TensorFlow 2 version
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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.
enable_control_flow_v2(...): Use control flow v2.
enable_eager_execution(...): Enables eager execution for the lifetime of this program.
enable_resource_variables(...): Creates resource variables by default.
enable_tensor_equality(...): Compare Tensors with element-wise comparison and thus be unhashable.
enable_v2_behavior(...): Enables TensorFlow 2.x behaviors.
enable_v2_tensorshape(...): In TensorFlow 2.0, iterating over a TensorShape instance returns values.
encode_base64(...): Encode strings into web-safe base64 format.
ensure_shape(...): Updates the shape of a tensor and checks at runtime that the shape holds.
equal(...): Returns the truth value of (x == y) element-wise.
erf(...): Computes the Gauss error function of x element-wise.
erfc(...): Computes the complementary error function of x element-wise.
executing_eagerly(...): Returns True if the current thread has eager execution enabled.
exp(...): Computes exponential of x element-wise. \(y = e^x\).
expand_dims(...): Inserts a dimension of 1 into a tensor's shape. (deprecated arguments)
expm1(...): Computes exp(x) - 1 element-wise.
extract_image_patches(...): Extract patches from images and put them in the "depth" output dimension.
extract_volume_patches(...): Extract patches from input and put them in the "depth" output dimension. 3D extension of extract_image_patches.
eye(...): Construct an identity matrix, or a batch of matrices.
fake_quant_with_min_max_args(...): Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type.
fake_quant_with_min_max_args_gradient(...): Compute gradients for a FakeQuantWithMinMaxArgs operation.
fake_quant_with_min_max_vars(...): Fake-quantize the 'inputs' tensor of type float via global float scalars min
fake_quant_with_min_max_vars_gradient(...): Compute gradients for a FakeQuantWithMinMaxVars operation.
fake_quant_with_min_max_vars_per_channel(...): Fake-quantize the 'inputs' tensor of type float and one of the shapes: [d],
fake_quant_with_min_max_vars_per_channel_gradient(...): Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation.
fft(...): Fast Fourier transform.