This page contains style decisions that both developers and users of TensorFlow should follow to increase the readability of their code, reduce the number of errors, and promote consistency.
Generally follow PEP8 Python style guide, except for using 2 spaces.
Python 2 and 3 compatible
All code needs to be compatible with Python 2 and 3.
Next lines should be present in all Python files:
from __future__ import absolute_import from __future__ import division from __future__ import print_function
sixto write compatible code (for example
Bazel BUILD rules
TensorFlow uses Bazel build system and enforces next requirements:
- Every BUILD file should contain next header:
# Description: # <...> package( default_visibility = ["//visibility:private"], ) licenses(["notice"]) # Apache 2.0 exports_files(["LICENSE"])
- At the end of every BUILD file, should contain:
filegroup( name = "all_files", srcs = glob( ["**/*"], exclude = [ "**/METADATA", "**/OWNERS", ], ), visibility = ["//third_party/tensorflow:__subpackages__"], )
- When adding new BUILD file, add this line to
- For all Python BUILD targets (libraries and tests) add next line:
srcs_version = "PY2AND3",
- Operations that deal with batches may assume that the first dimension of a Tensor is the batch dimension.
A Python operation is a function that, given input tensors and parameters, creates a part of the graph and returns output tensors.
The first arguments should be tensors, followed by basic python parameters. The last argument is
namewith a default value of
None. If operation needs to save some
Tensors to Graph collections, put the arguments with names of the collections right before
Tensor arguments should be either a single tensor or an iterable of tensors. E.g. a "Tensor or list of Tensors" is too broad. See
Operations that take tensors as arguments should call
convert_to_tensorto convert non-tensor inputs into tensors if they are using C++ operations. Note that the arguments are still described as a
Tensorobject of a specific dtype in the documentation.
Each Python operation should have a
name_scopelike below. Pass as arguments
name, a default name of the op, and a list of the input tensors.
Operations should contain an extensive Python comment with Args and Returns declarations that explain both the type and meaning of each value. Possible shapes, dtypes, or ranks should be specified in the description. See documentation details
For increased usability include an example of usage with inputs / outputs of the op in Example section.
def my_op(tensor_in, other_tensor_in, my_param, other_param=0.5, output_collections=(), name=None): """My operation that adds two tensors with given coefficients. Args: tensor_in: `Tensor`, input tensor. other_tensor_in: `Tensor`, same shape as `tensor_in`, other input tensor. my_param: `float`, coefficient for `tensor_in`. other_param: `float`, coefficient for `other_tensor_in`. output_collections: `tuple` of `string`s, name of the collection to collect result of this op. name: `string`, name of the operation. Returns: `Tensor` of same shape as `tensor_in`, sum of input values with coefficients. Example: >>> my_op([1., 2.], [3., 4.], my_param=0.5, other_param=0.6, output_collections=['MY_OPS'], name='add_t1t2') [2.3, 3.4] """ with tf.name_scope(name, "my_op", [tensor_in, other_tensor_in]): tensor_in = tf.convert_to_tensor(tensor_in) other_tensor_in = tf.convert_to_tensor(other_tensor_in) result = my_param * tensor_in + other_param * other_tensor_in tf.add_to_collection(output_collections, result) return result
output = my_op(t1, t2, my_param=0.5, other_param=0.6, output_collections=['MY_OPS'], name='add_t1t2')
A Layer is a Python operation that combines variable creation and/or one or many other graph operations. Follow the same requirements as for regular Python operation.
- If a layer creates one or more variables, the layer function should take next arguments also following order:
initializers: Optionally allow to specify initializers for the variables.
regularizers: Optionally allow to specify regularizers for the variables.
trainable: which control if their variables are trainable or not.
VariableScopeobject that variable will be put under.
boolindicator if the variable should be reused if it's present in the scope.
Layers that behave differently during training should take:
boolindicator to conditionally choose different computation paths (e.g. using
tf.cond) during execution.
def conv2d(inputs, num_filters_out, kernel_size, stride=1, padding='SAME', activation_fn=tf.nn.relu, normalization_fn=add_bias, normalization_params=None, initializers=None, regularizers=None, trainable=True, scope=None, reuse=None): ... see implementation at tensorflow/contrib/layers/python/layers/layers.py ...