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TensorFlow root package
Modules
app
module: Generic entry point script.
audio
module: Public API for tf.audio namespace.
autograph
module: Conversion of plain Python into TensorFlow graph code.
bitwise
module: Operations for manipulating the binary representations of integers.
compat
module: Functions for Python 2 vs. 3 compatibility.
config
module: Public API for tf.config namespace.
contrib
module: Contrib module containing volatile or experimental code.
data
module: tf.data.Dataset
API for input pipelines.
debugging
module: Public API for tf.debugging namespace.
distribute
module: Library for running a computation across multiple devices.
distributions
module: Core module for TensorFlow distribution objects and helpers.
dtypes
module: Public API for tf.dtypes namespace.
errors
module: Exception types for TensorFlow errors.
estimator
module: Estimator: High level tools for working with models.
experimental
module: Public API for tf.experimental namespace.
feature_column
module: Public API for tf.feature_column namespace.
gfile
module: Import router for file_io.
graph_util
module: Helpers to manipulate a tensor graph in python.
image
module: Image processing and decoding ops.
initializers
module: Public API for tf.initializers namespace.
io
module: Public API for tf.io namespace.
keras
module: Implementation of the Keras API meant to be a high-level API for TensorFlow.
layers
module: Public API for tf.layers namespace.
linalg
module: Operations for linear algebra.
lite
module: Public API for tf.lite namespace.
logging
module: Logging and Summary Operations.
lookup
module: Public API for tf.lookup namespace.
losses
module: Loss operations for use in neural networks.
manip
module: Operators for manipulating tensors.
math
module: Math Operations.
metrics
module: Evaluation-related metrics.
nest
module: Public API for tf.nest namespace.
nn
module: Wrappers for primitive Neural Net (NN) Operations.
profiler
module: Public API for tf.profiler namespace.
python_io
module: Python functions for directly manipulating TFRecord-formatted files.
quantization
module: Public API for tf.quantization namespace.
queue
module: Public API for tf.queue namespace.
ragged
module: Ragged Tensors.
random
module: Public API for tf.random namespace.
raw_ops
module: Note: tf.raw_ops
provides direct/low level access to all TensorFlow ops. See the RFC
resource_loader
module: Resource management library.
saved_model
module: Public API for tf.saved_model namespace.
sets
module: Tensorflow set operations.
signal
module: Signal processing operations.
sparse
module: Sparse Tensor Representation.
spectral
module: Public API for tf.spectral namespace.
strings
module: Operations for working with string Tensors.
summary
module: Operations for writing summary data, for use in analysis and visualization.
sysconfig
module: System configuration library.
test
module: Testing.
tpu
module: Ops related to Tensor Processing Units.
train
module: Support for training models.
user_ops
module: Public API for tf.user_ops namespace.
version
module: Public API for tf.version namespace.
xla
module: Public API for tf.xla namespace.
Classes
class AggregationMethod
: A class listing aggregation methods used to combine gradients.
class AttrValue
: A ProtocolMessage
class ConditionalAccumulator
: A conditional accumulator for aggregating gradients.
class ConditionalAccumulatorBase
: A conditional accumulator for aggregating gradients.
class ConfigProto
: A ProtocolMessage
class CriticalSection
: Critical section.
class DType
: Represents the type of the elements in a Tensor
.
class DeviceSpec
: Represents a (possibly partial) specification for a TensorFlow device.
class Dimension
: Represents the value of one dimension in a TensorShape.
class Event
: A ProtocolMessage
class FIFOQueue
: A queue implementation that dequeues elements in first-in first-out order.
class FixedLenFeature
: Configuration for parsing a fixed-length input feature.
class FixedLenSequenceFeature
: Configuration for parsing a variable-length input feature into a Tensor
.
class FixedLengthRecordReader
: A Reader that outputs fixed-length records from a file.
class GPUOptions
: A ProtocolMessage
class GradientTape
: Record operations for automatic differentiation.
class Graph
: A TensorFlow computation, represented as a dataflow graph.
class GraphDef
: A ProtocolMessage
class GraphKeys
: Standard names to use for graph collections.
class GraphOptions
: A ProtocolMessage
class HistogramProto
: A ProtocolMessage
class IdentityReader
: A Reader that outputs the queued work as both the key and value.
class IndexedSlices
: A sparse representation of a set of tensor slices at given indices.
class IndexedSlicesSpec
: Type specification for a tf.IndexedSlices
.
class InteractiveSession
: A TensorFlow Session
for use in interactive contexts, such as a shell.
class LMDBReader
: A Reader that outputs the records from a LMDB file.
class LogMessage
: A ProtocolMessage
class MetaGraphDef
: A ProtocolMessage
class Module
: Base neural network module class.
class NameAttrList
: A ProtocolMessage
class NodeDef
: A ProtocolMessage
class OpError
: A generic error that is raised when TensorFlow execution fails.
class Operation
: Represents a graph node that performs computation on tensors.
class OptimizerOptions
: A ProtocolMessage
class OptionalSpec
: Represents an optional potentially containing a structured value.
class PaddingFIFOQueue
: A FIFOQueue that supports batching variable-sized tensors by padding.
class PriorityQueue
: A queue implementation that dequeues elements in prioritized order.
class QueueBase
: Base class for queue implementations.
class RaggedTensor
: Represents a ragged tensor.
class RaggedTensorSpec
: Type specification for a tf.RaggedTensor
.
class RandomShuffleQueue
: A queue implementation that dequeues elements in a random order.
class ReaderBase
: Base class for different Reader types, that produce a record every step.
class RegisterGradient
: A decorator for registering the gradient function for an op type.
class RunMetadata
: A ProtocolMessage
class RunOptions
: A ProtocolMessage
class Session
: A class for running TensorFlow operations.
class SessionLog
: A ProtocolMessage
class SparseConditionalAccumulator
: A conditional accumulator for aggregating sparse gradients.
class SparseFeature
: Configuration for parsing a sparse input feature from an Example
.
class SparseTensor
: Represents a sparse tensor.
class SparseTensorSpec
: Type specification for a tf.SparseTensor
.
class SparseTensorValue
: SparseTensorValue(indices, values, dense_shape)
class Summary
: A ProtocolMessage
class SummaryMetadata
: A ProtocolMessage
class TFRecordReader
: A Reader that outputs the records from a TFRecords file.
class Tensor
: Represents one of the outputs of an Operation
.
class TensorArray
: Class wrapping dynamic-sized, per-time-step, write-once Tensor arrays.
class TensorArraySpec
: Type specification for a tf.TensorArray
.
class TensorInfo
: A ProtocolMessage
class TensorShape
: Represents the shape of a Tensor
.
class TensorSpec
: Describes a tf.Tensor.
class TextLineReader
: A Reader that outputs the lines of a file delimited by newlines.
class TypeSpec
: Specifies a TensorFlow value type.
class UnconnectedGradients
: Controls how gradient computation behaves when y does not depend on x.
class VarLenFeature
: Configuration for parsing a variable-length input feature.
class Variable
: See the Variables Guide.
class VariableAggregation
: Indicates how a distributed variable will be aggregated.
class VariableScope
: Variable scope object to carry defaults to provide to get_variable
.
class VariableSynchronization
: Indicates when a distributed variable will be synced.
class WholeFileReader
: A Reader that outputs the entire contents of a file as a value.
class constant_initializer
: Initializer that generates tensors with constant values.
class glorot_normal_initializer
: The Glorot normal initializer, also called Xavier normal initializer.
class glorot_uniform_initializer
: The Glorot uniform initializer, also called Xavier uniform initializer.
class name_scope
: A context manager for use when defining a Python op.
class ones_initializer
: Initializer that generates tensors initialized to 1.
class orthogonal_initializer
: Initializer that generates an orthogonal matrix.
class random_normal_initializer
: Initializer that generates tensors with a normal distribution.
class random_uniform_initializer
: Initializer that generates tensors with a uniform distribution.
class truncated_normal_initializer
: Initializer that generates a truncated normal distribution.
class uniform_unit_scaling_initializer
: Initializer that generates tensors without scaling variance.
class variable_scope
: A context manager for defining ops that creates variables (layers).
class variance_scaling_initializer
: Initializer capable of adapting its scale to the shape of weights tensors.
class zeros_initializer
: Initializer that generates tensors initialized to 0.
Functions
Assert(...)
: Asserts that the given condition is true.
NoGradient(...)
: Specifies that ops of type op_type
is not differentiable.
NotDifferentiable(...)
: Specifies that ops of type op_type
is not differentiable.
Print(...)
: Prints a list of tensors. (deprecated)
abs(...)
: Computes the absolute value of a tensor.
accumulate_n(...)
: Returns the element-wise sum of a list of tensors.
acos(...)
: Computes acos of x element-wise.
acosh(...)
: Computes inverse hyperbolic cosine of x element-wise.
add(...)
: Returns x + y element-wise.
add_check_numerics_ops(...)
: Connect a tf.debugging.check_numerics
to every floating point tensor.
add_n(...)
: Adds all input tensors element-wise.
add_to_collection(...)
: Wrapper for Graph.add_to_collection()
using the default graph.
add_to_collections(...)
: Wrapper for Graph.add_to_collections()
using the default graph.
all_variables(...)
: Use tf.compat.v1.global_variables
instead. (deprecated)
angle(...)
: Returns the element-wise argument of a complex (or real) tensor.
arg_max(...)
: Returns the index with the largest value across dimensions of a tensor.
arg_min(...)
: Returns the index with the smallest value across dimensions of a tensor.
argmax(...)
: Returns the index with the largest value across axes of a tensor. (deprecated arguments)
argmin(...)
: Returns the index with the smallest value across axes of a tensor. (deprecated arguments)
argsort(...)
: Returns the indices of a tensor that give its sorted order along an axis.
as_dtype(...)
: Converts the given type_value
to a DType
.
as_string(...)
: Converts each entry in the given tensor to strings.
asin(...)
: Computes the trignometric inverse sine of x element-wise.
asinh(...)
: Computes inverse hyperbolic sine of x element-wise.
assert_equal(...)
: Assert the condition x == y
holds element-wise.
assert_greater(...)
: Assert the condition x > y
holds element-wise.
assert_greater_equal(...)
: Assert the condition x >= y
holds element-wise.
assert_integer(...)
: Assert that x
is of integer dtype.
assert_less(...)
: Assert the condition x < y
holds element-wise.
assert_less_equal(...)
: Assert the condition x <= y
holds element-wise.
assert_near(...)
: Assert the condition x
and y
are close element-wise.
assert_negative(...)
: Assert the condition x < 0
holds element-wise.
assert_non_negative(...)
: Assert the condition x >= 0
holds element-wise.
assert_non_positive(...)
: Assert the condition x <= 0
holds element-wise.
assert_none_equal(...)
: Assert the condition x != y
holds element-wise.
assert_positive(...)
: Assert the condition x > 0
holds element-wise.
assert_proper_iterable(...)
: Static assert that values is a "proper" iterable.
assert_rank(...)
: Assert x
has rank equal to rank
.
assert_rank_at_least(...)
: Assert x
has rank equal to rank
or higher.
assert_rank_in(...)
: Assert x
has rank in ranks
.
assert_same_float_dtype(...)
: Validate and return float type based on tensors
and dtype
.
assert_scalar(...)
: Asserts that the given tensor
is a scalar (i.e. zero-dimensional).
assert_type(...)
: Statically asserts that the given Tensor
is of the specified type.
assert_variables_initialized(...)
: Returns an Op to check if variables are initialized.
assign(...)
: Update ref
by assigning value
to it.
assign_add(...)
: Update ref
by adding value
to it.
assign_sub(...)
: Update ref
by subtracting value
from it.
atan(...)
: Computes the trignometric inverse tangent of x element-wise.
atan2(...)
: Computes arctangent of y/x
element-wise, respecting signs of the arguments.
atanh(...)
: Computes inverse hyperbolic tangent of x element-wise.
batch_gather(...)
: Gather slices from params according to indices with leading batch dims. (deprecated)
batch_scatter_update(...)
: Generalization of tf.compat.v1.scatter_update
to axis different than 0. (deprecated)
batch_to_space(...)
: BatchToSpace for 4-D tensors of type T.
batch_to_space_nd(...)
: BatchToSpace for N-D tensors of type T.
betainc(...)
: Compute the regularized incomplete beta integral \(I_x(a, b)\).
bincount(...)
: Counts the number of occurrences of each value in an integer array.
bitcast(...)
: Bitcasts a tensor from one type to another without copying data.
boolean_mask(...)
: Apply boolean mask to tensor.
broadcast_dynamic_shape(...)
: Computes the shape of a broadcast given symbolic shapes.
broadcast_static_shape(...)
: Computes the shape of a broadcast given known shapes.
broadcast_to(...)
: Broadcast an array for a compatible shape.
case(...)
: Create a case operation.
cast(...)
: Casts a tensor to a new type.
ceil(...)
: Returns element-wise smallest integer not less than x.
check_numerics(...)
: Checks a tensor for NaN and Inf values.
cholesky(...)
: Computes the Cholesky decomposition of one or more square matrices.
cholesky_solve(...)
: Solves systems of linear eqns A X = RHS
, given Cholesky factorizations.
clip_by_average_norm(...)
: Clips tensor values to a maximum average L2-norm. (deprecated)
clip_by_global_norm(...)
: Clips values of multiple tensors by the ratio of the sum of their norms.
clip_by_norm(...)
: Clips tensor values to a maximum L2-norm.
clip_by_value(...)
: Clips tensor values to a specified min and max.
colocate_with(...)
: DEPRECATED FUNCTION
complex(...)
: Converts two real numbers to a complex number.
concat(...)
: Concatenates tensors along one dimension.
cond(...)
: Return true_fn()
if the predicate pred
is true else false_fn()
. (deprecated arguments)
confusion_matrix(...)
: Computes the confusion matrix from predictions and labels.
conj(...)
: Returns the complex conjugate of a complex number.
constant(...)
: Creates a constant tensor.
container(...)
: Wrapper for Graph.container()
using the default graph.
control_dependencies(...)
: Wrapper for Graph.control_dependencies()
using the default graph.
control_flow_v2_enabled(...)
: Returns True
if v2 control flow is enabled.
convert_to_tensor(...)
: Converts the given value
to a Tensor
.
convert_to_tensor_or_indexed_slices(...)
: Converts the given object to a Tensor
or an IndexedSlices
.
convert_to_tensor_or_sparse_tensor(...)
: Converts value to a SparseTensor
or Tensor
.
cos(...)
: Computes cos of x element-wise.
cosh(...)
: Computes hyperbolic cosine of x element-wise.
count_nonzero(...)
: Computes number of nonzero elements across dimensions of a tensor. (deprecated arguments) (deprecated arguments)
count_up_to(...)
: Increments 'ref' until it reaches 'limit'. (deprecated)
create_partitioned_variables(...)
: Create a list of partitioned variables according to the given slicing
. (deprecated)
cross(...)
: Compute the pairwise cross product.
cumprod(...)
: Compute the cumulative product of the tensor x
along axis
.
cumsum(...)
: Compute the cumulative sum of the tensor x
along axis
.
custom_gradient(...)
: Decorator to define a function with a custom gradient.
decode_base64(...)
: Decode web-safe base64-encoded strings.
decode_compressed(...)
: Decompress strings.
decode_csv(...)
: Convert CSV records to tensors. Each column maps to one tensor.
decode_json_example(...)
: Convert JSON-encoded Example records to binary protocol buffer strings.
decode_raw(...)
: Convert raw byte strings into tensors. (deprecated arguments)
delete_session_tensor(...)
: Delete the tensor for the given tensor handle.
depth_to_space(...)
: DepthToSpace for tensors of type T.
dequantize(...)
: Dequantize the 'input' tensor into a float Tensor.
deserialize_many_sparse(...)
: Deserialize and concatenate SparseTensors
from a serialized minibatch.
device(...)
: Wrapper for Graph.device()
using the default graph.
diag(...)
: Returns a diagonal tensor with a given diagonal values.
diag_part(...)
: Returns the diagonal part of the tensor.
digamma(...)
: Computes Psi, the derivative of Lgamma (the log of the absolute value of
dimension_at_index(...)
: Compatibility utility required to allow for both V1 and V2 behavior in TF.
dimension_value(...)
: Compatibility utility required to allow for both V1 and V2 behavior in TF.
disable_control_flow_v2(...)
: Opts out of control flow v2.
disable_eager_execution(...)
: Disables eager execution.
disable_resource_variables(...)
: Opts out of resource variables. (deprecated)
disable_tensor_equality(...)
: Compare Tensors by their id and be hashable.
disable_v2_behavior(...)
: Disables TensorFlow 2.x behaviors.
disable_v2_tensorshape(...)
: Disables the V2 TensorShape behavior and reverts to V1 behavior.
div(...)
: Divides x / y elementwise (using Python 2 division operator semantics). (deprecated)
div_no_nan(...)
: Computes an unsafe divide which returns 0 if the y is zero.
divide(...)
: Computes Python style division of x
by y
.
dynamic_partition(...)
: Partitions data
into num_partitions
tensors using indices from partitions
.
dynamic_stitch(...)
: Interleave the values from the data
tensors into a single tensor.
edit_distance(...)
: Computes the Levenshtein distance between sequences.
einsum(...)
: Tensor contraction over specified indices and outer product.