Tensorflow 2.0 Preview

You can install the preview package with:

pip install tf-nightly-2.0-preview

This api listing was last updated from version: tf-nightly-2.0-preview-1.13.0.dev20181214

Modules

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.

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.

dtypes module: Public API for tf.dtypes namespace.

errors module: Exception types for TensorFlow errors.

experimental module: Public API for tf.experimental namespace.

feature_column module: Public API for tf.feature_column namespace.

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.

linalg module: Operations for linear algebra.

lite module: Public API for tf.lite namespace.

losses module: Loss operations for use in neural networks.

math module: Basic arithmetic operators.

nn module: Wrappers for primitive Neural Net (NN) Operations.

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.

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.

strings module: Operations for working with string Tensors.

summary module: Public API for tf.summary namespace.

sysconfig module: System configuration library.

test module: Testing.

tools module

train module: Support for training models.

version module: Public API for tf.version namespace.

Classes

class AggregationMethod : A class listing aggregation methods used to combine gradients.

class DType : Represents the type of the elements in a Tensor .

class Event : A ProtocolMessage

class GradientTape : Record operations for automatic differentiation.

class Graph : A TensorFlow computation, represented as a dataflow graph.

class IndexedSlices : A sparse representation of a set of tensor slices at given indices.

class Operation : Represents a graph node that performs computation on tensors.

class RaggedTensor : Represents a ragged tensor.

class RegisterGradient : A decorator for registering the gradient function for an op type.

class SparseTensor : Represents a sparse tensor.

class Summary : A ProtocolMessage

class SummaryMetadata : A ProtocolMessage

class Tensor : Represents one of the outputs of an Operation .

class TensorArray : Class wrapping dynamic-sized, per-time-step, write-once Tensor arrays.

class TensorShape : Represents the shape of a Tensor .

class TensorSpec : Describes a tf.Tensor.

class UnconnectedGradients : Controls how gradient computation behaves when y does not depend on x.

class Variable : See theVariables Guide.

class VariableAggregation : Indicates how a distributed variable will be aggregated.

class VariableSynchronization : Indicates when a distributed variable will be synced.

class constant_initializer : Initializer that generates tensors with constant values.

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 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 zeros_initializer : Initializer that generates tensors initialized to 0.

Functions

Assert(...) : Asserts that the given condition is true.

abs(...) : Computes the absolute value of a tensor.

acos(...) : Computes acos of x element-wise.

acosh(...) : Computes inverse hyperbolic cosine of x element-wise.

add(...) : Returns x + y element-wise.

add_n(...) : Adds all input tensors element-wise.

argmax(...) : Returns the index with the largest value across axes of a tensor.

argmin(...) : Returns the index with the smallest value across axes of a tensor.

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. Supports many numeric

asin(...) : Computes asin 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_less(...) : Assert the condition x < y holds element-wise.

assert_rank(...) : Assert that x has rank equal to rank .

atan(...) : Computes atan 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.

batch_to_space(...) : BatchToSpace for N-D tensors of type T.

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.

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.

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() .

constant(...) : Creates a constant tensor.

control_dependencies(...) : Wrapper for Graph.control_dependencies() using the default graph.

convert_to_tensor(...) : Converts the given value to a Tensor .

cos(...) : Computes cos of x element-wise.

cosh(...) : Computes hyperbolic cosine of x element-wise.

cumsum(...) : Compute the cumulative sum of the tensor x along axis .

custom_gradient(...) : Decorator to define a function with a custom gradient.

device(...) : Specifies the device for ops created/executed in this context.

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(...) : A generalized contraction between tensors of arbitrary dimension.

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.

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.

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.

fill(...) : Creates a tensor filled with a scalar value.

floor(...) : Returns element-wise largest integer not greater than x.

floor_div(...) : Returns x // y element-wise.

floormod(...) : Returns element-wise remainder of division. When x < 0 xor y < 0 is

foldl(...) : foldl on the list of tensors unpacked from elems on dimension 0.

foldr(...) : foldr on the list of tensors unpacked from elems on dimension 0.

function(...) : Creates a callable TensorFlow graph from a Python function.

gather(...) : Gather slices from params axis axis according to indices .

gather_nd(...) : Gather slices from params into a Tensor with shape specified by indices .

get_logger(...) : Return TF logger instance.

gradients(...) : Constructs symbolic derivatives of sum of ys w.r.t. x in xs .

greater(...) : Returns the truth value of (x > y) element-wise.

greater_equal(...) : Returns the truth value of (x >= y) element-wise.

group(...) : Create an op that groups multiple operations.

guarantee_const(...) : Gives a guarantee to the TF runtime that the input tensor is a constant.

hessians(...) : Constructs the Hessian of sum of ys with respect to x in xs .

histogram_fixed_width(...) : Return histogram of values.

histogram_fixed_width_bins(...) : Bins the given values for use in a histogram.

identity(...) : Return a tensor with the same shape and contents as input.

identity_n(...) : Returns a list of tensors with the same shapes and contents as the input

import_graph_def(...) : Imports the graph from graph_def into the current default Graph . (deprecated arguments)

init_scope(...) : A context manager that lifts ops out of control-flow scopes and function-building graphs.

less(...) : Returns the truth value of (x < y) element-wise.

less_equal(...) : Returns the truth value of (x <= y) element-wise.

linspace(...) : Generates values in an interval.

load_library(...) : Loads a TensorFlow plugin.

load_op_library(...) : Loads a TensorFlow plugin, containing custom ops and kernels.

logical_and(...) : Returns the truth value of x AND y element-wise.

logical_not(...) : Returns the truth value of NOT x element-wise.

logical_or(...) : Returns the truth value of x OR y element-wise.

make_ndarray(...) : Create a numpy ndarray from a tensor.

map_fn(...) : map on the list of tensors unpacked from elems on dimension 0.

matmul(...) : Multiplies matrix a by matrix b , producing a * b .

matrix_square_root(...) : Computes the matrix square root of one or more square matrices:

maximum(...) : Returns the max of x and y (i.e. x > y ? x : y) element-wise.

meshgrid(...) : Broadcasts parameters for evaluation on an N-D grid.

minimum(...) : Returns the min of x and y (i.e. x < y ? x : y) element-wise.

mod(...) : Returns element-wise remainder of division. When x < 0 xor y < 0 is

multiply(...) : Returns x * y element-wise.

negative(...) : Computes numerical negative value element-wise.

no_gradient(...) : Specifies that ops of type op_type is not differentiable.

no_op(...) : Does nothing. Only useful as a placeholder for control edges.

no_regularizer(...) : Use this function to prevent regularization of variables.

norm(...) : Computes the norm of vectors, matrices, and tensors.

not_equal(...) : Returns the truth value of (x != y) element-wise.

one_hot(...) : Returns a one-hot tensor.

ones(...) : Creates a tensor with all elements set to 1.

ones_like(...) : Creates a tensor with all elements set to zero.

pad(...) : Pads a tensor.

parallel_stack(...) : Stacks a list of rank- R tensors into one rank- (R+1) tensor in parallel.

pow(...) : Computes the power of one value to another.

print(...) : Print the specified inputs.

py_function(...) : Wraps a python function into a TensorFlow op that executes it eagerly.

range(...) : Creates a sequence of numbers.

rank(...) : Returns the rank of a tensor.

realdiv(...) : Returns x / y element-wise for real types.

reduce_all(...) : Computes the "logical and" of elements across dimensions of a tensor.

reduce_any(...) : Computes the "logical or" of elements across dimensions of a tensor.

reduce_logsumexp(...) : Computes log(sum(exp(elements across dimensions of a tensor))).

reduce_max(...) : Computes the maximum of elements across dimensions of a tensor.

reduce_mean(...) : Computes the mean of elements across dimensions of a tensor.

reduce_min(...) : Computes the minimum of elements across dimensions of a tensor.

reduce_prod(...) : Computes the product of elements across dimensions of a tensor.

reduce_sum(...) : Computes the sum of elements across dimensions of a tensor.

register_tensor_conversion_function(...) : Registers a function for converting objects of base_type to Tensor .

required_space_to_batch_paddings(...) : Calculate padding required to make block_shape divide input_shape.

reshape(...) : Reshapes a tensor.

reverse(...) : Reverses specific dimensions of a tensor.

reverse_sequence(...) : Reverses variable length slices.

roll(...) : Rolls the elements of a tensor along an axis.

round(...) : Rounds the values of a tensor to the nearest integer, element-wise.

saturate_cast(...) : Performs a safe saturating cast of value to dtype .

scalar_mul(...) : Multiplies a scalar times a Tensor or IndexedSlices object.

scan(...) : scan on the list of tensors unpacked from elems on dimension 0.

scatter_div(...) : Divides a variable reference by sparse updates.

scatter_max(...) : Reduces sparse updates into a variable reference using the max operation.

scatter_min(...) : Reduces sparse updates into a variable reference using the min operation.

scatter_mul(...) : Multiplies sparse updates into a variable reference.

scatter_nd(...) : Scatter updates into a new tensor according to indices .

searchsorted(...) : Searches input tensor for values on the innermost dimension.

sequence_mask(...) : Returns a mask tensor representing the first N positions of each cell.

shape(...)

shape_n(...) : Returns shape of tensors.

sigmoid(...) : Computes sigmoid of x element-wise.

sign(...) : Returns an element-wise indication of the sign of a number.

sin(...) : Computes sin of x element-wise.

sinh(...) : Computes hyperbolic sine of x element-wise.

size(...)

slice(...) : Extracts a slice from a tensor.

sort(...) : Sorts a tensor.

space_to_batch(...) : SpaceToBatch for N-D tensors of type T.

space_to_batch_nd(...) : SpaceToBatch for N-D tensors of type T.

split(...) : Splits a tensor into sub tensors.

sqrt(...) : Computes square root of x element-wise.

square(...) : Computes square of x element-wise.

squeeze(...)

stack(...) : Stacks a list of rank- R tensors into one rank- (R+1) tensor.

stop_gradient(...) : Stops gradient computation.

strided_slice(...) : Extracts a strided slice of a tensor (generalized python array indexing).

string_split(...) : Split elements of source based on delimiter into a SparseTensor .

subtract(...) : Returns x - y element-wise.

tan(...) : Computes tan of x element-wise.

tanh(...) : Computes hyperbolic tangent of x element-wise.

tensor_scatter_add(...) : Adds sparse updates to an existing tensor according to indices .

tensor_scatter_sub(...) : Subtracts sparse updates from an existing tensor according to indices .

tensor_scatter_update(...) : Scatter updates into an existing tensor according to indices .

tensordot(...) : Tensor contraction of a and b along specified axes.

tile(...) : Constructs a tensor by tiling a given tensor.

timestamp(...) : Provides the time since epoch in seconds.

transpose(...) : Transposes a . Permutes the dimensions according to perm .

truediv(...) : Divides x / y elementwise (using Python 3 division operator semantics).

truncatediv(...) : Returns x / y element-wise for integer types.

truncatemod(...) : Returns element-wise remainder of division. This emulates C semantics in that

tuple(...) : Group tensors together.

unique(...) : Finds unique elements in a 1-D tensor.

unique_with_counts(...) : Finds unique elements in a 1-D tensor.

unravel_index(...) : Converts a flat index or array of flat indices into a tuple of

unstack(...) : Unpacks the given dimension of a rank- R tensor into rank- (R-1) tensors.

variable_creator_scope(...) : Scope which defines a variable creation function to be used by variable().

where(...) : Return the elements, either from x or y , depending on the condition .

while_loop(...) : Repeat body while the condition cond is true.

zeros(...) : Creates a tensor with all elements set to zero.

zeros_like(...) : Creates a tensor with all elements set to zero.

Other Members

bfloat16

bool

complex128

complex64

double

float16

float32

float64

half

int16

int32

int64

int8

newaxis

qint16

qint32

qint8

quint16

quint8

resource

string

uint16

uint32

uint64

uint8

variant

__version__

__git_version__

__compiler_version__

__cxx11_abi_flag__

__monolithic_build__