tf.random.log_uniform_candidate_sampler     View source on GitHub    Samples a set of classes using a log-uniform (Zipfian) base distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.nn.log_uniform_candidate_sampler, tf.compat.v1.random.log_uniform_candidate_sampler
tf.random.log_uniform_candidate_sampler(
    true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None
)
 This operation randomly samples a tensor of sampled classes (sampled_candidates) from the range of integers [0, range_max). The elements of sampled_candidates are drawn without replacement (if unique=True) or with replacement (if unique=False) from the base distribution. The base distribution for this operation is an approximately log-uniform or Zipfian distribution: P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1) This sampler is useful when the target classes approximately follow such a distribution - for example, if the classes represent words in a lexicon sorted in decreasing order of frequency. If your classes are not ordered by decreasing frequency, do not use this op. In addition, this operation returns tensors true_expected_count and sampled_expected_count representing the number of times each of the target classes (true_classes) and the sampled classes (sampled_candidates) is expected to occur in an average tensor of sampled classes. These values correspond to Q(y|x) defined in this document. If unique=True, then these are post-rejection probabilities and we compute them approximately.

 Args
  true_classes   A Tensor of type int64 and shape [batch_size, num_true]. The target classes.
  num_true   An int. The number of target classes per training example.
  num_sampled   An int. The number of classes to randomly sample.
  unique   A bool. Determines whether all sampled classes in a batch are unique.
  range_max   An int. The number of possible classes.
  seed   An int. An operation-specific seed. Default is 0.
  name   A name for the operation (optional).

 Returns
  sampled_candidates   A tensor of type int64 and shape [num_sampled]. The sampled classes.
  true_expected_count   A tensor of type float. Same shape as true_classes. The expected counts under the sampling distribution of each of true_classes.
  sampled_expected_count   A tensor of type float. Same shape as sampled_candidates. The expected counts under the sampling distribution of each of sampled_candidates.


tf.random.normal     View source on GitHub    Outputs random values from a normal distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.random.normal, tf.compat.v1.random_normal
tf.random.normal(
    shape, mean=0.0, stddev=1.0, dtype=tf.dtypes.float32, seed=None, name=None
)
 Example that generates a new set of random values every time:
tf.random.set_seed(5);
tf.random.normal([4], 0, 1, tf.float32)
<tf.Tensor: shape=(4,), dtype=float32, numpy=..., dtype=float32)>
 Example that outputs a reproducible result:
tf.random.set_seed(5);
tf.random.normal([2,2], 0, 1, tf.float32, seed=1)
<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[-1.3768897 , -0.01258316],
      [-0.169515   ,  1.0824056 ]], dtype=float32)>
 In this case, we are setting both the global and operation-level seed to ensure this result is reproducible. See tf.random.set_seed for more information.

 Args
  shape   A 1-D integer Tensor or Python array. The shape of the output tensor.
  mean   A Tensor or Python value of type dtype, broadcastable with stddev. The mean of the normal distribution.
  stddev   A Tensor or Python value of type dtype, broadcastable with mean. The standard deviation of the normal distribution.
  dtype   The type of the output.
  seed   A Python integer. Used to create a random seed for the distribution. See tf.random.set_seed for behavior.
  name   A name for the operation (optional).

 Returns   A tensor of the specified shape filled with random normal values.


tf.random.poisson     View source on GitHub    Draws shape samples from each of the given Poisson distribution(s).
tf.random.poisson(
    shape, lam, dtype=tf.dtypes.float32, seed=None, name=None
)
 lam is the rate parameter describing the distribution(s). Example: samples = tf.random.poisson([10], [0.5, 1.5])
# samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents
# the samples drawn from each distribution

samples = tf.random.poisson([7, 5], [12.2, 3.3])
# samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1]
# represents the 7x5 samples drawn from each of the two distributions

 Args
  shape   A 1-D integer Tensor or Python array. The shape of the output samples to be drawn per "rate"-parameterized distribution.
  lam   A Tensor or Python value or N-D array of type dtype. lam provides the rate parameter(s) describing the poisson distribution(s) to sample.
  dtype   The type of the output: float16, float32, float64, int32 or int64.
  seed   A Python integer. Used to create a random seed for the distributions. See tf.random.set_seed for behavior.
  name   Optional name for the operation.

 Returns
  samples   a Tensor of shape tf.concat([shape, tf.shape(lam)], axis=0) with values of type dtype.


tf.random.set_global_generator Replaces the global generator with another Generator object.  View aliases  Main aliases
tf.random.experimental.set_global_generator Compat aliases for migration See Migration guide for more details. tf.compat.v1.random.experimental.set_global_generator, tf.compat.v1.random.set_global_generator
tf.random.set_global_generator(
    generator
)
 This function creates a new Generator object (and the Variable object within), which does not work well with tf.function because (1) tf.function puts restrictions on Variable creation thus reset_global_generator can't be freely used inside tf.function; (2) redirecting a global variable to a new object is problematic with tf.function because the old object may be captured by a 'tf.function'ed function and still be used by it. A 'tf.function'ed function only keeps weak references to variables, so deleting a variable and then calling that function again may raise an error, as demonstrated by random_test.py/RandomTest.testResetGlobalGeneratorBadWithDefun .

 Args
  generator   the new Generator object.


tf.random.set_seed Sets the global random seed.
tf.random.set_seed(
    seed
)
 Operations that rely on a random seed actually derive it from two seeds: the global and operation-level seeds. This sets the global seed. Its interactions with operation-level seeds is as follows:  If neither the global seed nor the operation seed is set: A randomly picked seed is used for this op. If the graph-level seed is set, but the operation seed is not: The system deterministically picks an operation seed in conjunction with the graph-level seed so that it gets a unique random sequence. Within the same version of tensorflow and user code, this sequence is deterministic. However across different versions, this sequence might change. If the code depends on particular seeds to work, specify both graph-level and operation-level seeds explicitly. If the operation seed is set, but the global seed is not set: A default global seed and the specified operation seed are used to determine the random sequence. If both the global and the operation seed are set: Both seeds are used in conjunction to determine the random sequence.  To illustrate the user-visible effects, consider these examples: If neither the global seed nor the operation seed is set, we get different results for every call to the random op and every re-run of the program: print(tf.random.uniform([1]))  # generates 'A1'
print(tf.random.uniform([1]))  # generates 'A2'
 (now close the program and run it again) print(tf.random.uniform([1]))  # generates 'A3'
print(tf.random.uniform([1]))  # generates 'A4'
 If the global seed is set but the operation seed is not set, we get different results for every call to the random op, but the same sequence for every re-run of the program: tf.random.set_seed(1234)
print(tf.random.uniform([1]))  # generates 'A1'
print(tf.random.uniform([1]))  # generates 'A2'
 (now close the program and run it again) tf.random.set_seed(1234)
print(tf.random.uniform([1]))  # generates 'A1'
print(tf.random.uniform([1]))  # generates 'A2'
 The reason we get 'A2' instead 'A1' on the second call of tf.random.uniform above is because the second call uses a different operation seed. Note that tf.function acts like a re-run of a program in this case. When the global seed is set but operation seeds are not set, the sequence of random numbers are the same for each tf.function. For example: tf.random.set_seed(1234)

@tf.function
def f():
  a = tf.random.uniform([1])
  b = tf.random.uniform([1])
  return a, b

@tf.function
def g():
  a = tf.random.uniform([1])
  b = tf.random.uniform([1])
  return a, b

print(f())  # prints '(A1, A2)'
print(g())  # prints '(A1, A2)'
 If the operation seed is set, we get different results for every call to the random op, but the same sequence for every re-run of the program: print(tf.random.uniform([1], seed=1))  # generates 'A1'
print(tf.random.uniform([1], seed=1))  # generates 'A2'
 (now close the program and run it again) print(tf.random.uniform([1], seed=1))  # generates 'A1'
print(tf.random.uniform([1], seed=1))  # generates 'A2'
 The reason we get 'A2' instead 'A1' on the second call of tf.random.uniform above is because the same tf.random.uniform kernel (i.e. internal representation) is used by TensorFlow for all calls of it with the same arguments, and the kernel maintains an internal counter which is incremented every time it is executed, generating different results. Calling tf.random.set_seed will reset any such counters: tf.random.set_seed(1234)
print(tf.random.uniform([1], seed=1))  # generates 'A1'
print(tf.random.uniform([1], seed=1))  # generates 'A2'
tf.random.set_seed(1234)
print(tf.random.uniform([1], seed=1))  # generates 'A1'
print(tf.random.uniform([1], seed=1))  # generates 'A2'
 When multiple identical random ops are wrapped in a tf.function, their behaviors change because the ops no long share the same counter. For example: @tf.function
def foo():
  a = tf.random.uniform([1], seed=1)
  b = tf.random.uniform([1], seed=1)
  return a, b
print(foo())  # prints '(A1, A1)'
print(foo())  # prints '(A2, A2)'

@tf.function
def bar():
  a = tf.random.uniform([1])
  b = tf.random.uniform([1])
  return a, b
print(bar())  # prints '(A1, A2)'
print(bar())  # prints '(A3, A4)'
 The second call of foo returns '(A2, A2)' instead of '(A1, A1)' because tf.random.uniform maintains an internal counter. If you want foo to return '(A1, A1)' every time, use the stateless random ops such as tf.random.stateless_uniform. Also see tf.random.experimental.Generator for a new set of stateful random ops that use external variables to manage their states.

 Args
  seed   integer.


tf.random.shuffle     View source on GitHub    Randomly shuffles a tensor along its first dimension.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.random.shuffle, tf.compat.v1.random_shuffle
tf.random.shuffle(
    value, seed=None, name=None
)
 The tensor is shuffled along dimension 0, such that each value[j] is mapped to one and only one output[i]. For example, a mapping that might occur for a 3x2 tensor is: [[1, 2],       [[5, 6],
 [3, 4],  ==>   [1, 2],
 [5, 6]]        [3, 4]]

 Args
  value   A Tensor to be shuffled.
  seed   A Python integer. Used to create a random seed for the distribution. See tf.random.set_seed for behavior.
  name   A name for the operation (optional).

 Returns   A tensor of same shape and type as value, shuffled along its first dimension.


tf.random.stateless_binomial Outputs deterministic pseudorandom values from a binomial distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.random.stateless_binomial
tf.random.stateless_binomial(
    shape, seed, counts, probs, output_dtype=tf.dtypes.int32, name=None
)
 The generated values follow a binomial distribution with specified count and probability of success parameters. This is a stateless version of tf.random.Generator.binomial: if run twice with the same seeds and shapes, it will produce the same pseudorandom numbers. The output is consistent across multiple runs on the same hardware (and between CPU and GPU), but may change between versions of TensorFlow or on non-CPU/GPU hardware. Example: counts = [10., 20.]
# Probability of success.
probs = [0.8]

binomial_samples = tf.random.stateless_binomial(
    shape=[2], seed=[123, 456], counts=counts, probs=probs)

counts = ... # Shape [3, 1, 2]
probs = ...  # Shape [1, 4, 2]
shape = [3, 4, 3, 4, 2]
# Sample shape will be [3, 4, 3, 4, 2]
binomial_samples = tf.random.stateless_binomial(
    shape=shape, seed=[123, 456], counts=counts, probs=probs)

 Args
  shape   A 1-D integer Tensor or Python array. The shape of the output tensor.
  seed   A shape [2] Tensor, the seed to the random number generator. Must have dtype int32 or int64. (When using XLA, only int32 is allowed.)
  counts   Tensor. The counts of the binomial distribution. Must be broadcastable with probs, and broadcastable with the rightmost dimensions of shape.
  probs   Tensor. The probability of success for the binomial distribution. Must be broadcastable with counts and broadcastable with the rightmost dimensions of shape.
  output_dtype   The type of the output. Default: tf.int32
  name   A name for the operation (optional).

 Returns
  samples   A Tensor of the specified shape filled with random binomial values. For each i, each samples[..., i] is an independent draw from the binomial distribution on counts[i] trials with probability of success probs[i].


tf.random.stateless_categorical     View source on GitHub    Draws deterministic pseudorandom samples from a categorical distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.random.stateless_categorical
tf.random.stateless_categorical(
    logits, num_samples, seed, dtype=tf.dtypes.int64, name=None
)
 This is a stateless version of tf.categorical: if run twice with the same seeds and shapes, it will produce the same pseudorandom numbers. The output is consistent across multiple runs on the same hardware (and between CPU and GPU), but may change between versions of TensorFlow or on non-CPU/GPU hardware. Example: # samples has shape [1, 5], where each value is either 0 or 1 with equal
# probability.
samples = tf.random.stateless_categorical(
    tf.math.log([[0.5, 0.5]]), 5, seed=[7, 17])

 Args
  logits   2-D Tensor with shape [batch_size, num_classes]. Each slice [i, :] represents the unnormalized log-probabilities for all classes.
  num_samples   0-D. Number of independent samples to draw for each row slice.
  seed   A shape [2] Tensor, the seed to the random number generator. Must have dtype int32 or int64. (When using XLA, only int32 is allowed.)
  dtype   integer type to use for the output. Defaults to int64.
  name   Optional name for the operation.

 Returns   The drawn samples of shape [batch_size, num_samples].


tf.random.stateless_gamma Outputs deterministic pseudorandom values from a gamma distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.random.stateless_gamma
tf.random.stateless_gamma(
    shape, seed, alpha, beta=None, dtype=tf.dtypes.float32, name=None
)
 The generated values follow a gamma distribution with specified concentration (alpha) and inverse scale (beta) parameters. This is a stateless version of tf.random.gamma: if run twice with the same seeds and shapes, it will produce the same pseudorandom numbers. The output is consistent across multiple runs on the same hardware (and between CPU and GPU), but may change between versions of TensorFlow or on non-CPU/GPU hardware. A slight difference exists in the interpretation of the shape parameter between stateless_gamma and gamma: in gamma, the shape is always prepended to the shape of the broadcast of alpha with beta; whereas in stateless_gamma the shape parameter must always encompass the shapes of each of alpha and beta (which must broadcast together to match the trailing dimensions of shape).
Note: Because internal calculations are done using float64 and casting has floor semantics, we must manually map zero outcomes to the smallest possible positive floating-point value, i.e., np.finfo(dtype).tiny. This means that np.finfo(dtype).tiny occurs more frequently than it otherwise should. This bias can only happen for small values of alpha, i.e., alpha << 1 or large values of beta, i.e., beta >> 1.
 The samples are differentiable w.r.t. alpha and beta. The derivatives are computed using the approach described in (Figurnov et al., 2018). Example: samples = tf.random.stateless_gamma([10, 2], seed=[12, 34], alpha=[0.5, 1.5])
# samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents
# the samples drawn from each distribution

samples = tf.random.stateless_gamma([7, 5, 2], seed=[12, 34], alpha=[.5, 1.5])
# samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1]
# represents the 7x5 samples drawn from each of the two distributions

alpha = tf.constant([[1.], [3.], [5.]])
beta = tf.constant([[3., 4.]])
samples = tf.random.stateless_gamma(
    [30, 3, 2], seed=[12, 34], alpha=alpha, beta=beta)
# samples has shape [30, 3, 2], with 30 samples each of 3x2 distributions.

with tf.GradientTape() as tape:
  tape.watch([alpha, beta])
  loss = tf.reduce_mean(tf.square(tf.random.stateless_gamma(
      [30, 3, 2], seed=[12, 34], alpha=alpha, beta=beta)))
dloss_dalpha, dloss_dbeta = tape.gradient(loss, [alpha, beta])
# unbiased stochastic derivatives of the loss function
alpha.shape == dloss_dalpha.shape  # True
beta.shape == dloss_dbeta.shape  # True

 Args
  shape   A 1-D integer Tensor or Python array. The shape of the output tensor.
  seed   A shape [2] Tensor, the seed to the random number generator. Must have dtype int32 or int64. (When using XLA, only int32 is allowed.)
  alpha   Tensor. The concentration parameter of the gamma distribution. Must be broadcastable with beta, and broadcastable with the rightmost dimensions of shape.
  beta   Tensor. The inverse scale parameter of the gamma distribution. Must be broadcastable with alpha and broadcastable with the rightmost dimensions of shape.
  dtype   Floating point dtype of alpha, beta, and the output.
  name   A name for the operation (optional).

 Returns
  samples   A Tensor of the specified shape filled with random gamma values. For each i, each `samples[..., i] is an independent draw from the gamma distribution with concentration alpha[i] and scale beta[i].


tf.random.stateless_normal     View source on GitHub    Outputs deterministic pseudorandom values from a normal distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.random.stateless_normal
tf.random.stateless_normal(
    shape, seed, mean=0.0, stddev=1.0, dtype=tf.dtypes.float32, name=None
)
 This is a stateless version of tf.random.normal: if run twice with the same seeds and shapes, it will produce the same pseudorandom numbers. The output is consistent across multiple runs on the same hardware (and between CPU and GPU), but may change between versions of TensorFlow or on non-CPU/GPU hardware.

 Args
  shape   A 1-D integer Tensor or Python array. The shape of the output tensor.
  seed   A shape [2] Tensor, the seed to the random number generator. Must have dtype int32 or int64. (When using XLA, only int32 is allowed.)
  mean   A 0-D Tensor or Python value of type dtype. The mean of the normal distribution.
  stddev   A 0-D Tensor or Python value of type dtype. The standard deviation of the normal distribution.
  dtype   The type of the output.
  name   A name for the operation (optional).

 Returns   A tensor of the specified shape filled with random normal values.


tf.random.stateless_parameterized_truncated_normal Outputs random values from a truncated normal distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.random.stateless_parameterized_truncated_normal
tf.random.stateless_parameterized_truncated_normal(
    shape, seed, means=0.0, stddevs=1.0, minvals=-2.0, maxvals=2.0, name=None
)
 The generated values follow a normal distribution with specified mean and standard deviation, except that values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked. Examples: Sample from a Truncated normal, with deferring shape parameters that broadcast.
means = 0.
stddevs = tf.math.exp(tf.random.uniform(shape=[2, 3]))
minvals = [-1., -2., -1000.]
maxvals = [[10000.], [1.]]
y = tf.random.stateless_parameterized_truncated_normal(
  shape=[10, 2, 3], seed=[7, 17],
  means=means, stddevs=stddevs, minvals=minvals, maxvals=maxvals)
y.shape
TensorShape([10, 2, 3])

 Args
  shape   A 1-D integer Tensor or Python array. The shape of the output tensor.
  seed   A shape [2] Tensor, the seed to the random number generator. Must have dtype int32 or int64. (When using XLA, only int32 is allowed.)
  means   A Tensor or Python value of type dtype. The mean of the truncated normal distribution. This must broadcast with stddevs, minvals and maxvals, and the broadcasted shape must be dominated by shape.
  stddevs   A Tensor or Python value of type dtype. The standard deviation of the truncated normal distribution. This must broadcast with means, minvals and maxvals, and the broadcasted shape must be dominated by shape.
  minvals   A Tensor or Python value of type dtype. The minimum value of the truncated normal distribution. This must broadcast with means, stddevs and maxvals, and the broadcasted shape must be dominated by shape.
  maxvals   A Tensor or Python value of type dtype. The maximum value of the truncated normal distribution. This must broadcast with means, stddevs and minvals, and the broadcasted shape must be dominated by shape.
  name   A name for the operation (optional).

 Returns   A tensor of the specified shape filled with random truncated normal values.


tf.random.stateless_poisson Outputs deterministic pseudorandom values from a Poisson distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.random.stateless_poisson
tf.random.stateless_poisson(
    shape, seed, lam, dtype=tf.dtypes.int32, name=None
)
 The generated values follow a Poisson distribution with specified rate parameter. This is a stateless version of tf.random.poisson: if run twice with the same seeds and shapes, it will produce the same pseudorandom numbers. The output is consistent across multiple runs on the same hardware, but may change between versions of TensorFlow or on non-CPU/GPU hardware. A slight difference exists in the interpretation of the shape parameter between stateless_poisson and poisson: in poisson, the shape is always prepended to the shape of lam; whereas in stateless_poisson the shape of lam must match the trailing dimensions of shape. Example: samples = tf.random.stateless_poisson([10, 2], seed=[12, 34], lam=[5, 15])
# samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents
# the samples drawn from each distribution

samples = tf.random.stateless_poisson([7, 5, 2], seed=[12, 34], lam=[5, 15])
# samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1]
# represents the 7x5 samples drawn from each of the two distributions

rate = tf.constant([[1.], [3.], [5.]])
samples = tf.random.stateless_poisson([30, 3, 1], seed=[12, 34], lam=rate)
# samples has shape [30, 3, 1], with 30 samples each of 3x1 distributions.

 Args
  shape   A 1-D integer Tensor or Python array. The shape of the output tensor.
  seed   A shape [2] Tensor, the seed to the random number generator. Must have dtype int32 or int64. (When using XLA, only int32 is allowed.)
  lam   Tensor. The rate parameter "lambda" of the Poisson distribution. Shape must match the rightmost dimensions of shape.
  dtype   Dtype of the samples (int or float dtypes are permissible, as samples are discrete). Default: int32.
  name   A name for the operation (optional).

 Returns
  samples   A Tensor of the specified shape filled with random Poisson values. For each i, each samples[..., i] is an independent draw from the Poisson distribution with rate lam[i].


tf.random.stateless_truncated_normal     View source on GitHub    Outputs deterministic pseudorandom values, truncated normally distributed.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.random.stateless_truncated_normal
tf.random.stateless_truncated_normal(
    shape, seed, mean=0.0, stddev=1.0, dtype=tf.dtypes.float32, name=None
)
 This is a stateless version of tf.random.truncated_normal: if run twice with the same seeds and shapes, it will produce the same pseudorandom numbers. The output is consistent across multiple runs on the same hardware (and between CPU and GPU), but may change between versions of TensorFlow or on non-CPU/GPU hardware. The generated values follow a normal distribution with specified mean and standard deviation, except that values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked.

 Args
  shape   A 1-D integer Tensor or Python array. The shape of the output tensor.
  seed   A shape [2] Tensor, the seed to the random number generator. Must have dtype int32 or int64. (When using XLA, only int32 is allowed.)
  mean   A 0-D Tensor or Python value of type dtype. The mean of the truncated normal distribution.
  stddev   A 0-D Tensor or Python value of type dtype. The standard deviation of the normal distribution, before truncation.
  dtype   The type of the output.
  name   A name for the operation (optional).

 Returns   A tensor of the specified shape filled with random truncated normal values.


tf.random.stateless_uniform     View source on GitHub    Outputs deterministic pseudorandom values from a uniform distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.random.stateless_uniform
tf.random.stateless_uniform(
    shape, seed, minval=0, maxval=None, dtype=tf.dtypes.float32, name=None
)
 This is a stateless version of tf.random.uniform: if run twice with the same seeds and shapes, it will produce the same pseudorandom numbers. The output is consistent across multiple runs on the same hardware (and between CPU and GPU), but may change between versions of TensorFlow or on non-CPU/GPU hardware. The generated values follow a uniform distribution in the range [minval, maxval). The lower bound minval is included in the range, while the upper bound maxval is excluded. For floats, the default range is [0, 1). For ints, at least maxval must be specified explicitly. In the integer case, the random integers are slightly biased unless maxval - minval is an exact power of two. The bias is small for values of maxval - minval significantly smaller than the range of the output (either 2**32 or 2**64). For full-range (i.e. inclusive of both max and min) random integers, pass minval=None and maxval=None with an integer dtype. For an integer dtype either both minval and maxval must be None or neither may be None. For example: ints = tf.random.stateless_uniform(
    [10], seed=(2, 3), minval=None, maxval=None, dtype=tf.int32)

 Args
  shape   A 1-D integer Tensor or Python array. The shape of the output tensor.
  seed   A shape [2] Tensor, the seed to the random number generator. Must have dtype int32 or int64. (When using XLA, only int32 is allowed.)
  minval   A Tensor or Python value of type dtype, broadcastable with shape (for integer types, broadcasting is not supported, so it needs to be a scalar). The lower bound on the range of random values to generate. Pass None for full-range integers. Defaults to 0.
  maxval   A Tensor or Python value of type dtype, broadcastable with shape (for integer types, broadcasting is not supported, so it needs to be a scalar). The upper bound on the range of random values to generate. Defaults to 1 if dtype is floating point. Pass None for full-range integers.
  dtype   The type of the output: float16, float32, float64, int32, or int64. For unbounded uniform ints (minval, maxval both None), uint32 and uint64 may be used.
  name   A name for the operation (optional).

 Returns   A tensor of the specified shape filled with random uniform values.

 Raises
  ValueError   If dtype is integral and only one of minval or maxval is specified.


tf.random.truncated_normal     View source on GitHub    Outputs random values from a truncated normal distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.random.truncated_normal, tf.compat.v1.truncated_normal
tf.random.truncated_normal(
    shape, mean=0.0, stddev=1.0, dtype=tf.dtypes.float32, seed=None, name=None
)
 The generated values follow a normal distribution with specified mean and standard deviation, except that values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked.

 Args
  shape   A 1-D integer Tensor or Python array. The shape of the output tensor.
  mean   A 0-D Tensor or Python value of type dtype. The mean of the truncated normal distribution.
  stddev   A 0-D Tensor or Python value of type dtype. The standard deviation of the normal distribution, before truncation.
  dtype   The type of the output.
  seed   A Python integer. Used to create a random seed for the distribution. See tf.random.set_seed for behavior.
  name   A name for the operation (optional).

 Returns   A tensor of the specified shape filled with random truncated normal values.


tf.random.uniform     View source on GitHub    Outputs random values from a uniform distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.random.uniform, tf.compat.v1.random_uniform
tf.random.uniform(
    shape, minval=0, maxval=None, dtype=tf.dtypes.float32, seed=None, name=None
)
 The generated values follow a uniform distribution in the range [minval, maxval). The lower bound minval is included in the range, while the upper bound maxval is excluded. For floats, the default range is [0, 1). For ints, at least maxval must be specified explicitly. In the integer case, the random integers are slightly biased unless maxval - minval is an exact power of two. The bias is small for values of maxval - minval significantly smaller than the range of the output (either 2**32 or 2**64). Examples:
tf.random.uniform(shape=[2])
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([..., ...], dtype=float32)>
tf.random.uniform(shape=[], minval=-1., maxval=0.)
<tf.Tensor: shape=(), dtype=float32, numpy=-...>
tf.random.uniform(shape=[], minval=5, maxval=10, dtype=tf.int64)
<tf.Tensor: shape=(), dtype=int64, numpy=...>
 The seed argument produces a deterministic sequence of tensors across multiple calls. To repeat that sequence, use tf.random.set_seed:
tf.random.set_seed(5)
tf.random.uniform(shape=[], maxval=3, dtype=tf.int32, seed=10)
<tf.Tensor: shape=(), dtype=int32, numpy=2>
tf.random.uniform(shape=[], maxval=3, dtype=tf.int32, seed=10)
<tf.Tensor: shape=(), dtype=int32, numpy=0>
tf.random.set_seed(5)
tf.random.uniform(shape=[], maxval=3, dtype=tf.int32, seed=10)
<tf.Tensor: shape=(), dtype=int32, numpy=2>
tf.random.uniform(shape=[], maxval=3, dtype=tf.int32, seed=10)
<tf.Tensor: shape=(), dtype=int32, numpy=0>
 Without tf.random.set_seed but with a seed argument is specified, small changes to function graphs or previously executed operations will change the returned value. See tf.random.set_seed for details.

 Args
  shape   A 1-D integer Tensor or Python array. The shape of the output tensor.
  minval   A Tensor or Python value of type dtype, broadcastable with shape (for integer types, broadcasting is not supported, so it needs to be a scalar). The lower bound on the range of random values to generate (inclusive). Defaults to 0.
  maxval   A Tensor or Python value of type dtype, broadcastable with shape (for integer types, broadcasting is not supported, so it needs to be a scalar). The upper bound on the range of random values to generate (exclusive). Defaults to 1 if dtype is floating point.
  dtype   The type of the output: float16, float32, float64, int32, or int64.
  seed   A Python integer. Used in combination with tf.random.set_seed to create a reproducible sequence of tensors across multiple calls.
  name   A name for the operation (optional).

 Returns   A tensor of the specified shape filled with random uniform values.

 Raises
  ValueError   If dtype is integral and maxval is not specified.


tf.random.uniform_candidate_sampler     View source on GitHub    Samples a set of classes using a uniform base distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.nn.uniform_candidate_sampler, tf.compat.v1.random.uniform_candidate_sampler
tf.random.uniform_candidate_sampler(
    true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None
)
 This operation randomly samples a tensor of sampled classes (sampled_candidates) from the range of integers [0, range_max). The elements of sampled_candidates are drawn without replacement (if unique=True) or with replacement (if unique=False) from the base distribution. The base distribution for this operation is the uniform distribution over the range of integers [0, range_max). In addition, this operation returns tensors true_expected_count and sampled_expected_count representing the number of times each of the target classes (true_classes) and the sampled classes (sampled_candidates) is expected to occur in an average tensor of sampled classes. These values correspond to Q(y|x) defined in this document. If unique=True, then these are post-rejection probabilities and we compute them approximately.

 Args
  true_classes   A Tensor of type int64 and shape [batch_size, num_true]. The target classes.
  num_true   An int. The number of target classes per training example.
  num_sampled   An int. The number of classes to randomly sample. The sampled_candidates return value will have shape [num_sampled]. If unique=True, num_sampled must be less than or equal to range_max.
  unique   A bool. Determines whether all sampled classes in a batch are unique.
  range_max   An int. The number of possible classes.
  seed   An int. An operation-specific seed. Default is 0.
  name   A name for the operation (optional).

 Returns
  sampled_candidates   A tensor of type int64 and shape [num_sampled]. The sampled classes, either with possible duplicates (unique=False) or all unique (unique=True). In either case, sampled_candidates is independent of the true classes.
  true_expected_count   A tensor of type float. Same shape as true_classes. The expected counts under the sampling distribution of each of true_classes.
  sampled_expected_count   A tensor of type float. Same shape as sampled_candidates. The expected counts under the sampling distribution of each of sampled_candidates.


tf.random_normal_initializer     View source on GitHub    Initializer that generates tensors with a normal distribution.
tf.random_normal_initializer(
    mean=0.0, stddev=0.05, seed=None
)
 Initializers allow you to pre-specify an initialization strategy, encoded in the Initializer object, without knowing the shape and dtype of the variable being initialized. Examples:
def make_variables(k, initializer):
  return (tf.Variable(initializer(shape=[k], dtype=tf.float32)),
          tf.Variable(initializer(shape=[k, k], dtype=tf.float32)))
v1, v2 = make_variables(3,
                        tf.random_normal_initializer(mean=1., stddev=2.))
v1
<tf.Variable ... shape=(3,) ... numpy=array([...], dtype=float32)>
v2
<tf.Variable ... shape=(3, 3) ... numpy=

make_variables(4, tf.random_uniform_initializer(minval=-1., maxval=1.))
(<tf.Variable...shape=(4,) dtype=float32...>, <tf.Variable...shape=(4, 4) ...

 Args
  mean   a python scalar or a scalar tensor. Mean of the random values to generate.
  stddev   a python scalar or a scalar tensor. Standard deviation of the random values to generate.
  seed   A Python integer. Used to create random seeds. See tf.random.set_seed for behavior.    Methods from_config View source
@classmethod
from_config(
    config
)
 Instantiates an initializer from a configuration dictionary. Example: initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)

 Args
  config   A Python dictionary. It will typically be the output of get_config.

 Returns   An Initializer instance.
 get_config View source
get_config()
 Returns the configuration of the initializer as a JSON-serializable dict.

 Returns   A JSON-serializable Python dict.
 __call__ View source
__call__(
    shape, dtype=tf.dtypes.float32, **kwargs
)
 Returns a tensor object initialized as specified by the initializer.

 Args
  shape   Shape of the tensor.
  dtype   Optional dtype of the tensor. Only floating point types are supported.
  **kwargs   Additional keyword arguments.

 Raises
  ValueError   If the dtype is not floating point


tf.random_uniform_initializer     View source on GitHub    Initializer that generates tensors with a uniform distribution.
tf.random_uniform_initializer(
    minval=-0.05, maxval=0.05, seed=None
)
 Initializers allow you to pre-specify an initialization strategy, encoded in the Initializer object, without knowing the shape and dtype of the variable being initialized. Examples:
def make_variables(k, initializer):
  return (tf.Variable(initializer(shape=[k], dtype=tf.float32)),
          tf.Variable(initializer(shape=[k, k], dtype=tf.float32)))
v1, v2 = make_variables(3, tf.ones_initializer())
v1
<tf.Variable ... shape=(3,) ... numpy=array([1., 1., 1.], dtype=float32)>
v2
<tf.Variable ... shape=(3, 3) ... numpy=
array([[1., 1., 1.],
       [1., 1., 1.],
       [1., 1., 1.]], dtype=float32)>
make_variables(4, tf.random_uniform_initializer(minval=-1., maxval=1.))
(<tf.Variable...shape=(4,) dtype=float32...>, <tf.Variable...shape=(4, 4) ...

 Args
  minval   A python scalar or a scalar tensor. Lower bound of the range of random values to generate (inclusive).
  maxval   A python scalar or a scalar tensor. Upper bound of the range of random values to generate (exclusive).
  seed   A Python integer. Used to create random seeds. See tf.random.set_seed for behavior.    Methods from_config View source
@classmethod
from_config(
    config
)
 Instantiates an initializer from a configuration dictionary. Example: initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)

 Args
  config   A Python dictionary. It will typically be the output of get_config.

 Returns   An Initializer instance.
 get_config View source
get_config()
 Returns the configuration of the initializer as a JSON-serializable dict.

 Returns   A JSON-serializable Python dict.
 __call__ View source
__call__(
    shape, dtype=tf.dtypes.float32, **kwargs
)
 Returns a tensor object initialized as specified by the initializer.

 Args
  shape   Shape of the tensor.
  dtype   Optional dtype of the tensor. Only floating point and integer types are supported.
  **kwargs   Additional keyword arguments.

 Raises
  ValueError   If the dtype is not numeric.


tf.range     View source on GitHub    Creates a sequence of numbers.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.range  tf.range(limit, delta=1, dtype=None, name='range')
tf.range(start, limit, delta=1, dtype=None, name='range')
 Creates a sequence of numbers that begins at start and extends by increments of delta up to but not including limit. The dtype of the resulting tensor is inferred from the inputs unless it is provided explicitly. Like the Python builtin range, start defaults to 0, so that range(n) = range(0, n). For example:
start = 3
limit = 18
delta = 3
tf.range(start, limit, delta)
<tf.Tensor: shape=(5,), dtype=int32,
numpy=array([ 3,  6,  9, 12, 15], dtype=int32)>

start = 3
limit = 1
delta = -0.5
tf.range(start, limit, delta)
<tf.Tensor: shape=(4,), dtype=float32,
numpy=array([3. , 2.5, 2. , 1.5], dtype=float32)>

limit = 5
tf.range(limit)
<tf.Tensor: shape=(5,), dtype=int32,
numpy=array([0, 1, 2, 3, 4], dtype=int32)>

 Args
  start   A 0-D Tensor (scalar). Acts as first entry in the range if limit is not None; otherwise, acts as range limit and first entry defaults to 0.
  limit   A 0-D Tensor (scalar). Upper limit of sequence, exclusive. If None, defaults to the value of start while the first entry of the range defaults to 0.
  delta   A 0-D Tensor (scalar). Number that increments start. Defaults to 1.
  dtype   The type of the elements of the resulting tensor.
  name   A name for the operation. Defaults to "range".

 Returns   An 1-D Tensor of type dtype.
 Numpy Compatibility Equivalent to np.arange


tf.rank     View source on GitHub    Returns the rank of a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.rank
tf.rank(
    input, name=None
)
 See also tf.shape. Returns a 0-D int32 Tensor representing the rank of input. For example: # shape of tensor 't' is [2, 2, 3]
t = tf.constant([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]])
tf.rank(t)  # 3

Note: The rank of a tensor is not the same as the rank of a matrix. The rank of a tensor is the number of indices required to uniquely select each element of the tensor. Rank is also known as "order", "degree", or "ndims."

 Args
  input   A Tensor or SparseTensor.
  name   A name for the operation (optional).

 Returns   A Tensor of type int32.
 Numpy Compatibility Equivalent to np.ndim


Module: tf.raw_ops Public API for tf.raw_ops namespace.
Note: tf.raw_ops provides direct/low level access to all TensorFlow ops. See the RFC for details. Unless you are library writer, you likely do not need to use these ops directly.
    Op Name Has Gradient     Abort ❌   Abs ✔   AccumulateNV2 ✔   AccumulatorApplyGradient ❌   AccumulatorNumAccumulated ❌   AccumulatorSetGlobalStep ❌   AccumulatorTakeGradient ❌   Acos ✔   Acosh ✔   Add ✔   AddManySparseToTensorsMap ❌   AddN ✔   AddSparseToTensorsMap ❌   AddV2 ✔   AdjustContrast ❌   AdjustContrastv2 ❌   AdjustHue ❌   AdjustSaturation ❌   All ❌   AllCandidateSampler ❌   AllToAll ✔   Angle ✔   AnonymousIterator ❌   AnonymousIteratorV2 ❌   AnonymousMemoryCache ❌   AnonymousMultiDeviceIterator ❌   AnonymousRandomSeedGenerator ❌   AnonymousSeedGenerator ❌   Any ❌   ApplyAdaMax ❌   ApplyAdadelta ❌   ApplyAdagrad ❌   ApplyAdagradDA ❌   ApplyAdagradV2 ❌   ApplyAdam ❌   ApplyAddSign ❌   ApplyCenteredRMSProp ❌   ApplyFtrl ❌   ApplyFtrlV2 ❌   ApplyGradientDescent ❌   ApplyMomentum ❌   ApplyPowerSign ❌   ApplyProximalAdagrad ❌   ApplyProximalGradientDescent ❌   ApplyRMSProp ❌   ApproximateEqual ✔   ArgMax ✔   ArgMin ✔   AsString ✔   Asin ✔   Asinh ✔   Assert ✔   AssertCardinalityDataset ❌   AssertNextDataset ❌   Assign ✔   AssignAdd ✔   AssignAddVariableOp ❌   AssignSub ✔   AssignSubVariableOp ❌   AssignVariableOp ❌   Atan ✔   Atan2 ✔   Atanh ✔   AudioSpectrogram ❌   AudioSummary ✔   AudioSummaryV2 ✔   AutoShardDataset ❌   AvgPool ✔   AvgPool3D ✔   AvgPool3DGrad ✔   AvgPoolGrad ✔   BandedTriangularSolve ✔   Barrier ❌   BarrierClose ❌   BarrierIncompleteSize ❌   BarrierInsertMany ❌   BarrierReadySize ❌   BarrierTakeMany ❌   Batch ❌   BatchCholesky ❌   BatchCholeskyGrad ❌   BatchDataset ❌   BatchDatasetV2 ❌   BatchFFT ❌   BatchFFT2D ❌   BatchFFT3D ❌   BatchFunction ❌   BatchIFFT ❌   BatchIFFT2D ❌   BatchIFFT3D ❌   BatchMatMul ✔   BatchMatMulV2 ✔   BatchMatrixBandPart ❌   BatchMatrixDeterminant ❌   BatchMatrixDiag ❌   BatchMatrixDiagPart ❌   BatchMatrixInverse ❌   BatchMatrixSetDiag ❌   BatchMatrixSolve ❌   BatchMatrixSolveLs ❌   BatchMatrixTriangularSolve ❌   BatchNormWithGlobalNormalization ✔   BatchNormWithGlobalNormalizationGrad ❌   BatchSelfAdjointEig ❌   BatchSelfAdjointEigV2 ❌   BatchSvd ❌   BatchToSpace ✔   BatchToSpaceND ✔   BesselI0 ✔   BesselI0e ✔   BesselI1 ✔   BesselI1e ✔   BesselJ0 ✔   BesselJ1 ✔   BesselK0 ✔   BesselK0e ✔   BesselK1 ✔   BesselK1e ✔   BesselY0 ✔   BesselY1 ✔   Betainc ✔   BiasAdd ✔   BiasAddGrad ✔   BiasAddV1 ✔   Bincount ❌   Bitcast ❌   BitwiseAnd ✔   BitwiseOr ✔   BitwiseXor ✔   BlockLSTM ✔   BlockLSTMGrad ❌   BlockLSTMGradV2 ❌   BlockLSTMV2 ✔   BoostedTreesAggregateStats ❌   BoostedTreesBucketize ❌   BoostedTreesCalculateBestFeatureSplit ❌   BoostedTreesCalculateBestFeatureSplitV2 ❌   BoostedTreesCalculateBestGainsPerFeature ❌   BoostedTreesCenterBias ❌   BoostedTreesCreateEnsemble ❌   BoostedTreesCreateQuantileStreamResource ❌   BoostedTreesDeserializeEnsemble ❌   BoostedTreesEnsembleResourceHandleOp ❌   BoostedTreesExampleDebugOutputs ❌   BoostedTreesFlushQuantileSummaries ❌   BoostedTreesGetEnsembleStates ❌   BoostedTreesMakeQuantileSummaries ❌   BoostedTreesMakeStatsSummary ❌   BoostedTreesPredict ❌   BoostedTreesQuantileStreamResourceAddSummaries ❌   BoostedTreesQuantileStreamResourceDeserialize ❌   BoostedTreesQuantileStreamResourceFlush ❌   BoostedTreesQuantileStreamResourceGetBucketBoundaries ❌   BoostedTreesQuantileStreamResourceHandleOp ❌   BoostedTreesSerializeEnsemble ❌   BoostedTreesSparseAggregateStats ❌   BoostedTreesSparseCalculateBestFeatureSplit ❌   BoostedTreesTrainingPredict ❌   BoostedTreesUpdateEnsemble ❌   BoostedTreesUpdateEnsembleV2 ❌   BroadcastArgs ❌   BroadcastGradientArgs ✔   BroadcastTo ✔   Bucketize ❌   BytesProducedStatsDataset ❌   CSRSparseMatrixComponents ❌   CSRSparseMatrixToDense ✔   CSRSparseMatrixToSparseTensor ❌   CSVDataset ❌   CSVDatasetV2 ❌   CTCBeamSearchDecoder ✔   CTCGreedyDecoder ✔   CTCLoss ✔   CTCLossV2 ✔   CacheDataset ❌   CacheDatasetV2 ❌   Case ✔   Cast ✔   Ceil ✔   CheckNumerics ✔   CheckNumericsV2 ✔   Cholesky ✔   CholeskyGrad ❌   ChooseFastestBranchDataset ❌   ChooseFastestDataset ❌   ClipByValue ❌   CloseSummaryWriter ❌   CollectiveBcastRecv ❌   CollectiveBcastSend ❌   CollectiveGather ❌   CollectiveGatherV2 ❌   CollectivePermute ✔   CollectiveReduce ❌   CollectiveReduceV2 ❌   CombinedNonMaxSuppression ❌   CompareAndBitpack ❌   Complex ✔   ComplexAbs ✔   CompressElement ❌   ComputeAccidentalHits ❌   ComputeBatchSize ❌   Concat ✔   ConcatOffset ✔   ConcatV2 ✔   ConcatenateDataset ❌   ConditionalAccumulator ❌   ConfigureDistributedTPU ❌   ConfigureTPUEmbedding ❌   Conj ✔   ConjugateTranspose ✔   Const ✔   ConsumeMutexLock ❌   ControlTrigger ❌   Conv2D ✔   Conv2DBackpropFilter ✔   Conv2DBackpropInput ✔   Conv3D ✔   Conv3DBackpropFilter ❌   Conv3DBackpropFilterV2 ✔   Conv3DBackpropInput ❌   Conv3DBackpropInputV2 ✔   Copy ❌   CopyHost ❌   Cos ✔   Cosh ✔   CountUpTo ❌   CreateSummaryDbWriter ❌   CreateSummaryFileWriter ❌   CropAndResize ✔   CropAndResizeGradBoxes ❌   CropAndResizeGradImage ❌   Cross ✔   CrossReplicaSum ✔   CudnnRNN ✔   CudnnRNNBackprop ❌   CudnnRNNBackpropV2 ❌   CudnnRNNBackpropV3 ❌   CudnnRNNCanonicalToParams ❌   CudnnRNNCanonicalToParamsV2 ❌   CudnnRNNParamsSize ❌   CudnnRNNParamsToCanonical ❌   CudnnRNNParamsToCanonicalV2 ❌   CudnnRNNV2 ✔   CudnnRNNV3 ✔   Cumprod ✔   Cumsum ✔   CumulativeLogsumexp ✔   DataFormatDimMap ❌   DataFormatVecPermute ❌   DataServiceDataset ❌   DatasetCardinality ❌   DatasetFromGraph ❌   DatasetToGraph ❌   DatasetToGraphV2 ❌   DatasetToSingleElement ❌   DatasetToTFRecord ❌   Dawsn ✔   DebugGradientIdentity ✔   DebugGradientRefIdentity ✔   DebugIdentity ❌   DebugIdentityV2 ✔   DebugNanCount ❌   DebugNumericSummary ❌   DebugNumericSummaryV2 ❌   DecodeAndCropJpeg ❌   DecodeBase64 ✔   DecodeBmp ❌   DecodeCSV ❌   DecodeCompressed ❌   DecodeGif ❌   DecodeImage ❌   DecodeJSONExample ❌   DecodeJpeg ❌   DecodePaddedRaw ✔   DecodePng ❌   DecodeProtoV2 ✔   DecodeRaw ✔   DecodeWav ❌   DeepCopy ❌   DeleteIterator ❌   DeleteMemoryCache ❌   DeleteMultiDeviceIterator ❌   DeleteRandomSeedGenerator ❌   DeleteSeedGenerator ❌   DeleteSessionTensor ✔   DenseBincount ❌   DenseCountSparseOutput ❌   DenseToCSRSparseMatrix ✔   DenseToDenseSetOperation ✔   DenseToSparseBatchDataset ❌   DenseToSparseSetOperation ✔   DepthToSpace ✔   DepthwiseConv2dNative ✔   DepthwiseConv2dNativeBackpropFilter ✔   DepthwiseConv2dNativeBackpropInput ✔   Dequantize ❌   DeserializeIterator ❌   DeserializeManySparse ❌   DeserializeSparse ❌   DestroyResourceOp ❌   DestroyTemporaryVariable ❌   DeviceIndex ❌   Diag ✔   DiagPart ✔   Digamma ✔   Dilation2D ✔   Dilation2DBackpropFilter ❌   Dilation2DBackpropInput ❌   DirectedInterleaveDataset ❌   Div ✔   DivNoNan ✔   DrawBoundingBoxes ✔   DrawBoundingBoxesV2 ❌   DummyIterationCounter ❌   DummyMemoryCache ❌   DummySeedGenerator ❌   DynamicPartition ✔   DynamicStitch ✔   EagerPyFunc ✔   EditDistance ✔   Eig ✔   Einsum ✔   Elu ✔   EluGrad ✔   Empty ❌   EmptyTensorList ❌   EncodeBase64 ✔   EncodeJpeg ❌   EncodeJpegVariableQuality ❌   EncodePng ❌   EncodeProto ✔   EncodeWav ❌   EnqueueTPUEmbeddingIntegerBatch ❌   EnqueueTPUEmbeddingRaggedTensorBatch ❌   EnqueueTPUEmbeddingSparseBatch ❌   EnqueueTPUEmbeddingSparseTensorBatch ❌   EnsureShape ✔   Enter ✔   Equal ✔   Erf ✔   Erfc ✔   Erfinv ✔   EuclideanNorm ✔   Exit ✔   Exp ✔   ExpandDims ✔   ExperimentalAssertNextDataset ❌   ExperimentalAutoShardDataset ❌   ExperimentalBytesProducedStatsDataset ❌   ExperimentalCSVDataset ❌   ExperimentalChooseFastestDataset ❌   ExperimentalDatasetCardinality ❌   ExperimentalDatasetToTFRecord ❌   ExperimentalDenseToSparseBatchDataset ❌   ExperimentalDirectedInterleaveDataset ❌   ExperimentalGroupByReducerDataset ❌   ExperimentalGroupByWindowDataset ❌   ExperimentalIgnoreErrorsDataset ❌   ExperimentalIteratorGetDevice ❌   ExperimentalLMDBDataset ❌   ExperimentalLatencyStatsDataset ❌   ExperimentalMapAndBatchDataset ❌   ExperimentalMapDataset ❌   ExperimentalMatchingFilesDataset ❌   ExperimentalMaxIntraOpParallelismDataset ❌   ExperimentalNonSerializableDataset ❌   ExperimentalParallelInterleaveDataset ❌   ExperimentalParseExampleDataset ❌   ExperimentalPrivateThreadPoolDataset ❌   ExperimentalRandomDataset ❌   ExperimentalRebatchDataset ❌   ExperimentalScanDataset ❌   ExperimentalSetStatsAggregatorDataset ❌   ExperimentalSleepDataset ❌   ExperimentalSlidingWindowDataset ❌   ExperimentalSqlDataset ❌   ExperimentalStatsAggregatorHandle ❌   ExperimentalStatsAggregatorSummary ❌   ExperimentalTakeWhileDataset ❌   ExperimentalThreadPoolDataset ❌   ExperimentalThreadPoolHandle ❌   ExperimentalUnbatchDataset ❌   ExperimentalUniqueDataset ❌   Expint ✔   Expm1 ✔   ExtractGlimpse ✔   ExtractGlimpseV2 ❌   ExtractImagePatches ✔   ExtractJpegShape ❌   ExtractVolumePatches ✔   FFT ✔   FFT2D ✔   FFT3D ✔   FIFOQueue ❌   FIFOQueueV2 ❌   Fact ❌   FakeParam ❌   FakeQuantWithMinMaxArgs ✔   FakeQuantWithMinMaxArgsGradient ❌   FakeQuantWithMinMaxVars ✔   FakeQuantWithMinMaxVarsGradient ❌   FakeQuantWithMinMaxVarsPerChannel ✔   FakeQuantWithMinMaxVarsPerChannelGradient ❌   FakeQueue ❌   Fill ✔   FilterByLastComponentDataset ❌   FilterDataset ❌   Fingerprint ❌   FixedLengthRecordDataset ❌   FixedLengthRecordDatasetV2 ❌   FixedLengthRecordReader ✔   FixedLengthRecordReaderV2 ❌   FixedUnigramCandidateSampler ❌   FlatMapDataset ❌   Floor ✔   FloorDiv ✔   FloorMod ✔   FlushSummaryWriter ❌   For ❌   FractionalAvgPool ✔   FractionalAvgPoolGrad ❌   FractionalMaxPool ✔   FractionalMaxPoolGrad ❌   FresnelCos ✔   FresnelSin ✔   FusedBatchNorm ✔   FusedBatchNormGrad ✔   FusedBatchNormGradV2 ✔   FusedBatchNormGradV3 ✔   FusedBatchNormV2 ✔   FusedBatchNormV3 ✔   FusedPadConv2D ❌   FusedResizeAndPadConv2D ❌   GRUBlockCell ❌   GRUBlockCellGrad ❌   Gather ✔   GatherNd ✔   GatherV2 ✔   GenerateBoundingBoxProposals ✔   GenerateVocabRemapping ✔   GeneratorDataset ❌   GetSessionHandle ✔   GetSessionHandleV2 ✔   GetSessionTensor ✔   Greater ✔   GreaterEqual ✔   GroupByReducerDataset ❌   GroupByWindowDataset ❌   GuaranteeConst ❌   HSVToRGB ✔   HashTable ✔   HashTableV2 ✔   HistogramFixedWidth ❌   HistogramSummary ✔   IFFT ✔   IFFT2D ✔   IFFT3D ✔   IRFFT ✔   IRFFT2D ✔   IRFFT3D ❌   Identity ✔   IdentityN ✔   IdentityReader ✔   IdentityReaderV2 ❌   If ✔   Igamma ✔   IgammaGradA ❌   Igammac ✔   IgnoreErrorsDataset ❌   Imag ✔   ImageProjectiveTransformV2 ✔   ImageProjectiveTransformV3 ✔   ImageSummary ✔   ImmutableConst ❌   ImportEvent ❌   InTopK ❌   InTopKV2 ❌   InfeedDequeue ❌   InfeedDequeueTuple ❌   InfeedEnqueue ❌   InfeedEnqueuePrelinearizedBuffer ❌   InfeedEnqueueTuple ❌   InitializeTable ✔   InitializeTableFromDataset ❌   InitializeTableFromTextFile ✔   InitializeTableFromTextFileV2 ✔   InitializeTableV2 ✔   InplaceAdd ❌   InplaceSub ❌   InplaceUpdate ❌   InterleaveDataset ❌   Inv ✔   InvGrad ✔   Invert ✔   InvertPermutation ✔   IsBoostedTreesEnsembleInitialized ❌   IsBoostedTreesQuantileStreamResourceInitialized ❌   IsFinite ❌   IsInf ❌   IsNan ❌   IsVariableInitialized ❌   IsotonicRegression ✔   Iterator ❌   IteratorFromStringHandle ❌   IteratorFromStringHandleV2 ❌   IteratorGetDevice ❌   IteratorGetNext ❌   IteratorGetNextAsOptional ❌   IteratorGetNextSync ❌   IteratorToStringHandle ❌   IteratorV2 ❌   L2Loss ✔   LMDBDataset ❌   LMDBReader ✔   LRN ✔   LRNGrad ❌   LSTMBlockCell ❌   LSTMBlockCellGrad ❌   LatencyStatsDataset ❌   LeakyRelu ✔   LeakyReluGrad ✔   LearnedUnigramCandidateSampler ❌   LeftShift ✔   LegacyParallelInterleaveDatasetV2 ❌   Less ✔   LessEqual ✔   Lgamma ✔   LinSpace ✔   ListDiff ❌   LoadAndRemapMatrix ✔   LoadDataset ❌   LoadTPUEmbeddingADAMParameters ❌   LoadTPUEmbeddingADAMParametersGradAccumDebug ❌   LoadTPUEmbeddingAdadeltaParameters ❌   LoadTPUEmbeddingAdadeltaParametersGradAccumDebug ❌   LoadTPUEmbeddingAdagradParameters ❌   LoadTPUEmbeddingAdagradParametersGradAccumDebug ❌   LoadTPUEmbeddingCenteredRMSPropParameters ❌   LoadTPUEmbeddingFTRLParameters ❌   LoadTPUEmbeddingFTRLParametersGradAccumDebug ❌   LoadTPUEmbeddingMDLAdagradLightParameters ❌   LoadTPUEmbeddingMomentumParameters ❌   LoadTPUEmbeddingMomentumParametersGradAccumDebug ❌   LoadTPUEmbeddingProximalAdagradParameters ❌   LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug ❌   LoadTPUEmbeddingProximalYogiParameters ❌   LoadTPUEmbeddingProximalYogiParametersGradAccumDebug ❌   LoadTPUEmbeddingRMSPropParameters ❌   LoadTPUEmbeddingRMSPropParametersGradAccumDebug ❌   LoadTPUEmbeddingStochasticGradientDescentParameters ❌   LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug ❌   Log ✔   Log1p ✔   LogMatrixDeterminant ✔   LogSoftmax ✔   LogUniformCandidateSampler ❌   LogicalAnd ✔   LogicalNot ✔   LogicalOr ✔   LookupTableExport ❌   LookupTableExportV2 ❌   LookupTableFind ✔   LookupTableFindV2 ✔   LookupTableImport ❌   LookupTableImportV2 ❌   LookupTableInsert ✔   LookupTableInsertV2 ✔   LookupTableRemoveV2 ❌   LookupTableSize ✔   LookupTableSizeV2 ✔   LoopCond ✔   LowerBound ❌   Lu ❌   MakeIterator ❌   MapAndBatchDataset ❌   MapClear ❌   MapDataset ❌   MapDefun ❌   MapIncompleteSize ❌   MapPeek ❌   MapSize ❌   MapStage ❌   MapUnstage ❌   MapUnstageNoKey ❌   MatMul ✔   MatchingFiles ❌   MatchingFilesDataset ❌   MatrixBandPart ✔   MatrixDeterminant ✔   MatrixDiag ✔   MatrixDiagPart ✔   MatrixDiagPartV2 ✔   MatrixDiagPartV3 ✔   MatrixDiagV2 ✔   MatrixDiagV3 ✔   MatrixExponential ❌   MatrixInverse ✔   MatrixLogarithm ❌   MatrixSetDiag ✔   MatrixSetDiagV2 ✔   MatrixSetDiagV3 ✔   MatrixSolve ✔   MatrixSolveLs ✔   MatrixSquareRoot ✔   MatrixTriangularSolve ✔   Max ✔   MaxIntraOpParallelismDataset ❌   MaxPool ✔   MaxPool3D ✔   MaxPool3DGrad ✔   MaxPool3DGradGrad ✔   MaxPoolGrad ✔   MaxPoolGradGrad ✔   MaxPoolGradGradV2 ❌   MaxPoolGradGradWithArgmax ❌   MaxPoolGradV2 ✔   MaxPoolGradWithArgmax ❌   MaxPoolV2 ✔   MaxPoolWithArgmax ✔   Maximum ✔   Mean ✔   Merge ✔   MergeSummary ✔   MergeV2Checkpoints ❌   Mfcc ❌   Min ✔   Minimum ✔   MirrorPad ✔   MirrorPadGrad ✔   Mod ❌   ModelDataset ❌   Mul ✔   MulNoNan ✔   MultiDeviceIterator ❌   MultiDeviceIteratorFromStringHandle ❌   MultiDeviceIteratorGetNextFromShard ❌   MultiDeviceIteratorInit ❌   MultiDeviceIteratorToStringHandle ❌   Multinomial ✔   MutableDenseHashTable ✔   MutableDenseHashTableV2 ✔   MutableHashTable ✔   MutableHashTableOfTensors ✔   MutableHashTableOfTensorsV2 ✔   MutableHashTableV2 ✔   MutexLock ❌   MutexV2 ❌   NcclAllReduce ✔   NcclBroadcast ✔   NcclReduce ✔   Ndtri ✔   Neg ✔   NextAfter ✔   NextIteration ✔   NoOp ❌   NonDeterministicInts ❌   NonMaxSuppression ✔   NonMaxSuppressionV2 ✔   NonMaxSuppressionV3 ❌   NonMaxSuppressionV4 ❌   NonMaxSuppressionV5 ❌   NonMaxSuppressionWithOverlaps ✔   NonSerializableDataset ❌   NotEqual ✔   NthElement ✔   OneHot ✔   OneShotIterator ❌   OnesLike ✔   OptimizeDataset ❌   OptimizeDatasetV2 ❌   OptionalFromValue ✔   OptionalGetValue ✔   OptionalHasValue ❌   OptionalNone ❌   OrderedMapClear ❌   OrderedMapIncompleteSize ❌   OrderedMapPeek ❌   OrderedMapSize ❌   OrderedMapStage ❌   OrderedMapUnstage ❌   OrderedMapUnstageNoKey ❌   OutfeedDequeue ❌   OutfeedDequeueTuple ❌   OutfeedDequeueTupleV2 ❌   OutfeedDequeueV2 ❌   OutfeedEnqueue ❌   OutfeedEnqueueTuple ❌   Pack ✔   Pad ✔   PadV2 ✔   PaddedBatchDataset ❌   PaddedBatchDatasetV2 ❌   PaddingFIFOQueue ❌   PaddingFIFOQueueV2 ❌   ParallelConcat ❌   ParallelDynamicStitch ✔   ParallelInterleaveDataset ❌   ParallelInterleaveDatasetV2 ❌   ParallelInterleaveDatasetV3 ❌   ParallelInterleaveDatasetV4 ❌   ParallelMapDataset ❌   ParallelMapDatasetV2 ❌   ParameterizedTruncatedNormal ✔   ParseExample ❌   ParseExampleDataset ❌   ParseExampleDatasetV2 ❌   ParseExampleV2 ❌   ParseSequenceExample ❌   ParseSequenceExampleV2 ❌   ParseSingleExample ❌   ParseSingleSequenceExample ❌   ParseTensor ✔   PartitionedCall ❌   Placeholder ❌   PlaceholderV2 ❌   PlaceholderWithDefault ✔   Polygamma ✔   PopulationCount ✔   Pow ✔   PrefetchDataset ❌   Prelinearize ❌   PrelinearizeTuple ❌   PreventGradient ✔   Print ✔   PrintV2 ❌   PriorityQueue ❌   PriorityQueueV2 ❌   PrivateThreadPoolDataset ❌   Prod ✔   PyFunc ✔   PyFuncStateless ✔   Qr ✔   QuantizeAndDequantize ✔   QuantizeAndDequantizeV2 ✔   QuantizeAndDequantizeV3 ✔   QuantizeAndDequantizeV4 ✔   QuantizeAndDequantizeV4Grad ✔   QuantizeDownAndShrinkRange ❌   QuantizeV2 ❌   QuantizedAdd ❌   QuantizedAvgPool ❌   QuantizedBatchNormWithGlobalNormalization ❌   QuantizedBiasAdd ❌   QuantizedConcat ❌   QuantizedConv2D ❌   QuantizedConv2DAndRelu ❌   QuantizedConv2DAndReluAndRequantize ❌   QuantizedConv2DAndRequantize ❌   QuantizedConv2DPerChannel ❌   QuantizedConv2DWithBias ❌   QuantizedConv2DWithBiasAndRelu ❌   QuantizedConv2DWithBiasAndReluAndRequantize ❌   QuantizedConv2DWithBiasAndRequantize ❌   QuantizedConv2DWithBiasSignedSumAndReluAndRequantize ❌   QuantizedConv2DWithBiasSumAndRelu ❌   QuantizedConv2DWithBiasSumAndReluAndRequantize ❌   QuantizedDepthwiseConv2D ❌   QuantizedDepthwiseConv2DWithBias ❌   QuantizedDepthwiseConv2DWithBiasAndRelu ❌   QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize ❌   QuantizedInstanceNorm ❌   QuantizedMatMul ❌   QuantizedMatMulWithBias ❌   QuantizedMatMulWithBiasAndDequantize ❌   QuantizedMatMulWithBiasAndRelu ❌   QuantizedMatMulWithBiasAndReluAndRequantize ❌   QuantizedMatMulWithBiasAndRequantize ❌   QuantizedMaxPool ❌   QuantizedMul ❌   QuantizedRelu ❌   QuantizedRelu6 ❌   QuantizedReluX ❌   QuantizedReshape ❌   QuantizedResizeBilinear ❌   QueueClose ✔   QueueCloseV2 ❌   QueueDequeue ✔   QueueDequeueMany ✔   QueueDequeueManyV2 ❌   QueueDequeueUpTo ✔   QueueDequeueUpToV2 ❌   QueueDequeueV2 ❌   QueueEnqueue ✔   QueueEnqueueMany ✔   QueueEnqueueManyV2 ❌   QueueEnqueueV2 ❌   QueueIsClosed ❌   QueueIsClosedV2 ❌   QueueSize ✔   QueueSizeV2 ❌   RFFT ✔   RFFT2D ✔   RFFT3D ❌   RGBToHSV ✔   RaggedBincount ❌   RaggedCountSparseOutput ❌   RaggedCross ❌   RaggedGather ✔   RaggedRange ✔   RaggedTensorFromVariant ✔   RaggedTensorToSparse ✔   RaggedTensorToTensor ✔   RaggedTensorToVariant ✔   RaggedTensorToVariantGradient ❌   RandomCrop ✔   RandomDataset ❌   RandomGamma ✔   RandomGammaGrad ❌   RandomPoisson ❌   RandomPoissonV2 ❌   RandomShuffle ❌   RandomShuffleQueue ❌   RandomShuffleQueueV2 ❌   RandomStandardNormal ✔   RandomUniform ✔   RandomUniformInt ❌   Range ✔   RangeDataset ❌   Rank ✔   ReadFile ❌   ReadVariableOp ✔   ReaderNumRecordsProduced ✔   ReaderNumRecordsProducedV2 ❌   ReaderNumWorkUnitsCompleted ✔   ReaderNumWorkUnitsCompletedV2 ❌   ReaderRead ✔   ReaderReadUpTo ✔   ReaderReadUpToV2 ❌   ReaderReadV2 ❌   ReaderReset ✔   ReaderResetV2 ❌   ReaderRestoreState ✔   ReaderRestoreStateV2 ❌   ReaderSerializeState ✔   ReaderSerializeStateV2 ❌   Real ✔   RealDiv ✔   RebatchDataset ❌   RebatchDatasetV2 ❌   Reciprocal ✔   ReciprocalGrad ✔   RecordInput ❌   Recv ❌   RecvTPUEmbeddingActivations ❌   ReduceDataset ✔   ReduceJoin ✔   RefEnter ✔   RefExit ✔   RefIdentity ✔   RefMerge ✔   RefNextIteration ✔   RefSelect ❌   RefSwitch ✔   RegexFullMatch ❌   RegexReplace ✔   RegisterDataset ❌   Relu ✔   Relu6 ✔   Relu6Grad ✔   ReluGrad ✔   RemoteCall ❌   RepeatDataset ❌   RequantizationRange ❌   RequantizationRangePerChannel ❌   Requantize ❌   RequantizePerChannel ❌   Reshape ✔   ResizeArea ❌   ResizeBicubic ✔   ResizeBicubicGrad ❌   ResizeBilinear ✔   ResizeBilinearGrad ❌   ResizeNearestNeighbor ✔   ResizeNearestNeighborGrad ❌   ResourceAccumulatorApplyGradient ❌   ResourceAccumulatorNumAccumulated ❌   ResourceAccumulatorSetGlobalStep ❌   ResourceAccumulatorTakeGradient ❌   ResourceApplyAdaMax ❌   ResourceApplyAdadelta ❌   ResourceApplyAdagrad ❌   ResourceApplyAdagradDA ❌   ResourceApplyAdagradV2 ❌   ResourceApplyAdam ❌   ResourceApplyAdamWithAmsgrad ❌   ResourceApplyAddSign ❌   ResourceApplyCenteredRMSProp ❌   ResourceApplyFtrl ❌   ResourceApplyFtrlV2 ❌   ResourceApplyGradientDescent ❌   ResourceApplyKerasMomentum ❌   ResourceApplyMomentum ❌   ResourceApplyPowerSign ❌   ResourceApplyProximalAdagrad ❌   ResourceApplyProximalGradientDescent ❌   ResourceApplyRMSProp ❌   ResourceConditionalAccumulator ❌   ResourceCountUpTo ❌   ResourceGather ✔   ResourceGatherNd ✔   ResourceScatterAdd ❌   ResourceScatterDiv ❌   ResourceScatterMax ❌   ResourceScatterMin ❌   ResourceScatterMul ❌   ResourceScatterNdAdd ❌   ResourceScatterNdMax ❌   ResourceScatterNdMin ❌   ResourceScatterNdSub ❌   ResourceScatterNdUpdate ❌   ResourceScatterSub ❌   ResourceScatterUpdate ❌   ResourceSparseApplyAdadelta ❌   ResourceSparseApplyAdagrad ❌   ResourceSparseApplyAdagradDA ❌   ResourceSparseApplyAdagradV2 ❌   ResourceSparseApplyCenteredRMSProp ❌   ResourceSparseApplyFtrl ❌   ResourceSparseApplyFtrlV2 ❌   ResourceSparseApplyKerasMomentum ❌   ResourceSparseApplyMomentum ❌   ResourceSparseApplyProximalAdagrad ❌   ResourceSparseApplyProximalGradientDescent ❌   ResourceSparseApplyRMSProp ❌   ResourceStridedSliceAssign ❌   Restore ❌   RestoreSlice ❌   RestoreV2 ❌   RetrieveTPUEmbeddingADAMParameters ❌   RetrieveTPUEmbeddingADAMParametersGradAccumDebug ❌   RetrieveTPUEmbeddingAdadeltaParameters ❌   RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug ❌   RetrieveTPUEmbeddingAdagradParameters ❌   RetrieveTPUEmbeddingAdagradParametersGradAccumDebug ❌   RetrieveTPUEmbeddingCenteredRMSPropParameters ❌   RetrieveTPUEmbeddingFTRLParameters ❌   RetrieveTPUEmbeddingFTRLParametersGradAccumDebug ❌   RetrieveTPUEmbeddingMDLAdagradLightParameters ❌   RetrieveTPUEmbeddingMomentumParameters ❌   RetrieveTPUEmbeddingMomentumParametersGradAccumDebug ❌   RetrieveTPUEmbeddingProximalAdagradParameters ❌   RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug ❌   RetrieveTPUEmbeddingProximalYogiParameters ❌   RetrieveTPUEmbeddingProximalYogiParametersGradAccumDebug ❌   RetrieveTPUEmbeddingRMSPropParameters ❌   RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug ❌   RetrieveTPUEmbeddingStochasticGradientDescentParameters ❌   RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug ❌   Reverse ✔   ReverseSequence ✔   ReverseV2 ✔   RightShift ✔   Rint ✔   RngReadAndSkip ❌   RngSkip ❌   Roll ✔   Round ✔   Rsqrt ✔   RsqrtGrad ✔   SampleDistortedBoundingBox ✔   SampleDistortedBoundingBoxV2 ✔   SamplingDataset ❌   Save ❌   SaveDataset ❌   SaveSlices ❌   SaveV2 ❌   ScalarSummary ✔   ScaleAndTranslate ✔   ScaleAndTranslateGrad ❌   ScanDataset ❌   ScatterAdd ✔   ScatterDiv ✔   ScatterMax ❌   ScatterMin ❌   ScatterMul ✔   ScatterNd ✔   ScatterNdAdd ✔   ScatterNdMax ❌   ScatterNdMin ❌   ScatterNdNonAliasingAdd ✔   ScatterNdSub ✔   ScatterNdUpdate ✔   ScatterSub ✔   ScatterUpdate ❌   SdcaFprint ✔   SdcaOptimizer ✔   SdcaOptimizerV2 ✔   SdcaShrinkL1 ✔   SegmentMax ✔   SegmentMean ✔   SegmentMin ✔   SegmentProd ❌   SegmentSum ✔   Select ✔   SelectV2 ✔   SelfAdjointEig ❌   SelfAdjointEigV2 ✔   Selu ✔   SeluGrad ✔   Send ❌   SendTPUEmbeddingGradients ❌   SerializeIterator ❌   SerializeManySparse ❌   SerializeSparse ❌   SerializeTensor ✔   SetSize ✔   SetStatsAggregatorDataset ❌   Shape ✔   ShapeN ✔   ShardDataset ❌   ShardedFilename ❌   ShardedFilespec ❌   ShuffleAndRepeatDataset ❌   ShuffleAndRepeatDatasetV2 ❌   ShuffleDataset ❌   ShuffleDatasetV2 ❌   ShuffleDatasetV3 ❌   ShutdownDistributedTPU ❌   Sigmoid ✔   SigmoidGrad ✔   Sign ✔   Sin ✔   Sinh ✔   Size ✔   SkipDataset ❌   SleepDataset ❌   Slice ✔   SlidingWindowDataset ❌   Snapshot ❌   SnapshotDataset ❌   SnapshotDatasetV2 ❌   SobolSample ❌   Softmax ✔   SoftmaxCrossEntropyWithLogits ✔   Softplus ✔   SoftplusGrad ✔   Softsign ✔   SoftsignGrad ❌   SpaceToBatch ✔   SpaceToBatchND ✔   SpaceToDepth ✔   SparseAccumulatorApplyGradient ❌   SparseAccumulatorTakeGradient ❌   SparseAdd ✔   SparseAddGrad ✔   SparseApplyAdadelta ❌   SparseApplyAdagrad ❌   SparseApplyAdagradDA ❌   SparseApplyAdagradV2 ❌   SparseApplyCenteredRMSProp ❌   SparseApplyFtrl ❌   SparseApplyFtrlV2 ❌   SparseApplyMomentum ❌   SparseApplyProximalAdagrad ❌   SparseApplyProximalGradientDescent ❌   SparseApplyRMSProp ❌   SparseBincount ❌   SparseConcat ✔   SparseConditionalAccumulator ❌   SparseCountSparseOutput ❌   SparseCross ❌   SparseCrossHashed ❌   SparseCrossV2 ❌   SparseDenseCwiseAdd ✔   SparseDenseCwiseDiv ✔   SparseDenseCwiseMul ✔   SparseFillEmptyRows ✔   SparseFillEmptyRowsGrad ❌   SparseMatMul ✔   SparseMatrixAdd ✔   SparseMatrixMatMul ✔   SparseMatrixMul ✔   SparseMatrixNNZ ✔   SparseMatrixOrderingAMD ❌   SparseMatrixSoftmax ✔   SparseMatrixSoftmaxGrad ❌   SparseMatrixSparseCholesky ❌   SparseMatrixSparseMatMul ✔   SparseMatrixTranspose ✔   SparseMatrixZeros ✔   SparseReduceMax ❌   SparseReduceMaxSparse ❌   SparseReduceSum ✔   SparseReduceSumSparse ❌   SparseReorder ✔   SparseReshape ❌   SparseSegmentMean ✔   SparseSegmentMeanGrad ❌   SparseSegmentMeanWithNumSegments ✔   SparseSegmentSqrtN ✔   SparseSegmentSqrtNGrad ❌   SparseSegmentSqrtNWithNumSegments ✔   SparseSegmentSum ✔   SparseSegmentSumWithNumSegments ✔   SparseSlice ✔   SparseSliceGrad ❌   SparseSoftmax ✔   SparseSoftmaxCrossEntropyWithLogits ✔   SparseSparseMaximum ✔   SparseSparseMinimum ✔   SparseSplit ❌   SparseTensorDenseAdd ✔   SparseTensorDenseMatMul ✔   SparseTensorSliceDataset ❌   SparseTensorToCSRSparseMatrix ❌   SparseToDense ✔   SparseToSparseSetOperation ✔   Spence ✔   Split ✔   SplitV ✔   SqlDataset ❌   Sqrt ✔   SqrtGrad ✔   Square ✔   SquaredDifference ✔   Squeeze ✔   Stack ✔   StackClose ✔   StackCloseV2 ❌   StackPop ✔   StackPopV2 ❌   StackPush ✔   StackPushV2 ❌   StackV2 ❌   Stage ❌   StageClear ❌   StagePeek ❌   StageSize ❌   StatefulPartitionedCall ❌   StatefulRandomBinomial ❌   StatefulStandardNormal ❌   StatefulStandardNormalV2 ❌   StatefulTruncatedNormal ❌   StatefulUniform ❌   StatefulUniformFullInt ❌   StatefulUniformInt ❌   StatelessCase ✔   StatelessIf ✔   StatelessMultinomial ✔   StatelessParameterizedTruncatedNormal ✔   StatelessRandomBinomial ✔   StatelessRandomGammaV2 ✔   StatelessRandomGetKeyCounterAlg ❌   StatelessRandomNormal ✔   StatelessRandomNormalV2 ✔   StatelessRandomPoisson ✔   StatelessRandomUniform ✔   StatelessRandomUniformFullInt ✔   StatelessRandomUniformFullIntV2 ✔   StatelessRandomUniformInt ✔   StatelessRandomUniformIntV2 ✔   StatelessRandomUniformV2 ✔   StatelessSampleDistortedBoundingBox ❌   StatelessTruncatedNormal ✔   StatelessTruncatedNormalV2 ✔   StatelessWhile ✔   StaticRegexFullMatch ❌   StaticRegexReplace ❌   StatsAggregatorHandle ❌   StatsAggregatorHandleV2 ❌   StatsAggregatorSetSummaryWriter ❌   StatsAggregatorSummary ❌   StopGradient ✔   StridedSlice ✔   StridedSliceAssign ❌   StridedSliceGrad ✔   StringFormat ❌   StringJoin ✔   StringLength ❌   StringLower ❌   StringNGrams ❌   StringSplit ✔   StringSplitV2 ❌   StringStrip ❌   StringToHashBucket ✔   StringToHashBucketFast ✔   StringToHashBucketStrong ✔   StringToNumber ✔   StringUpper ❌   Sub ✔   Substr ❌   Sum ✔   SummaryWriter ❌   Svd ✔   Switch ✔   SymbolicGradient ❌   TFRecordDataset ❌   TFRecordReader ✔   TFRecordReaderV2 ❌   TPUCompilationResult ❌   TPUEmbeddingActivations ✔   TPUOrdinalSelector ❌   TPUPartitionedCall ❌   TPUReplicateMetadata ❌   TPUReplicatedInput ✔   TPUReplicatedOutput ❌   TakeDataset ❌   TakeManySparseFromTensorsMap ❌   TakeWhileDataset ❌   Tan ✔   Tanh ✔   TanhGrad ✔   TemporaryVariable ❌   TensorArray ✔   TensorArrayClose ✔   TensorArrayCloseV2 ✔   TensorArrayCloseV3 ✔   TensorArrayConcat ✔   TensorArrayConcatV2 ✔   TensorArrayConcatV3 ✔   TensorArrayGather ✔   TensorArrayGatherV2 ✔   TensorArrayGatherV3 ✔   TensorArrayGrad ✔   TensorArrayGradV2 ✔   TensorArrayGradV3 ✔   TensorArrayGradWithShape ✔   TensorArrayPack ❌   TensorArrayRead ✔   TensorArrayReadV2 ✔   TensorArrayReadV3 ✔   TensorArrayScatter ✔   TensorArrayScatterV2 ✔   TensorArrayScatterV3 ✔   TensorArraySize ✔   TensorArraySizeV2 ✔   TensorArraySizeV3 ✔   TensorArraySplit ✔   TensorArraySplitV2 ✔   TensorArraySplitV3 ✔   TensorArrayUnpack ❌   TensorArrayV2 ✔   TensorArrayV3 ✔   TensorArrayWrite ✔   TensorArrayWriteV2 ✔   TensorArrayWriteV3 ✔   TensorDataset ❌   TensorListConcat ✔   TensorListConcatLists ✔   TensorListConcatV2 ✔   TensorListElementShape ✔   TensorListFromTensor ✔   TensorListGather ✔   TensorListGetItem ✔   TensorListLength ✔   TensorListPopBack ✔   TensorListPushBack ✔   TensorListPushBackBatch ✔   TensorListReserve ❌   TensorListResize ✔   TensorListScatter ✔   TensorListScatterIntoExistingList ✔   TensorListScatterV2 ✔   TensorListSetItem ✔   TensorListSplit ✔   TensorListStack ✔   TensorScatterAdd ✔   TensorScatterMax ✔   TensorScatterMin ✔   TensorScatterSub ✔   TensorScatterUpdate ✔   TensorSliceDataset ❌   TensorStridedSliceUpdate ✔   TensorSummary ✔   TensorSummaryV2 ✔   TextLineDataset ❌   TextLineReader ✔   TextLineReaderV2 ❌   ThreadPoolDataset ❌   ThreadPoolHandle ❌   ThreadUnsafeUnigramCandidateSampler ❌   Tile ✔   TileGrad ❌   Timestamp ✔   ToBool ❌   TopK ✔   TopKV2 ✔   Transpose ✔   TridiagonalMatMul ✔   TridiagonalSolve ✔   TruncateDiv ✔   TruncateMod ❌   TruncatedNormal ✔   Unbatch ❌   UnbatchDataset ❌   UnbatchGrad ❌   UncompressElement ❌   UnicodeDecode ❌   UnicodeDecodeWithOffsets ❌   UnicodeEncode ❌   UnicodeScript ❌   UnicodeTranscode ❌   UniformCandidateSampler ❌   Unique ❌   UniqueDataset ❌   UniqueV2 ❌   UniqueWithCounts ❌   UniqueWithCountsV2 ❌   Unpack ✔   UnravelIndex ❌   UnsortedSegmentJoin ❌   UnsortedSegmentMax ✔   UnsortedSegmentMin ✔   UnsortedSegmentProd ✔   UnsortedSegmentSum ✔   Unstage ❌   UnwrapDatasetVariant ❌   UpperBound ❌   VarHandleOp ❌   VarIsInitializedOp ✔   Variable ❌   VariableShape ✔   VariableV2 ❌   Where ❌   While ✔   WholeFileReader ✔   WholeFileReaderV2 ❌   WindowDataset ❌   WorkerHeartbeat ❌   WrapDatasetVariant ❌   WriteAudioSummary ❌   WriteFile ❌   WriteGraphSummary ❌   WriteHistogramSummary ❌   WriteImageSummary ❌   WriteRawProtoSummary ❌   WriteScalarSummary ❌   WriteSummary ❌   Xdivy ✔   Xlog1py ✔   Xlogy ✔   ZerosLike ✔   Zeta ✔   ZipDataset ❌


tf.raw_ops.Abort Raise a exception to abort the process when called.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Abort
tf.raw_ops.Abort(
    error_msg='', exit_without_error=False, name=None
)
 If exit_without_error is true, the process will exit normally, otherwise it will exit with a SIGABORT signal. Returns nothing but an exception.

 Args
  error_msg   An optional string. Defaults to "". A string which is the message associated with the exception.
  exit_without_error   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.Abs   Computes the absolute value of a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Abs
tf.raw_ops.Abs(
    x, name=None
)
 Given a tensor x, this operation returns a tensor containing the absolute value of each element in x. For example, if x is an input element and y is an output element, this operation computes \(y = |x|\).

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int8, int16, int32, int64.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.AccumulateNV2 Returns the element-wise sum of a list of tensors.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.AccumulateNV2
tf.raw_ops.AccumulateNV2(
    inputs, shape, name=None
)
 tf.accumulate_n_v2 performs the same operation as tf.add_n, but does not wait for all of its inputs to be ready before beginning to sum. This can save memory if inputs are ready at different times, since minimum temporary storage is proportional to the output size rather than the inputs size. Unlike the original accumulate_n, accumulate_n_v2 is differentiable. Returns a Tensor of same shape and type as the elements of inputs.

 Args
  inputs   A list of at least 1 Tensor objects with the same type in: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. A list of Tensor objects, each with same shape and type.
  shape   A tf.TensorShape or list of ints. Shape of elements of inputs.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as inputs.


tf.raw_ops.AccumulatorApplyGradient Applies a gradient to a given accumulator.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.AccumulatorApplyGradient
tf.raw_ops.AccumulatorApplyGradient(
    handle, local_step, gradient, name=None
)
 Does not add if local_step is lesser than the accumulator's global_step.

 Args
  handle   A Tensor of type mutable string. The handle to a accumulator.
  local_step   A Tensor of type int64. The local_step value at which the gradient was computed.
  gradient   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. A tensor of the gradient to be accumulated.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.AccumulatorNumAccumulated Returns the number of gradients aggregated in the given accumulators.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.AccumulatorNumAccumulated
tf.raw_ops.AccumulatorNumAccumulated(
    handle, name=None
)

 Args
  handle   A Tensor of type mutable string. The handle to an accumulator.
  name   A name for the operation (optional).

 Returns   A Tensor of type int32.


tf.raw_ops.AccumulatorSetGlobalStep Updates the accumulator with a new value for global_step.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.AccumulatorSetGlobalStep
tf.raw_ops.AccumulatorSetGlobalStep(
    handle, new_global_step, name=None
)
 Logs warning if the accumulator's value is already higher than new_global_step.

 Args
  handle   A Tensor of type mutable string. The handle to an accumulator.
  new_global_step   A Tensor of type int64. The new global_step value to set.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.AccumulatorTakeGradient Extracts the average gradient in the given ConditionalAccumulator.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.AccumulatorTakeGradient
tf.raw_ops.AccumulatorTakeGradient(
    handle, num_required, dtype, name=None
)
 The op blocks until sufficient (i.e., more than num_required) gradients have been accumulated. If the accumulator has already aggregated more than num_required gradients, it returns the average of the accumulated gradients. Also automatically increments the recorded global_step in the accumulator by 1, and resets the aggregate to 0.

 Args
  handle   A Tensor of type mutable string. The handle to an accumulator.
  num_required   A Tensor of type int32. Number of gradients required before we return an aggregate.
  dtype   A tf.DType from: tf.float32, tf.float64, tf.int32, tf.uint8, tf.int16, tf.int8, tf.complex64, tf.int64, tf.qint8, tf.quint8, tf.qint32, tf.bfloat16, tf.uint16, tf.complex128, tf.half, tf.uint32, tf.uint64. The data type of accumulated gradients. Needs to correspond to the type of the accumulator.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.Acos Computes acos of x element-wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Acos
tf.raw_ops.Acos(
    x, name=None
)
 Provided an input tensor, the tf.math.acos operation returns the inverse cosine of each element of the tensor. If y = tf.math.cos(x) then, x = tf.math.acos(y). Input range is [-1, 1] and the output has a range of [0, pi].

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int8, int16, int32, int64, complex64, complex128.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.Acosh Computes inverse hyperbolic cosine of x element-wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Acosh
tf.raw_ops.Acosh(
    x, name=None
)
 Given an input tensor, the function computes inverse hyperbolic cosine of every element. Input range is [1, inf]. It returns nan if the input lies outside the range. x = tf.constant([-2, -0.5, 1, 1.2, 200, 10000, float("inf")])
tf.math.acosh(x) ==> [nan nan 0. 0.62236255 5.9914584 9.903487 inf]

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.Add Returns x + y element-wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Add
tf.raw_ops.Add(
    x, y, name=None
)

Note: math.add supports broadcasting. AddN does not. More about broadcasting here
 Given two input tensors, the tf.add operation computes the sum for every element in the tensor. Both input and output have a range (-inf, inf).

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128, string.
  y   A Tensor. Must have the same type as x.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.AddManySparseToTensorsMap Add an N-minibatch SparseTensor to a SparseTensorsMap, return N handles.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.AddManySparseToTensorsMap
tf.raw_ops.AddManySparseToTensorsMap(
    sparse_indices, sparse_values, sparse_shape, container='',
    shared_name='', name=None
)
 A SparseTensor of rank R is represented by three tensors: sparse_indices, sparse_values, and sparse_shape, where sparse_indices.shape[1] == sparse_shape.shape[0] == R An N-minibatch of SparseTensor objects is represented as a SparseTensor having a first sparse_indices column taking values between [0, N), where the minibatch size N == sparse_shape[0]. The input SparseTensor must have rank R greater than 1, and the first dimension is treated as the minibatch dimension. Elements of the SparseTensor must be sorted in increasing order of this first dimension. The stored SparseTensor objects pointed to by each row of the output sparse_handles will have rank R-1. The SparseTensor values can then be read out as part of a minibatch by passing the given keys as vector elements to TakeManySparseFromTensorsMap. To ensure the correct SparseTensorsMap is accessed, ensure that the same container and shared_name are passed to that Op. If no shared_name is provided here, instead use the name of the Operation created by calling AddManySparseToTensorsMap as the shared_name passed to TakeManySparseFromTensorsMap. Ensure the Operations are colocated.

 Args
  sparse_indices   A Tensor of type int64. 2-D. The indices of the minibatch SparseTensor. sparse_indices[:, 0] must be ordered values in [0, N).
  sparse_values   A Tensor. 1-D. The values of the minibatch SparseTensor.
  sparse_shape   A Tensor of type int64. 1-D. The shape of the minibatch SparseTensor. The minibatch size N == sparse_shape[0].
  container   An optional string. Defaults to "". The container name for the SparseTensorsMap created by this op.
  shared_name   An optional string. Defaults to "". The shared name for the SparseTensorsMap created by this op. If blank, the new Operation's unique name is used.
  name   A name for the operation (optional).

 Returns   A Tensor of type int64.


tf.raw_ops.AddN Add all input tensors element wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.AddN
tf.raw_ops.AddN(
    inputs, name=None
)
 Inputs must be of same size and shape. x = [9, 7, 10]
tf.math.add_n(x) ==> 26

 Args
  inputs   A list of at least 1 Tensor objects with the same type in: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64, variant.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as inputs.


tf.raw_ops.AddSparseToTensorsMap Add a SparseTensor to a SparseTensorsMap return its handle.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.AddSparseToTensorsMap
tf.raw_ops.AddSparseToTensorsMap(
    sparse_indices, sparse_values, sparse_shape, container='',
    shared_name='', name=None
)
 A SparseTensor is represented by three tensors: sparse_indices, sparse_values, and sparse_shape. This operator takes the given SparseTensor and adds it to a container object (a SparseTensorsMap). A unique key within this container is generated in the form of an int64, and this is the value that is returned. The SparseTensor can then be read out as part of a minibatch by passing the key as a vector element to TakeManySparseFromTensorsMap. To ensure the correct SparseTensorsMap is accessed, ensure that the same container and shared_name are passed to that Op. If no shared_name is provided here, instead use the name of the Operation created by calling AddSparseToTensorsMap as the shared_name passed to TakeManySparseFromTensorsMap. Ensure the Operations are colocated.

 Args
  sparse_indices   A Tensor of type int64. 2-D. The indices of the SparseTensor.
  sparse_values   A Tensor. 1-D. The values of the SparseTensor.
  sparse_shape   A Tensor of type int64. 1-D. The shape of the SparseTensor.
  container   An optional string. Defaults to "". The container name for the SparseTensorsMap created by this op.
  shared_name   An optional string. Defaults to "". The shared name for the SparseTensorsMap created by this op. If blank, the new Operation's unique name is used.
  name   A name for the operation (optional).

 Returns   A Tensor of type int64.


tf.raw_ops.AddV2 Returns x + y element-wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.AddV2
tf.raw_ops.AddV2(
    x, y, name=None
)

Note: Add supports broadcasting. AddN does not. More about broadcasting here

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, uint8, int8, int16, uint32, int32, int64, complex64, complex128.
  y   A Tensor. Must have the same type as x.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.AdjustContrast Deprecated. Disallowed in GraphDef version >= 2.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.AdjustContrast
tf.raw_ops.AdjustContrast(
    images, contrast_factor, min_value, max_value, name=None
)

 Args
  images   A Tensor. Must be one of the following types: uint8, int8, int16, int32, int64, float32, float64.
  contrast_factor   A Tensor of type float32.
  min_value   A Tensor of type float32.
  max_value   A Tensor of type float32.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.AdjustContrastv2 Adjust the contrast of one or more images.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.AdjustContrastv2
tf.raw_ops.AdjustContrastv2(
    images, contrast_factor, name=None
)
 images is a tensor of at least 3 dimensions. The last 3 dimensions are interpreted as [height, width, channels]. The other dimensions only represent a collection of images, such as [batch, height, width, channels]. Contrast is adjusted independently for each channel of each image. For each channel, the Op first computes the mean of the image pixels in the channel and then adjusts each component of each pixel to (x - mean) * contrast_factor + mean.

 Args
  images   A Tensor. Must be one of the following types: half, float32. Images to adjust. At least 3-D.
  contrast_factor   A Tensor of type float32. A float multiplier for adjusting contrast.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as images.


tf.raw_ops.AdjustHue Adjust the hue of one or more images.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.AdjustHue
tf.raw_ops.AdjustHue(
    images, delta, name=None
)
 images is a tensor of at least 3 dimensions. The last dimension is interpreted as channels, and must be three. The input image is considered in the RGB colorspace. Conceptually, the RGB colors are first mapped into HSV. A delta is then applied all the hue values, and then remapped back to RGB colorspace.

 Args
  images   A Tensor. Must be one of the following types: half, float32. Images to adjust. At least 3-D.
  delta   A Tensor of type float32. A float delta to add to the hue.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as images.


tf.raw_ops.AdjustSaturation Adjust the saturation of one or more images.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.AdjustSaturation
tf.raw_ops.AdjustSaturation(
    images, scale, name=None
)
 images is a tensor of at least 3 dimensions. The last dimension is interpreted as channels, and must be three. The input image is considered in the RGB colorspace. Conceptually, the RGB colors are first mapped into HSV. A scale is then applied all the saturation values, and then remapped back to RGB colorspace.

 Args
  images   A Tensor. Must be one of the following types: half, float32. Images to adjust. At least 3-D.
  scale   A Tensor of type float32. A float scale to add to the saturation.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as images.


tf.raw_ops.All Computes the "logical and" of elements across dimensions of a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.All
tf.raw_ops.All(
    input, axis, keep_dims=False, name=None
)
 Reduces input along the dimensions given in axis. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keep_dims is true, the reduced dimensions are retained with length 1.

 Args
  input   A Tensor of type bool. The tensor to reduce.
  axis   A Tensor. Must be one of the following types: int32, int64. The dimensions to reduce. Must be in the range [-rank(input), rank(input)).
  keep_dims   An optional bool. Defaults to False. If true, retain reduced dimensions with length 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type bool.


tf.raw_ops.AllCandidateSampler Generates labels for candidate sampling with a learned unigram distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.AllCandidateSampler
tf.raw_ops.AllCandidateSampler(
    true_classes, num_true, num_sampled, unique, seed=0, seed2=0, name=None
)
 See explanations of candidate sampling and the data formats at go/candidate-sampling. For each batch, this op picks a single set of sampled candidate labels. The advantages of sampling candidates per-batch are simplicity and the possibility of efficient dense matrix multiplication. The disadvantage is that the sampled candidates must be chosen independently of the context and of the true labels.

 Args
  true_classes   A Tensor of type int64. A batch_size * num_true matrix, in which each row contains the IDs of the num_true target_classes in the corresponding original label.
  num_true   An int that is >= 1. Number of true labels per context.
  num_sampled   An int that is >= 1. Number of candidates to produce.
  unique   A bool. If unique is true, we sample with rejection, so that all sampled candidates in a batch are unique. This requires some approximation to estimate the post-rejection sampling probabilities.
  seed   An optional int. Defaults to 0. If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed.
  seed2   An optional int. Defaults to 0. An second seed to avoid seed collision.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (sampled_candidates, true_expected_count, sampled_expected_count).     sampled_candidates   A Tensor of type int64.
  true_expected_count   A Tensor of type float32.
  sampled_expected_count   A Tensor of type float32.


tf.raw_ops.AllToAll An Op to exchange data across TPU replicas.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.AllToAll
tf.raw_ops.AllToAll(
    input, group_assignment, concat_dimension, split_dimension, split_count,
    name=None
)
 On each replica, the input is split into split_count blocks along split_dimension and send to the other replicas given group_assignment. After receiving split_count - 1 blocks from other replicas, we concatenate the blocks along concat_dimension as the output. For example, suppose there are 2 TPU replicas: replica 0 receives input: [[A, B]] replica 1 receives input: [[C, D]] group_assignment=[[0, 1]] concat_dimension=0 split_dimension=1 split_count=2 replica 0's output: [[A], [C]] replica 1's output: [[B], [D]]

 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64, bool. The local input to the sum.
  group_assignment   A Tensor of type int32. An int32 tensor with shape [num_groups, num_replicas_per_group]. group_assignment[i] represents the replica ids in the ith subgroup.
  concat_dimension   An int. The dimension number to concatenate.
  split_dimension   An int. The dimension number to split.
  split_count   An int. The number of splits, this number must equal to the sub-group size(group_assignment.get_shape()[1])
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.Angle   Returns the argument of a complex number.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Angle
tf.raw_ops.Angle(
    input, Tout=tf.dtypes.float32, name=None
)
 Given a tensor input of complex numbers, this operation returns a tensor of type float that is the argument of each element in input. All elements in input must be complex numbers of the form \(a + bj\), where a is the real part and b is the imaginary part. The argument returned by this operation is of the form \(atan2(b, a)\). For example: # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
tf.angle(input) ==> [2.0132, 1.056]

 Args
  input   A Tensor. Must be one of the following types: complex64, complex128.
  Tout   An optional tf.DType from: tf.float32, tf.float64. Defaults to tf.float32.
  name   A name for the operation (optional).

 Returns   A Tensor of type Tout.
 Numpy Compatibility Equivalent to np.angle.


tf.raw_ops.AnonymousIterator A container for an iterator resource.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.AnonymousIterator
tf.raw_ops.AnonymousIterator(
    output_types, output_shapes, name=None
)

 Args
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type resource.


tf.raw_ops.AnonymousIteratorV2 A container for an iterator resource.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.AnonymousIteratorV2
tf.raw_ops.AnonymousIteratorV2(
    output_types, output_shapes, name=None
)

 Args
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (handle, deleter).     handle   A Tensor of type resource.
  deleter   A Tensor of type variant.


tf.raw_ops.AnonymousMemoryCache  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.AnonymousMemoryCache
tf.raw_ops.AnonymousMemoryCache(
    name=None
)

 Args
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (handle, deleter).     handle   A Tensor of type resource.
  deleter   A Tensor of type variant.


tf.raw_ops.AnonymousMultiDeviceIterator A container for a multi device iterator resource.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.AnonymousMultiDeviceIterator
tf.raw_ops.AnonymousMultiDeviceIterator(
    devices, output_types, output_shapes, name=None
)

 Args
  devices   A list of strings that has length >= 1.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (handle, deleter).     handle   A Tensor of type resource.
  deleter   A Tensor of type variant.


tf.raw_ops.AnonymousRandomSeedGenerator  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.AnonymousRandomSeedGenerator
tf.raw_ops.AnonymousRandomSeedGenerator(
    seed, seed2, name=None
)

 Args
  seed   A Tensor of type int64.
  seed2   A Tensor of type int64.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (handle, deleter).     handle   A Tensor of type resource.
  deleter   A Tensor of type variant.


tf.raw_ops.AnonymousSeedGenerator  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.AnonymousSeedGenerator
tf.raw_ops.AnonymousSeedGenerator(
    seed, seed2, reshuffle, name=None
)

 Args
  seed   A Tensor of type int64.
  seed2   A Tensor of type int64.
  reshuffle   A Tensor of type bool.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (handle, deleter).     handle   A Tensor of type resource.
  deleter   A Tensor of type variant.


tf.raw_ops.Any Computes the "logical or" of elements across dimensions of a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Any
tf.raw_ops.Any(
    input, axis, keep_dims=False, name=None
)
 Reduces input along the dimensions given in axis. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keep_dims is true, the reduced dimensions are retained with length 1.

 Args
  input   A Tensor of type bool. The tensor to reduce.
  axis   A Tensor. Must be one of the following types: int32, int64. The dimensions to reduce. Must be in the range [-rank(input), rank(input)).
  keep_dims   An optional bool. Defaults to False. If true, retain reduced dimensions with length 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type bool.


tf.raw_ops.ApplyAdadelta Update '*var' according to the adadelta scheme.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ApplyAdadelta
tf.raw_ops.ApplyAdadelta(
    var, accum, accum_update, lr, rho, epsilon, grad, use_locking=False, name=None
)
 accum = rho() * accum + (1 - rho()) * grad.square(); update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad; update_accum = rho() * update_accum + (1 - rho()) * update.square(); var -= update;

 Args
  var   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Should be from a Variable().
  accum   A mutable Tensor. Must have the same type as var. Should be from a Variable().
  accum_update   A mutable Tensor. Must have the same type as var. Should be from a Variable().
  lr   A Tensor. Must have the same type as var. Scaling factor. Must be a scalar.
  rho   A Tensor. Must have the same type as var. Decay factor. Must be a scalar.
  epsilon   A Tensor. Must have the same type as var. Constant factor. Must be a scalar.
  grad   A Tensor. Must have the same type as var. The gradient.
  use_locking   An optional bool. Defaults to False. If True, updating of the var, accum and update_accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   A mutable Tensor. Has the same type as var.