TensorFlow
pip install tensorflow
Modules
audio
module: Public API for tf.audio namespace.
autodiff
module: Public API for tf.autodiff namespace.
autograph
module: Conversion of eager-style Python into TensorFlow graph code.
bitwise
module: Operations for manipulating the binary representations of integers.
compat
module: Compatibility functions.
config
module: Public API for tf.config namespace.
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.
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.
graph_util
module: Helpers to manipulate a tensor graph in python.
image
module: Image ops.
initializers
module: Public API for tf.keras.initializers namespace.
io
module: Public API for tf.io namespace.
keras
module: Public API for tf.keras namespace.
linalg
module: Operations for linear algebra.
lite
module: Public API for tf.lite namespace.
lookup
module: Public API for tf.lookup namespace.
losses
module: Public API for tf.keras.losses namespace.
math
module: Math Operations.
metrics
module: Public API for tf.keras.metrics namespace.
mixed_precision
module: Public API for tf.mixed_precision namespace.
mlir
module: Public API for tf.mlir namespace.
nest
module: Public API for tf.nest namespace.
nn
module: Primitive Neural Net (NN) Operations.
optimizers
module: Public API for tf.keras.optimizers namespace.
profiler
module: Public API for tf.profiler namespace.
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: Public API for tf.raw_ops 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: 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.
types
module: Public TensorFlow type definitions.
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 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 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 IndexedSlicesSpec
: Type specification for a tf.IndexedSlices
.
class Module
: Base neural network module class.
class Operation
: Represents a graph node that performs computation on tensors.
class OptionalSpec
: Type specification for tf.experimental.Optional
.
class RaggedTensor
: Represents a ragged tensor.
class RaggedTensorSpec
: Type specification for a tf.RaggedTensor
.
class RegisterGradient
: A decorator for registering the gradient function for an op type.
class SparseTensor
: Represents a sparse tensor.
class SparseTensorSpec
: Type specification for a tf.sparse.SparseTensor
.
class Tensor
: A tf.Tensor
represents a multidimensional array of elements.
class TensorArray
: Class wrapping dynamic-sized, per-time-step, write-once Tensor arrays.
class TensorArraySpec
: Type specification for a tf.TensorArray
.
class TensorShape
: Represents the shape of a Tensor
.
class TensorSpec
: Describes a tf.Tensor.
class TypeSpec
: Specifies a TensorFlow value type.
class UnconnectedGradients
: Controls how gradient computation behaves when y does not depend on x.
class Variable
: See the variable 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 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 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.
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_less(...)
: Assert the condition x < y
holds element-wise.
assert_rank(...)
: Assert that x
has rank equal to rank
.
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_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 from a tensor-like object.
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.
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.
eig(...)
: Computes the eigen decomposition of a batch of matrices.
eigvals(...)
: Computes the eigenvalues of one or more matrices.
einsum(...)
: Tensor contraction over specified indices and outer product.
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(...)
: Checks whether the current thread has eager execution enabled.
exp(...)
: Computes exponential of x element-wise. y=ex.
expand_dims(...)
: Returns a tensor with a length 1 axis inserted at index axis
.
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.
fingerprint(...)
: Generates fingerprint values.
floor(...)
: Returns element-wise largest integer not greater than x.
foldl(...)
: foldl on the list of tensors unpacked from elems
on dimension 0. (deprecated argument values)
foldr(...)
: foldr on the list of tensors unpacked from elems
on dimension 0. (deprecated argument values)
function(...)
: Compiles a function into a callable TensorFlow graph. (deprecated arguments)
gather(...)
: Gather slices from params axis axis
according to indices. (deprecated arguments)
gather_nd(...)
: Gather slices from params
into a Tensor with shape specified by indices
.
get_current_name_scope(...)
: Returns current full name scope specified by tf.name_scope(...)
s.
get_logger(...)
: Return TF logger instance.
get_static_value(...)
: Returns the constant value of the given tensor, if efficiently calculable.
grad_pass_through(...)
: Creates a grad-pass-through op with the forward behavior provided in f.
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(...)
: Promise to the TF runtime that the input tensor is a constant. (deprecated)
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.
inside_function(...)
: Indicates whether the caller code is executing inside a tf.function
.
is_tensor(...)
: Checks whether x
is a TF-native type that can be passed to many TF ops.
less(...)
: Returns the truth value of (x < y) element-wise.
less_equal(...)
: Returns the truth value of (x <= y) element-wise.
linspace(...)
: Generates evenly-spaced values in an interval along a given axis.
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.
make_tensor_proto(...)
: Create a TensorProto.
map_fn(...)
: Transforms elems
by applying fn
to each element unstacked on axis 0. (deprecated arguments)
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.
multiply(...)
: Returns an element-wise x * y.
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.
nondifferentiable_batch_function(...)
: Batches the computation done by the decorated function.
norm(...)
: Computes the norm of vectors, matrices, and tensors.
not_equal(...)
: Returns the truth value of (x != y) element-wise.
numpy_function(...)
: Wraps a python function and uses it as a TensorFlow op.
one_hot(...)
: Returns a one-hot tensor.
ones(...)
: Creates a tensor with all elements set to one (1).
ones_like(...)
: Creates a tensor of all ones that has the same shape as the input.
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.
recompute_grad(...)
: An eager-compatible version of recompute_grad.
reduce_all(...)
: Computes tf.math.logical_and
of elements across dimensions of a tensor.
reduce_any(...)
: Computes tf.math.logical_or
of elements across dimensions of a tensor.
reduce_logsumexp(...)
: Computes log(sum(exp(elements across dimensions of a tensor))).
reduce_max(...)
: Computes tf.math.maximum
of elements across dimensions of a tensor.
reduce_mean(...)
: Computes the mean of elements across dimensions of a tensor.
reduce_min(...)
: Computes the tf.math.minimum
of elements across dimensions of a tensor.
reduce_prod(...)
: Computes tf.math.multiply
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
.
repeat(...)
: Repeat elements of input
.
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. (deprecated argument values)
scatter_nd(...)
: Scatters updates
into a tensor of shape shape
according to indices
.
searchsorted(...)
: Searches for where a value would go in a sorted sequence.
sequence_mask(...)
: Returns a mask tensor representing the first N positions of each cell.
shape(...)
: Returns a tensor containing the shape of the input tensor.
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 sine of x element-wise.
sinh(...)
: Computes hyperbolic sine of x element-wise.
size(...)
: Returns the size of a tensor.
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 value
into a list of sub tensors.
sqrt(...)
: Computes element-wise square root of the input tensor.
square(...)
: Computes square of x element-wise.
squeeze(...)
: Removes dimensions of size 1 from the shape of a tensor.
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).
subtract(...)
: Returns x - y element-wise.
switch_case(...)
: Create a switch/case operation, i.e. an integer-indexed conditional.
tan(...)
: Computes tan of x element-wise.
tanh(...)
: Computes hyperbolic tangent of x
element-wise.
tensor_scatter_nd_add(...)
: Adds sparse updates
to an existing tensor according to indices
.
tensor_scatter_nd_sub(...)
: Subtracts sparse updates
from an existing tensor according to indices
.
tensor_scatter_nd_update(...)
: "Scatter updates
into an existing tensor according to indices
.
tensordot(...)
: Tensor contraction of a and b along specified axes and outer product.
tile(...)
: Constructs a tensor by tiling a given tensor.
timestamp(...)
: Provides the time since epoch in seconds.
transpose(...)
: Transposes a
, where a
is a Tensor.
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(...)
: Groups tensors together.
type_spec_from_value(...)
: Returns a tf.TypeSpec
that represents the given value
.
unique(...)
: Finds unique elements in a 1-D tensor.
unique_with_counts(...)
: Finds unique elements in a 1-D tensor.
unravel_index(...)
: Converts an array of flat indices into a tuple of coordinate arrays.
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().
vectorized_map(...)
: Parallel map on the list of tensors unpacked from elems
on dimension 0.
where(...)
: Return the elements where condition
is True
(multiplexing x
and y
).
while_loop(...)
: Repeat body
while the condition cond
is true. (deprecated argument values)
zeros(...)
: Creates a tensor with all elements set to zero.
zeros_like(...)
: Creates a tensor with all elements set to zero.
Other Members | |
---|---|
version |
'2.7.4'
|
bfloat16 |
Instance of tf.dtypes.DType
16-bit bfloat (brain floating point). |
bool |
Instance of tf.dtypes.DType
Boolean. |
complex128 |
Instance of tf.dtypes.DType
128-bit complex. |
complex64 |
Instance of tf.dtypes.DType
64-bit complex. |
double |
Instance of tf.dtypes.DType
64-bit (double precision) floating-point. |
float16 |
Instance of tf.dtypes.DType
16-bit (half precision) floating-point. |
float32 |
Instance of tf.dtypes.DType
32-bit (single precision) floating-point. |
float64 |
Instance of tf.dtypes.DType
64-bit (double precision) floating-point. |
half |
Instance of tf.dtypes.DType
16-bit (half precision) floating-point. |
int16 |
Instance of tf.dtypes.DType
Signed 16-bit integer. |
int32 |
Instance of tf.dtypes.DType
Signed 32-bit integer. |
int64 |
Instance of tf.dtypes.DType
Signed 64-bit integer. |
int8 |
Instance of tf.dtypes.DType
Signed 8-bit integer. |
newaxis |
None
|
qint16 |
Instance of tf.dtypes.DType
Signed quantized 16-bit integer. |
qint32 |
Instance of tf.dtypes.DType
signed quantized 32-bit integer. |
qint8 |
Instance of tf.dtypes.DType
Signed quantized 8-bit integer. |
quint16 |
Instance of tf.dtypes.DType
Unsigned quantized 16-bit integer. |
quint8 |
Instance of tf.dtypes.DType
Unsigned quantized 8-bit integer. |
resource |
Instance of tf.dtypes.DType
Handle to a mutable, dynamically allocated resource. |
string |
Instance of tf.dtypes.DType
Variable-length string, represented as byte array. |
uint16 |
Instance of tf.dtypes.DType
Unsigned 32-bit (dword) integer. |
uint32 |
Instance of tf.dtypes.DType
|
uint64 |
Instance of tf.dtypes.DType
Unsigned 64-bit (qword) integer. |
uint8 |
Instance of tf.dtypes.DType
Unsigned 8-bit (byte) integer. |
variant |
Instance of tf.dtypes.DType
Data of arbitrary type (known at runtime). |