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sammchardy/python-binance | binance/websockets.py | BinanceSocketManager.close | def close(self):
"""Close all connections
"""
keys = set(self._conns.keys())
for key in keys:
self.stop_socket(key)
self._conns = {} | python | def close(self):
"""Close all connections
"""
keys = set(self._conns.keys())
for key in keys:
self.stop_socket(key)
self._conns = {} | [
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python-visualization/folium | folium/plugins/heat_map_withtime.py | HeatMapWithTime._get_self_bounds | 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]].
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bounds = [[None, None], [None, None]]
for point in self.data:
bounds = [
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"""
Computes the bounds of the object itself (not including it's children)
in the form [[lat_min, lon_min], [lat_max, lon_max]].
"""
bounds = [[None, None], [None, None]]
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python-visualization/folium | folium/vector_layers.py | path_options | def path_options(line=False, radius=False, **kwargs):
"""
Contains options and constants shared between vector overlays
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Parameters
----------
stroke: Bool, True
Whether to draw stroke along the path.
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"""
Contains options and constants shared between vector overlays
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stroke: Bool, True
Whether to draw stroke along the path.
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python-visualization/folium | folium/features.py | Vega.render | def render(self, **kwargs):
"""Renders the HTML representation of the element."""
self.json = json.dumps(self.data)
self._parent.html.add_child(Element(Template("""
<div id="{{this.get_name()}}"></div>
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"""Renders the HTML representation of the element."""
self.json = json.dumps(self.data)
self._parent.html.add_child(Element(Template("""
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python-visualization/folium | folium/features.py | VegaLite.render | def render(self, **kwargs):
"""Renders the HTML representation of the element."""
vegalite_major_version = self._get_vegalite_major_versions(self.data)
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"""Renders the HTML representation of the element."""
vegalite_major_version = self._get_vegalite_major_versions(self.data)
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python-visualization/folium | folium/features.py | GeoJson.process_data | def process_data(self, data):
"""Convert an unknown data input into a geojson dictionary."""
if isinstance(data, dict):
self.embed = True
return data
elif isinstance(data, str):
if data.lower().startswith(('http:', 'ftp:', 'https:')):
if not se... | python | def process_data(self, data):
"""Convert an unknown data input into a geojson dictionary."""
if isinstance(data, dict):
self.embed = True
return data
elif isinstance(data, str):
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python-visualization/folium | folium/features.py | GeoJson.convert_to_feature_collection | def convert_to_feature_collection(self):
"""Convert data into a FeatureCollection if it is not already."""
if self.data['type'] == 'FeatureCollection':
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"""Convert data into a FeatureCollection if it is not already."""
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python-visualization/folium | folium/features.py | GeoJson._validate_function | def _validate_function(self, func, name):
"""
Tests `self.style_function` and `self.highlight_function` to ensure
they are functions returning dictionaries.
"""
test_feature = self.data['features'][0]
if not callable(func) or not isinstance(func(test_feature), dict):
... | python | def _validate_function(self, func, name):
"""
Tests `self.style_function` and `self.highlight_function` to ensure
they are functions returning dictionaries.
"""
test_feature = self.data['features'][0]
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python-visualization/folium | folium/features.py | GeoJson.find_identifier | def find_identifier(self):
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"""Find a unique identifier for each feature, create it if needed."""
features = self.data['features']
n = len(features)
feature = features[0]
if 'id' in feature and len(set(feat['id'] for feat in features)) == n:
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python-visualization/folium | folium/features.py | GeoJsonStyleMapper._create_mapping | def _create_mapping(self, func, switch):
"""Internal function to create the mapping."""
mapping = {}
for feature in self.data['features']:
content = func(feature)
if switch == 'style':
for key, value in content.items():
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"""Internal function to create the mapping."""
mapping = {}
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python-visualization/folium | folium/features.py | GeoJsonStyleMapper.get_feature_id | def get_feature_id(self, feature):
"""Return a value identifying the feature."""
fields = self.feature_identifier.split('.')[1:]
return functools.reduce(operator.getitem, fields, feature) | python | def get_feature_id(self, feature):
"""Return a value identifying the feature."""
fields = self.feature_identifier.split('.')[1:]
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python-visualization/folium | folium/features.py | GeoJsonStyleMapper._to_key | def _to_key(d):
"""Convert dict to str and enable Jinja2 template syntax."""
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python-visualization/folium | folium/features.py | GeoJsonStyleMapper._set_default_key | def _set_default_key(mapping):
"""Replace the field with the most features with a 'default' field."""
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del (mapping[key_longest]) | python | def _set_default_key(mapping):
"""Replace the field with the most features with a 'default' field."""
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python-visualization/folium | folium/features.py | TopoJson.style_data | def style_data(self):
"""Applies self.style_function to each feature of self.data."""
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def recursive_get(data, keys):
if len(keys):
return recursive_get(data.get(keys[0]), keys[1:])
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python-visualization/folium | folium/features.py | TopoJson.get_bounds | def get_bounds(self):
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"""
if not self.embed:
raise ValueError('Cannot compute bounds of non-embedded TopoJSON.')
xmin, xmax, ymin,... | python | def get_bounds(self):
"""
Computes the bounds of the object itself (not including it's children)
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python-visualization/folium | folium/features.py | GeoJsonTooltip.warn_for_geometry_collections | def warn_for_geometry_collections(self):
"""Checks for GeoJson GeometryCollection features to warn user about incompatibility."""
geom_collections = [
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for key, feature in enumerate(self._parent.data['feat... | python | def warn_for_geometry_collections(self):
"""Checks for GeoJson GeometryCollection features to warn user about incompatibility."""
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python-visualization/folium | folium/features.py | GeoJsonTooltip.render | def render(self, **kwargs):
"""Renders the HTML representation of the element."""
if isinstance(self._parent, GeoJson):
keys = tuple(self._parent.data['features'][0]['properties'].keys())
self.warn_for_geometry_collections()
elif isinstance(self._parent, TopoJson):
... | python | def render(self, **kwargs):
"""Renders the HTML representation of the element."""
if isinstance(self._parent, GeoJson):
keys = tuple(self._parent.data['features'][0]['properties'].keys())
self.warn_for_geometry_collections()
elif isinstance(self._parent, TopoJson):
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python-visualization/folium | folium/features.py | Choropleth.render | def render(self, **kwargs):
"""Render the GeoJson/TopoJson and color scale objects."""
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# ColorMap needs Map as its parent
assert isinstance(self._parent, Map), ('Choropleth must be added'
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"""Render the GeoJson/TopoJson and color scale objects."""
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python-visualization/folium | folium/utilities.py | validate_location | def validate_location(location): # noqa: C901
"""Validate a single lat/lon coordinate pair and convert to a list
Validate that location:
* is a sized variable
* with size 2
* allows indexing (i.e. has an ordering)
* where both values are floats (or convertible to float)
* and both values a... | python | def validate_location(location): # noqa: C901
"""Validate a single lat/lon coordinate pair and convert to a list
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* is a sized variable
* with size 2
* allows indexing (i.e. has an ordering)
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python-visualization/folium | folium/utilities.py | validate_locations | def validate_locations(locations):
"""Validate an iterable with multiple lat/lon coordinate pairs.
Returns
-------
list[list[float, float]] or list[list[list[float, float]]]
"""
locations = if_pandas_df_convert_to_numpy(locations)
try:
iter(locations)
except TypeError:
... | python | def validate_locations(locations):
"""Validate an iterable with multiple lat/lon coordinate pairs.
Returns
-------
list[list[float, float]] or list[list[list[float, float]]]
"""
locations = if_pandas_df_convert_to_numpy(locations)
try:
iter(locations)
except TypeError:
... | [
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python-visualization/folium | folium/utilities.py | if_pandas_df_convert_to_numpy | def if_pandas_df_convert_to_numpy(obj):
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Iterating over a DataFrame has weird side effects, such as the first
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"""
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"""Return a Numpy array from a Pandas dataframe.
Iterating over a DataFrame has weird side effects, such as the first
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python-visualization/folium | folium/utilities.py | image_to_url | def image_to_url(image, colormap=None, origin='upper'):
"""
Infers the type of an image argument and transforms it into a URL.
Parameters
----------
image: string, file or array-like object
* If string, it will be written directly in the output file.
* If file, it's content will be ... | python | def image_to_url(image, colormap=None, origin='upper'):
"""
Infers the type of an image argument and transforms it into a URL.
Parameters
----------
image: string, file or array-like object
* If string, it will be written directly in the output file.
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python-visualization/folium | folium/utilities.py | write_png | def write_png(data, origin='upper', colormap=None):
"""
Transform an array of data into a PNG string.
This can be written to disk using binary I/O, or encoded using base64
for an inline PNG like this:
>>> png_str = write_png(array)
>>> "data:image/png;base64,"+png_str.encode('base64')
Insp... | python | def write_png(data, origin='upper', colormap=None):
"""
Transform an array of data into a PNG string.
This can be written to disk using binary I/O, or encoded using base64
for an inline PNG like this:
>>> png_str = write_png(array)
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python-visualization/folium | folium/utilities.py | mercator_transform | def mercator_transform(data, lat_bounds, origin='upper', height_out=None):
"""
Transforms an image computed in (longitude,latitude) coordinates into
the a Mercator projection image.
Parameters
----------
data: numpy array or equivalent list-like object.
Must be NxM (mono), NxMx3 (RGB) ... | python | def mercator_transform(data, lat_bounds, origin='upper', height_out=None):
"""
Transforms an image computed in (longitude,latitude) coordinates into
the a Mercator projection image.
Parameters
----------
data: numpy array or equivalent list-like object.
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python-visualization/folium | folium/utilities.py | iter_coords | def iter_coords(obj):
"""
Returns all the coordinate tuples from a geometry or feature.
"""
if isinstance(obj, (tuple, list)):
coords = obj
elif 'features' in obj:
coords = [geom['geometry']['coordinates'] for geom in obj['features']]
elif 'geometry' in obj:
coords = obj... | python | def iter_coords(obj):
"""
Returns all the coordinate tuples from a geometry or feature.
"""
if isinstance(obj, (tuple, list)):
coords = obj
elif 'features' in obj:
coords = [geom['geometry']['coordinates'] for geom in obj['features']]
elif 'geometry' in obj:
coords = obj... | [
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python-visualization/folium | folium/utilities.py | _locations_mirror | def _locations_mirror(x):
"""
Mirrors the points in a list-of-list-of-...-of-list-of-points.
For example:
>>> _locations_mirror([[[1, 2], [3, 4]], [5, 6], [7, 8]])
[[[2, 1], [4, 3]], [6, 5], [8, 7]]
"""
if hasattr(x, '__iter__'):
if hasattr(x[0], '__iter__'):
return list... | python | def _locations_mirror(x):
"""
Mirrors the points in a list-of-list-of-...-of-list-of-points.
For example:
>>> _locations_mirror([[[1, 2], [3, 4]], [5, 6], [7, 8]])
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"""
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python-visualization/folium | folium/utilities.py | get_bounds | def get_bounds(locations, lonlat=False):
"""
Computes the bounds of the object in the form
[[lat_min, lon_min], [lat_max, lon_max]]
"""
bounds = [[None, None], [None, None]]
for point in iter_coords(locations):
bounds = [
[
none_min(bounds[0][0], point[0]),
... | python | def get_bounds(locations, lonlat=False):
"""
Computes the bounds of the object in the form
[[lat_min, lon_min], [lat_max, lon_max]]
"""
bounds = [[None, None], [None, None]]
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python-visualization/folium | folium/utilities.py | camelize | def camelize(key):
"""Convert a python_style_variable_name to lowerCamelCase.
Examples
--------
>>> camelize('variable_name')
'variableName'
>>> camelize('variableName')
'variableName'
"""
return ''.join(x.capitalize() if i > 0 else x
for i, x in enumerate(key.spl... | python | def camelize(key):
"""Convert a python_style_variable_name to lowerCamelCase.
Examples
--------
>>> camelize('variable_name')
'variableName'
>>> camelize('variableName')
'variableName'
"""
return ''.join(x.capitalize() if i > 0 else x
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>>> camelize('variable_name')
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python-visualization/folium | folium/utilities.py | normalize | def normalize(rendered):
"""Return the input string without non-functional spaces or newlines."""
out = ''.join([line.strip()
for line in rendered.splitlines()
if line.strip()])
out = out.replace(', ', ',')
return out | python | def normalize(rendered):
"""Return the input string without non-functional spaces or newlines."""
out = ''.join([line.strip()
for line in rendered.splitlines()
if line.strip()])
out = out.replace(', ', ',')
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python-visualization/folium | folium/utilities.py | _tmp_html | def _tmp_html(data):
"""Yields the path of a temporary HTML file containing data."""
filepath = ''
try:
fid, filepath = tempfile.mkstemp(suffix='.html', prefix='folium_')
os.write(fid, data.encode('utf8'))
os.close(fid)
yield filepath
finally:
if os.path.isfile(fi... | python | def _tmp_html(data):
"""Yields the path of a temporary HTML file containing data."""
filepath = ''
try:
fid, filepath = tempfile.mkstemp(suffix='.html', prefix='folium_')
os.write(fid, data.encode('utf8'))
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python-visualization/folium | folium/utilities.py | deep_copy | def deep_copy(item_original):
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python-visualization/folium | folium/utilities.py | get_obj_in_upper_tree | def get_obj_in_upper_tree(element, cls):
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python-visualization/folium | folium/utilities.py | parse_options | def parse_options(**kwargs):
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python-visualization/folium | folium/map.py | LayerControl.render | def render(self, **kwargs):
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key = item.layer_name
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python-visualization/folium | folium/folium.py | Map._repr_html_ | def _repr_html_(self, **kwargs):
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python-visualization/folium | folium/folium.py | Map._to_png | def _to_png(self, delay=3):
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Examples
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... | python | def _to_png(self, delay=3):
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python-visualization/folium | folium/folium.py | Map.fit_bounds | 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
----------
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python-visualization/folium | folium/folium.py | Map.choropleth | def choropleth(self, *args, **kwargs):
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"""
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python-visualization/folium | folium/plugins/timestamped_geo_json.py | TimestampedGeoJson._get_self_bounds | 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... | python | def _get_self_bounds(self):
"""
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python-visualization/folium | folium/plugins/dual_map.py | DualMap.add_child | 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)
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index = len(self.m2._children)
self.children_for_m2.append((child, name, index)) | python | def add_child(self, child, name=None, index=None):
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yahoo/TensorFlowOnSpark | tensorflowonspark/TFManager.py | start | def start(authkey, queues, mode='local'):
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/data/process_bounding_boxes.py | ProcessXMLAnnotation | 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()
... | python | 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()
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/dataset.py | Dataset.data_files | def data_files(self):
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Returns:
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yahoo/TensorFlowOnSpark | examples/cifar10/cifar10.py | _activation_summary | 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... | python | 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
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yahoo/TensorFlowOnSpark | examples/cifar10/cifar10.py | _variable_on_cpu | 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... | python | 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
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yahoo/TensorFlowOnSpark | examples/cifar10/cifar10.py | _variable_with_weight_decay | 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
... | python | 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
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yahoo/TensorFlowOnSpark | examples/cifar10/cifar10.py | distorted_inputs | 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... | python | 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
"""
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yahoo/TensorFlowOnSpark | examples/cifar10/cifar10.py | inference | def inference(images):
"""Build the CIFAR-10 model.
Args:
images: Images returned from distorted_inputs() or inputs().
Returns:
Logits.
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# We instantiate all variables using tf.get_variable() instead of
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# If we onl... | python | def inference(images):
"""Build the CIFAR-10 model.
Args:
images: Images returned from distorted_inputs() or inputs().
Returns:
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yahoo/TensorFlowOnSpark | examples/cifar10/cifar10.py | loss | 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.
"""
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Add summary for "Loss" and "Loss/avg".
Args:
logits: Logits from inference().
labels: Labels from distorted_inputs or inputs(). 1-D tensor
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Returns:
Loss tensor of type float.
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yahoo/TensorFlowOnSpark | examples/cifar10/cifar10.py | _add_loss_summaries | def _add_loss_summaries(total_loss):
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Args:
total_loss: Total loss from loss().
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total_loss: Total loss from loss().
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yahoo/TensorFlowOnSpark | examples/cifar10/cifar10.py | train | def train(total_loss, global_step):
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Args:
total_loss: Total loss from loss().
global_step: Integer Variable counting the number of training steps
processed.
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"""Train CIFAR-10 model.
Create an optimizer and apply to all trainable variables. Add moving
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Args:
total_loss: Total loss from loss().
global_step: Integer Variable counting the number of training steps
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/slim/variables.py | add_variable | 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
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"""
collections... | python | def add_variable(var, restore=True):
"""Adds a variable to the MODEL_VARIABLES collection.
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var: a variable.
restore: whether the variable should be added to the
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scope: an optional scope for filtering the variables to return.
suffix: an optional suffix for filtering the variables to return.
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/slim/variables.py | get_unique_variable | 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... | python | 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.
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/slim/variables.py | variable_device | 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 | python | 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)
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/slim/variables.py | global_step | 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... | python | 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.
"""
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/slim/variables.py | variable | def variable(name, shape=None, dtype=tf.float32, initializer=None,
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/inception_eval.py | _eval_once | 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... | python | 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.
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/inception_eval.py | evaluate | 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... | python | 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.
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yahoo/TensorFlowOnSpark | tensorflowonspark/pipeline.py | _run_model | 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
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yahoo/TensorFlowOnSpark | tensorflowonspark/pipeline.py | single_node_env | 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):
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# setup ENV for Had... | python | 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):
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yahoo/TensorFlowOnSpark | tensorflowonspark/pipeline.py | get_meta_graph_def | def get_meta_graph_def(saved_model_dir, tag_set):
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From `saved_model_cli.py <https://github.com/tensorflow/tensorflow/blob/8e0e8d41a3a8f2d4a6100c2ea1dc9d6c6c4ad382/tensorflow/python/tools/saved_model_cli.py#L186>`_
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"""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>`_
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yahoo/TensorFlowOnSpark | tensorflowonspark/pipeline.py | yield_batch | 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... | python | def yield_batch(iterable, batch_size, num_tensors=1):
"""Generator that yields batches of a DataFrame iterator.
Args:
:iterable: Spark partition iterator.
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yahoo/TensorFlowOnSpark | tensorflowonspark/pipeline.py | TFEstimator._fit | def _fit(self, dataset):
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Args:
:dataset: A Spark DataFrame with columns that will be mapped to TensorFlow tensors.
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A TFModel representing the trained ... | python | 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.
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yahoo/TensorFlowOnSpark | tensorflowonspark/pipeline.py | TFModel._transform | def _transform(self, dataset):
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Args:
:dataset: A Spark DataFrame for TensorFlow inferencing.
"""
spark = SparkSession.builder.getOrCreate()
# set a deterministic order for input/output columns (lexicograph... | python | def _transform(self, dataset):
"""Transforms the input DataFrame by applying the _run_model() mapPartitions function.
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yahoo/TensorFlowOnSpark | tensorflowonspark/TFCluster.py | run | def run(sc, map_fun, tf_args, num_executors, num_ps, tensorboard=False, input_mode=InputMode.TENSORFLOW,
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yahoo/TensorFlowOnSpark | tensorflowonspark/TFCluster.py | TFCluster.train | def train(self, dataRDD, num_epochs=0, feed_timeout=600, qname='input'):
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yahoo/TensorFlowOnSpark | tensorflowonspark/TFCluster.py | TFCluster.inference | def inference(self, dataRDD, feed_timeout=600, qname='input'):
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yahoo/TensorFlowOnSpark | tensorflowonspark/TFCluster.py | TFCluster.shutdown | 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()` ... | python | 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.
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/data/build_imagenet_data.py | _int64_feature | 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)) | python | def _int64_feature(value):
"""Wrapper for inserting int64 features into Example proto."""
if not isinstance(value, list):
value = [value]
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/data/build_imagenet_data.py | _float_feature | def _float_feature(value):
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if not isinstance(value, list):
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"""Wrapper for inserting float features into Example proto."""
if not isinstance(value, list):
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/data/build_imagenet_data.py | _convert_to_example | def _convert_to_example(filename, image_buffer, label, synset, human, bbox,
height, width):
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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... | python | def _convert_to_example(filename, image_buffer, label, synset, human, bbox,
height, width):
"""Build an Example proto for an example.
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filename: string, path to an image file, e.g., '/path/to/example.JPG'
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/data/build_imagenet_data.py | _process_image | 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... | python | 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.
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/data/build_imagenet_data.py | _find_human_readable_labels | 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.,
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Returns:
List of human-readab... | python | 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.,
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/data/build_imagenet_data.py | _find_image_bounding_boxes | 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... | python | def _find_image_bounding_boxes(filenames, image_to_bboxes):
"""Find the bounding boxes for a given image file.
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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
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/data/build_imagenet_data.py | _process_dataset | 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 ... | python | 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 ... | [
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/data/build_imagenet_data.py | _build_synset_lookup | 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... | python | 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.
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n02119247 black fox
n021193... | [
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/data/build_imagenet_data.py | _build_bounding_box_lookup | 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... | python | 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.
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yahoo/TensorFlowOnSpark | examples/mnist/estimator/mnist_estimator.py | cnn_model_fn | 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... | python | 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])
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yahoo/TensorFlowOnSpark | examples/cifar10/cifar10_input.py | read_cifar10 | 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
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yahoo/TensorFlowOnSpark | examples/cifar10/cifar10_input.py | _generate_image_and_label_batch | def _generate_image_and_label_batch(image, label, min_queue_examples,
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Args:
image: 3-D Tensor of [height, width, 3] of type.float32.
label: 1-D Tensor of type.int32
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batch_size, shuffle):
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image: 3-D Tensor of [height, width, 3] of type.float32.
label: 1-D Tensor of type.int32
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yahoo/TensorFlowOnSpark | examples/cifar10/cifar10_input.py | distorted_inputs | 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... | python | 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.
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yahoo/TensorFlowOnSpark | examples/cifar10/cifar10_input.py | inputs | 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... | python | 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.
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images: Image... | [
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yahoo/TensorFlowOnSpark | tensorflowonspark/dfutil.py | saveAsTFRecords | 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... | python | 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))
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yahoo/TensorFlowOnSpark | tensorflowonspark/dfutil.py | loadTFRecords | def loadTFRecords(sc, input_dir, binary_features=[]):
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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... | python | def loadTFRecords(sc, input_dir, binary_features=[]):
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yahoo/TensorFlowOnSpark | tensorflowonspark/dfutil.py | toTFExample | 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... | python | 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,
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yahoo/TensorFlowOnSpark | tensorflowonspark/dfutil.py | infer_schema | 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... | python | def infer_schema(example, binary_features=[]):
"""Given a tf.train.Example, infer the Spark DataFrame schema (StructFields).
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yahoo/TensorFlowOnSpark | tensorflowonspark/dfutil.py | fromTFExample | 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
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"""mapPartition function to convert an RDD of serialized tf.train.Example bytestring into an RDD of Row.
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yahoo/TensorFlowOnSpark | examples/wide_deep/census_main.py | build_estimator | 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... | python | 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... | [
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yahoo/TensorFlowOnSpark | examples/wide_deep/census_main.py | run_census | 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... | python | 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)
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/slim/inception_model.py | inception_v3 | 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... | python | 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.
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/slim/inception_model.py | inception_v3_parameters | 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... | python | 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.
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yahoo/TensorFlowOnSpark | tensorflowonspark/util.py | single_node_env | 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 =... | python | 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:
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yahoo/TensorFlowOnSpark | tensorflowonspark/util.py | get_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... | python | 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]
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yahoo/TensorFlowOnSpark | tensorflowonspark/util.py | find_in_path | 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 | python | 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):
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return False | [
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/slim/losses.py | l1_regularizer | 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.... | python | 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):
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"l1_weight... | Define a L1 regularizer.
Args:
weight: scale the loss by this factor.
scope: Optional scope for name_scope.
Returns:
a regularizer function. | [
"Define",
"a",
"L1",
"regularizer",
"."
] | 5e4b6c185ab722fd0104ede0377e1149ea8d6f7c | https://github.com/yahoo/TensorFlowOnSpark/blob/5e4b6c185ab722fd0104ede0377e1149ea8d6f7c/examples/imagenet/inception/slim/losses.py#L37-L53 | train |
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