length, width])
output += token_type_embeddings
if use_position_embeddings:
assert_op = tf.assert_less_equal(seq_length, max_position_embeddings)
with tf.control_dependencies([assert_op]):
full_position_embeddings = tf.get_variable(
name=position_embedding_name,
shape=[max_position_embeddings, width],
initializer=create_initializer(initializer_range))
# Since the position embedding table is a learned variable, we create it
# using a (long) sequence length `max_position_embeddings`. The actual
# sequence length might be shorter than this, for faster training of
# tasks that do not have long sequences.
#
# So `full_position_embeddings` is effectively an embedding table
# for position [0, 1, 2, ..., max_position_embeddings-1], and the current
# sequence has positions [0, 1, 2, ... seq_length-1], so we can just
# perform a slice.
position_embeddings = tf.slice(full_position_embeddings, [0, 0],
[seq_length, -1])
num_dims = len(output.shape.as_list())
# Only the last two dimensions are relevant (`seq_length` and `width`), so
# we broadcast among the first dimensions, which is typically just
# the batch size.
position_broadcast_shape = []
for _ in range(num_dims - 2):
position_broadcast_shape.append(1)
position_broadcast_shape.extend([seq_length, width])
# position_embeddings : [1,seq_length,width]
position_embeddings = tf.reshape(position_embeddings,
position_broadcast_shape)
# output : [batch_size, seq_length, width], broadcast position_embeddings
output += position_embeddings
output = layer_norm_and_dropout(output, dropout_prob)
return output
def fgsm_generate(x, model, y=None, eps=0.3, ord=np.inf, clip_min=None, clip_max=None, clip_grad=False, targeted=False, sanity_checks=True):
asserts = []
# If a data range was specified, check that the input was in that range
if clip_min is not None:
asserts.append(tf.assert_greater_equal(x, tf.cast(clip_min, x.dtype)))
if clip_max is not None:
asserts.append(tf.assert_less_equal(x, tf.cast(clip_max, x.dtype)))
logits = model(x)._op.inputs[0]
if y is None:
# Using model predictions as ground truth to avoid label leaking
preds_max = reduce_max(logits, 1, keepdims=True)
y = tf.to_float(tf.equal(logits, preds_max))
y = tf.stop_gradient(y)
y = y / reduce_sum(y, 1, keepdims=True)
# Compute loss
#################
## CE-loss ###
#################
loss = softmax_cross_entropy_with_logits(labels=y, logits=logits)
if targeted:
loss = -loss
# ##################
# ### CW-loss ###
# ##################
# logits_sort = tf.contrib.framework.sort(logits, axis=1, direction="DESCENDING")
# logits_max = tf.gather(logits_sort, axis=1, indices=[0])
# logits_secondmax = tf.gather(logits_sort, axis=1, indices=[1])
#
# logits_loss = logits_max - logits_secondmax
# loss = -tf.reduce_mean(logits_loss)
# if targeted:
# loss = -loss
# ##################
# ### DLR-loss ###
# ##################
# logits_sort = tf.contrib.framework.sort(logits, axis=1, direction="DESCENDING")
# logits_max = tf.gather(logits_sort, axis=1, indices=[0])
# logits_secondmax = tf.gather(logits_sort, axis=1, indices=[1])
# logits_thirdmax = tf.gather(logits_sort, axis=1, indices=[2])
#
# logits_loss = tf.divide(logits_max - logits_secondmax, logits_max - logits_thirdmax + 1e12)
#
# loss = -tf.reduce_mean(logits_loss)
# if targeted:
# loss = -loss
# Define gradient of loss wrt input
grad, = tf.gradients(loss, x)
if clip_grad:
grad = zero_out_clipped_grads(grad, x, clip_min, clip_max)
optimal_perturbation = optimize_linear(grad, eps, ord)
# Add perturbation to original example to obtain adversarial example
adv_x = x + optimal_perturbation
# If clipping is needed, reset all values outside of [clip_min, clip_max]
if (clip_min is not None) or (clip_max is not None):
# We don't currently support one-sided clipping
assert clip_min is not None and clip_max is not None
adv_x = clip_by_value(adv_x, clip_min, clip_max)
if sanity_checks:
with tf.control_dependencies(asserts):
adv_x = tf.identity(adv_x)
return adv_x
def __init__(self,
mean_direction,
concentration,
validate_args=False,
allow_nan_stats=True,
name='VonMisesFisher'):
"""Creates a new `VonMisesFisher` instance.
Args:
mean_direction: Floating-point `Tensor` with shape [B1, ... Bn, D].
A unit vector indicating the mode of the distribution, or the
unit-normalized direction of the mean. (This is *not* in general the
mean of the distribution; the mean is not generally in the support of
the distribution.) NOTE: `D` is currently restricted to <= 5.
concentration: Floating-point `Tensor` having batch shape [B1, ... Bn]
broadcastable with `mean_direction`. The level of concentration of
samples around the `mean_direction`. `concentration=0` indicates a
uniform distribution over the unit hypersphere, and `concentration=+inf`
indicates a `Deterministic` distribution (delta function) at
`mean_direction`.
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
allow_nan_stats: Python `bool`, default `True`. When `True`,
statistics (e.g., mean, mode, variance) use the value "`NaN`" to
indicate the result is undefined. When `False`, an exception is raised
if one or more of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class.
Raises:
ValueError: For known-bad arguments, i.e. unsupported event dimension.
"""
parameters = dict(locals())
with tf.name_scope(name, values=[mean_direction, concentration]) as name:
dtype = dtype_util.common_dtype([mean_direction, concentration],
tf.float32)
mean_direction = tf.convert_to_tensor(
mean_direction, name='mean_direction', dtype=dtype)
concentration = tf.convert_to_tensor(
concentration, name='concentration', dtype=dtype)
assertions = [
tf.assert_non_negative(
concentration, message='`concentration` must be non-negative'),
tf.assert_greater(
tf.shape(mean_direction)[-1], 1,
message='`mean_direction` may not have scalar event shape'),
tf.assert_near(
1., tf.linalg.norm(mean_direction, axis=-1),
message='`mean_direction` must be unit-length')
] if validate_args else []
if mean_direction.shape.with_rank_at_least(1)[-1].value is not None:
if mean_direction.shape.with_rank_at_least(1)[-1].value > 5:
raise ValueError('vMF ndims > 5 is not currently supported')
elif validate_args:
assertions += [tf.assert_less_equal(
tf.shape(mean_direction)[-1], 5,
message='vMF ndims > 5 is not currently supported')]
with tf.control_dependencies(assertions):
self._mean_direction = tf.identity(mean_direction)
self._concentration = tf.identity(concentration)
tf.assert_same_float_dtype([self._mean_direction, self._concentration])
# mean_direction is always reparameterized.
# concentration is only for event_dim==3, via an inversion sampler.
reparameterization_type = (
reparameterization.FULLY_REPARAMETERIZED
if mean_direction.shape.with_rank_at_least(1)[-1].value == 3 else
reparameterization.NOT_REPARAMETERIZED)
super(VonMisesFisher, self).__init__(
dtype=self._concentration.dtype,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
reparameterization_type=reparameterization_type,
parameters=parameters,
graph_parents=[self._mean_direction, self._concentration],
name=name)
def _sample_n(self, n, seed=None):
seed = seed_stream.SeedStream(seed, salt='vom_mises_fisher')
# The sampling strategy relies on the fact that vMF variates are symmetric
# about the mean direction. Accordingly, if we have a sampling strategy for
# the away-from-mean angle, then we can uniformly sample the remaining
# dimensions on the S^{dim-2} sphere for , and rotate these samples from a
# (1, 0, 0, ..., 0)-mode distribution into the target orientation.
#
# This is easy to imagine on the 1-sphere (S^1; in 2-D space): sample a
# von-Mises distributed `x` value in [-1, 1], then uniformly select what
# amounts to a "up" or "down" additional degree of freedom after unit
# normalizing, followed by a final rotation to the desired mean direction
# from a basis of (1, 0).
#
# On S^2 (in 3-D), selecting a vMF `x` identifies a circle in `yz` on the
# unit sphere over which the distribution is uniform, in particular the
# circle where x = \hat{x} intersects the unit sphere. We pick a point on
# that circle, then rotate to the desired mean direction from a basis of
# (1, 0, 0).
event_dim = self.event_shape[0].value or self._event_shape_tensor()[0]
sample_batch_shape = tf.concat([[n], self._batch_shape_tensor()], axis=0)
dim = tf.cast(event_dim - 1, self.dtype)
if event_dim == 3:
samples_dim0 = self._sample_3d(n, seed=seed)
else:
# Wood'94 provides a rejection algorithm to sample the x coordinate.
# Wood'94 definition of b:
# b = (-2 * kappa + tf.sqrt(4 * kappa**2 + dim**2)) / dim
# https://stats.stackexchange.com/questions/156729 suggests:
b = dim / (2 * self.concentration +
tf.sqrt(4 * self.concentration**2 + dim**2))
# TODO(bjp): Integrate any useful numerical tricks from hyperspherical VAE
# https://github.com/nicola-decao/s-vae-tf/
x = (1 - b) / (1 + b)
c = self.concentration * x + dim * tf.log1p(-x**2)
beta = beta_lib.Beta(dim / 2, dim / 2)
def cond_fn(w, should_continue):
del w
return tf.reduce_any(should_continue)
def body_fn(w, should_continue):
z = beta.sample(sample_shape=sample_batch_shape, seed=seed())
w = tf.where(should_continue, (1 - (1 + b) * z) / (1 - (1 - b) * z), w)
w = tf.check_numerics(w, 'w')
should_continue = tf.logical_and(
should_continue,
self.concentration * w + dim * tf.log1p(-x * w) - c <
tf.log(tf.random_uniform(sample_batch_shape, seed=seed(),
dtype=self.dtype)))
return w, should_continue
w = tf.zeros(sample_batch_shape, dtype=self.dtype)
should_continue = tf.ones(sample_batch_shape, dtype=tf.bool)
samples_dim0 = tf.while_loop(cond_fn, body_fn, (w, should_continue))[0]
samples_dim0 = samples_dim0[..., tf.newaxis]
if not self._allow_nan_stats:
# Verify samples are w/in -1, 1, with useful error output tensors (top
# value rather than all values).
with tf.control_dependencies([
tf.assert_less_equal(
samples_dim0, self.dtype.as_numpy_dtype(1.01),
data=[tf.nn.top_k(tf.reshape(samples_dim0, [-1]))[0]]),
tf.assert_greater_equal(
samples_dim0, self.dtype.as_numpy_dtype(-1.01),
data=[-tf.nn.top_k(tf.reshape(-samples_dim0, [-1]))[0]])]):
samples_dim0 = tf.identity(samples_dim0)
samples_otherdims_shape = tf.concat([sample_batch_shape, [event_dim - 1]],
axis=0)
unit_otherdims = tf.nn.l2_normalize(
tf.random_normal(samples_otherdims_shape, seed=seed(),
dtype=self.dtype),
axis=-1)
samples = tf.concat([
samples_dim0, # we must avoid sqrt(1 - (>1)**2)
tf.sqrt(tf.maximum(1 - samples_dim0**2, 0.)) * unit_otherdims
], axis=-1)
samples = tf.nn.l2_normalize(samples, axis=-1)
if not self._allow_nan_stats:
samples = tf.check_numerics(samples, 'samples')
# Runtime assert that samples are unit length.
if not self._allow_nan_stats:
worst, idx = tf.nn.top_k(
tf.reshape(tf.abs(1 - tf.linalg.norm(samples, axis=-1)), [-1]))
with tf.control_dependencies([
tf.assert_near(
self.dtype.as_numpy_dtype(0), worst,
data=[worst, idx,
tf.gather(tf.reshape(samples, [-1, event_dim]), idx)],
atol=1e-4, summarize=100)]):
samples = tf.identity(samples)
# The samples generated are symmetric around a mode at (1, 0, 0, ...., 0).
# Now, we move the mode to `self.mean_direction` using a rotation matrix.
if not self._allow_nan_stats:
# Assert that the basis vector rotates to the mean direction, as expected.
basis = tf.cast(tf.concat([[1.], tf.zeros([event_dim - 1])], axis=0),
self.dtype)
with tf.control_dependencies([
tf.assert_less(
tf.linalg.norm(self._rotate(basis) - self.mean_direction,
axis=-1),
self.dtype.as_numpy_dtype(1e-5))
]):
return self._rotate(samples)
return self._rotate(samples)
def __init__(self,
batch_size,
total_num_examples,
max_learning_rate=1.,
preconditioner_decay_rate=0.95,
burnin=25,
burnin_max_learning_rate=1e-6,
use_single_learning_rate=False,
name=None,
variable_scope=None):
default_name = 'VariationalSGD'
with tf.name_scope(name, default_name, [
max_learning_rate, preconditioner_decay_rate, batch_size,
burnin, burnin_max_learning_rate
]):
if variable_scope is None:
var_scope_name = tf.get_default_graph().unique_name(
name or default_name)
with tf.variable_scope(var_scope_name) as scope:
self._variable_scope = scope
else:
self._variable_scope = variable_scope
self._preconditioner_decay_rate = tf.convert_to_tensor(
preconditioner_decay_rate, name='preconditioner_decay_rate')
self._batch_size = tf.convert_to_tensor(batch_size,
name='batch_size')
self._total_num_examples = tf.convert_to_tensor(
total_num_examples, name='total_num_examples')
self._burnin = tf.convert_to_tensor(burnin, name='burnin')
self._burnin_max_learning_rate = tf.convert_to_tensor(
burnin_max_learning_rate, name='burnin_max_learning_rate')
self._max_learning_rate = tf.convert_to_tensor(
max_learning_rate, name='max_learning_rate')
self._use_single_learning_rate = use_single_learning_rate
with tf.variable_scope(self._variable_scope):
self._counter = tf.get_variable('counter',
initializer=0,
trainable=False)
self._preconditioner_decay_rate = control_flow_ops.with_dependencies([
tf.assert_non_negative(
self._preconditioner_decay_rate,
message='`preconditioner_decay_rate` must be non-negative'
),
tf.assert_less_equal(
self._preconditioner_decay_rate,
1.,
message='`preconditioner_decay_rate` must be at most 1.'),
], self._preconditioner_decay_rate)
self._batch_size = control_flow_ops.with_dependencies([
tf.assert_greater(
self._batch_size,
0,
message='`batch_size` must be greater than zero')
], self._batch_size)
self._total_num_examples = control_flow_ops.with_dependencies([
tf.assert_greater(
self._total_num_examples,
0,
message='`total_num_examples` must be greater than zero')
], self._total_num_examples)
self._burnin = control_flow_ops.with_dependencies([
tf.assert_non_negative(
self._burnin, message='`burnin` must be non-negative'),
tf.assert_integer(self._burnin,
message='`burnin` must be an integer')
], self._burnin)
self._burnin_max_learning_rate = control_flow_ops.with_dependencies([
tf.assert_non_negative(
self._burnin_max_learning_rate,
message='`burnin_max_learning_rate` must be non-negative')
], self._burnin_max_learning_rate)
self._max_learning_rate = control_flow_ops.with_dependencies([
tf.assert_non_negative(
self._max_learning_rate,
message='`max_learning_rate` must be non-negative')
], self._max_learning_rate)
super(VariationalSGD, self).__init__(use_locking=False,
name=name or default_name)
def __init__(self,
df,
scale_operator,
input_output_cholesky=False,
validate_args=False,
allow_nan_stats=True,
name=None):
"""Construct Wishart distributions.
Args:
df: `float` or `double` tensor, the degrees of freedom of the
distribution(s). `df` must be greater than or equal to `k`.
scale_operator: `float` or `double` instance of `LinearOperator`.
input_output_cholesky: Python `bool`. If `True`, functions whose input or
output have the semantics of samples assume inputs are in Cholesky form
and return outputs in Cholesky form. In particular, if this flag is
`True`, input to `log_prob` is presumed of Cholesky form and output from
`sample`, `mean`, and `mode` are of Cholesky form. Setting this
argument to `True` is purely a computational optimization and does not
change the underlying distribution; for instance, `mean` returns the
Cholesky of the mean, not the mean of Cholesky factors. The `variance`
and `stddev` methods are unaffected by this flag.
Default value: `False` (i.e., input/output does not have Cholesky
semantics).
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
(e.g., mean, mode, variance) use the value "`NaN`" to indicate the
result is undefined. When `False`, an exception is raised if one or
more of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class.
Raises:
TypeError: if scale is not floating-type
TypeError: if scale.dtype != df.dtype
ValueError: if df < k, where scale operator event shape is
`(k, k)`
"""
parameters = dict(locals())
self._input_output_cholesky = input_output_cholesky
with tf.name_scope(name) as name:
with tf.name_scope("init", values=[df, scale_operator]):
if not scale_operator.dtype.is_floating:
raise TypeError(
"scale_operator.dtype=%s is not a floating-point type"
% scale_operator.dtype)
if not scale_operator.is_square:
print(scale_operator.to_dense().eval())
raise ValueError("scale_operator must be square.")
self._scale_operator = scale_operator
self._df = tf.convert_to_tensor(df,
dtype=scale_operator.dtype,
name="df")
contrib_tensor_util.assert_same_float_dtype(
(self._df, self._scale_operator))
if (self._scale_operator.shape.ndims is None
or self._scale_operator.shape[-1].value is None):
self._dimension = tf.cast(
self._scale_operator.domain_dimension_tensor(),
dtype=self._scale_operator.dtype,
name="dimension")
else:
self._dimension = tf.convert_to_tensor(
self._scale_operator.shape[-1].value,
dtype=self._scale_operator.dtype,
name="dimension")
df_val = tensor_util.constant_value(self._df)
dim_val = tensor_util.constant_value(self._dimension)
if df_val is not None and dim_val is not None:
df_val = np.asarray(df_val)
if not df_val.shape:
df_val = [df_val]
if any(df_val < dim_val):
raise ValueError(
"Degrees of freedom (df = %s) cannot be less than "
"dimension of scale matrix (scale.dimension = %s)"
% (df_val, dim_val))
elif validate_args:
assertions = tf.assert_less_equal(
self._dimension,
self._df,
message=("Degrees of freedom (df = %s) cannot be "
"less than dimension of scale matrix "
"(scale.dimension = %s)" %
(self._dimension, self._df)))
self._df = control_flow_ops.with_dependencies([assertions],
self._df)
super(_WishartLinearOperator, self).__init__(
dtype=self._scale_operator.dtype,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
reparameterization_type=tf.distributions.FULLY_REPARAMETERIZED,
parameters=parameters,
graph_parents=([self._df, self._dimension] +
self._scale_operator.graph_parents),
name=name)
def __init__(self,
learning_rate,
preconditioner_decay_rate=0.95,
data_size=1,
burnin=25,
diagonal_bias=1e-8,
name=None,
parallel_iterations=10,
variable_scope=None):
default_name = 'StochasticGradientLangevinDynamics'
with tf.name_scope(name, default_name, [
learning_rate, preconditioner_decay_rate, data_size, burnin,
diagonal_bias
]):
if tf.executing_eagerly():
raise NotImplementedError(
'Eager execution currently not supported for '
' SGLD optimizer.')
if variable_scope is None:
var_scope_name = tf.get_default_graph().unique_name(
name or default_name)
with tf.variable_scope(var_scope_name) as scope:
self._variable_scope = scope
else:
self._variable_scope = variable_scope
self._preconditioner_decay_rate = tf.convert_to_tensor(
preconditioner_decay_rate, name='preconditioner_decay_rate')
self._data_size = tf.convert_to_tensor(data_size, name='data_size')
self._burnin = tf.convert_to_tensor(burnin, name='burnin')
self._diagonal_bias = tf.convert_to_tensor(diagonal_bias,
name='diagonal_bias')
self._learning_rate = tf.convert_to_tensor(learning_rate,
name='learning_rate')
self._parallel_iterations = parallel_iterations
with tf.variable_scope(self._variable_scope):
self._counter = tf.get_variable('counter',
initializer=0,
trainable=False)
self._preconditioner_decay_rate = control_flow_ops.with_dependencies([
tf.assert_non_negative(
self._preconditioner_decay_rate,
message='`preconditioner_decay_rate` must be non-negative'
),
tf.assert_less_equal(
self._preconditioner_decay_rate,
1.,
message='`preconditioner_decay_rate` must be at most 1.'),
], self._preconditioner_decay_rate)
self._data_size = control_flow_ops.with_dependencies([
tf.assert_greater(
self._data_size,
0,
message='`data_size` must be greater than zero')
], self._data_size)
self._burnin = control_flow_ops.with_dependencies([
tf.assert_non_negative(
self._burnin, message='`burnin` must be non-negative'),
tf.assert_integer(self._burnin,
message='`burnin` must be an integer')
], self._burnin)
self._diagonal_bias = control_flow_ops.with_dependencies([
tf.assert_non_negative(
self._diagonal_bias,
message='`diagonal_bias` must be non-negative')
], self._diagonal_bias)
super(StochasticGradientLangevinDynamics,
self).__init__(use_locking=False, name=name or default_name)
def percentile(x,
q,
axis=None,
interpolation=None,
keep_dims=False,
validate_args=False,
name=None):
"""Compute the `q`-th percentile of `x`.
Given a vector `x`, the `q`-th percentile of `x` is the value `q / 100` of the
way from the minimum to the maximum in a sorted copy of `x`.
The values and distances of the two nearest neighbors as well as the
`interpolation` parameter will determine the percentile if the normalized
ranking does not match the location of `q` exactly.
This function is the same as the median if `q = 50`, the same as the minimum
if `q = 0` and the same as the maximum if `q = 100`.
```python
# Get 30th percentile with default ('nearest') interpolation.
x = [1., 2., 3., 4.]
percentile(x, q=30.)
==> 2.0
# Get 30th percentile with 'lower' interpolation
x = [1., 2., 3., 4.]
percentile(x, q=30., interpolation='lower')
==> 1.0
# Get 100th percentile (maximum). By default, this is computed over every dim
x = [[1., 2.]
[3., 4.]]
percentile(x, q=100.)
==> 4.0
# Treat the leading dim as indexing samples, and find the 100th quantile (max)
# over all such samples.
x = [[1., 2.]
[3., 4.]]
percentile(x, q=100., axis=[0])
==> [3., 4.]
```
Compare to `numpy.percentile`.
Args:
x: Floating point `N-D` `Tensor` with `N > 0`. If `axis` is not `None`,
`x` must have statically known number of dimensions.
q: Scalar `Tensor` in `[0, 100]`. The percentile.
axis: Optional `0-D` or `1-D` integer `Tensor` with constant values.
The axis that hold independent samples over which to return the desired
percentile. If `None` (the default), treat every dimension as a sample
dimension, returning a scalar.
interpolation : {"lower", "higher", "nearest"}. Default: "nearest"
This optional parameter specifies the interpolation method to
use when the desired quantile lies between two data points `i < j`:
* lower: `i`.
* higher: `j`.
* nearest: `i` or `j`, whichever is nearest.
keep_dims: Python `bool`. If `True`, the last dimension is kept with size 1
If `False`, the last dimension is removed from the output shape.
validate_args: Whether to add runtime checks of argument validity.
If False, and arguments are incorrect, correct behavior is not guaranteed.
name: A Python string name to give this `Op`. Default is "percentile"
Returns:
A `(N - len(axis))` dimensional `Tensor` of same dtype as `x`, or, if
`axis` is `None`, a scalar.
Raises:
ValueError: If argument 'interpolation' is not an allowed type.
"""
name = name or "percentile"
allowed_interpolations = {"lower", "higher", "nearest"}
if interpolation is None:
interpolation = "nearest"
else:
if interpolation not in allowed_interpolations:
raise ValueError(
"Argument 'interpolation' must be in %s. Found %s" %
(allowed_interpolations, interpolation))
with tf.name_scope(name, [x, q]):
x = tf.convert_to_tensor(x, name="x")
# Double is needed here and below, else we get the wrong index if the array
# is huge along axis.
q = tf.to_double(q, name="q")
_get_static_ndims(q, expect_ndims=0)
if validate_args:
q = control_flow_ops.with_dependencies([
tf.assert_rank(q, 0),
tf.assert_greater_equal(q, tf.to_double(0.)),
tf.assert_less_equal(q, tf.to_double(100.))
], q)
if axis is None:
y = tf.reshape(x, [-1])
else:
axis = tf.convert_to_tensor(axis, name="axis")
tf.assert_integer(axis)
axis_ndims = _get_static_ndims(axis,
expect_static=True,
expect_ndims_no_more_than=1)
axis_const = tensor_util.constant_value(axis)
if axis_const is None:
raise ValueError(
"Expected argument 'axis' to be statically available. Found: %s"
% axis)
axis = axis_const
if axis_ndims == 0:
axis = [axis]
axis = [int(a) for a in axis]
x_ndims = _get_static_ndims(x,
expect_static=True,
expect_ndims_at_least=1)
axis = _make_static_axis_non_negative(axis, x_ndims)
y = _move_dims_to_flat_end(x, axis, x_ndims)
frac_at_q_or_above = 1. - q / 100.
d = tf.to_double(tf.shape(y)[-1])
if interpolation == "lower":
index = tf.ceil((d - 1) * frac_at_q_or_above)
elif interpolation == "higher":
index = tf.floor((d - 1) * frac_at_q_or_above)
elif interpolation == "nearest":
index = tf.round((d - 1) * frac_at_q_or_above)
# If d is gigantic, then we would have d == d - 1, even in double... So
# let's use max/min to avoid out of bounds errors.
d = tf.shape(y)[-1]
# d - 1 will be distinct from d in int32.
index = tf.clip_by_value(tf.to_int32(index), 0, d - 1)
# Sort everything, not just the top 'k' entries, which allows multiple calls
# to sort only once (under the hood) and use CSE.
sorted_y = _sort_tensor(y)
# result.shape = B
result = sorted_y[..., index]
result.set_shape(y.get_shape()[:-1])
if keep_dims:
if axis is None:
# ones_vec = [1, 1,..., 1], total length = len(S) + len(B).
ones_vec = tf.ones(shape=[_get_best_effort_ndims(x)],
dtype=tf.int32)
result *= tf.ones(ones_vec, dtype=x.dtype)
else:
result = _insert_back_keep_dims(result, axis)
return result
def embed(input_ids,
vocab_size,
embedding_size,
position_offset=0,
initializer_range=0.02,
max_position_embeddings=512,
use_one_hot_embeddings=True):
"""reur and position embeddings
:param input_ids: int Tensor of shape [batch_size, seq_length].
:param vocab_size: number of words in vocab
:param embedding_size: dimensionality of the embedding
:param position_offset: aka number of cached tokens.
:param initializer_range: float. Range of the weight initialization.
:param max_position_embeddings: int. Maximum sequence length.
:param use_one_hot_embeddings: probably want this to be true
:return: [batch_size, seq_length, embedding_size] embedded tensor
"""
(batch_size, seq_length) = get_shape_list(input_ids, expected_rank=2)
embedding_table = tf.get_variable(
name='word_embed',
shape=[vocab_size, embedding_size],
initializer=create_initializer(initializer_range),
)
assert_op = tf.assert_less_equal(tf.reduce_max(input_ids), vocab_size - 1)
with tf.control_dependencies([assert_op]):
if use_one_hot_embeddings:
flat_input_ids = tf.reshape(input_ids, [-1])
one_hot_input_ids = tf.one_hot(flat_input_ids, depth=vocab_size)
output_flat = tf.matmul(one_hot_input_ids, embedding_table)
else:
output_flat = tf.nn.embedding_lookup(embedding_table, input_ids)
embedded_input = tf.reshape(output_flat,
[batch_size, seq_length, embedding_size])
assert_op = tf.assert_less_equal(seq_length, max_position_embeddings)
with tf.control_dependencies([assert_op]):
full_position_embeddings = tf.get_variable(
name='pos_embed',
shape=[max_position_embeddings, embedding_size],
initializer=create_initializer(initializer_range),
)
# Since the position embedding table is a learned variable, we create it
# using a (long) sequence length `max_position_embeddings`. The actual
# sequence length might be shorter than this, for faster training of
# tasks that do not have long sequences.
#
# So `full_position_embeddings` is effectively an embedding table
# for position [0, 1, 2, ..., max_position_embeddings-1], and the current
# sequence has positions [0, 1, 2, ... seq_length-1], so we can just
# perform a slice.
if position_offset == 0:
embedded_input += tf.slice(full_position_embeddings, [0, 0],
[seq_length, -1])[None]
else:
# Tensorflow is too stupid to allow slicing
flat_pos_ids = (tf.range(seq_length, dtype=tf.int32) +
position_offset)
one_hot_pos_ids = tf.one_hot(flat_pos_ids,
depth=max_position_embeddings)
# [seq_length, full_position_embeddings], [full_position_embeddings, dim]
seq_embeds = tf.matmul(one_hot_pos_ids, full_position_embeddings)
embedded_input += seq_embeds[None]
# embedded_input += tf.slice(full_position_embeddings[position_offset:], [0, 0], [seq_length, -1])[None]
return layer_norm(embedded_input, name='embed_norm'), embedding_table
def __call__(self, inputs, state, scope=None):
(
past_cand_symbols, # [batch_size, max_len]
past_cand_logprobs,# [batch_size]
past_beam_symbols, # [batch_size*self.beam_size, max_len], right-aligned!!!
past_beam_logprobs,# [batch_size*self.beam_size]
past_cell_state,
) = state
batch_size = tf.shape(past_cand_logprobs)[0] # TODO: get as int, if possible
full_size = batch_size * self.beam_size
cell_inputs = inputs
cell_outputs, raw_cell_state = self.cell(cell_inputs, past_cell_state)
logprobs = tf.nn.log_softmax(cell_outputs)
logprobs_batched = tf.reshape(logprobs + tf.expand_dims(past_beam_logprobs, 1),
[-1, self.beam_size * self.num_classes])
logprobs_batched.set_shape((None, self.beam_size * self.num_classes))
# prints and asserts
tf.assert_less_equal(logprobs, 0.0)
tf.assert_less_equal(past_beam_logprobs, 0.0)
masked_logprobs = tf.reshape(logprobs_batched, [-1, self.beam_size * self.num_classes])
# print masked_logprobs.get_shape()
beam_logprobs, indices = tf.nn.top_k(
masked_logprobs,
self.beam_size
)
beam_logprobs = tf.reshape(beam_logprobs, [-1])
# For continuing to the next symbols
symbols = indices % self.num_classes # [batch_size, self.beam_size]
parent_refs = tf.reshape(indices // self.num_classes, [-1]) # [batch_size*self.beam_size]
# TODO: this technically doesn't need to be recalculated every loop
parent_refs_offsets = tf.mul(tf.floordiv(tf.range(full_size), self.beam_size), self.beam_size)
parent_refs = parent_refs + parent_refs_offsets
if past_beam_symbols is not None:
symbols_history = tf.gather(past_beam_symbols, parent_refs)
beam_symbols = tf.concat(1, [tf.reshape(symbols, [-1, 1]), symbols_history])
else:
beam_symbols = tf.reshape(symbols, [-1, 1])
# Above ends up outputting reversed. Below doesn't work though because tf doesn't support negative indexing.
# last = past_beam_symbols.get_shape()[1]
# symbols_history = tf.gather(past_beam_symbols[:,last - 1], parent_refs)
# beam_symbols = tf.concat(1, [past_beam_symbols[:,:last-1], tf.reshape(symbols_history, [-1, 1]), tf.reshape(symbols, [-1, 1]), ])
# Handle the output and the cell state shuffling
outputs = tf.reshape(symbols, [-1]) # [batch_size*beam_size, 1]
cell_state = nest_map(
lambda element: tf.gather(element, parent_refs),
raw_cell_state
)
# Handling for getting a done token
# logprobs_done = tf.reshape(logprobs_batched, [-1, self.beam_size, self.num_classes])[:,:,self.stop_token]
# done_parent_refs = tf.to_int32(tf.argmax(logprobs_done, 1))
# done_parent_refs_offsets = tf.range(batch_size) * self.beam_size
# done_symbols = tf.gather(past_beam_symbols, done_parent_refs + done_parent_refs_offsets)
# logprobs_done_max = tf.reduce_max(logprobs_done, 1)
# cand_symbols = tf.select(logprobs_done_max > past_cand_logprobs,
# done_symbols,
# past_cand_symbols)
# cand_logprobs = tf.maximum(logprobs_done_max, past_cand_logprobs)
cand_symbols = past_cand_symbols # current last symbol in the beam [batch_size*self.beam_size]
cand_logprobs = past_cand_logprobs
return outputs, (
cand_symbols,
cand_logprobs,
beam_symbols,
beam_logprobs,
cell_state,
)
def model_fn(features, labels, mode, params):
image = features['image']
num_classes = params['model']['num_classes']
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
# build convolutional layers
conv = build_conv_layers(image, params['model']['conv_layers'], is_training)
# load convolutional and dense layers from a checkpoint
freeze_variables = {}
checkpoint_path = params['training'].get('checkpoint_path')
freeze_restored_variables = params['training'].get('freeze_restored_variables', False)
if checkpoint_path:
tvars = tf.trainable_variables()
assignment_map = {}
for var in tvars:
assignment_map[var.name[:-2]] = var
if freeze_restored_variables:
freeze_variables[var.name] = True
tf.train.init_from_checkpoint(root_dir(checkpoint_path), assignment_map)
# build dense layers
dense = build_dense_layers(conv, params['model']['dense_layers'], is_training)
# get logits
if 'subnet' in params:
# build NN for each neuron
subnet_dropout_rate = params['model']['subnet'].get('subnet_dropout_rate', 0)
if subnet_dropout_rate:
dense = tf.layers.dropout(inputs=dense, rate=subnet_dropout_rate, training=is_training)
logits_layer_params = dict(params['model']['logits_layer'])
logits_layer_params['num_units'] = 1
logits_concat = []
for i in range(num_classes):
subnet_dense = build_dense_layers(dense, params['model']['subnet']['dense_layers'], is_training)
subnet_logits = build_dense_layers(subnet_dense, [logits_layer_params], is_training)
logits_concat.append(subnet_logits)
logits = tf.concat(logits_concat, axis=-1)
else:
# a single layer to get a spike
logits_layer_params = dict(params['model']['logits_layer'])
logits_layer_params['num_units'] = num_classes
logits = build_dense_layers(dense, [logits_layer_params], is_training)
# return prediction specification
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions={'spikes': logits})
# make sure that images were distorted correctly and display them in TensorBoard
max_images = 12
images = image[:max_images]
assert_min = tf.assert_greater_equal(tf.reduce_min(images), 0.0, message='Image contains values less than 0')
assert_max = tf.assert_less_equal(tf.reduce_max(images), 1.0, message='Image contains values greater than 1')
with tf.control_dependencies([assert_min, assert_max]):
tf.summary.image('images', tf.cast(images * 255, dtype=tf.uint8), max_outputs=max_images)
# compute the loss
nan_mask = tf.cast(features['nan_mask'], tf.float32)
mse_loss = tf.losses.mean_squared_error(labels=labels, predictions=logits, weights=nan_mask)
loss = mse_loss + tf.losses.get_regularization_loss()
# get train variables
train_vars = [var for var in tf.trainable_variables() if var.name not in freeze_variables]
# return training specification
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = tf.contrib.layers.optimize_loss(
loss=loss,
global_step=tf.train.get_global_step(),
learning_rate=params['training']['learning_rate'],
optimizer='Adam',
summaries=['learning_rate', 'loss', 'gradients', 'gradient_norm'],
variables=train_vars,
)
# perform update ops for batch normalization
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
train_op = tf.group([train_op, update_ops])
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
# evaluation metrics
eval_metric_ops = {
'rmse': tf.metrics.root_mean_squared_error(labels=labels, predictions=logits, weights=nan_mask),
}
# RMSE per column
for i in range(num_classes):
eval_metric_ops['rmse/column%d' % i] = tf.metrics.root_mean_squared_error(labels=labels[:, i],
predictions=logits[:, i],
weights=nan_mask[:, i])
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
def test_doesnt_raise_when_equal(self):
with self.test_session():
small = tf.constant([1, 2], name="small")
with tf.control_dependencies([tf.assert_less_equal(small, small)]):
out = tf.identity(small)
out.eval()
def test_doesnt_raise_when_equal(self):
with self.test_session():
small = tf.constant([1, 2], name="small")
with tf.control_dependencies([tf.assert_less_equal(small, small)]):
out = tf.identity(small)
out.eval()
