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train | Booster.get_score | Get feature importance of each feature.
Importance type can be defined as:
* 'weight': the number of times a feature is used to split the data across all trees.
* 'gain': the average gain across all splits the feature is used in.
* 'cover': the average coverage across all splits the fea... | python-package/xgboost/core.py | def get_score(self, fmap='', importance_type='weight'):
"""Get feature importance of each feature.
Importance type can be defined as:
* 'weight': the number of times a feature is used to split the data across all trees.
* 'gain': the average gain across all splits the feature is used in... | def get_score(self, fmap='', importance_type='weight'):
"""Get feature importance of each feature.
Importance type can be defined as:
* 'weight': the number of times a feature is used to split the data across all trees.
* 'gain': the average gain across all splits the feature is used in... | [
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train | Booster.trees_to_dataframe | Parse a boosted tree model text dump into a pandas DataFrame structure.
This feature is only defined when the decision tree model is chosen as base
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Param... | python-package/xgboost/core.py | def trees_to_dataframe(self, fmap=''):
"""Parse a boosted tree model text dump into a pandas DataFrame structure.
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"""Parse a boosted tree model text dump into a pandas DataFrame structure.
This feature is only defined when the decision tree model is chosen as base
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train | Booster._validate_features | Validate Booster and data's feature_names are identical.
Set feature_names and feature_types from DMatrix | python-package/xgboost/core.py | def _validate_features(self, data):
"""
Validate Booster and data's feature_names are identical.
Set feature_names and feature_types from DMatrix
"""
if self.feature_names is None:
self.feature_names = data.feature_names
self.feature_types = data.feature_t... | def _validate_features(self, data):
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Validate Booster and data's feature_names are identical.
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train | Booster.get_split_value_histogram | Get split value histogram of a feature
Parameters
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feature: str
The name of the feature.
fmap: str (optional)
The name of feature map file.
bin: int, default None
The maximum number of bins.
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"""Get split value histogram of a feature
Parameters
----------
feature: str
The name of the feature.
fmap: str (optional)
The name of feature map file.
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feature: str
The name of the feature.
fmap: str (optional)
The name of feature map file.
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train | plot_importance | Plot importance based on fitted trees.
Parameters
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booster : Booster, XGBModel or dict
Booster or XGBModel instance, or dict taken by Booster.get_fscore()
ax : matplotlib Axes, default None
Target axes instance. If None, new figure and axes will be created.
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xlabel='F score', ylabel='Features',
importance_type='weight', max_num_features=None,
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xlim=None, ylim=None, title='Feature importance',
xlabel='F score', ylabel='Features',
importance_type='weight', max_num_features=None,
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train | _parse_node | parse dumped node | python-package/xgboost/plotting.py | def _parse_node(graph, text, condition_node_params, leaf_node_params):
"""parse dumped node"""
match = _NODEPAT.match(text)
if match is not None:
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"""parse dumped node"""
match = _NODEPAT.match(text)
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match = _LEAFPAT.match(text)
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train | _parse_edge | parse dumped edge | python-package/xgboost/plotting.py | def _parse_edge(graph, node, text, yes_color='#0000FF', no_color='#FF0000'):
"""parse dumped edge"""
try:
match = _EDGEPAT.match(text)
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yes, no, missing = match.groups()
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train | to_graphviz | Convert specified tree to graphviz instance. IPython can automatically plot the
returned graphiz instance. Otherwise, you should call .render() method
of the returned graphiz instance.
Parameters
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booster : Booster, XGBModel
Booster or XGBModel instance
fmap: str (optional)
... | python-package/xgboost/plotting.py | def to_graphviz(booster, fmap='', num_trees=0, rankdir='UT',
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train | newAction | Create a new action and assign callbacks, shortcuts, etc. | libs/utils.py | def newAction(parent, text, slot=None, shortcut=None, icon=None,
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a = QAction(text, parent)
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train | natural_sort | Sort the list into natural alphanumeric order. | libs/utils.py | def natural_sort(list, key=lambda s:s):
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train | Canvas.mouseMoveEvent | Update line with last point and current coordinates. | libs/canvas.py | def mouseMoveEvent(self, ev):
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train | Canvas.selectShapePoint | Select the first shape created which contains this point. | libs/canvas.py | def selectShapePoint(self, point):
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train | get_main_app | Standard boilerplate Qt application code.
Do everything but app.exec_() -- so that we can test the application in one thread | labelImg.py | def get_main_app(argv=[]):
"""
Standard boilerplate Qt application code.
Do everything but app.exec_() -- so that we can test the application in one thread
"""
app = QApplication(argv)
app.setApplicationName(__appname__)
app.setWindowIcon(newIcon("app"))
# Tzutalin 201705+: Accept extra ... | def get_main_app(argv=[]):
"""
Standard boilerplate Qt application code.
Do everything but app.exec_() -- so that we can test the application in one thread
"""
app = QApplication(argv)
app.setApplicationName(__appname__)
app.setWindowIcon(newIcon("app"))
# Tzutalin 201705+: Accept extra ... | [
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train | MainWindow.toggleActions | Enable/Disable widgets which depend on an opened image. | labelImg.py | def toggleActions(self, value=True):
"""Enable/Disable widgets which depend on an opened image."""
for z in self.actions.zoomActions:
z.setEnabled(value)
for action in self.actions.onLoadActive:
action.setEnabled(value) | def toggleActions(self, value=True):
"""Enable/Disable widgets which depend on an opened image."""
for z in self.actions.zoomActions:
z.setEnabled(value)
for action in self.actions.onLoadActive:
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train | MainWindow.toggleDrawingSensitive | In the middle of drawing, toggling between modes should be disabled. | labelImg.py | def toggleDrawingSensitive(self, drawing=True):
"""In the middle of drawing, toggling between modes should be disabled."""
self.actions.editMode.setEnabled(not drawing)
if not drawing and self.beginner():
# Cancel creation.
print('Cancel creation.')
self.canva... | def toggleDrawingSensitive(self, drawing=True):
"""In the middle of drawing, toggling between modes should be disabled."""
self.actions.editMode.setEnabled(not drawing)
if not drawing and self.beginner():
# Cancel creation.
print('Cancel creation.')
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train | MainWindow.btnstate | Function to handle difficult examples
Update on each object | labelImg.py | def btnstate(self, item= None):
""" Function to handle difficult examples
Update on each object """
if not self.canvas.editing():
return
item = self.currentItem()
if not item: # If not selected Item, take the first one
item = self.labelList.item(self.labe... | def btnstate(self, item= None):
""" Function to handle difficult examples
Update on each object """
if not self.canvas.editing():
return
item = self.currentItem()
if not item: # If not selected Item, take the first one
item = self.labelList.item(self.labe... | [
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train | MainWindow.newShape | Pop-up and give focus to the label editor.
position MUST be in global coordinates. | labelImg.py | def newShape(self):
"""Pop-up and give focus to the label editor.
position MUST be in global coordinates.
"""
if not self.useDefaultLabelCheckbox.isChecked() or not self.defaultLabelTextLine.text():
if len(self.labelHist) > 0:
self.labelDialog = LabelDialog(
... | def newShape(self):
"""Pop-up and give focus to the label editor.
position MUST be in global coordinates.
"""
if not self.useDefaultLabelCheckbox.isChecked() or not self.defaultLabelTextLine.text():
if len(self.labelHist) > 0:
self.labelDialog = LabelDialog(
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train | MainWindow.loadFile | Load the specified file, or the last opened file if None. | labelImg.py | def loadFile(self, filePath=None):
"""Load the specified file, or the last opened file if None."""
self.resetState()
self.canvas.setEnabled(False)
if filePath is None:
filePath = self.settings.get(SETTING_FILENAME)
# Make sure that filePath is a regular python string... | def loadFile(self, filePath=None):
"""Load the specified file, or the last opened file if None."""
self.resetState()
self.canvas.setEnabled(False)
if filePath is None:
filePath = self.settings.get(SETTING_FILENAME)
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train | MainWindow.scaleFitWindow | Figure out the size of the pixmap in order to fit the main widget. | labelImg.py | def scaleFitWindow(self):
"""Figure out the size of the pixmap in order to fit the main widget."""
e = 2.0 # So that no scrollbars are generated.
w1 = self.centralWidget().width() - e
h1 = self.centralWidget().height() - e
a1 = w1 / h1
# Calculate a new scale value based... | def scaleFitWindow(self):
"""Figure out the size of the pixmap in order to fit the main widget."""
e = 2.0 # So that no scrollbars are generated.
w1 = self.centralWidget().width() - e
h1 = self.centralWidget().height() - e
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train | ustr | py2/py3 unicode helper | libs/ustr.py | def ustr(x):
'''py2/py3 unicode helper'''
if sys.version_info < (3, 0, 0):
from PyQt4.QtCore import QString
if type(x) == str:
return x.decode(DEFAULT_ENCODING)
if type(x) == QString:
#https://blog.csdn.net/friendan/article/details/51088476
#https://b... | def ustr(x):
'''py2/py3 unicode helper'''
if sys.version_info < (3, 0, 0):
from PyQt4.QtCore import QString
if type(x) == str:
return x.decode(DEFAULT_ENCODING)
if type(x) == QString:
#https://blog.csdn.net/friendan/article/details/51088476
#https://b... | [
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train | PascalVocWriter.prettify | Return a pretty-printed XML string for the Element. | libs/pascal_voc_io.py | def prettify(self, elem):
"""
Return a pretty-printed XML string for the Element.
"""
rough_string = ElementTree.tostring(elem, 'utf8')
root = etree.fromstring(rough_string)
return etree.tostring(root, pretty_print=True, encoding=ENCODE_METHOD).replace(" ".encode(), ... | def prettify(self, elem):
"""
Return a pretty-printed XML string for the Element.
"""
rough_string = ElementTree.tostring(elem, 'utf8')
root = etree.fromstring(rough_string)
return etree.tostring(root, pretty_print=True, encoding=ENCODE_METHOD).replace(" ".encode(), ... | [
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train | PascalVocWriter.genXML | Return XML root | libs/pascal_voc_io.py | def genXML(self):
"""
Return XML root
"""
# Check conditions
if self.filename is None or \
self.foldername is None or \
self.imgSize is None:
return None
top = Element('annotation')
if self.verified:
top... | def genXML(self):
"""
Return XML root
"""
# Check conditions
if self.filename is None or \
self.foldername is None or \
self.imgSize is None:
return None
top = Element('annotation')
if self.verified:
top... | [
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train | Exchange.fetch | Perform a HTTP request and return decoded JSON data | python/ccxt/async_support/base/exchange.py | async def fetch(self, url, method='GET', headers=None, body=None):
"""Perform a HTTP request and return decoded JSON data"""
request_headers = self.prepare_request_headers(headers)
url = self.proxy + url
if self.verbose:
print("\nRequest:", method, url, headers, body)
... | async def fetch(self, url, method='GET', headers=None, body=None):
"""Perform a HTTP request and return decoded JSON data"""
request_headers = self.prepare_request_headers(headers)
url = self.proxy + url
if self.verbose:
print("\nRequest:", method, url, headers, body)
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train | Exchange.fetch2 | A better wrapper over request for deferred signing | python/ccxt/base/exchange.py | def fetch2(self, path, api='public', method='GET', params={}, headers=None, body=None):
"""A better wrapper over request for deferred signing"""
if self.enableRateLimit:
self.throttle()
self.lastRestRequestTimestamp = self.milliseconds()
request = self.sign(path, api, method,... | def fetch2(self, path, api='public', method='GET', params={}, headers=None, body=None):
"""A better wrapper over request for deferred signing"""
if self.enableRateLimit:
self.throttle()
self.lastRestRequestTimestamp = self.milliseconds()
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train | Exchange.request | Exchange.request is the entry point for all generated methods | python/ccxt/base/exchange.py | def request(self, path, api='public', method='GET', params={}, headers=None, body=None):
"""Exchange.request is the entry point for all generated methods"""
return self.fetch2(path, api, method, params, headers, body) | def request(self, path, api='public', method='GET', params={}, headers=None, body=None):
"""Exchange.request is the entry point for all generated methods"""
return self.fetch2(path, api, method, params, headers, body) | [
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train | Exchange.find_broadly_matched_key | A helper method for matching error strings exactly vs broadly | python/ccxt/base/exchange.py | def find_broadly_matched_key(self, broad, string):
"""A helper method for matching error strings exactly vs broadly"""
keys = list(broad.keys())
for i in range(0, len(keys)):
key = keys[i]
if string.find(key) >= 0:
return key
return None | def find_broadly_matched_key(self, broad, string):
"""A helper method for matching error strings exactly vs broadly"""
keys = list(broad.keys())
for i in range(0, len(keys)):
key = keys[i]
if string.find(key) >= 0:
return key
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train | Exchange.fetch | Perform a HTTP request and return decoded JSON data | python/ccxt/base/exchange.py | def fetch(self, url, method='GET', headers=None, body=None):
"""Perform a HTTP request and return decoded JSON data"""
request_headers = self.prepare_request_headers(headers)
url = self.proxy + url
if self.verbose:
print("\nRequest:", method, url, request_headers, body)
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"""Perform a HTTP request and return decoded JSON data"""
request_headers = self.prepare_request_headers(headers)
url = self.proxy + url
if self.verbose:
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train | Exchange.safe_either | A helper-wrapper for the safe_value_2() family. | python/ccxt/base/exchange.py | def safe_either(method, dictionary, key1, key2, default_value=None):
"""A helper-wrapper for the safe_value_2() family."""
value = method(dictionary, key1)
return value if value is not None else method(dictionary, key2, default_value) | def safe_either(method, dictionary, key1, key2, default_value=None):
"""A helper-wrapper for the safe_value_2() family."""
value = method(dictionary, key1)
return value if value is not None else method(dictionary, key2, default_value) | [
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train | Exchange.truncate | Deprecated, use decimal_to_precision instead | python/ccxt/base/exchange.py | def truncate(num, precision=0):
"""Deprecated, use decimal_to_precision instead"""
if precision > 0:
decimal_precision = math.pow(10, precision)
return math.trunc(num * decimal_precision) / decimal_precision
return int(Exchange.truncate_to_string(num, precision)) | def truncate(num, precision=0):
"""Deprecated, use decimal_to_precision instead"""
if precision > 0:
decimal_precision = math.pow(10, precision)
return math.trunc(num * decimal_precision) / decimal_precision
return int(Exchange.truncate_to_string(num, precision)) | [
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train | Exchange.truncate_to_string | Deprecated, todo: remove references from subclasses | python/ccxt/base/exchange.py | def truncate_to_string(num, precision=0):
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train | Exchange.check_address | Checks an address is not the same character repeated or an empty sequence | python/ccxt/base/exchange.py | def check_address(self, address):
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train | reduce_filename | r'''
Expects something like /tmp/tmpAjry4Gdsbench/test.weights.e5.XXX.YYY.pb
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r'''
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r'''
Expects something like /tmp/tmpAjry4Gdsbench/test.weights.e5.XXX.YYY.pb
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train | keep_only_digits | r'''
local helper to just keep digits | util/benchmark.py | def keep_only_digits(s):
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r'''
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train | parse_stm_file | r"""
Parses an STM file at ``stm_file`` into a list of :class:`STMSegment`. | util/stm.py | def parse_stm_file(stm_file):
r"""
Parses an STM file at ``stm_file`` into a list of :class:`STMSegment`.
"""
stm_segments = []
with codecs.open(stm_file, encoding="utf-8") as stm_lines:
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if not "ignore_time_... | def parse_stm_file(stm_file):
r"""
Parses an STM file at ``stm_file`` into a list of :class:`STMSegment`.
"""
stm_segments = []
with codecs.open(stm_file, encoding="utf-8") as stm_lines:
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train | read_wave | Reads a .wav file.
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train | write_wave | Writes a .wav file.
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train | frame_generator | Generates audio frames from PCM audio data.
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Yields Frames of the requested duration. | examples/vad_transcriber/wavSplit.py | def frame_generator(frame_duration_ms, audio, sample_rate):
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train | Worker.run | Initialise the runner function with the passed args, kwargs | examples/vad_transcriber/audioTranscript_gui.py | def run(self):
'''
Initialise the runner function with the passed args, kwargs
'''
# Retrieve args/kwargs here; and fire up the processing using them
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Initialise the runner function with the passed args, kwargs
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transcript = self.fn(*self.args, **self.kwargs)
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train | exec_command | r'''
Helper to exec locally (subprocess) or remotely (paramiko) | bin/benchmark_nc.py | def exec_command(command, cwd=None):
r'''
Helper to exec locally (subprocess) or remotely (paramiko)
'''
rc = None
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if ssh_conn is None:
ld_library_path = {'LD_LIBRARY_PATH': '.:%s' % os.environ.get('LD_LIBRARY_PATH', '')}
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Helper to exec locally (subprocess) or remotely (paramiko)
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train | get_arch_string | r'''
Check local or remote system arch, to produce TaskCluster proper link. | bin/benchmark_nc.py | def get_arch_string():
r'''
Check local or remote system arch, to produce TaskCluster proper link.
'''
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if rc > 0:
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r'''
Check local or remote system arch, to produce TaskCluster proper link.
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train | extract_native_client_tarball | r'''
Download a native_client.tar.xz file from TaskCluster and extract it to dir. | bin/benchmark_nc.py | def extract_native_client_tarball(dir):
r'''
Download a native_client.tar.xz file from TaskCluster and extract it to dir.
'''
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target_tarball = os.path.join(dir, 'native_client.tar.xz')
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r'''
Download a native_client.tar.xz file from TaskCluster and extract it to dir.
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train | is_zip_file | r'''
Ensure that a path is a zip file by:
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r'''
Ensure that a path is a zip file by:
- checking length is 1
- checking extension is '.zip'
'''
ext = os.path.splitext(models[0])[1]
return (len(models) == 1) and (ext == '.zip') | def is_zip_file(models):
r'''
Ensure that a path is a zip file by:
- checking length is 1
- checking extension is '.zip'
'''
ext = os.path.splitext(models[0])[1]
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train | maybe_inspect_zip | r'''
Detect if models is a list of protocolbuffer files or a ZIP file.
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r'''
Detect if models is a list of protocolbuffer files or a ZIP file.
If the latter, then unzip it and return the list of protocolbuffer files
that were inside.
'''
if not(is_zip_file(models)):
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if len(models) > 1:
return models
... | def maybe_inspect_zip(models):
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Detect if models is a list of protocolbuffer files or a ZIP file.
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'''
if not(is_zip_file(models)):
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train | all_files | r'''
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r'''
Return a list of full path of files matching 'models', sorted in human
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Files are supposed to be named identically except one variable component
e.g. the list,
test.weights.e5.lstm1200.ldc93s1.pb
... | def all_files(models=[]):
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Return a list of full path of files matching 'models', sorted in human
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train | setup_tempdir | r'''
Copy models, libs and binary to a directory (new one if dir is None) | bin/benchmark_nc.py | def setup_tempdir(dir, models, wav, alphabet, lm_binary, trie, binaries):
r'''
Copy models, libs and binary to a directory (new one if dir is None)
'''
if dir is None:
dir = tempfile.mkdtemp(suffix='dsbench')
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maybe_do... | def setup_tempdir(dir, models, wav, alphabet, lm_binary, trie, binaries):
r'''
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train | teardown_tempdir | r'''
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r'''
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if ssh_conn:
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assert_valid_dir(dir)
shutil.rmtree(dir) | def teardown_tempdir(dir):
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train | get_sshconfig | r'''
Read user's SSH configuration file | bin/benchmark_nc.py | def get_sshconfig():
r'''
Read user's SSH configuration file
'''
with open(os.path.expanduser('~/.ssh/config')) as f:
cfg = paramiko.SSHConfig()
cfg.parse(f)
ret_dict = {}
for d in cfg._config:
_copy = dict(d)
# Avoid buggy behavior with strange h... | def get_sshconfig():
r'''
Read user's SSH configuration file
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with open(os.path.expanduser('~/.ssh/config')) as f:
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cfg.parse(f)
ret_dict = {}
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train | establish_ssh | r'''
Establish a SSH connection to a remote host. It should be able to use
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r'''
Establish a SSH connection to a remote host. It should be able to use
SSH's config file Host name declarations. By default, will not automatically
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... | def establish_ssh(target=None, auto_trust=False, allow_agent=True, look_keys=True):
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train | run_benchmarks | r'''
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assert_valid_dir(dir)
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Core of the running of the benchmarks. We will run on all of models, against
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train | produce_csv | r'''
Take an input dictionnary and write it to the object-file output. | bin/benchmark_nc.py | def produce_csv(input, output):
r'''
Take an input dictionnary and write it to the object-file output.
'''
output.write('"model","mean","std"\n')
for model_data in input:
output.write('"%s",%f,%f\n' % (model_data['name'], model_data['mean'], model_data['stddev']))
output.flush()
outp... | def produce_csv(input, output):
r'''
Take an input dictionnary and write it to the object-file output.
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output.write('"model","mean","std"\n')
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train | to_sparse_tuple | r"""Creates a sparse representention of ``sequence``.
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r"""Creates a sparse representention of ``sequence``.
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indices = np.asarray(list(zip([0]*len(sequence), range(len(sequence)))), dtype=np.int64)
shape = np.asarray([1, len(sequence)], dtype=np.int64)
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train | _parallel_downloader | Generate a function to download a file based on given parameters
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:param voxforge_url: the base voxforge URL
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This works by currying the above given arguments into a closure
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:param voxforge_url: the base voxforge URL
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train | AtomicCounter.increment | Increments the counter by the given amount
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"""Increments the counter by the given amount
:param amount: the amount to increment by (default 1)
:return: the incremented value of the counter
"""
self.__lock.acquire()
self.__count += amount
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self.__lock.acquire()
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train | calculate_report | r'''
This routine will calculate a WER report.
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loss items from the provided WER results tuple (only items with WER!=0 and ordered by their WER). | util/evaluate_tools.py | def calculate_report(labels, decodings, distances, losses):
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This routine will calculate a WER report.
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loss items from the provided WER results tuple (only items with WER!=0 and ordered by their WER).
'... | def calculate_report(labels, decodings, distances, losses):
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This routine will calculate a WER report.
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train | sparse_tensor_value_to_texts | r"""
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train | parse_args | Parse command line parameters
Args:
args ([str]): Command line parameters as list of strings
Returns:
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"""Parse command line parameters
Args:
args ([str]): Command line parameters as list of strings
Returns:
:obj:`argparse.Namespace`: command line parameters namespace
"""
parser = argparse.ArgumentParser(
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... | def parse_args(args):
"""Parse command line parameters
Args:
args ([str]): Command line parameters as list of strings
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:obj:`argparse.Namespace`: command line parameters namespace
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train | setup_logging | Setup basic logging
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level (int): minimum log level for emitting messages | bin/import_gram_vaani.py | def setup_logging(level):
"""Setup basic logging
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level (int): minimum log level for emitting messages
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format = "[%(asctime)s] %(levelname)s:%(name)s:%(message)s"
logging.basicConfig(
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train | main | Main entry point allowing external calls
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args ([str]): command line parameter list
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train | GramVaaniDownloader.download | Downloads the data associated with this instance
Return:
mp3_directory (os.path): The directory into which the associated mp3's were downloaded | bin/import_gram_vaani.py | def download(self):
"""Downloads the data associated with this instance
Return:
mp3_directory (os.path): The directory into which the associated mp3's were downloaded
"""
mp3_directory = self._pre_download()
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mp3_directory (os.path): The directory into which the associated mp3's were downloaded
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mp3_directory = self._pre_download()
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train | GramVaaniConverter.convert | Converts the mp3's associated with this instance to wav's
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wav_directory = self._pre_convert()
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wav_directory = self._pre_convert()
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train | text_to_char_array | r"""
Given a Python string ``original``, remove unsupported characters, map characters
to integers and return a numpy array representing the processed string. | util/text.py | def text_to_char_array(original, alphabet):
r"""
Given a Python string ``original``, remove unsupported characters, map characters
to integers and return a numpy array representing the processed string.
"""
return np.asarray([alphabet.label_from_string(c) for c in original]) | def text_to_char_array(original, alphabet):
r"""
Given a Python string ``original``, remove unsupported characters, map characters
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train | wer_cer_batch | r"""
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In case of the original having more words (N) than the result and both
being totally different (all N words resulting in 1 edit operation each),
the WER will always be 1 (N ... | util/text.py | def wer_cer_batch(originals, results):
r"""
The WER is defined as the editing/Levenshtein distance on word level
divided by the amount of words in the original text.
In case of the original having more words (N) than the result and both
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r"""
The WER is defined as the editing/Levenshtein distance on word level
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train | variable_on_cpu | r"""
Next we concern ourselves with graph creation.
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r"""
Next we concern ourselves with graph creation.
However, before we do so we must introduce a utility function ``variable_on_cpu()``
used to create a variable in CPU memory.
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Next we concern ourselves with graph creation.
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train | calculate_mean_edit_distance_and_loss | r'''
This routine beam search decodes a mini-batch and calculates the loss and mean edit distance.
Next to total and average loss it returns the mean edit distance,
the decoded result and the batch's original Y. | DeepSpeech.py | def calculate_mean_edit_distance_and_loss(iterator, dropout, reuse):
r'''
This routine beam search decodes a mini-batch and calculates the loss and mean edit distance.
Next to total and average loss it returns the mean edit distance,
the decoded result and the batch's original Y.
'''
# Obtain th... | def calculate_mean_edit_distance_and_loss(iterator, dropout, reuse):
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This routine beam search decodes a mini-batch and calculates the loss and mean edit distance.
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train | get_tower_results | r'''
With this preliminary step out of the way, we can for each GPU introduce a
tower for which's batch we calculate and return the optimization gradients
and the average loss across towers. | DeepSpeech.py | def get_tower_results(iterator, optimizer, dropout_rates):
r'''
With this preliminary step out of the way, we can for each GPU introduce a
tower for which's batch we calculate and return the optimization gradients
and the average loss across towers.
'''
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... | def get_tower_results(iterator, optimizer, dropout_rates):
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With this preliminary step out of the way, we can for each GPU introduce a
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train | average_gradients | r'''
A routine for computing each variable's average of the gradients obtained from the GPUs.
Note also that this code acts as a synchronization point as it requires all
GPUs to be finished with their mini-batch before it can run to completion. | DeepSpeech.py | def average_gradients(tower_gradients):
r'''
A routine for computing each variable's average of the gradients obtained from the GPUs.
Note also that this code acts as a synchronization point as it requires all
GPUs to be finished with their mini-batch before it can run to completion.
'''
# List ... | def average_gradients(tower_gradients):
r'''
A routine for computing each variable's average of the gradients obtained from the GPUs.
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train | log_variable | r'''
We introduce a function for logging a tensor variable's current state.
It logs scalar values for the mean, standard deviation, minimum and maximum.
Furthermore it logs a histogram of its state and (if given) of an optimization gradient. | DeepSpeech.py | def log_variable(variable, gradient=None):
r'''
We introduce a function for logging a tensor variable's current state.
It logs scalar values for the mean, standard deviation, minimum and maximum.
Furthermore it logs a histogram of its state and (if given) of an optimization gradient.
'''
name = ... | def log_variable(variable, gradient=None):
r'''
We introduce a function for logging a tensor variable's current state.
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train | export | r'''
Restores the trained variables into a simpler graph that will be exported for serving. | DeepSpeech.py | def export():
r'''
Restores the trained variables into a simpler graph that will be exported for serving.
'''
log_info('Exporting the model...')
from tensorflow.python.framework.ops import Tensor, Operation
inputs, outputs, _ = create_inference_graph(batch_size=FLAGS.export_batch_size, n_steps=... | def export():
r'''
Restores the trained variables into a simpler graph that will be exported for serving.
'''
log_info('Exporting the model...')
from tensorflow.python.framework.ops import Tensor, Operation
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train | ctc_beam_search_decoder | Wrapper for the CTC Beam Search Decoder.
:param probs_seq: 2-D list of probability distributions over each time
step, with each element being a list of normalized
probabilities over alphabet and blank.
:type probs_seq: 2-D list
:param alphabet: alphabet list.
... | native_client/ctcdecode/__init__.py | def ctc_beam_search_decoder(probs_seq,
alphabet,
beam_size,
cutoff_prob=1.0,
cutoff_top_n=40,
scorer=None):
"""Wrapper for the CTC Beam Search Decoder.
:param probs_seq: 2... | def ctc_beam_search_decoder(probs_seq,
alphabet,
beam_size,
cutoff_prob=1.0,
cutoff_top_n=40,
scorer=None):
"""Wrapper for the CTC Beam Search Decoder.
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train | ctc_beam_search_decoder_batch | Wrapper for the batched CTC beam search decoder.
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:type probs_seq: 3-D list
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train | Audio.resample | Microphone may not support our native processing sampling rate, so
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deepspeech
Args:
data (binary): Input audio stream
input_rate (int): Input audio rate to resample from | examples/mic_vad_streaming/mic_vad_streaming.py | def resample(self, data, input_rate):
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Args:
data (binary): Input audio stream
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train | Audio.read_resampled | Return a block of audio data resampled to 16000hz, blocking if necessary. | examples/mic_vad_streaming/mic_vad_streaming.py | def read_resampled(self):
"""Return a block of audio data resampled to 16000hz, blocking if necessary."""
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train | VADAudio.frame_generator | Generator that yields all audio frames from microphone. | examples/mic_vad_streaming/mic_vad_streaming.py | def frame_generator(self):
"""Generator that yields all audio frames from microphone."""
if self.input_rate == self.RATE_PROCESS:
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train | VADAudio.vad_collector | Generator that yields series of consecutive audio frames comprising each utterence, separated by yielding a single None.
Determines voice activity by ratio of frames in padding_ms. Uses a buffer to include padding_ms prior to being triggered.
Example: (frame, ..., frame, None, frame, ..., frame,... | examples/mic_vad_streaming/mic_vad_streaming.py | def vad_collector(self, padding_ms=300, ratio=0.75, frames=None):
"""Generator that yields series of consecutive audio frames comprising each utterence, separated by yielding a single None.
Determines voice activity by ratio of frames in padding_ms. Uses a buffer to include padding_ms prior to being... | def vad_collector(self, padding_ms=300, ratio=0.75, frames=None):
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train | cut | Global `cut` function that supports parallel processing.
Note that this only works using dt, custom POSTokenizer
instances are not supported. | jieba/posseg/__init__.py | def cut(sentence, HMM=True):
"""
Global `cut` function that supports parallel processing.
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"""
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Global `cut` function that supports parallel processing.
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"""
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train | enable_parallel | Change the module's `cut` and `cut_for_search` functions to the
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Note that this only works using dt, custom Tokenizer
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"""
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Note that this only works using dt, custom Tokenizer
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"""
global pool, dt, cut, cut_for_search
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"""
global pool, dt, cut, cut_for_search
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train | Tokenizer.cut | The main function that segments an entire sentence that contains
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Parameter:
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- HMM: Whether to use the Hidd... | jieba/__init__.py | def cut(self, sentence, cut_all=False, HMM=True):
'''
The main function that segments an entire sentence that contains
Chinese characters into separated words.
Parameter:
- sentence: The str(unicode) to be segmented.
- cut_all: Model type. True for full pattern, ... | def cut(self, sentence, cut_all=False, HMM=True):
'''
The main function that segments an entire sentence that contains
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train | Tokenizer.cut_for_search | Finer segmentation for search engines. | jieba/__init__.py | def cut_for_search(self, sentence, HMM=True):
"""
Finer segmentation for search engines.
"""
words = self.cut(sentence, HMM=HMM)
for w in words:
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gram2 = w[i:i + 2]
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"""
Finer segmentation for search engines.
"""
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for w in words:
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train | Tokenizer.load_userdict | Load personalized dict to improve detect rate.
Parameter:
- f : A plain text file contains words and their ocurrences.
Can be a file-like object, or the path of the dictionary file,
whose encoding must be utf-8.
Structure of dict file:
word1 freq... | jieba/__init__.py | def load_userdict(self, f):
'''
Load personalized dict to improve detect rate.
Parameter:
- f : A plain text file contains words and their ocurrences.
Can be a file-like object, or the path of the dictionary file,
whose encoding must be utf-8.
... | def load_userdict(self, f):
'''
Load personalized dict to improve detect rate.
Parameter:
- f : A plain text file contains words and their ocurrences.
Can be a file-like object, or the path of the dictionary file,
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train | Tokenizer.add_word | Add a word to dictionary.
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train | Tokenizer.suggest_freq | Suggest word frequency to force the characters in a word to be
joined or splitted.
Parameter:
- segment : The segments that the word is expected to be cut into,
If the word should be treated as a whole, use a str.
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"""
Suggest word frequency to force the characters in a word to be
joined or splitted.
Parameter:
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If the word should be treated as a whole,... | def suggest_freq(self, segment, tune=False):
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Suggest word frequency to force the characters in a word to be
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train | Tokenizer.tokenize | Tokenize a sentence and yields tuples of (word, start, end)
Parameter:
- sentence: the str(unicode) to be segmented.
- mode: "default" or "search", "search" is for finer segmentation.
- HMM: whether to use the Hidden Markov Model. | jieba/__init__.py | def tokenize(self, unicode_sentence, mode="default", HMM=True):
"""
Tokenize a sentence and yields tuples of (word, start, end)
Parameter:
- sentence: the str(unicode) to be segmented.
- mode: "default" or "search", "search" is for finer segmentation.
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"""
Tokenize a sentence and yields tuples of (word, start, end)
Parameter:
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train | TextRank.textrank | Extract keywords from sentence using TextRank algorithm.
Parameter:
- topK: return how many top keywords. `None` for all possible words.
- withWeight: if True, return a list of (word, weight);
if False, return a list of words.
- allowPOS: the allowed... | jieba/analyse/textrank.py | def textrank(self, sentence, topK=20, withWeight=False, allowPOS=('ns', 'n', 'vn', 'v'), withFlag=False):
"""
Extract keywords from sentence using TextRank algorithm.
Parameter:
- topK: return how many top keywords. `None` for all possible words.
- withWeight: if True, re... | def textrank(self, sentence, topK=20, withWeight=False, allowPOS=('ns', 'n', 'vn', 'v'), withFlag=False):
"""
Extract keywords from sentence using TextRank algorithm.
Parameter:
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train | TFIDF.extract_tags | Extract keywords from sentence using TF-IDF algorithm.
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Extract keywords from sentence using TF-IDF algorithm.
Parameter:
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Extract keywords from sentence using TF-IDF algorithm.
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train | paracrawl_v3_pairs | Generates raw (English, other) pairs from a ParaCrawl V3.0 data file.
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paracrawl_file: A ParaCrawl V3.0 en-.. data file.
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creating a translation p... | tensor2tensor/data_generators/cleaner_en_xx.py | def paracrawl_v3_pairs(paracrawl_file):
"""Generates raw (English, other) pairs from a ParaCrawl V3.0 data file.
Args:
paracrawl_file: A ParaCrawl V3.0 en-.. data file.
Yields:
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Args:
paracrawl_file: A ParaCrawl V3.0 en-.. data file.
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train | _raw_sentences | Generates Unicode strings, one for each <seg> in a ParaCrawl data file.
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paracrawl_file: A ParaCrawl V3.0 en-.. data file.
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paracrawl_file: A ParaCrawl V3.0 en-.. data file.
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train | clean_en_xx_pairs | Generates a cleaned-up stream of (English, other) translation pairs.
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minimal assumptions on the non-English pair member: (1) All filtering is
done based on the English member of the pair, and (2) sentence splitting
assumes only that sente... | tensor2tensor/data_generators/cleaner_en_xx.py | def clean_en_xx_pairs(en_xx_pairs):
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Cleaning includes both filtering and simplistic sentence splitting, with
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train | _get_case_file_paths | Obtain a list of image paths corresponding to training or eval case.
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tmp_dir: str, the root path to which raw images were written, at the
top level having meta/ and raw/ subdirs.
case: bool, whether obtaining file paths for training (true) or eval
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training_fraction: float, the ... | tensor2tensor/data_generators/allen_brain.py | def _get_case_file_paths(tmp_dir, case, training_fraction=0.95):
"""Obtain a list of image paths corresponding to training or eval case.
Args:
tmp_dir: str, the root path to which raw images were written, at the
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case: bool, whether obtaining file paths for tra... | def _get_case_file_paths(tmp_dir, case, training_fraction=0.95):
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train | maybe_download_image_dataset | Download a set of images from api.brain-map.org to `target_dir`.
Args:
image_ids: list, a list of image ids.
target_dir: str, a directory to which to download the images. | tensor2tensor/data_generators/allen_brain.py | def maybe_download_image_dataset(image_ids, target_dir):
"""Download a set of images from api.brain-map.org to `target_dir`.
Args:
image_ids: list, a list of image ids.
target_dir: str, a directory to which to download the images.
"""
tf.gfile.MakeDirs(target_dir)
num_images = len(image_ids)
for... | def maybe_download_image_dataset(image_ids, target_dir):
"""Download a set of images from api.brain-map.org to `target_dir`.
Args:
image_ids: list, a list of image ids.
target_dir: str, a directory to which to download the images.
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train | random_square_mask | Create a numpy array with specified shape and masked fraction.
Args:
shape: tuple, shape of the mask to create.
fraction: float, fraction of the mask area to populate with `mask_scalar`.
Returns:
numpy.array: A numpy array storing the mask. | tensor2tensor/data_generators/allen_brain.py | def random_square_mask(shape, fraction):
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Args:
shape: tuple, shape of the mask to create.
fraction: float, fraction of the mask area to populate with `mask_scalar`.
Returns:
numpy.array: A numpy array storing the mask.
"""
mask =... | def random_square_mask(shape, fraction):
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Args:
shape: tuple, shape of the mask to create.
fraction: float, fraction of the mask area to populate with `mask_scalar`.
Returns:
numpy.array: A numpy array storing the mask.
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train | _generator | Base problem example generator for Allen Brain Atlas problems.
Args:
tmp_dir: str, a directory where raw example input data has been stored.
training: bool, whether the mode of operation is training (or,
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prefixed with train or d... | tensor2tensor/data_generators/allen_brain.py | def _generator(tmp_dir, training, size=_BASE_EXAMPLE_IMAGE_SIZE,
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"""Base problem example generator for Allen Brain Atlas problems.
Args:
tmp_dir: str, a directory where raw example input data has been stored.
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train | transformer_moe_base | Set of hyperparameters. | tensor2tensor/models/research/transformer_moe.py | def transformer_moe_base():
"""Set of hyperparameters."""
hparams = common_hparams.basic_params1()
hparams.norm_type = "layer"
hparams.hidden_size = 512
hparams.batch_size = 4096
hparams.max_length = 2001
hparams.max_input_seq_length = 2000
hparams.max_target_seq_length = 2000
hparams.dropout = 0.0
... | def transformer_moe_base():
"""Set of hyperparameters."""
hparams = common_hparams.basic_params1()
hparams.norm_type = "layer"
hparams.hidden_size = 512
hparams.batch_size = 4096
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train | transformer_moe_8k | Hyper parameters specifics for long sequence generation. | tensor2tensor/models/research/transformer_moe.py | def transformer_moe_8k():
"""Hyper parameters specifics for long sequence generation."""
hparams = transformer_moe_base()
hparams.batch_size = 8192
hparams.max_length = 0 # max_length == batch_size
hparams.eval_drop_long_sequences = True
hparams.min_length_bucket = 256 # Avoid cyclic problems for big bat... | def transformer_moe_8k():
"""Hyper parameters specifics for long sequence generation."""
hparams = transformer_moe_base()
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train | transformer_moe_2k | Base transformers model with moe.
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* No encoder.
* Layer 0: a - sep (self-attention - unmasked separable convolutions)
* Layer 1: a - sep
* Layer 2: a - sep
* Layer 3: a - sep
* Layer 4: a - sep
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"""Base transformers model with moe.
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* No encoder.
* Layer 0: a - sep (self-attention - unmasked separable convolutions)
* Layer 1: a - sep
* Layer 2: a - sep
* Layer 3: a - sep
* Layer 4: a - sep
* Decoder architecture:
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"""Base transformers model with moe.
Will have the following architecture:
* No encoder.
* Layer 0: a - sep (self-attention - unmasked separable convolutions)
* Layer 1: a - sep
* Layer 2: a - sep
* Layer 3: a - sep
* Layer 4: a - sep
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*... | [
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train | transformer_moe_prepend_8k | Model which formulate a seq2seq problem as language modeling. | tensor2tensor/models/research/transformer_moe.py | def transformer_moe_prepend_8k():
"""Model which formulate a seq2seq problem as language modeling."""
hparams = transformer_moe_8k()
hparams.prepend_mode = "prepend_inputs_masked_attention"
hparams.eval_drop_long_sequences = False
hparams.max_input_seq_length = 7500
hparams.default_ff = "sepm"
hparams.lay... | def transformer_moe_prepend_8k():
"""Model which formulate a seq2seq problem as language modeling."""
hparams = transformer_moe_8k()
hparams.prepend_mode = "prepend_inputs_masked_attention"
hparams.eval_drop_long_sequences = False
hparams.max_input_seq_length = 7500
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train | f | Applies residual function for RevNet.
Args:
x: input tensor
depth1: Number of output channels for the first and second conv layers.
depth2: Number of output channels for the third conv layer.
dim: '2d' if 2-dimensional, '3d' if 3-dimensional.
first_batch_norm: Whether to keep the first batch norm... | tensor2tensor/models/revnet.py | def f(x, depth1, depth2, dim='2d', first_batch_norm=True, stride=1,
training=True, bottleneck=True, padding='SAME'):
"""Applies residual function for RevNet.
Args:
x: input tensor
depth1: Number of output channels for the first and second conv layers.
depth2: Number of output channels for the thi... | def f(x, depth1, depth2, dim='2d', first_batch_norm=True, stride=1,
training=True, bottleneck=True, padding='SAME'):
"""Applies residual function for RevNet.
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x: input tensor
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] | tensorflow/tensor2tensor | python | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/revnet.py#L72-L122 | [
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train | downsample_bottleneck | Downsamples 'x' by `stride` using a 1x1 convolution filter.
Args:
x: input tensor of size [N, H, W, C]
output_channels: Desired number of output channels.
dim: '2d' if 2-dimensional, '3d' if 3-dimensional.
stride: What stride to use. Usually 1 or 2.
scope: Optional variable scope.
Returns:
... | tensor2tensor/models/revnet.py | def downsample_bottleneck(x, output_channels, dim='2d', stride=1, scope='h'):
"""Downsamples 'x' by `stride` using a 1x1 convolution filter.
Args:
x: input tensor of size [N, H, W, C]
output_channels: Desired number of output channels.
dim: '2d' if 2-dimensional, '3d' if 3-dimensional.
stride: What... | def downsample_bottleneck(x, output_channels, dim='2d', stride=1, scope='h'):
"""Downsamples 'x' by `stride` using a 1x1 convolution filter.
Args:
x: input tensor of size [N, H, W, C]
output_channels: Desired number of output channels.
dim: '2d' if 2-dimensional, '3d' if 3-dimensional.
stride: What... | [
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] | tensorflow/tensor2tensor | python | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/revnet.py#L125-L144 | [
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"... | 272500b6efe353aeb638d2745ed56e519462ca31 |
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