TensorFlow Core 2.0 Guide

Welcome!

TensorFlow Core contains :

44 Modules
32 Classes
178 Functions and more are coming…

I am Paras, and I will be explaining to you every module, classes, and functions of TensorFlow. Here, I will be giving the links of my all posts where you can find the explanations. I am taking this structured content information from TensorFlow official documentation and later on, I will be explaining them. Let’s start…

Modules

tf Module
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.
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 processing and decoding ops.
initializers module: Keras initializer serialization / deserialization.
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.
lookup module: Public API for tf.lookup namespace.
losses module: Built-in loss functions.
math module: Math Operations.
metrics module: Built-in metrics.
nest module: Public API for tf.nest namespace.
nn module: Wrappers for primitive Neural Net (NN) Operations.
optimizers module: Built-in optimizer classes.
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.
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: Represents an optional potentially containing a structured value.
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.SparseTensor.
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 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 Variables 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.
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.
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=ex
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.
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.
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.
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(…): 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.
is_tensor(…): Checks whether x is a tensor or “tensor-like”.
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.
make_tensor_proto(…): Create a TensorProto.
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.
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.
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 1.
ones_like(…): Creates a tensor with all elements set to one.
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 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_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(…): 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 sine 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(…): 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.
tile(…): Constructs a tensor by tiling a given tensor.
timestamp(…): Provides the time since epoch in seconds.
transpose(…): Transposes a.
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().
vectorized_map(…): Parallel map on the list of tensors unpacked from elems on dimension 0.
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.