id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 75 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
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21,600 | tzutalin/labelImg | libs/utils.py | natural_sort | def natural_sort(list, key=lambda s:s):
"""
Sort the list into natural alphanumeric order.
"""
def get_alphanum_key_func(key):
convert = lambda text: int(text) if text.isdigit() else text
return lambda s: [convert(c) for c in re.split('([0-9]+)', key(s))]
sort_key = get_alphanum_key_... | python | def natural_sort(list, key=lambda s:s):
"""
Sort the list into natural alphanumeric order.
"""
def get_alphanum_key_func(key):
convert = lambda text: int(text) if text.isdigit() else text
return lambda s: [convert(c) for c in re.split('([0-9]+)', key(s))]
sort_key = get_alphanum_key_... | [
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21,601 | tzutalin/labelImg | libs/canvas.py | Canvas.selectShapePoint | def selectShapePoint(self, point):
"""Select the first shape created which contains this point."""
self.deSelectShape()
if self.selectedVertex(): # A vertex is marked for selection.
index, shape = self.hVertex, self.hShape
shape.highlightVertex(index, shape.MOVE_VERTEX)
... | python | def selectShapePoint(self, point):
"""Select the first shape created which contains this point."""
self.deSelectShape()
if self.selectedVertex(): # A vertex is marked for selection.
index, shape = self.hVertex, self.hShape
shape.highlightVertex(index, shape.MOVE_VERTEX)
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21,602 | tzutalin/labelImg | labelImg.py | MainWindow.toggleDrawingSensitive | 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... | python | 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... | [
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21,603 | tzutalin/labelImg | labelImg.py | MainWindow.btnstate | 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... | python | 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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21,604 | tzutalin/labelImg | labelImg.py | MainWindow.newShape | 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(
... | python | 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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21,605 | tzutalin/labelImg | labelImg.py | MainWindow.scaleFitWindow | 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... | python | 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... | [
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21,606 | tzutalin/labelImg | libs/pascal_voc_io.py | PascalVocWriter.genXML | 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... | python | 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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21,607 | ccxt/ccxt | python/ccxt/base/exchange.py | Exchange.fetch2 | 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,... | python | 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,... | [
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21,608 | ccxt/ccxt | python/ccxt/base/exchange.py | Exchange.request | 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) | python | 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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21,609 | ccxt/ccxt | python/ccxt/base/exchange.py | Exchange.find_broadly_matched_key | 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 | python | 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 | [
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21,610 | ccxt/ccxt | python/ccxt/base/exchange.py | Exchange.truncate | 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)) | python | 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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21,611 | ccxt/ccxt | python/ccxt/base/exchange.py | Exchange.check_address | def check_address(self, address):
"""Checks an address is not the same character repeated or an empty sequence"""
if address is None:
self.raise_error(InvalidAddress, details='address is None')
if all(letter == address[0] for letter in address) or len(address) < self.minFundingAddres... | python | def check_address(self, address):
"""Checks an address is not the same character repeated or an empty sequence"""
if address is None:
self.raise_error(InvalidAddress, details='address is None')
if all(letter == address[0] for letter in address) or len(address) < self.minFundingAddres... | [
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21,612 | mozilla/DeepSpeech | util/benchmark.py | keep_only_digits | def keep_only_digits(s):
r'''
local helper to just keep digits
'''
fs = ''
for c in s:
if c.isdigit():
fs += c
return int(fs) | python | def keep_only_digits(s):
r'''
local helper to just keep digits
'''
fs = ''
for c in s:
if c.isdigit():
fs += c
return int(fs) | [
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21,613 | mozilla/DeepSpeech | examples/vad_transcriber/wavSplit.py | read_wave | def read_wave(path):
"""Reads a .wav file.
Takes the path, and returns (PCM audio data, sample rate).
"""
with contextlib.closing(wave.open(path, 'rb')) as wf:
num_channels = wf.getnchannels()
assert num_channels == 1
sample_width = wf.getsampwidth()
assert sample_width ... | python | def read_wave(path):
"""Reads a .wav file.
Takes the path, and returns (PCM audio data, sample rate).
"""
with contextlib.closing(wave.open(path, 'rb')) as wf:
num_channels = wf.getnchannels()
assert num_channels == 1
sample_width = wf.getsampwidth()
assert sample_width ... | [
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21,614 | mozilla/DeepSpeech | examples/vad_transcriber/wavSplit.py | write_wave | def write_wave(path, audio, sample_rate):
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"""
with contextlib.closing(wave.open(path, 'wb')) as wf:
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wf.writeframes(audio) | python | def write_wave(path, audio, sample_rate):
"""Writes a .wav file.
Takes path, PCM audio data, and sample rate.
"""
with contextlib.closing(wave.open(path, 'wb')) as wf:
wf.setnchannels(1)
wf.setsampwidth(2)
wf.setframerate(sample_rate)
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21,615 | mozilla/DeepSpeech | examples/vad_transcriber/wavSplit.py | frame_generator | def frame_generator(frame_duration_ms, audio, sample_rate):
"""Generates audio frames from PCM audio data.
Takes the desired frame duration in milliseconds, the PCM data, and
the sample rate.
Yields Frames of the requested duration.
"""
n = int(sample_rate * (frame_duration_ms / 1000.0) * 2)
... | python | def frame_generator(frame_duration_ms, audio, sample_rate):
"""Generates audio frames from PCM audio data.
Takes the desired frame duration in milliseconds, the PCM data, and
the sample rate.
Yields Frames of the requested duration.
"""
n = int(sample_rate * (frame_duration_ms / 1000.0) * 2)
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21,616 | mozilla/DeepSpeech | examples/vad_transcriber/audioTranscript_gui.py | Worker.run | def run(self):
'''
Initialise the runner function with the passed args, kwargs
'''
# Retrieve args/kwargs here; and fire up the processing using them
try:
transcript = self.fn(*self.args, **self.kwargs)
except:
traceback.print_exc()
ex... | python | def run(self):
'''
Initialise the runner function with the passed args, kwargs
'''
# Retrieve args/kwargs here; and fire up the processing using them
try:
transcript = self.fn(*self.args, **self.kwargs)
except:
traceback.print_exc()
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21,617 | mozilla/DeepSpeech | bin/benchmark_nc.py | get_arch_string | def get_arch_string():
r'''
Check local or remote system arch, to produce TaskCluster proper link.
'''
rc, stdout, stderr = exec_command('uname -sm')
if rc > 0:
raise AssertionError('Error checking OS')
stdout = stdout.lower().strip()
if not 'linux' in stdout:
raise Assertio... | python | def get_arch_string():
r'''
Check local or remote system arch, to produce TaskCluster proper link.
'''
rc, stdout, stderr = exec_command('uname -sm')
if rc > 0:
raise AssertionError('Error checking OS')
stdout = stdout.lower().strip()
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21,618 | mozilla/DeepSpeech | bin/benchmark_nc.py | extract_native_client_tarball | def extract_native_client_tarball(dir):
r'''
Download a native_client.tar.xz file from TaskCluster and extract it to dir.
'''
assert_valid_dir(dir)
target_tarball = os.path.join(dir, 'native_client.tar.xz')
if os.path.isfile(target_tarball) and os.stat(target_tarball).st_size == 0:
retu... | python | def extract_native_client_tarball(dir):
r'''
Download a native_client.tar.xz file from TaskCluster and extract it to dir.
'''
assert_valid_dir(dir)
target_tarball = os.path.join(dir, 'native_client.tar.xz')
if os.path.isfile(target_tarball) and os.stat(target_tarball).st_size == 0:
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21,619 | mozilla/DeepSpeech | bin/benchmark_nc.py | maybe_inspect_zip | def maybe_inspect_zip(models):
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)):
return models
if len(models) > 1:
return models
... | python | def maybe_inspect_zip(models):
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)):
return models
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return models
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21,620 | mozilla/DeepSpeech | bin/benchmark_nc.py | teardown_tempdir | def teardown_tempdir(dir):
r'''
Cleanup temporary directory.
'''
if ssh_conn:
delete_tree(dir)
assert_valid_dir(dir)
shutil.rmtree(dir) | python | def teardown_tempdir(dir):
r'''
Cleanup temporary directory.
'''
if ssh_conn:
delete_tree(dir)
assert_valid_dir(dir)
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21,621 | mozilla/DeepSpeech | bin/benchmark_nc.py | get_sshconfig | 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... | python | 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)
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21,622 | mozilla/DeepSpeech | bin/benchmark_nc.py | establish_ssh | def establish_ssh(target=None, auto_trust=False, allow_agent=True, look_keys=True):
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
add trust for hosts, will use SSH agent and will try to load keys.
... | python | def establish_ssh(target=None, auto_trust=False, allow_agent=True, look_keys=True):
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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21,623 | mozilla/DeepSpeech | bin/benchmark_nc.py | run_benchmarks | def run_benchmarks(dir, models, wav, alphabet, lm_binary=None, trie=None, iters=-1):
r'''
Core of the running of the benchmarks. We will run on all of models, against
the WAV file provided as wav, and the provided alphabet.
'''
assert_valid_dir(dir)
inference_times = [ ]
for model in mode... | python | def run_benchmarks(dir, models, wav, alphabet, lm_binary=None, trie=None, iters=-1):
r'''
Core of the running of the benchmarks. We will run on all of models, against
the WAV file provided as wav, and the provided alphabet.
'''
assert_valid_dir(dir)
inference_times = [ ]
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21,624 | mozilla/DeepSpeech | bin/benchmark_nc.py | produce_csv | 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... | python | 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()
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21,625 | mozilla/DeepSpeech | bin/import_voxforge.py | _parallel_downloader | def _parallel_downloader(voxforge_url, archive_dir, total, counter):
"""Generate a function to download a file based on given parameters
This works by currying the above given arguments into a closure
in the form of the following function.
:param voxforge_url: the base voxforge URL
:param archive_d... | python | def _parallel_downloader(voxforge_url, archive_dir, total, counter):
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21,626 | mozilla/DeepSpeech | bin/import_voxforge.py | _parallel_extracter | def _parallel_extracter(data_dir, number_of_test, number_of_dev, total, counter):
"""Generate a function to extract a tar file based on given parameters
This works by currying the above given arguments into a closure
in the form of the following function.
:param data_dir: the target directory to ... | python | def _parallel_extracter(data_dir, number_of_test, number_of_dev, total, counter):
"""Generate a function to extract a tar file based on given parameters
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21,627 | mozilla/DeepSpeech | util/text.py | text_to_char_array | 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]) | python | def text_to_char_array(original, alphabet):
r"""
Given a Python string ``original``, remove unsupported characters, map characters
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"""
return np.asarray([alphabet.label_from_string(c) for c in original]) | [
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21,628 | mozilla/DeepSpeech | DeepSpeech.py | calculate_mean_edit_distance_and_loss | 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... | python | 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.
'''
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21,629 | mozilla/DeepSpeech | DeepSpeech.py | get_tower_results | 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.
'''
# To calculate the mean of the losses
... | python | def get_tower_results(iterator, optimizer, dropout_rates):
r'''
With this preliminary step out of the way, we can for each GPU introduce a
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21,630 | mozilla/DeepSpeech | DeepSpeech.py | average_gradients | 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 ... | python | 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.
'''
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21,631 | mozilla/DeepSpeech | native_client/ctcdecode/__init__.py | ctc_beam_search_decoder | 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... | python | 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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21,632 | mozilla/DeepSpeech | native_client/ctcdecode/__init__.py | ctc_beam_search_decoder_batch | def ctc_beam_search_decoder_batch(probs_seq,
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beam_size,
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cutoff_prob=1.0,
cutof... | [
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21,633 | mozilla/DeepSpeech | examples/mic_vad_streaming/mic_vad_streaming.py | Audio.resample | def resample(self, data, input_rate):
"""
Microphone may not support our native processing sampling rate, so
resample from input_rate to RATE_PROCESS here for webrtcvad and
deepspeech
Args:
data (binary): Input audio stream
input_rate (int): Input audio r... | python | def resample(self, data, input_rate):
"""
Microphone may not support our native processing sampling rate, so
resample from input_rate to RATE_PROCESS here for webrtcvad and
deepspeech
Args:
data (binary): Input audio stream
input_rate (int): Input audio r... | [
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21,634 | mozilla/DeepSpeech | examples/mic_vad_streaming/mic_vad_streaming.py | Audio.read_resampled | def read_resampled(self):
"""Return a block of audio data resampled to 16000hz, blocking if necessary."""
return self.resample(data=self.buffer_queue.get(),
input_rate=self.input_rate) | python | def read_resampled(self):
"""Return a block of audio data resampled to 16000hz, blocking if necessary."""
return self.resample(data=self.buffer_queue.get(),
input_rate=self.input_rate) | [
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21,635 | mozilla/DeepSpeech | examples/mic_vad_streaming/mic_vad_streaming.py | VADAudio.frame_generator | def frame_generator(self):
"""Generator that yields all audio frames from microphone."""
if self.input_rate == self.RATE_PROCESS:
while True:
yield self.read()
else:
while True:
yield self.read_resampled() | python | def frame_generator(self):
"""Generator that yields all audio frames from microphone."""
if self.input_rate == self.RATE_PROCESS:
while True:
yield self.read()
else:
while True:
yield self.read_resampled() | [
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21,636 | fxsjy/jieba | jieba/posseg/__init__.py | cut | def cut(sentence, HMM=True):
"""
Global `cut` function that supports parallel processing.
Note that this only works using dt, custom POSTokenizer
instances are not supported.
"""
global dt
if jieba.pool is None:
for w in dt.cut(sentence, HMM=HMM):
yield w
else:
... | python | def cut(sentence, HMM=True):
"""
Global `cut` function that supports parallel processing.
Note that this only works using dt, custom POSTokenizer
instances are not supported.
"""
global dt
if jieba.pool is None:
for w in dt.cut(sentence, HMM=HMM):
yield w
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21,637 | fxsjy/jieba | jieba/__init__.py | enable_parallel | def enable_parallel(processnum=None):
"""
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
instances are not supported.
"""
global pool, dt, cut, cut_for_search
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... | python | def enable_parallel(processnum=None):
"""
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
instances are not supported.
"""
global pool, dt, cut, cut_for_search
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21,638 | fxsjy/jieba | jieba/__init__.py | Tokenizer.cut | 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, ... | python | def cut(self, sentence, cut_all=False, HMM=True):
'''
The main function that segments an entire sentence that contains
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Parameter:
- sentence: The str(unicode) to be segmented.
- cut_all: Model type. True for full pattern, ... | [
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21,639 | fxsjy/jieba | jieba/__init__.py | Tokenizer.cut_for_search | def cut_for_search(self, sentence, HMM=True):
"""
Finer segmentation for search engines.
"""
words = self.cut(sentence, HMM=HMM)
for w in words:
if len(w) > 2:
for i in xrange(len(w) - 1):
gram2 = w[i:i + 2]
if s... | python | def cut_for_search(self, sentence, HMM=True):
"""
Finer segmentation for search engines.
"""
words = self.cut(sentence, HMM=HMM)
for w in words:
if len(w) > 2:
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21,640 | fxsjy/jieba | jieba/__init__.py | Tokenizer.load_userdict | 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.
... | python | 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.
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21,641 | fxsjy/jieba | jieba/__init__.py | Tokenizer.add_word | def add_word(self, word, freq=None, tag=None):
"""
Add a word to dictionary.
freq and tag can be omitted, freq defaults to be a calculated value
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"""
self.check_initialized()
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freq = int(freq) if ... | python | def add_word(self, word, freq=None, tag=None):
"""
Add a word to dictionary.
freq and tag can be omitted, freq defaults to be a calculated value
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"""
self.check_initialized()
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21,642 | fxsjy/jieba | jieba/__init__.py | Tokenizer.suggest_freq | def suggest_freq(self, segment, tune=False):
"""
Suggest word frequency to force the characters in a word to be
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Parameter:
- segment : The segments that the word is expected to be cut into,
If the word should be treated as a whole,... | python | def suggest_freq(self, segment, tune=False):
"""
Suggest word frequency to force the characters in a word to be
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21,643 | tensorflow/tensor2tensor | tensor2tensor/data_generators/allen_brain.py | _get_case_file_paths | 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
top level having meta/ and raw/ subdirs.
case: bool, whether obtaining file paths for tra... | python | 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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21,644 | tensorflow/tensor2tensor | tensor2tensor/data_generators/allen_brain.py | maybe_download_image_dataset | 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... | python | 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)
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21,645 | tensorflow/tensor2tensor | tensor2tensor/data_generators/allen_brain.py | random_square_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.
"""
mask =... | python | def random_square_mask(shape, fraction):
"""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.
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21,646 | tensorflow/tensor2tensor | tensor2tensor/data_generators/allen_brain.py | _generator | def _generator(tmp_dir, training, size=_BASE_EXAMPLE_IMAGE_SIZE,
training_fraction=0.95):
"""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... | python | def _generator(tmp_dir, training, size=_BASE_EXAMPLE_IMAGE_SIZE,
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"""Base problem example generator for Allen Brain Atlas problems.
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tmp_dir: str, a directory where raw example input data has been stored.
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21,647 | tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_moe.py | transformer_moe_2k | def transformer_moe_2k():
"""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
* Decoder architecture:
*... | python | def transformer_moe_2k():
"""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
* Decoder architecture:
*... | [
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21,648 | tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_moe.py | transformer_moe_prepend_8k | 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... | python | 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"
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21,649 | tensorflow/tensor2tensor | tensor2tensor/models/revnet.py | f | def f(x, depth1, depth2, dim='2d', first_batch_norm=True, stride=1,
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"""Applies residual function for RevNet.
Args:
x: input tensor
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training=True, bottleneck=True, padding='SAME'):
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21,650 | tensorflow/tensor2tensor | tensor2tensor/models/revnet.py | downsample_bottleneck | 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... | python | 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.
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21,651 | tensorflow/tensor2tensor | tensor2tensor/models/revnet.py | downsample_residual | def downsample_residual(x, output_channels, dim='2d', stride=1, scope='h'):
"""Downsamples 'x' by `stride` using average pooling.
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 ... | python | def downsample_residual(x, output_channels, dim='2d', stride=1, scope='h'):
"""Downsamples 'x' by `stride` using average pooling.
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x: input tensor of size [N, H, W, C]
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21,652 | tensorflow/tensor2tensor | tensor2tensor/models/revnet.py | init | def init(images, num_channels, dim='2d', stride=2,
kernel_size=7, maxpool=True, training=True, scope='init'):
"""Standard ResNet initial block used as first RevNet block.
Args:
images: [N, H, W, 3] tensor of input images to the model.
num_channels: Output depth of convolutional layer in initial bl... | python | def init(images, num_channels, dim='2d', stride=2,
kernel_size=7, maxpool=True, training=True, scope='init'):
"""Standard ResNet initial block used as first RevNet block.
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images: [N, H, W, 3] tensor of input images to the model.
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21,653 | tensorflow/tensor2tensor | tensor2tensor/models/revnet.py | unit | def unit(x1, x2, block_num, depth, num_layers, dim='2d',
bottleneck=True, first_batch_norm=True, stride=1, training=True):
"""Implements bottleneck RevNet unit from authors' RevNet architecture.
Args:
x1: [N, H, W, C] tensor of network activations.
x2: [N, H, W, C] tensor of network activations.
... | python | def unit(x1, x2, block_num, depth, num_layers, dim='2d',
bottleneck=True, first_batch_norm=True, stride=1, training=True):
"""Implements bottleneck RevNet unit from authors' RevNet architecture.
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x1: [N, H, W, C] tensor of network activations.
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21,654 | tensorflow/tensor2tensor | tensor2tensor/models/revnet.py | final_block | def final_block(x1, x2, dim='2d', training=True, scope='final_block'):
"""Converts activations from last RevNet block to pre-logits.
Args:
x1: [NxHxWxC] tensor of network activations.
x2: [NxHxWxC] tensor of network activations.
dim: '2d' if 2-dimensional, '3d' if 3-dimensional.
training: True for ... | python | def final_block(x1, x2, dim='2d', training=True, scope='final_block'):
"""Converts activations from last RevNet block to pre-logits.
Args:
x1: [NxHxWxC] tensor of network activations.
x2: [NxHxWxC] tensor of network activations.
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21,655 | tensorflow/tensor2tensor | tensor2tensor/models/revnet.py | revnet | def revnet(inputs, hparams, reuse=None):
"""Uses Tensor2Tensor memory optimized RevNet block to build a RevNet.
Args:
inputs: [NxHxWx3] tensor of input images to the model.
hparams: HParams object that contains the following parameters,
in addition to the parameters contained in the basic_params1() o... | python | def revnet(inputs, hparams, reuse=None):
"""Uses Tensor2Tensor memory optimized RevNet block to build a RevNet.
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inputs: [NxHxWx3] tensor of input images to the model.
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21,656 | tensorflow/tensor2tensor | tensor2tensor/models/revnet.py | revnet_base | def revnet_base():
"""Default hparams for Revnet."""
hparams = common_hparams.basic_params1()
hparams.add_hparam('num_channels', [64, 128, 256, 416])
hparams.add_hparam('num_layers_per_block', [1, 1, 10, 1])
hparams.add_hparam('bottleneck', True)
hparams.add_hparam('first_batch_norm', [False, True, True, Tr... | python | def revnet_base():
"""Default hparams for Revnet."""
hparams = common_hparams.basic_params1()
hparams.add_hparam('num_channels', [64, 128, 256, 416])
hparams.add_hparam('num_layers_per_block', [1, 1, 10, 1])
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21,657 | tensorflow/tensor2tensor | tensor2tensor/models/revnet.py | revnet_range | def revnet_range(rhp):
"""Hyperparameters for tuning revnet."""
rhp.set_float('learning_rate', 0.05, 0.2, scale=rhp.LOG_SCALE)
rhp.set_float('weight_decay', 1e-5, 1e-3, scale=rhp.LOG_SCALE)
rhp.set_discrete('num_channels_init_block', [64, 128])
return rhp | python | def revnet_range(rhp):
"""Hyperparameters for tuning revnet."""
rhp.set_float('learning_rate', 0.05, 0.2, scale=rhp.LOG_SCALE)
rhp.set_float('weight_decay', 1e-5, 1e-3, scale=rhp.LOG_SCALE)
rhp.set_discrete('num_channels_init_block', [64, 128])
return rhp | [
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21,658 | tensorflow/tensor2tensor | tensor2tensor/models/video/basic_deterministic_params.py | next_frame_basic_deterministic | def next_frame_basic_deterministic():
"""Basic 2-frame conv model."""
hparams = base.next_frame_base()
hparams.video_num_input_frames = 4
hparams.video_num_target_frames = 1
hparams.hidden_size = 64
hparams.batch_size = 4
hparams.num_hidden_layers = 2
hparams.optimizer = "Adafactor"
hparams.learning_r... | python | def next_frame_basic_deterministic():
"""Basic 2-frame conv model."""
hparams = base.next_frame_base()
hparams.video_num_input_frames = 4
hparams.video_num_target_frames = 1
hparams.hidden_size = 64
hparams.batch_size = 4
hparams.num_hidden_layers = 2
hparams.optimizer = "Adafactor"
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21,659 | tensorflow/tensor2tensor | tensor2tensor/models/video/basic_deterministic_params.py | next_frame_pixel_noise | def next_frame_pixel_noise():
"""Basic 2-frame conv model with pixel noise."""
hparams = next_frame_basic_deterministic()
hparams.add_hparam("video_modality_input_noise", 0.05)
hparams.bottom["inputs"] = modalities.video_pixel_noise_bottom
hparams.top["inputs"] = modalities.video_top
return hparams | python | def next_frame_pixel_noise():
"""Basic 2-frame conv model with pixel noise."""
hparams = next_frame_basic_deterministic()
hparams.add_hparam("video_modality_input_noise", 0.05)
hparams.bottom["inputs"] = modalities.video_pixel_noise_bottom
hparams.top["inputs"] = modalities.video_top
return hparams | [
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21,660 | tensorflow/tensor2tensor | tensor2tensor/models/video/basic_deterministic_params.py | next_frame_sampling | def next_frame_sampling():
"""Basic conv model with scheduled sampling."""
hparams = next_frame_basic_deterministic()
hparams.scheduled_sampling_mode = "prob_inverse_exp"
hparams.scheduled_sampling_max_prob = 1.0
hparams.scheduled_sampling_decay_steps = 10000
return hparams | python | def next_frame_sampling():
"""Basic conv model with scheduled sampling."""
hparams = next_frame_basic_deterministic()
hparams.scheduled_sampling_mode = "prob_inverse_exp"
hparams.scheduled_sampling_max_prob = 1.0
hparams.scheduled_sampling_decay_steps = 10000
return hparams | [
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21,661 | tensorflow/tensor2tensor | tensor2tensor/models/video/basic_deterministic_params.py | next_frame_ae | def next_frame_ae():
"""Conv autoencoder."""
hparams = next_frame_basic_deterministic()
hparams.bottom["inputs"] = modalities.video_bitwise_bottom
hparams.top["inputs"] = modalities.video_top
hparams.hidden_size = 256
hparams.batch_size = 8
hparams.num_hidden_layers = 4
hparams.num_compress_steps = 4
... | python | def next_frame_ae():
"""Conv autoencoder."""
hparams = next_frame_basic_deterministic()
hparams.bottom["inputs"] = modalities.video_bitwise_bottom
hparams.top["inputs"] = modalities.video_top
hparams.hidden_size = 256
hparams.batch_size = 8
hparams.num_hidden_layers = 4
hparams.num_compress_steps = 4
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21,662 | tensorflow/tensor2tensor | tensor2tensor/models/video/basic_deterministic_params.py | next_frame_ae_tiny | def next_frame_ae_tiny():
"""Conv autoencoder, tiny set for testing."""
hparams = next_frame_tiny()
hparams.bottom["inputs"] = modalities.video_bitwise_bottom
hparams.top["inputs"] = modalities.video_top
hparams.batch_size = 8
hparams.dropout = 0.4
return hparams | python | def next_frame_ae_tiny():
"""Conv autoencoder, tiny set for testing."""
hparams = next_frame_tiny()
hparams.bottom["inputs"] = modalities.video_bitwise_bottom
hparams.top["inputs"] = modalities.video_top
hparams.batch_size = 8
hparams.dropout = 0.4
return hparams | [
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21,663 | tensorflow/tensor2tensor | tensor2tensor/models/video/basic_deterministic_params.py | next_frame_tiny | def next_frame_tiny():
"""Tiny for testing."""
hparams = next_frame_basic_deterministic()
hparams.hidden_size = 32
hparams.num_hidden_layers = 1
hparams.num_compress_steps = 2
hparams.filter_double_steps = 1
return hparams | python | def next_frame_tiny():
"""Tiny for testing."""
hparams = next_frame_basic_deterministic()
hparams.hidden_size = 32
hparams.num_hidden_layers = 1
hparams.num_compress_steps = 2
hparams.filter_double_steps = 1
return hparams | [
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21,664 | tensorflow/tensor2tensor | tensor2tensor/models/video/basic_deterministic_params.py | next_frame_l1 | def next_frame_l1():
"""Basic conv model with L1 modality."""
hparams = next_frame_basic_deterministic()
hparams.loss["targets"] = modalities.video_l1_loss
hparams.top["targets"] = modalities.video_l1_top
hparams.video_modality_loss_cutoff = 2.4
return hparams | python | def next_frame_l1():
"""Basic conv model with L1 modality."""
hparams = next_frame_basic_deterministic()
hparams.loss["targets"] = modalities.video_l1_loss
hparams.top["targets"] = modalities.video_l1_top
hparams.video_modality_loss_cutoff = 2.4
return hparams | [
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21,665 | tensorflow/tensor2tensor | tensor2tensor/models/video/basic_deterministic_params.py | next_frame_l2 | def next_frame_l2():
"""Basic conv model with L2 modality."""
hparams = next_frame_basic_deterministic()
hparams.loss["targets"] = modalities.video_l2_loss
hparams.top["targets"] = modalities.video_l1_top
hparams.video_modality_loss_cutoff = 2.4
return hparams | python | def next_frame_l2():
"""Basic conv model with L2 modality."""
hparams = next_frame_basic_deterministic()
hparams.loss["targets"] = modalities.video_l2_loss
hparams.top["targets"] = modalities.video_l1_top
hparams.video_modality_loss_cutoff = 2.4
return hparams | [
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21,666 | tensorflow/tensor2tensor | tensor2tensor/models/video/basic_deterministic_params.py | next_frame_base_range | def next_frame_base_range(rhp):
"""Basic tuning grid."""
rhp.set_float("dropout", 0.2, 0.6)
rhp.set_discrete("hidden_size", [64, 128, 256])
rhp.set_int("num_compress_steps", 5, 8)
rhp.set_discrete("batch_size", [4, 8, 16, 32])
rhp.set_int("num_hidden_layers", 1, 3)
rhp.set_int("filter_double_steps", 1, 6)... | python | def next_frame_base_range(rhp):
"""Basic tuning grid."""
rhp.set_float("dropout", 0.2, 0.6)
rhp.set_discrete("hidden_size", [64, 128, 256])
rhp.set_int("num_compress_steps", 5, 8)
rhp.set_discrete("batch_size", [4, 8, 16, 32])
rhp.set_int("num_hidden_layers", 1, 3)
rhp.set_int("filter_double_steps", 1, 6)... | [
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21,667 | tensorflow/tensor2tensor | tensor2tensor/models/video/basic_deterministic_params.py | next_frame_ae_range | def next_frame_ae_range(rhp):
"""Autoencoder world model tuning grid."""
rhp.set_float("dropout", 0.3, 0.5)
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rhp.set_int("num_hidden_layers", 2, 6)
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"""Autoencoder world model tuning grid."""
rhp.set_float("dropout", 0.3, 0.5)
rhp.set_int("num_compress_steps", 1, 3)
rhp.set_int("num_hidden_layers", 2, 6)
rhp.set_float("learning_rate_constant", 1., 2.)
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21,668 | tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_free.py | initialize_env_specs | def initialize_env_specs(hparams, env_problem_name):
"""Initializes env_specs using the appropriate env."""
if env_problem_name:
env = registry.env_problem(env_problem_name, batch_size=hparams.batch_size)
else:
env = rl_utils.setup_env(hparams, hparams.batch_size,
hparams.eval... | python | def initialize_env_specs(hparams, env_problem_name):
"""Initializes env_specs using the appropriate env."""
if env_problem_name:
env = registry.env_problem(env_problem_name, batch_size=hparams.batch_size)
else:
env = rl_utils.setup_env(hparams, hparams.batch_size,
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21,669 | tensorflow/tensor2tensor | tensor2tensor/utils/learning_rate.py | learning_rate_factor | def learning_rate_factor(name, step_num, hparams):
"""Compute the designated learning rate factor from hparams."""
if name == "constant":
tf.logging.info("Base learning rate: %f", hparams.learning_rate_constant)
return hparams.learning_rate_constant
elif name == "linear_warmup":
return tf.minimum(1.0,... | python | def learning_rate_factor(name, step_num, hparams):
"""Compute the designated learning rate factor from hparams."""
if name == "constant":
tf.logging.info("Base learning rate: %f", hparams.learning_rate_constant)
return hparams.learning_rate_constant
elif name == "linear_warmup":
return tf.minimum(1.0,... | [
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21,670 | tensorflow/tensor2tensor | tensor2tensor/utils/learning_rate.py | learning_rate_schedule | def learning_rate_schedule(hparams):
"""Learning rate schedule based on hparams."""
mlperf_log.transformer_print(key=mlperf_log.OPT_LR, deferred=True)
mlperf_log.transformer_print(
key=mlperf_log.OPT_LR_WARMUP_STEPS,
value=hparams.learning_rate_warmup_steps)
step_num = _global_step(hparams)
schedu... | python | def learning_rate_schedule(hparams):
"""Learning rate schedule based on hparams."""
mlperf_log.transformer_print(key=mlperf_log.OPT_LR, deferred=True)
mlperf_log.transformer_print(
key=mlperf_log.OPT_LR_WARMUP_STEPS,
value=hparams.learning_rate_warmup_steps)
step_num = _global_step(hparams)
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21,671 | tensorflow/tensor2tensor | tensor2tensor/utils/learning_rate.py | legacy_learning_rate_schedule | def legacy_learning_rate_schedule(hparams):
"""Backwards-compatible learning-rate schedule."""
step_num = _global_step(hparams)
warmup_steps = tf.to_float(hparams.learning_rate_warmup_steps)
if hparams.learning_rate_decay_scheme == "noam":
ret = 5000.0 * hparams.hidden_size**-0.5 * tf.minimum(
(step... | python | def legacy_learning_rate_schedule(hparams):
"""Backwards-compatible learning-rate schedule."""
step_num = _global_step(hparams)
warmup_steps = tf.to_float(hparams.learning_rate_warmup_steps)
if hparams.learning_rate_decay_scheme == "noam":
ret = 5000.0 * hparams.hidden_size**-0.5 * tf.minimum(
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21,672 | tensorflow/tensor2tensor | tensor2tensor/utils/learning_rate.py | _global_step | def _global_step(hparams):
"""Adjust global step if a multi-step optimizer is used."""
step = tf.to_float(tf.train.get_or_create_global_step())
multiplier = hparams.optimizer_multistep_accumulate_steps
if not multiplier:
return step
tf.logging.info("Dividing global step by %d for multi-step optimizer."
... | python | def _global_step(hparams):
"""Adjust global step if a multi-step optimizer is used."""
step = tf.to_float(tf.train.get_or_create_global_step())
multiplier = hparams.optimizer_multistep_accumulate_steps
if not multiplier:
return step
tf.logging.info("Dividing global step by %d for multi-step optimizer."
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21,673 | tensorflow/tensor2tensor | tensor2tensor/utils/learning_rate.py | _piecewise_learning_rate | def _piecewise_learning_rate(step, boundaries, values):
"""Scale learning rate according to the given schedule.
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step: global step
boundaries: List of steps to transition on.
values: Multiplier to apply at each boundary transition.
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"""Scale learning rate according to the given schedule.
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21,674 | tensorflow/tensor2tensor | tensor2tensor/utils/learning_rate.py | _learning_rate_decay | def _learning_rate_decay(hparams, warmup_steps=0):
"""Learning rate decay multiplier."""
scheme = hparams.learning_rate_decay_scheme
warmup_steps = tf.to_float(warmup_steps)
global_step = _global_step(hparams)
if not scheme or scheme == "none":
return tf.constant(1.)
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"""Learning rate decay multiplier."""
scheme = hparams.learning_rate_decay_scheme
warmup_steps = tf.to_float(warmup_steps)
global_step = _global_step(hparams)
if not scheme or scheme == "none":
return tf.constant(1.)
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21,675 | tensorflow/tensor2tensor | tensor2tensor/utils/learning_rate.py | _learning_rate_warmup | def _learning_rate_warmup(warmup_steps, warmup_schedule="exp", hparams=None):
"""Learning rate warmup multiplier."""
if not warmup_steps:
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tf.logging.info("Applying %s learning rate warmup for %d steps",
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warmup_steps = tf.to_float(warm... | python | def _learning_rate_warmup(warmup_steps, warmup_schedule="exp", hparams=None):
"""Learning rate warmup multiplier."""
if not warmup_steps:
return tf.constant(1.)
tf.logging.info("Applying %s learning rate warmup for %d steps",
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21,676 | tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | is_in_expr | def is_in_expr(expr, find):
"""Returns True if `find` is a subtree of `expr`."""
return expr == find or (isinstance(expr, ExprNode) and expr.is_in(find)) | python | def is_in_expr(expr, find):
"""Returns True if `find` is a subtree of `expr`."""
return expr == find or (isinstance(expr, ExprNode) and expr.is_in(find)) | [
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21,677 | tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | random_expr_with_required_var | def random_expr_with_required_var(depth, required_var, optional_list, ops):
"""Generate a random expression tree with a required variable.
The required variable appears exactly once in the expression.
Args:
depth: At least one leaf will be this many levels down from the top.
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"""Generate a random expression tree with a required variable.
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depth: At least one leaf will be this many levels down from the top.
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21,678 | tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | random_expr | def random_expr(depth, vlist, ops):
"""Generate a random expression tree.
Args:
depth: At least one leaf will be this many levels down from the top.
vlist: A list of chars. These chars are randomly selected as leaf values.
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"""Generate a random expression tree.
Args:
depth: At least one leaf will be this many levels down from the top.
vlist: A list of chars. These chars are randomly selected as leaf values.
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21,679 | tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | algebra_inverse_solve | def algebra_inverse_solve(left, right, var, solve_ops):
"""Solves for the value of the given var in an expression.
Args:
left: The root of the ExprNode tree on the left side of the equals sign.
right: The root of the ExprNode tree on the right side of the equals sign.
var: A char. The variable to solve... | python | def algebra_inverse_solve(left, right, var, solve_ops):
"""Solves for the value of the given var in an expression.
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left: The root of the ExprNode tree on the left side of the equals sign.
right: The root of the ExprNode tree on the right side of the equals sign.
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21,680 | tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | format_sympy_expr | def format_sympy_expr(sympy_expr, functions=None):
"""Convert sympy expression into a string which can be encoded.
Args:
sympy_expr: Any sympy expression tree or string.
functions: Defines special functions. A dict mapping human readable string
names, like "log", "exp", "sin", "cos", etc., to singl... | python | def format_sympy_expr(sympy_expr, functions=None):
"""Convert sympy expression into a string which can be encoded.
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sympy_expr: Any sympy expression tree or string.
functions: Defines special functions. A dict mapping human readable string
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21,681 | tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | generate_algebra_inverse_sample | def generate_algebra_inverse_sample(vlist, ops, solve_ops, min_depth,
max_depth):
"""Randomly generate an algebra inverse dataset sample.
Given an input equation and variable, produce the expression equal to the
variable.
Args:
vlist: Variable list. List of chars that c... | python | def generate_algebra_inverse_sample(vlist, ops, solve_ops, min_depth,
max_depth):
"""Randomly generate an algebra inverse dataset sample.
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21,682 | tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | generate_algebra_simplify_sample | def generate_algebra_simplify_sample(vlist, ops, min_depth, max_depth):
"""Randomly generate an algebra simplify dataset sample.
Given an input expression, produce the simplified expression.
Args:
vlist: Variable list. List of chars that can be used in the expression.
ops: List of ExprOp instances. The ... | python | def generate_algebra_simplify_sample(vlist, ops, min_depth, max_depth):
"""Randomly generate an algebra simplify dataset sample.
Given an input expression, produce the simplified expression.
Args:
vlist: Variable list. List of chars that can be used in the expression.
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21,683 | tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | generate_calculus_integrate_sample | def generate_calculus_integrate_sample(vlist, ops, min_depth, max_depth,
functions):
"""Randomly generate a symbolic integral dataset sample.
Given an input expression, produce the indefinite integral.
Args:
vlist: Variable list. List of chars that can be used in the e... | python | def generate_calculus_integrate_sample(vlist, ops, min_depth, max_depth,
functions):
"""Randomly generate a symbolic integral dataset sample.
Given an input expression, produce the indefinite integral.
Args:
vlist: Variable list. List of chars that can be used in the e... | [
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21,684 | tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | algebra_inverse | def algebra_inverse(alphabet_size=26, min_depth=0, max_depth=2,
nbr_cases=10000):
"""Generate the algebra inverse dataset.
Each sample is a symbolic math equation involving unknown variables. The
task is to solve for the given variable. The target is the resulting
expression.
Args:
a... | python | def algebra_inverse(alphabet_size=26, min_depth=0, max_depth=2,
nbr_cases=10000):
"""Generate the algebra inverse dataset.
Each sample is a symbolic math equation involving unknown variables. The
task is to solve for the given variable. The target is the resulting
expression.
Args:
a... | [
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21,685 | tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | algebra_simplify | def algebra_simplify(alphabet_size=26,
min_depth=0,
max_depth=2,
nbr_cases=10000):
"""Generate the algebra simplify dataset.
Each sample is a symbolic math expression involving unknown variables. The
task is to simplify the expression. The target is ... | python | def algebra_simplify(alphabet_size=26,
min_depth=0,
max_depth=2,
nbr_cases=10000):
"""Generate the algebra simplify dataset.
Each sample is a symbolic math expression involving unknown variables. The
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21,686 | tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | calculus_integrate | def calculus_integrate(alphabet_size=26,
min_depth=0,
max_depth=2,
nbr_cases=10000):
"""Generate the calculus integrate dataset.
Each sample is a symbolic math expression involving unknown variables. The
task is to take the indefinite integral ... | python | def calculus_integrate(alphabet_size=26,
min_depth=0,
max_depth=2,
nbr_cases=10000):
"""Generate the calculus integrate dataset.
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21,687 | tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | ExprNode.is_in | def is_in(self, expr):
"""Returns True if `expr` is a subtree."""
if expr == self:
return True
is_in_left = is_in_expr(self.left, expr)
is_in_right = is_in_expr(self.right, expr)
return is_in_left or is_in_right | python | def is_in(self, expr):
"""Returns True if `expr` is a subtree."""
if expr == self:
return True
is_in_left = is_in_expr(self.left, expr)
is_in_right = is_in_expr(self.right, expr)
return is_in_left or is_in_right | [
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21,688 | tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | preprocess_example_common | def preprocess_example_common(example, mode, hparams):
"""Preprocessing steps common to all models."""
if "inputs" in example and hparams.max_input_seq_length > 0:
example["inputs"] = example["inputs"][:hparams.max_input_seq_length]
if hparams.prepend_mode != "none":
if mode == tf.estimator.ModeKeys.PREDI... | python | def preprocess_example_common(example, mode, hparams):
"""Preprocessing steps common to all models."""
if "inputs" in example and hparams.max_input_seq_length > 0:
example["inputs"] = example["inputs"][:hparams.max_input_seq_length]
if hparams.prepend_mode != "none":
if mode == tf.estimator.ModeKeys.PREDI... | [
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21,689 | tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | _copy_problem_hparams | def _copy_problem_hparams(p_hparams):
"""Use input modality, vocab, and space id for target."""
p = p_hparams
# Duplicate input modality.
p.modality["targets"] = p.modality["inputs"]
# Duplicate input vocab size.
p.vocab_size["targets"] = p.vocab_size["inputs"]
# Duplicate input vocabulary.
p.vocabulary... | python | def _copy_problem_hparams(p_hparams):
"""Use input modality, vocab, and space id for target."""
p = p_hparams
# Duplicate input modality.
p.modality["targets"] = p.modality["inputs"]
# Duplicate input vocab size.
p.vocab_size["targets"] = p.vocab_size["inputs"]
# Duplicate input vocabulary.
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21,690 | tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | _default_hparams | def _default_hparams():
"""A set of basic model hyperparameters."""
return hparam.HParams(
# Use this parameter to get comparable perplexity numbers with different
# tokenizations. This value should be set to the ratio of the number of
# tokens in the test set according to the tokenization used t... | python | def _default_hparams():
"""A set of basic model hyperparameters."""
return hparam.HParams(
# Use this parameter to get comparable perplexity numbers with different
# tokenizations. This value should be set to the ratio of the number of
# tokens in the test set according to the tokenization used t... | [
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21,691 | tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | Problem.tpu_batch_size_per_shard | def tpu_batch_size_per_shard(self, model_hparams):
"""Batch size in examples per TPU core.
Args:
model_hparams: model hyperparameters
Returns:
an integer
"""
if self.batch_size_means_tokens and not model_hparams.use_fixed_batch_size:
return model_hparams.batch_size // self.max_len... | python | def tpu_batch_size_per_shard(self, model_hparams):
"""Batch size in examples per TPU core.
Args:
model_hparams: model hyperparameters
Returns:
an integer
"""
if self.batch_size_means_tokens and not model_hparams.use_fixed_batch_size:
return model_hparams.batch_size // self.max_len... | [
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21,692 | tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | Problem.preprocess | def preprocess(self, dataset, mode, hparams, interleave=True):
"""Runtime preprocessing on the whole dataset.
Return a tf.data.Datset -- the preprocessed version of the given one.
By default this function calls preprocess_example.
Args:
dataset: the Dataset of already decoded but not yet preproc... | python | def preprocess(self, dataset, mode, hparams, interleave=True):
"""Runtime preprocessing on the whole dataset.
Return a tf.data.Datset -- the preprocessed version of the given one.
By default this function calls preprocess_example.
Args:
dataset: the Dataset of already decoded but not yet preproc... | [
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Return a tf.data.Datset -- the preprocessed version of the given one.
By default this function calls preprocess_example.
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dataset: the Dataset of already decoded but not yet preprocessed features.
mode: tf.estimator.ModeKeys
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21,693 | tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | Problem.filepattern | def filepattern(self, data_dir, mode, shard=None):
"""Get filepattern for data files for mode.
Matches mode to a suffix.
* DatasetSplit.TRAIN: train
* DatasetSplit.EVAL: dev
* DatasetSplit.TEST: test
* tf.estimator.ModeKeys.PREDICT: dev
Args:
data_dir: str, data directory.
mode... | python | def filepattern(self, data_dir, mode, shard=None):
"""Get filepattern for data files for mode.
Matches mode to a suffix.
* DatasetSplit.TRAIN: train
* DatasetSplit.EVAL: dev
* DatasetSplit.TEST: test
* tf.estimator.ModeKeys.PREDICT: dev
Args:
data_dir: str, data directory.
mode... | [
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21,694 | tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | Problem.maybe_reverse_features | def maybe_reverse_features(self, feature_map):
"""Reverse features between inputs and targets if the problem is '_rev'."""
if not self._was_reversed:
return
inputs = feature_map.pop("inputs", None)
targets = feature_map.pop("targets", None)
inputs_seg = feature_map.pop("inputs_segmentation", N... | python | def maybe_reverse_features(self, feature_map):
"""Reverse features between inputs and targets if the problem is '_rev'."""
if not self._was_reversed:
return
inputs = feature_map.pop("inputs", None)
targets = feature_map.pop("targets", None)
inputs_seg = feature_map.pop("inputs_segmentation", N... | [
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21,695 | tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | Problem.dataset | def dataset(self,
mode,
data_dir=None,
num_threads=None,
output_buffer_size=None,
shuffle_files=None,
hparams=None,
preprocess=True,
dataset_split=None,
shard=None,
partition_id=0,... | python | def dataset(self,
mode,
data_dir=None,
num_threads=None,
output_buffer_size=None,
shuffle_files=None,
hparams=None,
preprocess=True,
dataset_split=None,
shard=None,
partition_id=0,... | [
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Args:
mode: tf.estimator.ModeKeys; determines which files to read from.
data_dir: directory that contains data files.
num_threads: int, number of threads to use for decode and preprocess
Dataset.map calls.
output_buffer_size: int, how many elements ... | [
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21,696 | tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | Problem.decode_example | def decode_example(self, serialized_example):
"""Return a dict of Tensors from a serialized tensorflow.Example."""
data_fields, data_items_to_decoders = self.example_reading_spec()
# Necessary to rejoin examples in the correct order with the Cloud ML Engine
# batch prediction API.
data_fields["batch... | python | def decode_example(self, serialized_example):
"""Return a dict of Tensors from a serialized tensorflow.Example."""
data_fields, data_items_to_decoders = self.example_reading_spec()
# Necessary to rejoin examples in the correct order with the Cloud ML Engine
# batch prediction API.
data_fields["batch... | [
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21,697 | tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | Problem.make_estimator_input_fn | def make_estimator_input_fn(self,
mode,
hparams,
data_dir=None,
force_repeat=False,
prevent_repeat=False,
dataset_kwargs=None):
"""Retur... | python | def make_estimator_input_fn(self,
mode,
hparams,
data_dir=None,
force_repeat=False,
prevent_repeat=False,
dataset_kwargs=None):
"""Retur... | [
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21,698 | tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | Problem._dataset_partition | def _dataset_partition(self, mode, config, params):
"""Which part of the training data to read.
If there are multiple parallel calls to input_fn (multiple TPU hosts),
then we want each one to read from a separate partition of the training
data.
Args:
mode: tf.estimator.ModeKeys
config:... | python | def _dataset_partition(self, mode, config, params):
"""Which part of the training data to read.
If there are multiple parallel calls to input_fn (multiple TPU hosts),
then we want each one to read from a separate partition of the training
data.
Args:
mode: tf.estimator.ModeKeys
config:... | [
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mode: tf.estimator.ModeKeys
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21,699 | tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | Problem.serving_input_fn | def serving_input_fn(self, hparams, decode_hparams=None, use_tpu=False):
"""Input fn for serving export, starting from serialized example."""
mode = tf.estimator.ModeKeys.PREDICT
serialized_example = tf.placeholder(
dtype=tf.string, shape=[None], name="serialized_example")
dataset = tf.data.Data... | python | def serving_input_fn(self, hparams, decode_hparams=None, use_tpu=False):
"""Input fn for serving export, starting from serialized example."""
mode = tf.estimator.ModeKeys.PREDICT
serialized_example = tf.placeholder(
dtype=tf.string, shape=[None], name="serialized_example")
dataset = tf.data.Data... | [
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