tensorflow开发API
架构
Modules
app module: Generic entry point script.
bitwise module: Operations for manipulating the binary representations of integers.
compat module: Functions for Python 2 vs. 3 compatibility.
contrib module: contrib module containing volatile or experimental code.
data module: tf.data.Dataset API for input pipelines.
distributions module: Core module for TensorFlow distribution objects and helpers.
errors module: Exception types for TensorFlow errors.
estimator module: Estimator: High level tools for working with models.
feature_column module: FeatureColumns: tools for ingesting and representing features.
flags module: Implementation of the flags interface.
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.initializer namespace.
keras module: Implementation of the Keras API meant to be a high-level API for TensorFlow.
layers module: This library provides a set of high-level neural networks layers.
linalg module: Public API for tf.linalg namespace.
logging module: Logging utilities.
losses module: Loss operations for use in neural networks.
metrics module: Evaluation-related metrics.
nn module: Neural network support.
profiler module: profiler python module provides APIs to profile TensorFlow models.
python_io module: Python functions for directly manipulating TFRecord-formatted files.
pywrap_tensorflow module: A wrapper for TensorFlow SWIG-generated bindings.
resource_loader module: Resource management library.
saved_model module: Convenience functions to save a model.
sets module: Tensorflow set operations.
spectral module: Spectral operators (e.g. DCT, FFT, RFFT).
summary module: Tensor summaries for exporting information about a model.
sysconfig module: System configuration library.
test module: Testing.
tools module
train module: Support for training models.
user_ops module: All user ops.
Classes
class AggregationMethod: A class listing aggregation methods used to combine gradients.
class AttrValue
class ConditionalAccumulator: A conditional accumulator for aggregating gradients.
class ConditionalAccumulatorBase: A conditional accumulator for aggregating gradients.
class ConfigProto
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
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
class Graph: A TensorFlow computation, represented as a dataflow graph.
class GraphDef
class GraphKeys: Standard names to use for graph collections.
class GraphOptions
class HistogramProto
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 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
class MetaGraphDef
class NameAttrList
class NodeDef
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
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 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
class RunOptions
class Session: A class for running TensorFlow operations.
class SessionLog
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 SparseTensorValue: SparseTensorValue(indices, values, dense_shape)
class Summary
class SummaryMetadata
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 TensorInfo
class TensorShape: Represents the shape of a Tensor.
class TextLineReader: A Reader that outputs the lines of a file delimited by newlines.
class VarLenFeature: Configuration for parsing a variable-length input feature.
class Variable: See the Variables How To for a high level overview.
class VariableScope: Variable scope object to carry defaults to provide to get_variable.
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 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.
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 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.
all_variables(...): See tf.global_variables. (deprecated)
angle(...): Returns the argument of a complex number.
arg_max(...): Returns the index with the largest value across dimensions of a tensor. (deprecated)
arg_min(...): Returns the index with the smallest value across dimensions of a tensor. (deprecated)
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)
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_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_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 for all elements.
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(...)
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 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_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
.
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. Numpy equivalent is tensor[mask].
broadcast_dynamic_shape(...): Returns the broadcasted dynamic shape between shape_x and shape_y.
broadcast_static_shape(...): Returns the broadcasted static shape between shape_x and shape_y.
case(...): Create a case operation.
cast(...): Casts a tensor to a new type.
ceil(...): Returns element-wise smallest integer in 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.
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(...)
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.
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.
count_up_to(...): Increments 'ref' until it reaches 'limit'.
create_partitioned_variables(...): Create a list of partitioned variables according to the given slicing.
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.
decode_base64(...): Decode web-safe base64-encoded 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(...): Reinterpret the bytes of a string as a vector of numbers.
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
div(...): Divides x / y elementwise (using Python 2 division operator semantics).
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.
encode_base64(...): Encode strings into web-safe base64 format.
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.
exp(...): Computes exponential of x element-wise.
.
expand_dims(...): Inserts a dimension of 1 into a tensor's shape.
expm1(...): Computes exponential of x - 1 element-wise.
extract_image_patches(...): Extract patches from images and put them in the "depth" output dimension.
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.
fft2d(...): 2D fast Fourier transform.
fft3d(...): 3D fast Fourier transform.
fill(...): Creates a tensor filled with a scalar value.
fixed_size_partitioner(...): Partitioner to specify a fixed number of shards along given axis.
floor(...): Returns element-wise largest integer not greater than x.
floor_div(...): Returns x // y element-wise.
floordiv(...): Divides x / y elementwise, rounding toward the most negative integer.
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.
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_collection(...): Wrapper for Graph.get_collection() using the default graph.
get_collection_ref(...): Wrapper for Graph.get_collection_ref() using the default graph.
get_default_graph(...): Returns the default graph for the current thread.
get_default_session(...): Returns the default session for the current thread.
get_local_variable(...): Gets an existing local variable or creates a new one.
get_seed(...): Returns the local seeds an operation should use given an op-specific seed.
get_session_handle(...): Return the handle of data.
get_session_tensor(...): Get the tensor of type dtype by feeding a tensor handle.
get_variable(...): Gets an existing variable with these parameters or create a new one.
get_variable_scope(...): Returns the current variable scope.
global_norm(...): Computes the global norm of multiple tensors.
global_variables(...): Returns global variables.
global_variables_initializer(...): Returns an Op that initializes global variables.
glorot_normal_initializer(...): The Glorot normal initializer, also called Xavier normal initializer.
glorot_uniform_initializer(...): The Glorot uniform initializer, also called Xavier uniform initializer.
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.
hessians(...): Constructs the Hessian of sum of ys with respect to x in xs.
histogram_fixed_width(...): Return histogram of values.
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
ifft(...): Inverse fast Fourier transform.
ifft2d(...): Inverse 2D fast Fourier transform.
ifft3d(...): Inverse 3D fast Fourier transform.
igamma(...): Compute the lower regularized incomplete Gamma function Q(a, x).
igammac(...): Compute the upper regularized incomplete Gamma function Q(a, x).
imag(...): Returns the imaginary part of a complex number.
import_graph_def(...): Imports the graph from graph_def into the current default Graph.
initialize_all_tables(...): Returns an Op that initializes all tables of the default graph. (deprecated)
initialize_all_variables(...): See tf.global_variables_initializer. (deprecated)
initialize_local_variables(...): See tf.local_variables_initializer. (deprecated)
initialize_variables(...): See tf.variables_initializer. (deprecated)
invert_permutation(...): Computes the inverse permutation of a tensor.
is_finite(...): Returns which elements of x are finite.
is_inf(...): Returns which elements of x are Inf.
is_nan(...): Returns which elements of x are NaN.
is_non_decreasing(...): Returns True if x is non-decreasing.
is_numeric_tensor(...)
is_strictly_increasing(...): Returns True if x is strictly increasing.
is_variable_initialized(...): Tests if a variable has been initialized.
lbeta(...): Computes
, reducing along the last dimension.
less(...): Returns the truth value of (x < y) element-wise.
less_equal(...): Returns the truth value of (x <= y) element-wise.
lgamma(...): Computes the log of the absolute value of Gamma(x) element-wise.
lin_space(...): Generates values in an interval.
linspace(...): Generates values in an interval.
load_file_system_library(...): Loads a TensorFlow plugin, containing file system implementation.
load_op_library(...): Loads a TensorFlow plugin, containing custom ops and kernels.
local_variables(...): Returns local variables.
local_variables_initializer(...): Returns an Op that initializes all local variables.
log(...): Computes natural logarithm of x element-wise.
log1p(...): Computes natural logarithm of (1 + x) element-wise.
log_sigmoid(...): Computes log sigmoid of x element-wise.
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.
logical_xor(...): x ^ y = (x | y) & ~(x & y).
make_ndarray(...): Create a numpy ndarray from a tensor.
make_template(...): Given an arbitrary function, wrap it so that it does variable sharing.
make_tensor_proto(...): Create a TensorProto.
map_fn(...): map on the list of tensors unpacked from elems on dimension 0.
matching_files(...): Returns the set of files matching one or more glob patterns.
matmul(...): Multiplies matrix a by matrix b, producing a * b.
matrix_band_part(...): Copy a tensor setting everything outside a central band in each innermost matrix
matrix_determinant(...): Computes the determinant of one or more square matrices.
matrix_diag(...): Returns a batched diagonal tensor with a given batched diagonal values.
matrix_diag_part(...): Returns the batched diagonal part of a batched tensor.
matrix_inverse(...): Computes the inverse of one or more square invertible matrices or their
matrix_set_diag(...): Returns a batched matrix tensor with new batched diagonal values.
matrix_solve(...): Solves systems of linear equations.
matrix_solve_ls(...): Solves one or more linear least-squares problems.
matrix_transpose(...): Transposes last two dimensions of tensor a.
matrix_triangular_solve(...): Solves systems of linear equations with upper or lower triangular matrices by
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.
min_max_variable_partitioner(...): Partitioner to allocate minimum size per slice.
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
model_variables(...): Returns all variables in the MODEL_VARIABLES collection.
moving_average_variables(...): Returns all variables that maintain their moving averages.
multinomial(...): Draws samples from a multinomial distribution.
multiply(...): Returns x * y element-wise.
negative(...): Computes numerical negative value element-wise.
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 1.
op_scope(...): DEPRECATED. Same as name_scope above, just different argument order.
pad(...): Pads a tensor.
parallel_stack(...): Stacks a list of rank-R tensors into one rank-(R+1) tensor in parallel.
parse_example(...): Parses Example protos into a dict of tensors.
parse_single_example(...): Parses a single Example proto.
parse_single_sequence_example(...): Parses a single SequenceExample proto.
parse_tensor(...): Transforms a serialized tensorflow.TensorProto proto into a Tensor.
placeholder(...): Inserts a placeholder for a tensor that will be always fed.
placeholder_with_default(...): A placeholder op that passes through input when its output is not fed.
polygamma(...): Compute the polygamma function
.
pow(...): Computes the power of one value to another.
py_func(...): Wraps a python function and uses it as a TensorFlow op.
qr(...): Computes the QR decompositions of one or more matrices.
quantize_v2(...): Quantize the 'input' tensor of type float to 'output' tensor of type 'T'.
quantized_concat(...): Concatenates quantized tensors along one dimension.
random_crop(...): Randomly crops a tensor to a given size.
random_gamma(...): Draws shape samples from each of the given Gamma distribution(s).
random_normal(...): Outputs random values from a normal distribution.
random_poisson(...): Draws shape samples from each of the given Poisson distribution(s).
random_shuffle(...): Randomly shuffles a tensor along its first dimension.
random_uniform(...): Outputs random values from a uniform distribution.
range(...): Creates a sequence of numbers.
rank(...): Returns the rank of a tensor.
read_file(...): Reads and outputs the entire contents of the input filename.
real(...): Returns the real part of a complex number.
realdiv(...): Returns x / y element-wise for real types.
reciprocal(...): Computes the reciprocal of x element-wise.
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_join(...): Joins a string Tensor across the given dimensions.
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.
report_uninitialized_variables(...): Adds ops to list the names of uninitialized variables.
required_space_to_batch_paddings(...): Calculate padding required to make block_shape divide input_shape.
reset_default_graph(...): Clears the default graph stack and resets the global default graph.
reshape(...): Reshapes a tensor.
reverse(...): Reverses specific dimensions of a tensor.
reverse_sequence(...): Reverses variable length slices.
reverse_v2(...): Reverses specific dimensions of a tensor.
rint(...): Returns element-wise integer closest to x.
round(...): Rounds the values of a tensor to the nearest integer, element-wise.
rsqrt(...): Computes reciprocal of square root of x 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_add(...): Adds sparse updates to a variable reference.
scatter_div(...): Divides a variable reference by sparse updates.
scatter_mul(...): Multiplies sparse updates into a variable reference.
scatter_nd(...): Scatter updates into a new (initially zero) tensor according to indices.
scatter_nd_add(...): Applies sparse addition between updates and individual values or slices
scatter_nd_sub(...): Applies sparse subtraction between updates and individual values or slices
scatter_nd_update(...): Applies sparse updates to individual values or slices within a given
scatter_sub(...): Subtracts sparse updates to a variable reference.
scatter_update(...): Applies sparse updates to a variable reference.
segment_max(...): Computes the maximum along segments of a tensor.
segment_mean(...): Computes the mean along segments of a tensor.
segment_min(...): Computes the minimum along segments of a tensor.
segment_prod(...): Computes the product along segments of a tensor.
segment_sum(...): Computes the sum along segments of a tensor.
self_adjoint_eig(...): Computes the eigen decomposition of a batch of self-adjoint matrices.
self_adjoint_eigvals(...): Computes the eigenvalues of one or more self-adjoint matrices.
sequence_mask(...): Returns a mask tensor representing the first N positions of each cell.
serialize_many_sparse(...): Serialize an N-minibatch SparseTensor into an [N, 3] string Tensor.
serialize_sparse(...): Serialize a SparseTensor into a string 3-vector (1-D Tensor) object.
serialize_tensor(...): Transforms a Tensor into a serialized TensorProto proto.
set_random_seed(...): Sets the graph-level random seed.
setdiff1d(...): Computes the difference between two lists of numbers or strings.
shape(...): Returns the shape of a 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 sin 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.
space_to_batch(...): SpaceToBatch for 4-D tensors of type T.
space_to_batch_nd(...): SpaceToBatch for N-D tensors of type T.
space_to_depth(...): SpaceToDepth for tensors of type T.
sparse_add(...): Adds two tensors, at least one of each is a SparseTensor.
sparse_concat(...): Concatenates a list of SparseTensor along the specified dimension.
sparse_fill_empty_rows(...): Fills empty rows in the input 2-D SparseTensor with a default value.
sparse_mask(...): Masks elements of IndexedSlices.
sparse_matmul(...): Multiply matrix "a" by matrix "b".
sparse_maximum(...): Returns the element-wise max of two SparseTensors.
sparse_merge(...): Combines a batch of feature ids and values into a single SparseTensor.
sparse_minimum(...): Returns the element-wise min of two SparseTensors.
sparse_placeholder(...): Inserts a placeholder for a sparse tensor that will be always fed.
sparse_reduce_max(...): Computes the max of elements across dimensions of a SparseTensor.
sparse_reduce_max_sparse(...): Computes the max of elements across dimensions of a SparseTensor.
sparse_reduce_sum(...): Computes the sum of elements across dimensions of a SparseTensor.
sparse_reduce_sum_sparse(...): Computes the sum of elements across dimensions of a SparseTensor.
sparse_reorder(...): Reorders a SparseTensor into the canonical, row-major ordering.
sparse_reset_shape(...): Resets the shape of a SparseTensor with indices and values unchanged.
sparse_reshape(...): Reshapes a SparseTensor to represent values in a new dense shape.
sparse_retain(...): Retains specified non-empty values within a SparseTensor.
sparse_segment_mean(...): Computes the mean along sparse segments of a tensor.
sparse_segment_sqrt_n(...): Computes the sum along sparse segments of a tensor divided by the sqrt of N.
sparse_segment_sum(...): Computes the sum along sparse segments of a tensor.
sparse_slice(...): Slice a SparseTensor based on the start and `size.
sparse_softmax(...): Applies softmax to a batched N-D SparseTensor.
sparse_split(...): Split a SparseTensor into num_split tensors along axis.
sparse_tensor_dense_matmul(...): Multiply SparseTensor (of rank 2) "A" by dense matrix "B".
sparse_tensor_to_dense(...): Converts a SparseTensor into a dense tensor.
sparse_to_dense(...): Converts a sparse representation into a dense tensor.
sparse_to_indicator(...): Converts a SparseTensor of ids into a dense bool indicator tensor.
sparse_transpose(...): Transposes a SparseTensor
split(...): Splits a tensor into sub tensors.
sqrt(...): Computes square root of x element-wise.
square(...): Computes square of x element-wise.
squared_difference(...): Returns (x - y)(x - y) 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).
string_join(...): Joins the strings in the given list of string tensors into one tensor;
string_split(...): Split elements of source based on delimiter into a SparseTensor.
string_to_hash_bucket(...): Converts each string in the input Tensor to its hash mod by a number of buckets.
string_to_hash_bucket_fast(...): Converts each string in the input Tensor to its hash mod by a number of buckets.
string_to_hash_bucket_strong(...): Converts each string in the input Tensor to its hash mod by a number of buckets.
string_to_number(...): Converts each string in the input Tensor to the specified numeric type.
substr(...): Return substrings from Tensor of strings.
subtract(...): Returns x - y element-wise.
svd(...): Computes the singular value decompositions of one or more matrices.
tables_initializer(...): Returns an Op that initializes all tables of the default graph.
tan(...): Computes tan of x element-wise.
tanh(...): Computes hyperbolic tangent of x element-wise.
tensordot(...): Tensor contraction of a and b along specified axes.
tile(...): Constructs a tensor by tiling a given tensor.
to_bfloat16(...): Casts a tensor to type bfloat16.
to_double(...): Casts a tensor to type float64.
to_float(...): Casts a tensor to type float32.
to_int32(...): Casts a tensor to type int32.
to_int64(...): Casts a tensor to type int64.
trace(...): Compute the trace of a tensor x.
trainable_variables(...): Returns all variables created with trainable=True.
transpose(...): Transposes a. Permutes the dimensions according to perm.
truediv(...): Divides x / y elementwise (using Python 3 division operator semantics).
truncated_normal(...): Outputs random values from a truncated normal distribution.
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.
unsorted_segment_max(...): Computes the Max along segments of a tensor.
unsorted_segment_sum(...): Computes the sum along segments of a tensor.
unstack(...): Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors.
variable_axis_size_partitioner(...): Get a partitioner for VariableScope to keep shards below max_shard_bytes.
variable_op_scope(...): Deprecated: context manager for defining an op that creates variables.
variables_initializer(...): Returns an Op that initializes a list of variables.
verify_tensor_all_finite(...): Assert that the tensor does not contain any NaN's or Inf's.
where(...): Return the elements, either from x or y, depending on the condition.
while_loop(...): Repeat body while the condition cond is true.
write_file(...): Writes contents to the file at input filename. Creates file and recursively
zeros(...): Creates a tensor with all elements set to zero.
zeros_like(...): Creates a tensor with all elements set to zero.
zeta(...): Compute the Hurwitz zeta function
.
Other Members
AUTO_REUSE
COMPILER_VERSION
GIT_VERSION
GRAPH_DEF_VERSION
GRAPH_DEF_VERSION_MIN_CONSUMER
GRAPH_DEF_VERSION_MIN_PRODUCER
QUANTIZED_DTYPES
VERSION
compiler_version
git_version
version
bfloat16
bool
complex128
complex64
double
float16
float32
float64
half
int16
int32
int64
int8
newaxis
qint16
qint32
qint8
quint16
quint8
resource
string
uint16
uint8
variant
sennchi
