DocInherit(kclass) |
Class decorator to enable kclass and all its sub-classes to automatically inherit docstrings from base classes. |
add_name_and_scope_arg_doc(method) |
Add name and scope argument to the doc of method. |
add_name_arg_doc(method) |
Add name argument to the doc of method. |
append_arg_to_doc(doc, arg_doc) |
Add the doc for name and scope argument to the doc string. |
append_to_doc(doc, content) |
Append content to the doc string. |
assert_deps(*args, **kwds) |
If is_assertion_enabled() == True, open a context that will run assert_ops on exit. |
broadcast_to_shape(x, shape[, name]) |
Broadcast x to match shape. |
broadcast_to_shape_strict(x, shape[, name]) |
Broadcast x to match shape. |
camel_to_underscore(name) |
Convert a camel-case name to underscore. |
concat_shapes(shapes[, name]) |
Concat shapes from shapes. |
create_session([lock_memory, …]) |
A convenient method to create a TensorFlow session. |
deprecated_arg(old_arg[, new_arg, version]) |
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ensure_variables_initialized([variables, name]) |
Ensure variables are initialized. |
flatten_to_ndims(x, ndims[, name]) |
Flatten the front dimensions of x, such that the resulting tensor will have at most ndims dimensions. |
get_batch_size(tensor[, axis, name]) |
Infer the mini-batch size according to tensor. |
get_cache_root() |
Get the cache root directory. |
get_config_defaults(config) |
Get the default config values of config. |
get_config_validator(type) |
Get an instance of ConfigValidator for specified type. |
get_default_scope_name(name[, cls_or_instance]) |
Generate a valid default scope name. |
get_default_session_or_error() |
Get the default session. |
get_dimensions_size(tensor[, axis, name]) |
Get the size of tensor of specified axis. |
get_model_variables([scope]) |
Get all model variables (i.e., variables in MODEL_VARIABLES collection). |
get_rank(tensor[, name]) |
Get the rank of the tensor. |
get_reuse_stack_top() |
Get the top of the reuse scope stack. |
get_static_shape(tensor) |
Get the the static shape of specified tensor as a tuple. |
get_uninitialized_variables([variables, name]) |
Get uninitialized variables as a list. |
get_variable_ddi(name, initial_value[, …]) |
Wraps tf.get_variable() to support data-dependent initialization. |
get_variables_as_dict([scope, collection]) |
Get TensorFlow variables as dict. |
global_reuse([method_or_scope, _sentinel, scope]) |
Decorate a function to reuse a variable scope automatically. |
humanize_duration(seconds[, short_units]) |
Format specified time duration as human readable text. |
instance_reuse([method_or_scope, _sentinel, …]) |
Decorate an instance method to reuse a variable scope automatically. |
is_assertion_enabled(*args, **kwargs) |
Whether or not to enable assertions? |
is_float(x) |
Test whether or not x is a Python or NumPy float. |
is_integer(x) |
Test whether or not x is a Python or NumPy integer. |
is_shape_equal(x, y[, name]) |
Check whether the shape of x equals to y. |
is_tensor_object(x) |
Test whether or not x is a tensor object. |
is_tensorflow_version_higher_or_equal(version) |
Check whether the version of TensorFlow is higher than or equal to version. |
iter_files(root_dir[, sep]) |
Iterate through all files in root_dir, returning the relative paths of each file. |
makedirs(name[, mode, exist_ok]) |
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maybe_check_numerics(tensor, message[, name]) |
Check the numerics of tensor, if should_check_numerics(). |
maybe_close(*args, **kwds) |
Enter a context, and if obj has .close() method, close it when exiting the context. |
minibatch_slices_iterator(length, batch_size) |
Iterate through all the mini-batch slices. |
model_variable(name[, shape, dtype, …]) |
Get or create a model variable. |
register_config_arguments(config, parser[, …]) |
Register config to the specified argument parser. |
register_config_validator(type, validator_class) |
Register a config value validator. |
register_tensor_wrapper_class(cls) |
Register a sub-class of TensorWrapper into TensorFlow type system. |
reopen_variable_scope(*args, **kwds) |
Reopen the specified var_scope and its original name scope. |
reshape_tail(input, ndims, shape[, name]) |
Reshape the tail (last) ndims into specified shape. |
resolve_negative_axis(ndims, axis) |
Resolve all negative axis indices according to ndims into positive. |
root_variable_scope(*args, **kwds) |
Open the root variable scope and its name scope. |
scoped_set_config(*args, **kwds) |
Set config values within a context scope. |
set_assertion_enabled(*args, **kwargs) |
Set whether or not to enable assertions? |
set_cache_root(cache_root) |
Set the root cache directory. |
set_check_numerics(*args, **kwargs) |
Set whether or not to check numerics? |
set_random_seed(seed) |
Generate random seeds for NumPy, TensorFlow and TFSnippet. |
should_check_numerics(*args, **kwargs) |
Whether or not to check numerics? |
split_numpy_array(array[, portion, size, …]) |
Split numpy array into two halves, by portion or by size. |
split_numpy_arrays(arrays[, portion, size, …]) |
Split numpy arrays into two halves, by portion or by size. |
transpose_conv2d_axis(input, …[, name]) |
Ensure the channels axis of input tensor to be placed at the desired axis. |
transpose_conv2d_channels_last_to_x(input, …) |
Ensure the channels axis (known to be the last axis) of input tensor to be placed at the desired axis. |
transpose_conv2d_channels_x_to_last(input, …) |
Ensure the channels axis of input tensor to be placed at the last axis. |
unflatten_from_ndims(x, static_front_shape, …) |
The inverse transformation of flatten(). |
validate_enum_arg(arg_name, arg_value, choices) |
Validate the value of a enumeration argument. |
validate_group_ndims_arg(group_ndims[, name]) |
Validate the specified value for group_ndims argument. |
validate_int_tuple_arg(arg_name, arg_value) |
Validate an integer or a tuple of integers, as a tuple of integers. |
validate_n_samples_arg(value, name) |
Validate the n_samples argument. |
validate_positive_int_arg(arg_name, arg_value) |
Validate a positive integer argument. |
