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Framework utilities.
Modules
nest module: ## Functions for working with arbitrarily nested sequences of elements.
Classes
class BoundedTensorSpec: A TensorSpec that specifies minimum and maximum values.
class RecordInput: RecordInput asynchronously reads and randomly yields TFRecords.
class TensorSpec: Describes a tf.Tensor.
class VariableDeviceChooser: Device chooser for variables.
class convolutional_delta_orthogonal: Initializer that generates a delta orthogonal kernel for ConvNets.
class convolutional_orthogonal_1d: Initializer that generates a 1D orthogonal kernel for ConvNets.
class convolutional_orthogonal_2d: Initializer that generates a 2D orthogonal kernel for ConvNets.
class convolutional_orthogonal_3d: Initializer that generates a 3D orthogonal kernel for ConvNets.
Functions
add_arg_scope(...): Decorates a function with args so it can be used within an arg_scope.
add_model_variable(...): Adds a variable to the GraphKeys.MODEL_VARIABLES collection.
arg_scope(...): Stores the default arguments for the given set of list_ops.
arg_scoped_arguments(...): Returns the list kwargs that arg_scope can set for a func.
argsort(...): Returns the indices of a tensor that give its sorted order along an axis.
assert_global_step(...): DEPRECATED FUNCTION
assert_or_get_global_step(...): Verifies that a global step tensor is valid or gets one if None is given.
assert_same_float_dtype(...): Validate and return float type based on tensors and dtype.
assert_scalar(...): Asserts that the given tensor is a scalar (i.e. zero-dimensional).
assert_scalar_int(...): Assert tensor is 0-D, of type tf.int32 or tf.int64.
assign_from_checkpoint(...): Creates an operation to assign specific variables from a checkpoint.
assign_from_checkpoint_fn(...): Returns a function that assigns specific variables from a checkpoint.
assign_from_values(...): Creates an assignment operation from a given mapping.
assign_from_values_fn(...): Returns a function that assigns specific variables from the given values.
convert_to_tensor_or_sparse_tensor(...): Converts value to a SparseTensor or Tensor.
create_global_step(...): Create global step tensor in graph. (deprecated)
deprecated(...): Decorator for marking functions or methods deprecated.
deprecated_arg_values(...): Decorator for marking specific function argument values as deprecated.
deprecated_args(...): Decorator for marking specific function arguments as deprecated.
filter_variables(...): Filter a list of variables using regular expressions.
fuse_op(...): Fuse subgraph between input_nodes and output_nodes into a single custom op.
get_global_step(...): DEPRECATED FUNCTION
get_graph_from_inputs(...): Returns the appropriate graph to use for the given inputs.
get_local_variables(...): Gets the list of local variables, filtered by scope and/or suffix.
get_model_variables(...): Gets the list of model variables, filtered by scope and/or suffix.
get_name_scope(...): Returns the current name scope of the default graph.
get_or_create_global_step(...): Returns and create (if necessary) the global step tensor. (deprecated)
get_placeholders(...): Get placeholders of a graph.
get_trainable_variables(...): Gets the list of trainable variables, filtered by scope and/or suffix.
get_unique_variable(...): Gets the variable uniquely identified by that var_op_name.
get_variable_full_name(...): Returns the full name of a variable.
get_variables(...): Gets the list of variables, filtered by scope and/or suffix.
get_variables_by_name(...): Gets the list of variables that were given that name.
get_variables_by_suffix(...): Gets the list of variables that end with the given suffix.
get_variables_to_restore(...): Gets the list of the variables to restore.
global_variable(...): Create a variable with a value and add it to GraphKeys.GLOBAL_VARIABLES.
has_arg_scope(...): Checks whether a func has been decorated with @add_arg_scope or not.
init_from_checkpoint(...): Using assignment map initializes current variables with loaded tensors.
is_tensor(...): Checks whether x is a tensor or "tensor-like".
list_variables(...): Returns list of all variables in the latest checkpoint.
load_and_remap_matrix_initializer(...): Returns a var initializer for loading and remapping a 2-D (matrix) tensor.
load_checkpoint(...): Returns CheckpointReader for latest checkpoint.
load_embedding_initializer(...): Returns a variable initializer for loading pre-trained embeddings.
load_linear_multiclass_bias_initializer(...): Loads pre-trained multi-class biases for linear models from checkpoint.
load_variable(...): Returns a Tensor with the contents of the given variable in the checkpoint.
load_variable_slot_initializer(...): Loads pre-trained multi-class slots for linear models from checkpoint.
local_variable(...): Create a variable with a value and add it to GraphKeys.LOCAL_VARIABLES.
model_variable(...): Gets an existing model variable with these parameters or creates a new one.
prepend_name_scope(...): Prepends name scope to a name.
py_func(...): Wraps a python function and uses it as a TensorFlow op.
reduce_sum_n(...): Reduce tensors to a scalar sum.
remove_squeezable_dimensions(...): Squeeze last dim if ranks of predictions and labels differ by 1. (deprecated)
smart_case(...): Like tf.case, except attempts to statically evaluate predicates.
smart_cond(...): Return either true_fn() if predicate pred is true else false_fn().
smart_constant_value(...): Return the bool value for pred, or None if pred had a dynamic value.
sort(...): Sorts a tensor.
strip_name_scope(...): Removes name scope from a name.
variable(...): Gets an existing variable with these parameters or creates a new one.
with_same_shape(...): Assert tensors are the same shape, from the same graph.
with_shape(...): Asserts tensor has expected shape.
zero_initializer(...): Initialize 'ref' with all zeros, ref tensor should be uninitialized.