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"""Updates generated docs from Python doc comments."""
import os.path
import tensorflow.python.platform
import sys
import tensorflow as tf
from tensorflow.python.framework import docs
from tensorflow.python.framework import framework_lib
from tensorflow.python.client import client_lib
tf.flags.DEFINE_string("out_dir", None,
"Directory to which docs should be written.")
tf.flags.DEFINE_boolean("print_hidden_regex", False,
"Dump a regular expression matching any hidden symbol")
FLAGS = tf.flags.FLAGS
def get_module_to_name():
return {tf: 'tf',
tf.errors: 'tf.errors',
tf.image: 'tf.image',
tf.nn: 'tf.nn',
tf.train: 'tf.train',
tf.python_io: 'tf.python_io'}
def all_libraries(module_to_name, members, documented):
# A list of (filename, docs.Library) pairs representing the individual files
# that we want to create.
def library(name, title, module=None, **args):
if module is None:
module = sys.modules["tensorflow.python.ops" +
("" if name == "ops" else "." + name)]
return (name + ".md", docs.Library(title=title,
module_to_name=module_to_name,
members=members,
documented=documented,
module=module,
**args))
return [
# Splits of module 'tf'.
library("framework", "Building Graphs", framework_lib),
library("constant_op", "Constants, Sequences, and Random Values"),
library("state_ops", "Variables"),
library("array_ops", "Tensor Transformations",
exclude_symbols=["list_diff"]),
library("math_ops", "Math",
exclude_symbols=["sparse_matmul", "arg_min", "arg_max",
"lin_space", "sparse_segment_mean_grad"]),
library("control_flow_ops", "Control Flow"),
library("image", "Images", tf.image, exclude_symbols=["ResizeMethod"]),
library("sparse_ops", "Sparse Tensors"),
library("io_ops", "Inputs and Readers",
exclude_symbols=["LookupTableBase", "HashTable",
"initialize_all_tables",
"string_to_hash_bucket"]),
library("python_io", "Data IO (Python functions)", tf.python_io),
library("nn", "Neural Network", tf.nn,
exclude_symbols=["deconv2d", "conv2d_backprop_input",
"conv2d_backprop_filter", "avg_pool_grad",
"max_pool_grad", "max_pool_grad_with_argmax",
"batch_norm_with_global_normalization_grad",
"lrn_grad", "relu6_grad", "softplus_grad",
"xw_plus_b", "relu_layer", "lrn",
"batch_norm_with_global_normalization",
"batch_norm_with_global_normalization_grad",
"all_candidate_sampler"]),
library('client', "Running Graphs", client_lib,
exclude_symbols=["InteractiveSession"]),
library("train", "Training", tf.train,
exclude_symbols=["Feature", "Features", "BytesList", "FloatList",
"Int64List", "Example", "InferenceExample",
"RankingExample", "SequenceExample"]),
]
_hidden_symbols = ["Event", "Summary",
"HistogramProto", "ConfigProto", "NodeDef", "GraphDef",
"GPUOptions", "SessionInterface", "BaseSession"]
def main(unused_argv):
if not FLAGS.out_dir:
tf.logging.error("out_dir not specified")
return -1
# Document libraries
documented = set()
module_to_name = get_module_to_name()
members = docs.collect_members(module_to_name)
libraries = all_libraries(module_to_name, members, documented)
docs.write_libraries(FLAGS.out_dir, libraries)
# Make it easy to search for hidden symbols
if FLAGS.print_hidden_regex:
hidden = set(_hidden_symbols)
for _, lib in libraries:
hidden.update(lib.exclude_symbols)
print r"hidden symbols regex = r'\b(%s)\b'" % "|".join(sorted(hidden))
# Verify that all symbols are mentioned in some library doc.
catch_all = docs.Library(title="Catch All", module=None,
exclude_symbols=_hidden_symbols,
module_to_name=module_to_name, members=members,
documented=documented)
catch_all.assert_no_leftovers()
# Generate index
with open(os.path.join(FLAGS.out_dir, "index.md"), "w") as f:
docs.Index(module_to_name, members, libraries).write_markdown_to_file(f)
if __name__ == "__main__":
tf.app.run()
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