INSTRUCTION stringlengths 1 46.3k | RESPONSE stringlengths 75 80.2k |
|---|---|
Renders the HTML representation of the element. | def render(self, **kwargs):
"""Renders the HTML representation of the element."""
figure = self.get_root()
assert isinstance(figure, Figure), ('You cannot render this Element '
'if it is not in a Figure.')
# Set global switches
figure.... |
Fit the map to contain a bounding box with the
maximum zoom level possible.
Parameters
----------
bounds: list of (latitude, longitude) points
Bounding box specified as two points [southwest, northeast]
padding_top_left: (x, y) point, default None
Padding... | def fit_bounds(self, bounds, padding_top_left=None,
padding_bottom_right=None, padding=None, max_zoom=None):
"""Fit the map to contain a bounding box with the
maximum zoom level possible.
Parameters
----------
bounds: list of (latitude, longitude) points
... |
Call the Choropleth class with the same arguments.
This method may be deleted after a year from now (Nov 2018). | def choropleth(self, *args, **kwargs):
"""Call the Choropleth class with the same arguments.
This method may be deleted after a year from now (Nov 2018).
"""
warnings.warn(
'The choropleth method has been deprecated. Instead use the new '
'Choropleth class, whic... |
Computes the bounds of the object itself (not including it's children)
in the form [[lat_min, lon_min], [lat_max, lon_max]]. | def _get_self_bounds(self):
"""
Computes the bounds of the object itself (not including it's children)
in the form [[lat_min, lon_min], [lat_max, lon_max]].
"""
if not self.embed:
raise ValueError('Cannot compute bounds of non-embedded GeoJSON.')
data = json... |
Add object `child` to the first map and store it for the second. | def add_child(self, child, name=None, index=None):
"""Add object `child` to the first map and store it for the second."""
self.m1.add_child(child, name, index)
if index is None:
index = len(self.m2._children)
self.children_for_m2.append((child, name, index)) |
Create a new multiprocess.Manager (or return existing one).
Args:
:authkey: string authorization key
:queues: *INTERNAL_USE*
:mode: 'local' indicates that the manager will only be accessible from the same host, otherwise remotely accessible.
Returns:
A TFManager instance, which is also cached in l... | def start(authkey, queues, mode='local'):
"""Create a new multiprocess.Manager (or return existing one).
Args:
:authkey: string authorization key
:queues: *INTERNAL_USE*
:mode: 'local' indicates that the manager will only be accessible from the same host, otherwise remotely accessible.
Returns:
... |
Connect to a multiprocess.Manager.
Args:
:address: unique address to the TFManager, either a unique connection string for 'local', or a (host, port) tuple for remote.
:authkey: string authorization key
Returns:
A TFManager instance referencing the remote TFManager at the supplied address. | def connect(address, authkey):
"""Connect to a multiprocess.Manager.
Args:
:address: unique address to the TFManager, either a unique connection string for 'local', or a (host, port) tuple for remote.
:authkey: string authorization key
Returns:
A TFManager instance referencing the remote TFManager a... |
Process a single XML file containing a bounding box. | def ProcessXMLAnnotation(xml_file):
"""Process a single XML file containing a bounding box."""
# pylint: disable=broad-except
try:
tree = ET.parse(xml_file)
except Exception:
print('Failed to parse: ' + xml_file, file=sys.stderr)
return None
# pylint: enable=broad-except
root = tree.getroot()
... |
Helper to create summaries for activations.
Creates a summary that provides a histogram of activations.
Creates a summary that measures the sparsity of activations.
Args:
x: Tensor
Returns:
nothing | def _activation_summary(x):
"""Helper to create summaries for activations.
Creates a summary that provides a histogram of activations.
Creates a summary that measures the sparsity of activations.
Args:
x: Tensor
Returns:
nothing
"""
# Remove 'tower_[0-9]/' from the name in case this is a multi-G... |
Returns a python list of all (sharded) data subset files.
Returns:
python list of all (sharded) data set files.
Raises:
ValueError: if there are not data_files matching the subset. | def data_files(self):
"""Returns a python list of all (sharded) data subset files.
Returns:
python list of all (sharded) data set files.
Raises:
ValueError: if there are not data_files matching the subset.
"""
tf_record_pattern = os.path.join(FLAGS.data_dir, '%s-*' % self.subset)
da... |
Helper to create a Variable stored on CPU memory.
Args:
name: name of the variable
shape: list of ints
initializer: initializer for Variable
Returns:
Variable Tensor | def _variable_on_cpu(name, shape, initializer):
"""Helper to create a Variable stored on CPU memory.
Args:
name: name of the variable
shape: list of ints
initializer: initializer for Variable
Returns:
Variable Tensor
"""
with tf.device('/cpu:0'):
dtype = tf.float16 if FLAGS.use_fp16 else... |
Helper to create an initialized Variable with weight decay.
Note that the Variable is initialized with a truncated normal distribution.
A weight decay is added only if one is specified.
Args:
name: name of the variable
shape: list of ints
stddev: standard deviation of a truncated Gaussian
wd: ad... | def _variable_with_weight_decay(name, shape, stddev, wd):
"""Helper to create an initialized Variable with weight decay.
Note that the Variable is initialized with a truncated normal distribution.
A weight decay is added only if one is specified.
Args:
name: name of the variable
shape: list of ints
... |
Construct distorted input for CIFAR training using the Reader ops.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
Raises:
ValueError: If no data_dir | def distorted_inputs():
"""Construct distorted input for CIFAR training using the Reader ops.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
Raises:
ValueError: If no data_dir
"""
if not FLAGS.data_dir:
rais... |
Build the CIFAR-10 model.
Args:
images: Images returned from distorted_inputs() or inputs().
Returns:
Logits. | def inference(images):
"""Build the CIFAR-10 model.
Args:
images: Images returned from distorted_inputs() or inputs().
Returns:
Logits.
"""
# We instantiate all variables using tf.get_variable() instead of
# tf.Variable() in order to share variables across multiple GPU training runs.
# If we onl... |
Add L2Loss to all the trainable variables.
Add summary for "Loss" and "Loss/avg".
Args:
logits: Logits from inference().
labels: Labels from distorted_inputs or inputs(). 1-D tensor
of shape [batch_size]
Returns:
Loss tensor of type float. | def loss(logits, labels):
"""Add L2Loss to all the trainable variables.
Add summary for "Loss" and "Loss/avg".
Args:
logits: Logits from inference().
labels: Labels from distorted_inputs or inputs(). 1-D tensor
of shape [batch_size]
Returns:
Loss tensor of type float.
"""
# Calcula... |
Add summaries for losses in CIFAR-10 model.
Generates moving average for all losses and associated summaries for
visualizing the performance of the network.
Args:
total_loss: Total loss from loss().
Returns:
loss_averages_op: op for generating moving averages of losses. | def _add_loss_summaries(total_loss):
"""Add summaries for losses in CIFAR-10 model.
Generates moving average for all losses and associated summaries for
visualizing the performance of the network.
Args:
total_loss: Total loss from loss().
Returns:
loss_averages_op: op for generating moving averages ... |
Train CIFAR-10 model.
Create an optimizer and apply to all trainable variables. Add moving
average for all trainable variables.
Args:
total_loss: Total loss from loss().
global_step: Integer Variable counting the number of training steps
processed.
Returns:
train_op: op for training. | def train(total_loss, global_step):
"""Train CIFAR-10 model.
Create an optimizer and apply to all trainable variables. Add moving
average for all trainable variables.
Args:
total_loss: Total loss from loss().
global_step: Integer Variable counting the number of training steps
processed.
Return... |
Adds a variable to the MODEL_VARIABLES collection.
Optionally it will add the variable to the VARIABLES_TO_RESTORE collection.
Args:
var: a variable.
restore: whether the variable should be added to the
VARIABLES_TO_RESTORE collection. | def add_variable(var, restore=True):
"""Adds a variable to the MODEL_VARIABLES collection.
Optionally it will add the variable to the VARIABLES_TO_RESTORE collection.
Args:
var: a variable.
restore: whether the variable should be added to the
VARIABLES_TO_RESTORE collection.
"""
collections... |
Gets the list of variables, filtered by scope and/or suffix.
Args:
scope: an optional scope for filtering the variables to return.
suffix: an optional suffix for filtering the variables to return.
Returns:
a copied list of variables with scope and suffix. | def get_variables(scope=None, suffix=None):
"""Gets the list of variables, filtered by scope and/or suffix.
Args:
scope: an optional scope for filtering the variables to return.
suffix: an optional suffix for filtering the variables to return.
Returns:
a copied list of variables with scope and suffi... |
Gets the variable uniquely identified by that name.
Args:
name: a name that uniquely identifies the variable.
Returns:
a tensorflow variable.
Raises:
ValueError: if no variable uniquely identified by the name exists. | def get_unique_variable(name):
"""Gets the variable uniquely identified by that name.
Args:
name: a name that uniquely identifies the variable.
Returns:
a tensorflow variable.
Raises:
ValueError: if no variable uniquely identified by the name exists.
"""
candidates = tf.get_collection(tf.Grap... |
Fix the variable device to colocate its ops. | def variable_device(device, name):
"""Fix the variable device to colocate its ops."""
if callable(device):
var_name = tf.get_variable_scope().name + '/' + name
var_def = tf.NodeDef(name=var_name, op='Variable')
device = device(var_def)
if device is None:
device = ''
return device |
Returns the global step variable.
Args:
device: Optional device to place the variable. It can be an string or a
function that is called to get the device for the variable.
Returns:
the tensor representing the global step variable. | def global_step(device=''):
"""Returns the global step variable.
Args:
device: Optional device to place the variable. It can be an string or a
function that is called to get the device for the variable.
Returns:
the tensor representing the global step variable.
"""
global_step_ref = tf.get_col... |
Gets an existing variable with these parameters or creates a new one.
It also add itself to a group with its name.
Args:
name: the name of the new or existing variable.
shape: shape of the new or existing variable.
dtype: type of the new or existing variable (defaults to `DT_FLOAT`).
initializer... | def variable(name, shape=None, dtype=tf.float32, initializer=None,
regularizer=None, trainable=True, collections=None, device='',
restore=True):
"""Gets an existing variable with these parameters or creates a new one.
It also add itself to a group with its name.
Args:
name: the n... |
Runs Eval once.
Args:
saver: Saver.
summary_writer: Summary writer.
top_1_op: Top 1 op.
top_5_op: Top 5 op.
summary_op: Summary op. | def _eval_once(saver, summary_writer, top_1_op, top_5_op, summary_op):
"""Runs Eval once.
Args:
saver: Saver.
summary_writer: Summary writer.
top_1_op: Top 1 op.
top_5_op: Top 5 op.
summary_op: Summary op.
"""
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(FLAGS.checkpo... |
Evaluate model on Dataset for a number of steps. | def evaluate(dataset):
"""Evaluate model on Dataset for a number of steps."""
with tf.Graph().as_default():
# Get images and labels from the dataset.
images, labels = image_processing.inputs(dataset)
# Number of classes in the Dataset label set plus 1.
# Label 0 is reserved for an (unused) backgrou... |
mapPartitions function to run single-node inferencing from a checkpoint/saved_model, using the model's input/output mappings.
Args:
:iterator: input RDD partition iterator.
:args: arguments for TFModel, in argparse format
:tf_args: arguments for TensorFlow inferencing code, in argparse or ARGV format.
... | def _run_model(iterator, args, tf_args):
"""mapPartitions function to run single-node inferencing from a checkpoint/saved_model, using the model's input/output mappings.
Args:
:iterator: input RDD partition iterator.
:args: arguments for TFModel, in argparse format
:tf_args: arguments for TensorFlow in... |
Sets up environment for a single-node TF session.
Args:
:args: command line arguments as either argparse args or argv list | def single_node_env(args):
"""Sets up environment for a single-node TF session.
Args:
:args: command line arguments as either argparse args or argv list
"""
# setup ARGV for the TF process
if isinstance(args, list):
sys.argv = args
elif args.argv:
sys.argv = args.argv
# setup ENV for Had... |
Utility function to read a meta_graph_def from disk.
From `saved_model_cli.py <https://github.com/tensorflow/tensorflow/blob/8e0e8d41a3a8f2d4a6100c2ea1dc9d6c6c4ad382/tensorflow/python/tools/saved_model_cli.py#L186>`_
Args:
:saved_model_dir: path to saved_model.
:tag_set: list of string tags identifying th... | def get_meta_graph_def(saved_model_dir, tag_set):
"""Utility function to read a meta_graph_def from disk.
From `saved_model_cli.py <https://github.com/tensorflow/tensorflow/blob/8e0e8d41a3a8f2d4a6100c2ea1dc9d6c6c4ad382/tensorflow/python/tools/saved_model_cli.py#L186>`_
Args:
:saved_model_dir: path to saved_... |
Generator that yields batches of a DataFrame iterator.
Args:
:iterable: Spark partition iterator.
:batch_size: number of items to retrieve per invocation.
:num_tensors: number of tensors (columns) expected in each item.
Returns:
An array of ``num_tensors`` arrays, each of length `batch_size` | def yield_batch(iterable, batch_size, num_tensors=1):
"""Generator that yields batches of a DataFrame iterator.
Args:
:iterable: Spark partition iterator.
:batch_size: number of items to retrieve per invocation.
:num_tensors: number of tensors (columns) expected in each item.
Returns:
An array o... |
Trains a TensorFlow model and returns a TFModel instance with the same args/params pointing to a checkpoint or saved_model on disk.
Args:
:dataset: A Spark DataFrame with columns that will be mapped to TensorFlow tensors.
Returns:
A TFModel representing the trained model, backed on disk by a Tenso... | def _fit(self, dataset):
"""Trains a TensorFlow model and returns a TFModel instance with the same args/params pointing to a checkpoint or saved_model on disk.
Args:
:dataset: A Spark DataFrame with columns that will be mapped to TensorFlow tensors.
Returns:
A TFModel representing the trained ... |
Transforms the input DataFrame by applying the _run_model() mapPartitions function.
Args:
:dataset: A Spark DataFrame for TensorFlow inferencing. | def _transform(self, dataset):
"""Transforms the input DataFrame by applying the _run_model() mapPartitions function.
Args:
:dataset: A Spark DataFrame for TensorFlow inferencing.
"""
spark = SparkSession.builder.getOrCreate()
# set a deterministic order for input/output columns (lexicograph... |
*For InputMode.SPARK only*. Feeds Spark RDD partitions into the TensorFlow worker nodes
It is the responsibility of the TensorFlow "main" function to interpret the rows of the RDD.
Since epochs are implemented via ``RDD.union()`` and the entire RDD must generally be processed in full, it is recommended
t... | def train(self, dataRDD, num_epochs=0, feed_timeout=600, qname='input'):
"""*For InputMode.SPARK only*. Feeds Spark RDD partitions into the TensorFlow worker nodes
It is the responsibility of the TensorFlow "main" function to interpret the rows of the RDD.
Since epochs are implemented via ``RDD.union()``... |
Stops the distributed TensorFlow cluster.
For InputMode.SPARK, this will be executed AFTER the `TFCluster.train()` or `TFCluster.inference()` method completes.
For InputMode.TENSORFLOW, this will be executed IMMEDIATELY after `TFCluster.run()` and will wait until the TF worker nodes complete.
Args:
... | def shutdown(self, ssc=None, grace_secs=0, timeout=259200):
"""Stops the distributed TensorFlow cluster.
For InputMode.SPARK, this will be executed AFTER the `TFCluster.train()` or `TFCluster.inference()` method completes.
For InputMode.TENSORFLOW, this will be executed IMMEDIATELY after `TFCluster.run()` ... |
*For InputMode.SPARK only*: Feeds Spark RDD partitions into the TensorFlow worker nodes and returns an RDD of results
It is the responsibility of the TensorFlow "main" function to interpret the rows of the RDD and provide valid data for the output RDD.
This will use the distributed TensorFlow cluster for infe... | def inference(self, dataRDD, feed_timeout=600, qname='input'):
"""*For InputMode.SPARK only*: Feeds Spark RDD partitions into the TensorFlow worker nodes and returns an RDD of results
It is the responsibility of the TensorFlow "main" function to interpret the rows of the RDD and provide valid data for the outp... |
Wrapper for inserting int64 features into Example proto. | def _int64_feature(value):
"""Wrapper for inserting int64 features into Example proto."""
if not isinstance(value, list):
value = [value]
return tf.train.Feature(int64_list=tf.train.Int64List(value=value)) |
Wrapper for inserting float features into Example proto. | def _float_feature(value):
"""Wrapper for inserting float features into Example proto."""
if not isinstance(value, list):
value = [value]
return tf.train.Feature(float_list=tf.train.FloatList(value=value)) |
Build an Example proto for an example.
Args:
filename: string, path to an image file, e.g., '/path/to/example.JPG'
image_buffer: string, JPEG encoding of RGB image
label: integer, identifier for the ground truth for the network
synset: string, unique WordNet ID specifying the label, e.g., 'n02323233'... | def _convert_to_example(filename, image_buffer, label, synset, human, bbox,
height, width):
"""Build an Example proto for an example.
Args:
filename: string, path to an image file, e.g., '/path/to/example.JPG'
image_buffer: string, JPEG encoding of RGB image
label: integer, iden... |
Process a single image file.
Args:
filename: string, path to an image file e.g., '/path/to/example.JPG'.
coder: instance of ImageCoder to provide TensorFlow image coding utils.
Returns:
image_buffer: string, JPEG encoding of RGB image.
height: integer, image height in pixels.
width: integer, im... | def _process_image(filename, coder):
"""Process a single image file.
Args:
filename: string, path to an image file e.g., '/path/to/example.JPG'.
coder: instance of ImageCoder to provide TensorFlow image coding utils.
Returns:
image_buffer: string, JPEG encoding of RGB image.
height: integer, imag... |
Build a list of human-readable labels.
Args:
synsets: list of strings; each string is a unique WordNet ID.
synset_to_human: dict of synset to human labels, e.g.,
'n02119022' --> 'red fox, Vulpes vulpes'
Returns:
List of human-readable strings corresponding to each synset. | def _find_human_readable_labels(synsets, synset_to_human):
"""Build a list of human-readable labels.
Args:
synsets: list of strings; each string is a unique WordNet ID.
synset_to_human: dict of synset to human labels, e.g.,
'n02119022' --> 'red fox, Vulpes vulpes'
Returns:
List of human-readab... |
Find the bounding boxes for a given image file.
Args:
filenames: list of strings; each string is a path to an image file.
image_to_bboxes: dictionary mapping image file names to a list of
bounding boxes. This list contains 0+ bounding boxes.
Returns:
List of bounding boxes for each image. Note th... | def _find_image_bounding_boxes(filenames, image_to_bboxes):
"""Find the bounding boxes for a given image file.
Args:
filenames: list of strings; each string is a path to an image file.
image_to_bboxes: dictionary mapping image file names to a list of
bounding boxes. This list contains 0+ bounding box... |
Process a complete data set and save it as a TFRecord.
Args:
name: string, unique identifier specifying the data set.
directory: string, root path to the data set.
num_shards: integer number of shards for this data set.
synset_to_human: dict of synset to human labels, e.g.,
'n02119022' --> 'red... | def _process_dataset(name, directory, num_shards, synset_to_human,
image_to_bboxes):
"""Process a complete data set and save it as a TFRecord.
Args:
name: string, unique identifier specifying the data set.
directory: string, root path to the data set.
num_shards: integer number of ... |
Build lookup for synset to human-readable label.
Args:
imagenet_metadata_file: string, path to file containing mapping from
synset to human-readable label.
Assumes each line of the file looks like:
n02119247 black fox
n02119359 silver fox
n02119477 red fox, Vulpes f... | def _build_synset_lookup(imagenet_metadata_file):
"""Build lookup for synset to human-readable label.
Args:
imagenet_metadata_file: string, path to file containing mapping from
synset to human-readable label.
Assumes each line of the file looks like:
n02119247 black fox
n021193... |
Build a lookup from image file to bounding boxes.
Args:
bounding_box_file: string, path to file with bounding boxes annotations.
Assumes each line of the file looks like:
n00007846_64193.JPEG,0.0060,0.2620,0.7545,0.9940
where each line corresponds to one bounding box annotation associated
... | def _build_bounding_box_lookup(bounding_box_file):
"""Build a lookup from image file to bounding boxes.
Args:
bounding_box_file: string, path to file with bounding boxes annotations.
Assumes each line of the file looks like:
n00007846_64193.JPEG,0.0060,0.2620,0.7545,0.9940
where each lin... |
Model function for CNN. | def cnn_model_fn(features, labels, mode):
"""Model function for CNN."""
# Input Layer
# Reshape X to 4-D tensor: [batch_size, width, height, channels]
# MNIST images are 28x28 pixels, and have one color channel
input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
# Convolutional Layer #1
# Computes 3... |
Reads and parses examples from CIFAR10 data files.
Recommendation: if you want N-way read parallelism, call this function
N times. This will give you N independent Readers reading different
files & positions within those files, which will give better mixing of
examples.
Args:
filename_queue: A queue of... | def read_cifar10(filename_queue):
"""Reads and parses examples from CIFAR10 data files.
Recommendation: if you want N-way read parallelism, call this function
N times. This will give you N independent Readers reading different
files & positions within those files, which will give better mixing of
examples.
... |
Construct a queued batch of images and labels.
Args:
image: 3-D Tensor of [height, width, 3] of type.float32.
label: 1-D Tensor of type.int32
min_queue_examples: int32, minimum number of samples to retain
in the queue that provides of batches of examples.
batch_size: Number of images per batch.... | def _generate_image_and_label_batch(image, label, min_queue_examples,
batch_size, shuffle):
"""Construct a queued batch of images and labels.
Args:
image: 3-D Tensor of [height, width, 3] of type.float32.
label: 1-D Tensor of type.int32
min_queue_examples: int32, min... |
Construct distorted input for CIFAR training using the Reader ops.
Args:
data_dir: Path to the CIFAR-10 data directory.
batch_size: Number of images per batch.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size. | def distorted_inputs(data_dir, batch_size):
"""Construct distorted input for CIFAR training using the Reader ops.
Args:
data_dir: Path to the CIFAR-10 data directory.
batch_size: Number of images per batch.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
label... |
Construct input for CIFAR evaluation using the Reader ops.
Args:
eval_data: bool, indicating if one should use the train or eval data set.
data_dir: Path to the CIFAR-10 data directory.
batch_size: Number of images per batch.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZ... | def inputs(eval_data, data_dir, batch_size):
"""Construct input for CIFAR evaluation using the Reader ops.
Args:
eval_data: bool, indicating if one should use the train or eval data set.
data_dir: Path to the CIFAR-10 data directory.
batch_size: Number of images per batch.
Returns:
images: Image... |
Save a Spark DataFrame as TFRecords.
This will convert the DataFrame rows to TFRecords prior to saving.
Args:
:df: Spark DataFrame
:output_dir: Path to save TFRecords | def saveAsTFRecords(df, output_dir):
"""Save a Spark DataFrame as TFRecords.
This will convert the DataFrame rows to TFRecords prior to saving.
Args:
:df: Spark DataFrame
:output_dir: Path to save TFRecords
"""
tf_rdd = df.rdd.mapPartitions(toTFExample(df.dtypes))
tf_rdd.saveAsNewAPIHadoopFile(out... |
Load TFRecords from disk into a Spark DataFrame.
This will attempt to automatically convert the tf.train.Example features into Spark DataFrame columns of equivalent types.
Note: TensorFlow represents both strings and binary types as tf.train.BytesList, and we need to
disambiguate these types for Spark DataFrame... | def loadTFRecords(sc, input_dir, binary_features=[]):
"""Load TFRecords from disk into a Spark DataFrame.
This will attempt to automatically convert the tf.train.Example features into Spark DataFrame columns of equivalent types.
Note: TensorFlow represents both strings and binary types as tf.train.BytesList, an... |
mapPartition function to convert a Spark RDD of Row into an RDD of serialized tf.train.Example bytestring.
Note that tf.train.Example is a fairly flat structure with limited datatypes, e.g. tf.train.FloatList,
tf.train.Int64List, and tf.train.BytesList, so most DataFrame types will be coerced into one of these typ... | def toTFExample(dtypes):
"""mapPartition function to convert a Spark RDD of Row into an RDD of serialized tf.train.Example bytestring.
Note that tf.train.Example is a fairly flat structure with limited datatypes, e.g. tf.train.FloatList,
tf.train.Int64List, and tf.train.BytesList, so most DataFrame types will be... |
Given a tf.train.Example, infer the Spark DataFrame schema (StructFields).
Note: TensorFlow represents both strings and binary types as tf.train.BytesList, and we need to
disambiguate these types for Spark DataFrames DTypes (StringType and BinaryType), so we require a "hint"
from the caller in the ``binary_featu... | def infer_schema(example, binary_features=[]):
"""Given a tf.train.Example, infer the Spark DataFrame schema (StructFields).
Note: TensorFlow represents both strings and binary types as tf.train.BytesList, and we need to
disambiguate these types for Spark DataFrames DTypes (StringType and BinaryType), so we requ... |
mapPartition function to convert an RDD of serialized tf.train.Example bytestring into an RDD of Row.
Note: TensorFlow represents both strings and binary types as tf.train.BytesList, and we need to
disambiguate these types for Spark DataFrames DTypes (StringType and BinaryType), so we require a "hint"
from the c... | def fromTFExample(iter, binary_features=[]):
"""mapPartition function to convert an RDD of serialized tf.train.Example bytestring into an RDD of Row.
Note: TensorFlow represents both strings and binary types as tf.train.BytesList, and we need to
disambiguate these types for Spark DataFrames DTypes (StringType an... |
Build an estimator appropriate for the given model type. | def build_estimator(model_dir, model_type, model_column_fn, inter_op, intra_op, ctx):
"""Build an estimator appropriate for the given model type."""
wide_columns, deep_columns = model_column_fn()
hidden_units = [100, 75, 50, 25]
# Create a tf.estimator.RunConfig to ensure the model is run on CPU, which
# tra... |
Construct all necessary functions and call run_loop.
Args:
flags_obj: Object containing user specified flags. | def run_census(flags_obj, ctx):
"""Construct all necessary functions and call run_loop.
Args:
flags_obj: Object containing user specified flags.
"""
train_file = os.path.join(flags_obj.data_dir, census_dataset.TRAINING_FILE)
test_file = os.path.join(flags_obj.data_dir, census_dataset.EVAL_FILE)
# Trai... |
Latest Inception from http://arxiv.org/abs/1512.00567.
"Rethinking the Inception Architecture for Computer Vision"
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens,
Zbigniew Wojna
Args:
inputs: a tensor of size [batch_size, height, width, channels].
dropout_keep_prob: dropout... | def inception_v3(inputs,
dropout_keep_prob=0.8,
num_classes=1000,
is_training=True,
restore_logits=True,
scope=''):
"""Latest Inception from http://arxiv.org/abs/1512.00567.
"Rethinking the Inception Architecture for Computer Vi... |
Yields the scope with the default parameters for inception_v3.
Args:
weight_decay: the weight decay for weights variables.
stddev: standard deviation of the truncated guassian weight distribution.
batch_norm_decay: decay for the moving average of batch_norm momentums.
batch_norm_epsilon: small float ... | def inception_v3_parameters(weight_decay=0.00004, stddev=0.1,
batch_norm_decay=0.9997, batch_norm_epsilon=0.001):
"""Yields the scope with the default parameters for inception_v3.
Args:
weight_decay: the weight decay for weights variables.
stddev: standard deviation of the trunc... |
Setup environment variables for Hadoop compatibility and GPU allocation | def single_node_env(num_gpus=1):
"""Setup environment variables for Hadoop compatibility and GPU allocation"""
import tensorflow as tf
# ensure expanded CLASSPATH w/o glob characters (required for Spark 2.1 + JNI)
if 'HADOOP_PREFIX' in os.environ and 'TFOS_CLASSPATH_UPDATED' not in os.environ:
classpath =... |
Simple utility to get host IP address. | def get_ip_address():
"""Simple utility to get host IP address."""
try:
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
s.connect(("8.8.8.8", 80))
ip_address = s.getsockname()[0]
except socket_error as sockerr:
if sockerr.errno != errno.ENETUNREACH:
raise sockerr
ip_address = socket... |
Find a file in a given path string. | def find_in_path(path, file):
"""Find a file in a given path string."""
for p in path.split(os.pathsep):
candidate = os.path.join(p, file)
if os.path.exists(candidate) and os.path.isfile(candidate):
return candidate
return False |
Define a L1 regularizer.
Args:
weight: scale the loss by this factor.
scope: Optional scope for name_scope.
Returns:
a regularizer function. | def l1_regularizer(weight=1.0, scope=None):
"""Define a L1 regularizer.
Args:
weight: scale the loss by this factor.
scope: Optional scope for name_scope.
Returns:
a regularizer function.
"""
def regularizer(tensor):
with tf.name_scope(scope, 'L1Regularizer', [tensor]):
l1_weight = tf.... |
Define a L2 regularizer.
Args:
weight: scale the loss by this factor.
scope: Optional scope for name_scope.
Returns:
a regularizer function. | def l2_regularizer(weight=1.0, scope=None):
"""Define a L2 regularizer.
Args:
weight: scale the loss by this factor.
scope: Optional scope for name_scope.
Returns:
a regularizer function.
"""
def regularizer(tensor):
with tf.name_scope(scope, 'L2Regularizer', [tensor]):
l2_weight = tf.... |
Define a L1L2 regularizer.
Args:
weight_l1: scale the L1 loss by this factor.
weight_l2: scale the L2 loss by this factor.
scope: Optional scope for name_scope.
Returns:
a regularizer function. | def l1_l2_regularizer(weight_l1=1.0, weight_l2=1.0, scope=None):
"""Define a L1L2 regularizer.
Args:
weight_l1: scale the L1 loss by this factor.
weight_l2: scale the L2 loss by this factor.
scope: Optional scope for name_scope.
Returns:
a regularizer function.
"""
def regularizer(tensor):
... |
Define a L1Loss, useful for regularize, i.e. lasso.
Args:
tensor: tensor to regularize.
weight: scale the loss by this factor.
scope: Optional scope for name_scope.
Returns:
the L1 loss op. | def l1_loss(tensor, weight=1.0, scope=None):
"""Define a L1Loss, useful for regularize, i.e. lasso.
Args:
tensor: tensor to regularize.
weight: scale the loss by this factor.
scope: Optional scope for name_scope.
Returns:
the L1 loss op.
"""
with tf.name_scope(scope, 'L1Loss', [tensor]):
... |
Define a L2Loss, useful for regularize, i.e. weight decay.
Args:
tensor: tensor to regularize.
weight: an optional weight to modulate the loss.
scope: Optional scope for name_scope.
Returns:
the L2 loss op. | def l2_loss(tensor, weight=1.0, scope=None):
"""Define a L2Loss, useful for regularize, i.e. weight decay.
Args:
tensor: tensor to regularize.
weight: an optional weight to modulate the loss.
scope: Optional scope for name_scope.
Returns:
the L2 loss op.
"""
with tf.name_scope(scope, 'L2Loss... |
Define a Cross Entropy loss using softmax_cross_entropy_with_logits.
It can scale the loss by weight factor, and smooth the labels.
Args:
logits: [batch_size, num_classes] logits outputs of the network .
one_hot_labels: [batch_size, num_classes] target one_hot_encoded labels.
label_smoothing: if great... | def cross_entropy_loss(logits, one_hot_labels, label_smoothing=0,
weight=1.0, scope=None):
"""Define a Cross Entropy loss using softmax_cross_entropy_with_logits.
It can scale the loss by weight factor, and smooth the labels.
Args:
logits: [batch_size, num_classes] logits outputs of t... |
Stores the default arguments for the given set of list_ops.
For usage, please see examples at top of the file.
Args:
list_ops_or_scope: List or tuple of operations to set argument scope for or
a dictionary containg the current scope. When list_ops_or_scope is a dict,
kwargs must be empty. When lis... | def arg_scope(list_ops_or_scope, **kwargs):
"""Stores the default arguments for the given set of list_ops.
For usage, please see examples at top of the file.
Args:
list_ops_or_scope: List or tuple of operations to set argument scope for or
a dictionary containg the current scope. When list_ops_or_scop... |
Decorates a function with args so it can be used within an arg_scope.
Args:
func: function to decorate.
Returns:
A tuple with the decorated function func_with_args(). | def add_arg_scope(func):
"""Decorates a function with args so it can be used within an arg_scope.
Args:
func: function to decorate.
Returns:
A tuple with the decorated function func_with_args().
"""
@functools.wraps(func)
def func_with_args(*args, **kwargs):
current_scope = _current_arg_scope(... |
Returns this executor's "singleton" instance of the multiprocessing.Manager, reconnecting per python-worker if needed.
Args:
:cluster_info: cluster node reservations
:host: host IP address
:executor_id: unique id per executor (created during initial call to run())
Returns:
TFManager instance for t... | def _get_manager(cluster_info, host, executor_id):
"""Returns this executor's "singleton" instance of the multiprocessing.Manager, reconnecting per python-worker if needed.
Args:
:cluster_info: cluster node reservations
:host: host IP address
:executor_id: unique id per executor (created during initial... |
Wraps the user-provided TensorFlow main function in a Spark mapPartitions function.
Args:
:fn: TensorFlow "main" function provided by the user.
:tf_args: ``argparse`` args, or command line ``ARGV``. These will be passed to the ``fn``.
:cluster_meta: dictionary of cluster metadata (e.g. cluster_id, reser... | def run(fn, tf_args, cluster_meta, tensorboard, log_dir, queues, background):
"""Wraps the user-provided TensorFlow main function in a Spark mapPartitions function.
Args:
:fn: TensorFlow "main" function provided by the user.
:tf_args: ``argparse`` args, or command line ``ARGV``. These will be passed to th... |
Feeds Spark partitions into the shared multiprocessing.Queue.
Args:
:cluster_info: node reservation information for the cluster (e.g. host, executor_id, pid, ports, etc)
:cluster_meta: dictionary of cluster metadata (e.g. cluster_id, reservation.Server address, etc)
:feed_timeout: number of seconds after... | def train(cluster_info, cluster_meta, feed_timeout=600, qname='input'):
"""Feeds Spark partitions into the shared multiprocessing.Queue.
Args:
:cluster_info: node reservation information for the cluster (e.g. host, executor_id, pid, ports, etc)
:cluster_meta: dictionary of cluster metadata (e.g. cluster_id... |
Feeds Spark partitions into the shared multiprocessing.Queue and returns inference results.
Args:
:cluster_info: node reservation information for the cluster (e.g. host, executor_id, pid, ports, etc)
:feed_timeout: number of seconds after which data feeding times out (600 sec default)
:qname: *INTERNAL_U... | def inference(cluster_info, feed_timeout=600, qname='input'):
"""Feeds Spark partitions into the shared multiprocessing.Queue and returns inference results.
Args:
:cluster_info: node reservation information for the cluster (e.g. host, executor_id, pid, ports, etc)
:feed_timeout: number of seconds after whi... |
Stops all TensorFlow nodes by feeding ``None`` into the multiprocessing.Queues.
Args:
:cluster_info: node reservation information for the cluster (e.g. host, executor_id, pid, ports, etc).
:queues: *INTERNAL_USE*
Returns:
A nodeRDD.mapPartitions() function | def shutdown(cluster_info, queues=['input']):
"""Stops all TensorFlow nodes by feeding ``None`` into the multiprocessing.Queues.
Args:
:cluster_info: node reservation information for the cluster (e.g. host, executor_id, pid, ports, etc).
:queues: *INTERNAL_USE*
Returns:
A nodeRDD.mapPartitions() fun... |
Convenience function to access ``TFNode.start_cluster_server`` directly from this object instance. | def start_cluster_server(self, num_gpus=1, rdma=False):
"""Convenience function to access ``TFNode.start_cluster_server`` directly from this object instance."""
return TFNode.start_cluster_server(self, num_gpus, rdma) |
Convenience function to access ``TFNode.export_saved_model`` directly from this object instance. | def export_saved_model(self, sess, export_dir, tag_set, signatures):
"""Convenience function to access ``TFNode.export_saved_model`` directly from this object instance."""
TFNode.export_saved_model(sess, export_dir, tag_set, signatures) |
Convenience function to access ``TFNode.DataFeed`` directly from this object instance. | def get_data_feed(self, train_mode=True, qname_in='input', qname_out='output', input_mapping=None):
"""Convenience function to access ``TFNode.DataFeed`` directly from this object instance."""
return TFNode.DataFeed(self.mgr, train_mode, qname_in, qname_out, input_mapping) |
Downloads data from url, and makes changes to match the CSV format. | def _download_and_clean_file(filename, url):
"""Downloads data from url, and makes changes to match the CSV format."""
temp_file, _ = urllib.request.urlretrieve(url)
with tf.gfile.Open(temp_file, 'r') as temp_eval_file:
with tf.gfile.Open(filename, 'w') as eval_file:
for line in temp_eval_file:
... |
Download census data if it is not already present. | def download(data_dir):
"""Download census data if it is not already present."""
tf.gfile.MakeDirs(data_dir)
training_file_path = os.path.join(data_dir, TRAINING_FILE)
if not tf.gfile.Exists(training_file_path):
_download_and_clean_file(training_file_path, TRAINING_URL)
eval_file_path = os.path.join(dat... |
Builds a set of wide and deep feature columns. | def build_model_columns():
"""Builds a set of wide and deep feature columns."""
# Continuous variable columns
age = tf.feature_column.numeric_column('age')
education_num = tf.feature_column.numeric_column('education_num')
capital_gain = tf.feature_column.numeric_column('capital_gain')
capital_loss = tf.feat... |
Generate an input function for the Estimator. | def input_fn(data_file, num_epochs, shuffle, batch_size):
"""Generate an input function for the Estimator."""
assert tf.gfile.Exists(data_file), (
'%s not found. Please make sure you have run census_dataset.py and '
'set the --data_dir argument to the correct path.' % data_file)
def parse_csv(value):... |
Receive a message on ``sock``. | def receive(self, sock):
"""Receive a message on ``sock``."""
msg = None
data = b''
recv_done = False
recv_len = -1
while not recv_done:
buf = sock.recv(BUFSIZE)
if buf is None or len(buf) == 0:
raise Exception("socket closed")
if recv_len == -1:
recv_len = stru... |
Send ``msg`` to destination ``sock``. | def send(self, sock, msg):
"""Send ``msg`` to destination ``sock``."""
data = pickle.dumps(msg)
buf = struct.pack('>I', len(data)) + data
sock.sendall(buf) |
Block until all reservations are received. | def await_reservations(self, sc, status={}, timeout=600):
"""Block until all reservations are received."""
timespent = 0
while not self.reservations.done():
logging.info("waiting for {0} reservations".format(self.reservations.remaining()))
# check status flags for any errors
if 'error' in ... |
Start listener in a background thread
Returns:
address of the Server as a tuple of (host, port) | def start(self):
"""Start listener in a background thread
Returns:
address of the Server as a tuple of (host, port)
"""
server_sock = self.start_listening_socket()
# hostname may not be resolvable but IP address probably will be
host = self.get_server_ip()
port = server_sock.getsockn... |
Helper function to wrap msg w/ msg_type. | def _request(self, msg_type, msg_data=None):
"""Helper function to wrap msg w/ msg_type."""
msg = {}
msg['type'] = msg_type
if msg_data:
msg['data'] = msg_data
done = False
tries = 0
while not done and tries < MAX_RETRIES:
try:
MessageSocket.send(self, self.sock, msg)
... |
Poll until all reservations completed, then return cluster_info. | def await_reservations(self):
"""Poll until all reservations completed, then return cluster_info."""
done = False
while not done:
done = self._request('QUERY')
time.sleep(1)
return self.get_reservations() |
Serializes an image/label as a TFExample byte string | def toTFExample(image, label):
"""Serializes an image/label as a TFExample byte string"""
example = tf.train.Example(
features=tf.train.Features(
feature={
'label': tf.train.Feature(int64_list=tf.train.Int64List(value=label.astype("int64"))),
'image': tf.train.Feature(int64_list=tf.train.I... |
Deserializes a TFExample from a byte string | def fromTFExample(bytestr):
"""Deserializes a TFExample from a byte string"""
example = tf.train.Example()
example.ParseFromString(bytestr)
return example |
Writes MNIST image/label vectors into parallelized files on HDFS | def writeMNIST(sc, input_images, input_labels, output, format, num_partitions):
"""Writes MNIST image/label vectors into parallelized files on HDFS"""
# load MNIST gzip into memory
with open(input_images, 'rb') as f:
images = numpy.array(mnist.extract_images(f))
with open(input_labels, 'rb') as f:
if f... |
Reads/verifies previously created output | def readMNIST(sc, output, format):
"""Reads/verifies previously created output"""
output_images = output + "/images"
output_labels = output + "/labels"
imageRDD = None
labelRDD = None
if format == "pickle":
imageRDD = sc.pickleFile(output_images)
labelRDD = sc.pickleFile(output_labels)
elif form... |
Build Inception v3 model architecture.
See here for reference: http://arxiv.org/abs/1512.00567
Args:
images: Images returned from inputs() or distorted_inputs().
num_classes: number of classes
for_training: If set to `True`, build the inference model for training.
Kernels that operate differentl... | def inference(images, num_classes, for_training=False, restore_logits=True,
scope=None):
"""Build Inception v3 model architecture.
See here for reference: http://arxiv.org/abs/1512.00567
Args:
images: Images returned from inputs() or distorted_inputs().
num_classes: number of classes
f... |
Adds all losses for the model.
Note the final loss is not returned. Instead, the list of losses are collected
by slim.losses. The losses are accumulated in tower_loss() and summed to
calculate the total loss.
Args:
logits: List of logits from inference(). Each entry is a 2-D float Tensor.
labels: Labe... | def loss(logits, labels, batch_size=None):
"""Adds all losses for the model.
Note the final loss is not returned. Instead, the list of losses are collected
by slim.losses. The losses are accumulated in tower_loss() and summed to
calculate the total loss.
Args:
logits: List of logits from inference(). Ea... |
Convenience function to create a Tensorflow-compatible absolute HDFS path from relative paths
Args:
:ctx: TFNodeContext containing the metadata specific to this node in the cluster.
:path: path to convert
Returns:
An absolute path prefixed with the correct filesystem scheme. | def hdfs_path(ctx, path):
"""Convenience function to create a Tensorflow-compatible absolute HDFS path from relative paths
Args:
:ctx: TFNodeContext containing the metadata specific to this node in the cluster.
:path: path to convert
Returns:
An absolute path prefixed with the correct filesystem sch... |
Function that wraps the creation of TensorFlow ``tf.train.Server`` for a node in a distributed TensorFlow cluster.
This is intended to be invoked from within the TF ``map_fun``, replacing explicit code to instantiate ``tf.train.ClusterSpec``
and ``tf.train.Server`` objects.
Args:
:ctx: TFNodeContext contain... | def start_cluster_server(ctx, num_gpus=1, rdma=False):
"""Function that wraps the creation of TensorFlow ``tf.train.Server`` for a node in a distributed TensorFlow cluster.
This is intended to be invoked from within the TF ``map_fun``, replacing explicit code to instantiate ``tf.train.ClusterSpec``
and ``tf.trai... |
Convenience function to export a saved_model using provided arguments
The caller specifies the saved_model signatures in a simplified python dictionary form, as follows::
signatures = {
'signature_def_key': {
'inputs': { 'input_tensor_alias': input_tensor_name },
'outputs': { 'output_tenso... | def export_saved_model(sess, export_dir, tag_set, signatures):
"""Convenience function to export a saved_model using provided arguments
The caller specifies the saved_model signatures in a simplified python dictionary form, as follows::
signatures = {
'signature_def_key': {
'inputs': { 'input_te... |
Gets a batch of items from the input RDD.
If multiple tensors are provided per row in the input RDD, e.g. tuple of (tensor1, tensor2, ..., tensorN) and:
* no ``input_mapping`` was provided to the DataFeed constructor, this will return an array of ``batch_size`` tuples,
and the caller is responsible for ... | def next_batch(self, batch_size):
"""Gets a batch of items from the input RDD.
If multiple tensors are provided per row in the input RDD, e.g. tuple of (tensor1, tensor2, ..., tensorN) and:
* no ``input_mapping`` was provided to the DataFeed constructor, this will return an array of ``batch_size`` tuples,... |
Push a batch of output results to the Spark output RDD of ``TFCluster.inference()``.
Note: this currently expects a one-to-one mapping of input to output data, so the length of the ``results`` array should match the length of
the previously retrieved batch of input data.
Args:
:results: array of out... | def batch_results(self, results):
"""Push a batch of output results to the Spark output RDD of ``TFCluster.inference()``.
Note: this currently expects a one-to-one mapping of input to output data, so the length of the ``results`` array should match the length of
the previously retrieved batch of input data... |
Terminate data feeding early.
Since TensorFlow applications can often terminate on conditions unrelated to the training data (e.g. steps, accuracy, etc),
this method signals the data feeding process to ignore any further incoming data. Note that Spark itself does not have a mechanism
to terminate an RDD o... | def terminate(self):
"""Terminate data feeding early.
Since TensorFlow applications can often terminate on conditions unrelated to the training data (e.g. steps, accuracy, etc),
this method signals the data feeding process to ignore any further incoming data. Note that Spark itself does not have a mechani... |
Define/export a single-node TF graph for inferencing | def export_fun(args):
"""Define/export a single-node TF graph for inferencing"""
# Input placeholder for inferencing
x = tf.placeholder(tf.float32, [None, IMAGE_PIXELS * IMAGE_PIXELS], name="x")
# Variables of the hidden layer
hid_w = tf.Variable(tf.truncated_normal([IMAGE_PIXELS * IMAGE_PIXELS, hidden_units... |
Train Inception on a dataset for a number of steps. | def train(target, dataset, cluster_spec, ctx):
"""Train Inception on a dataset for a number of steps."""
# Number of workers and parameter servers are infered from the workers and ps
# hosts string.
num_workers = len(cluster_spec.as_dict()['worker'])
num_parameter_servers = len(cluster_spec.as_dict()['ps'])
... |
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