tf.md

API of tf

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 tensor is a multidimensional array of elements represented by a
• 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 = e^x).
• 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.
• 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.
• 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(...): Logical AND function.
• 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.
• quantize_and_dequantize_v4(...): Returns the gradient of QuantizeAndDequantizeV4.
• 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.
• 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(...): 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 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_max(...)
• tensor_scatter_nd_min(...)
• 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(...): Group 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.