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def create_testing_instances(sentence, tokenizer, max_seq_length=128):
'Create `TestInstance`s from raw text.'
max_token_num = (max_seq_length - 2)
tokens = tokenizer.tokenize(sentence)
if (len(tokens) > max_token_num):
tokens = tokens[:max_token_num]
if (tokens[0] is not '[SOS]'):
tokens.insert(0, '[SOS]')
if (tokens[(- 1)] is not '[EOS]'):
tokens.append('[EOS]')
instances = []
instances.append(create_instances_from_tokens(tokens))
return instances
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def create_instances_from_tokens(tokens):
'Creates `TestInstance`s for a single sentence.'
instance = TestingInstance(tokens)
return instance
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def validate_case_matches_checkpoint(do_lower_case, init_checkpoint):
'Checks whether the casing config is consistent with the checkpoint name.'
if (not init_checkpoint):
return
m = re.match('^.*?([A-Za-z0-9_-]+)/bert_model.ckpt', init_checkpoint)
if (m is None):
return
model_name = m.group(1)
lower_models = ['uncased_L-24_H-1024_A-16', 'uncased_L-12_H-768_A-12', 'multilingual_L-12_H-768_A-12', 'chinese_L-12_H-768_A-12']
cased_models = ['cased_L-12_H-768_A-12', 'cased_L-24_H-1024_A-16', 'multi_cased_L-12_H-768_A-12']
is_bad_config = False
if ((model_name in lower_models) and (not do_lower_case)):
is_bad_config = True
actual_flag = 'False'
case_name = 'lowercased'
opposite_flag = 'True'
if ((model_name in cased_models) and do_lower_case):
is_bad_config = True
actual_flag = 'True'
case_name = 'cased'
opposite_flag = 'False'
if is_bad_config:
raise ValueError(('You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. However, `%s` seems to be a %s model, so you should pass in `--do_lower_case=%s` so that the fine-tuning matches how the model was pre-training. If this error is wrong, please just comment out this check.' % (actual_flag, init_checkpoint, model_name, case_name, opposite_flag)))
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def convert_to_unicode(text):
"Converts `text` to Unicode (if it's not already), assuming utf-8 input."
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode('utf-8', 'ignore')
else:
raise ValueError(('Unsupported string type: %s' % type(text)))
elif six.PY2:
if isinstance(text, str):
return text.decode('utf-8', 'ignore')
elif isinstance(text, unicode):
return text
else:
raise ValueError(('Unsupported string type: %s' % type(text)))
else:
raise ValueError('Not running on Python2 or Python 3?')
|
def printable_text(text):
'Returns text encoded in a way suitable for print or `tf.logging`.'
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode('utf-8', 'ignore')
else:
raise ValueError(('Unsupported string type: %s' % type(text)))
elif six.PY2:
if isinstance(text, str):
return text
elif isinstance(text, unicode):
return text.encode('utf-8')
else:
raise ValueError(('Unsupported string type: %s' % type(text)))
else:
raise ValueError('Not running on Python2 or Python 3?')
|
def load_vocab(vocab_file):
'Loads a vocabulary file into a dictionary.'
vocab = collections.OrderedDict()
index = 0
with tf.gfile.GFile(vocab_file, 'r') as reader:
while True:
token = convert_to_unicode(reader.readline())
if (not token):
break
token = token.strip()
vocab[token] = index
index += 1
return vocab
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def convert_by_vocab(vocab, items):
'Converts a sequence of [tokens|ids] using the vocab.'
output = []
for item in items:
output.append(vocab[item])
return output
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def convert_tokens_to_ids(vocab, tokens):
return convert_by_vocab(vocab, tokens)
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def convert_ids_to_tokens(inv_vocab, ids):
return convert_by_vocab(inv_vocab, ids)
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def whitespace_tokenize(text):
'Runs basic whitespace cleaning and splitting on a piece of text.'
text = text.strip()
if (not text):
return []
tokens = text.split()
return tokens
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class FullTokenizer(object):
'Runs end-to-end tokenziation.'
def __init__(self, vocab_file, do_lower_case=True):
self.vocab = load_vocab(vocab_file)
self.inv_vocab = {v: k for (k, v) in self.vocab.items()}
self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
def tokenize(self, text):
split_tokens = []
for token in self.basic_tokenizer.tokenize(text):
for sub_token in self.wordpiece_tokenizer.tokenize(token):
split_tokens.append(sub_token)
return split_tokens
def convert_tokens_to_ids(self, tokens):
return convert_by_vocab(self.vocab, tokens)
def convert_ids_to_tokens(self, ids):
return convert_by_vocab(self.inv_vocab, ids)
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class BasicTokenizer(object):
'Runs basic tokenization (punctuation splitting, lower casing, etc.).'
def __init__(self, do_lower_case=True):
'Constructs a BasicTokenizer.\n\n Args:\n do_lower_case: Whether to lower case the input.\n '
self.do_lower_case = do_lower_case
def tokenize(self, text):
'Tokenizes a piece of text.'
text = convert_to_unicode(text)
text = self._clean_text(text)
text = self._tokenize_chinese_chars(text)
orig_tokens = whitespace_tokenize(text)
split_tokens = []
for token in orig_tokens:
if self.do_lower_case:
token = token.lower()
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token))
output_tokens = whitespace_tokenize(' '.join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
'Strips accents from a piece of text.'
text = unicodedata.normalize('NFD', text)
output = []
for char in text:
cat = unicodedata.category(char)
if (cat == 'Mn'):
continue
output.append(char)
return ''.join(output)
def _run_split_on_punc(self, text):
'Splits punctuation on a piece of text.'
chars = list(text)
i = 0
start_new_word = True
output = []
while (i < len(chars)):
char = chars[i]
if _is_punctuation(char):
output.append([char])
start_new_word = True
else:
if start_new_word:
output.append([])
start_new_word = False
output[(- 1)].append(char)
i += 1
return [''.join(x) for x in output]
def _tokenize_chinese_chars(self, text):
'Adds whitespace around any CJK character.'
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(' ')
output.append(char)
output.append(' ')
else:
output.append(char)
return ''.join(output)
def _is_chinese_char(self, cp):
'Checks whether CP is the codepoint of a CJK character.'
if (((cp >= 19968) and (cp <= 40959)) or ((cp >= 13312) and (cp <= 19903)) or ((cp >= 131072) and (cp <= 173791)) or ((cp >= 173824) and (cp <= 177983)) or ((cp >= 177984) and (cp <= 178207)) or ((cp >= 178208) and (cp <= 183983)) or ((cp >= 63744) and (cp <= 64255)) or ((cp >= 194560) and (cp <= 195103))):
return True
return False
def _clean_text(self, text):
'Performs invalid character removal and whitespace cleanup on text.'
output = []
for char in text:
cp = ord(char)
if ((cp == 0) or (cp == 65533) or _is_control(char)):
continue
if _is_whitespace(char):
output.append(' ')
else:
output.append(char)
return ''.join(output)
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class WordpieceTokenizer(object):
'Runs WordPiece tokenziation.'
def __init__(self, vocab, unk_token='[UNK]', max_input_chars_per_word=200):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
'Tokenizes a piece of text into its word pieces.\n\n This uses a greedy longest-match-first algorithm to perform tokenization\n using the given vocabulary.\n\n For example:\n input = "unaffable"\n output = ["un", "##aff", "##able"]\n\n Args:\n text: A single token or whitespace separated tokens. This should have\n already been passed through `BasicTokenizer.\n\n Returns:\n A list of wordpiece tokens.\n '
text = convert_to_unicode(text)
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
if (len(chars) > self.max_input_chars_per_word):
output_tokens.append(self.unk_token)
continue
is_bad = False
start = 0
sub_tokens = []
while (start < len(chars)):
end = len(chars)
cur_substr = None
while (start < end):
substr = ''.join(chars[start:end])
if (start > 0):
substr = ('##' + substr)
if (substr in self.vocab):
cur_substr = substr
break
end -= 1
if (cur_substr is None):
is_bad = True
break
sub_tokens.append(cur_substr)
start = end
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens
|
def _is_whitespace(char):
'Checks whether `chars` is a whitespace character.'
if ((char == ' ') or (char == '\t') or (char == '\n') or (char == '\r')):
return True
cat = unicodedata.category(char)
if (cat == 'Zs'):
return True
return False
|
def _is_control(char):
'Checks whether `chars` is a control character.'
if ((char == '\t') or (char == '\n') or (char == '\r')):
return False
cat = unicodedata.category(char)
if cat.startswith('C'):
return True
return False
|
def _is_punctuation(char):
'Checks whether `chars` is a punctuation character.'
cp = ord(char)
if (((cp >= 33) and (cp <= 47)) or ((cp >= 58) and (cp <= 64)) or ((cp >= 91) and (cp <= 96)) or ((cp >= 123) and (cp <= 126))):
return True
cat = unicodedata.category(char)
if cat.startswith('P'):
return True
return False
|
def load(in_file=None, format='tsv'):
' Load a clustering from a file. By default the input file is a\n tab-separated listing of words and their cluster ID. Returns a dictionary of\n the clustering.\n\n Args:\n in_file (string): path to input file\n format (string): input file format (default: tsv)\n\n Returns:\n dict: word-to-tag mapping\n '
mapping = {}
if (format == 'tsv'):
with open(in_file) as f:
for line in f:
tokens = line.split()
mapping[tokens[0]] = int(tokens[1])
return mapping
|
def save(mapping=None, out=None, format='tsv'):
' Save a clustering (dictionary) to file. By default the output file is\n a tab-separated listing of words and their cluster ID.\n\n Args:\n mapping (dict): word-to-tag mapping\n out (string): path to output file\n format (string): output file format (default: tsv)\n '
if (format == 'tsv'):
with open(out, 'w') as outfile:
for key in sorted(sorted(mapping), key=mapping.get):
line = (((str(key) + '\t') + str(mapping[key])) + '\n')
outfile.write(line)
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def tag_string(mapping=None, text=None, unk=unk):
"Tag a string with the corresponding cluster ID's. If a word is not\n found in the clustering, use unk.\n\n Args:\n mapping (dict): word-to-tag mapping\n text (string): the string to be tagged\n unk (string): what to label unknown/unseen words that are not in\n mapping (default: <unk>)\n\n Returns:\n string: sequence of tags\n "
newsent = ''
for word in text.split():
if (word in mapping):
newsent += (' ' + str(mapping[word]))
elif (unk in mapping):
newsent += (' ' + str(mapping[unk]))
else:
newsent += (' ' + '<unk>')
return newsent.lstrip()
|
def tag_stdin(mapping=None, unk=unk):
' This calls tag_string() for each line in stdin, and prints the\n result to stdout.\n\n Args:\n mapping (dict): word-to-tag mapping\n unk (string): what to label unknown/unseen words that are not in\n mapping (default: <unk>)\n '
for line in sys.stdin:
print(tag_string(mapping=mapping, text=line, unk=unk))
|
def cluster(text=None, in_file=None, classes=None, class_file=None, class_offset=None, forward_lambda=None, ngram_input=None, min_count=None, out=None, print_freqs=None, quiet=None, refine=None, rev_alternate=None, threads=None, tune_cycles=None, unidirectional=None, verbose=None, word_vectors=None):
'\n Produce a clustering, given a textual input. There is one required argument\n (the training input text), and many optional arguments. The one required\n argument is either text or in_file. The argument text is a list of Python\n strings. The argument in_file is a path to a text file, consisting of\n preprocessed (eg. tokenized) one-sentence-per-line text. The use of text\n is probably not a good idea for large corpora.\n\n The other optional arguments are described by running the compiled\n clustercat binary with the --help argument, except that the\n leading -- from the shell argument is removed, and - is replaced with _.\n So for example, instead of --tune-cycles 15, the Python function argument\n would be tune_cycles=15 .\n\n Returns a dictionary of the form { word : cluster_id } .\n '
cc_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
cc_bin = os.path.join(cc_dir, 'bin', 'clustercat')
if os.path.isfile(cc_bin):
cmd_str = [cc_bin]
elif distutils.spawn.find_executable('clustercat'):
cmd_str = ['clustercat']
else:
print('Error: Unable to access clustercat binary from either ', cc_dir, " or $PATH. In the parent directory, first run 'make install', and then add $HOME/bin/ to your $PATH, by typing the following command:\necho 'PATH=$PATH:$HOME/bin' >> $HOME/.bashrc && source $HOME/.bashrc")
exit(1)
clustercat_params = {'in_file': '--in', 'out': '--out', 'classes': '--classes', 'class_file': '--class-file', 'class_offset': '--class-offset', 'forward_lambda': '--forward-lambda', 'ngram_input': '--ngram-input', 'min_count': '--min-count', 'refine': '--refine', 'rev_alternate': '--rev-alternate', 'threads': '--threads', 'tune_cycles': '--tune-cycles', 'word_vectors': '--word-vectors'}
boolean_params = {'print_freqs': '--print-freqs', 'quiet': '--quiet', 'unidirectional': '--unidirectional', 'verbose': '--verbose'}
for (arg, value) in locals().items():
if ((arg in boolean_params) and (value is True)):
cmd_str.append(boolean_params[arg])
elif ((arg in clustercat_params) and (value is not None)):
cmd_str.append(clustercat_params[arg])
cmd_str.append(str(value))
cmd_out = ''
if (text and (not in_file)):
p1 = subprocess.Popen(['printf', '\n'.join(text)], stdout=subprocess.PIPE, universal_newlines=True)
p2 = subprocess.Popen(cmd_str, stdin=p1.stdout, stdout=subprocess.PIPE, universal_newlines=True)
p1.stdout.close()
cmd_out = p2.communicate()[0]
elif (in_file and (not text)):
cmd_out = subprocess.check_output(cmd_str, universal_newlines=True)
else:
print('Error: supply either text or in_file argument to clustercat.cluster(), but not both')
clusters = {}
for line in cmd_out.split('\n'):
split_line = line.split('\t')
try:
clusters[split_line[0]] = int(split_line[1])
except:
pass
return clusters
|
def main():
' No real reason to use this as a standalone script. Just invoke the\n C-compiled binary for standalone applications. But here you\n go, anyways.\n '
import argparse
parser = argparse.ArgumentParser(description='Clusters words, or tags them')
parser.add_argument('-i', '--in', help='Load input training file')
parser.add_argument('-o', '--out', help='Save final mapping to file')
parser.add_argument('-t', '--tag', help='Tag stdin input, using clustering in supplied argument')
args = parser.parse_args()
if args.tag:
mapping = load(in_file=args.tag)
tag_stdin(mapping=mapping)
else:
mapping = cluster(text=sys.stdin)
if args.out:
save(mapping=mapping, out=args.out)
else:
print(mapping)
|
class BlobProto(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTO
|
class BlobProtoVector(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTOVECTOR
|
class Datum(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATUM
|
class FillerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FILLERPARAMETER
|
class NetParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETPARAMETER
|
class SolverParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERPARAMETER
|
class SolverState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERSTATE
|
class NetState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATE
|
class NetStateRule(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATERULE
|
class LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERPARAMETER
|
class TransformationParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TRANSFORMATIONPARAMETER
|
class AccuracyParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ACCURACYPARAMETER
|
class ArgMaxParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ARGMAXPARAMETER
|
class ConcatParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONCATPARAMETER
|
class ContrastiveLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONTRASTIVELOSSPARAMETER
|
class ConvolutionParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONVOLUTIONPARAMETER
|
class DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATAPARAMETER
|
class DropoutParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DROPOUTPARAMETER
|
class DummyDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DUMMYDATAPARAMETER
|
class EltwiseParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ELTWISEPARAMETER
|
class ThresholdParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _THRESHOLDPARAMETER
|
class HDF5DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5DATAPARAMETER
|
class HDF5OutputParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5OUTPUTPARAMETER
|
class HingeLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HINGELOSSPARAMETER
|
class ImageDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _IMAGEDATAPARAMETER
|
class InfogainLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INFOGAINLOSSPARAMETER
|
class InnerProductParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INNERPRODUCTPARAMETER
|
class LRNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LRNPARAMETER
|
class MemoryDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MEMORYDATAPARAMETER
|
class MVNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MVNPARAMETER
|
class PoolingParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POOLINGPARAMETER
|
class PowerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POWERPARAMETER
|
class ReLUParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _RELUPARAMETER
|
class SigmoidParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SIGMOIDPARAMETER
|
class SliceParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SLICEPARAMETER
|
class SoftmaxParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOFTMAXPARAMETER
|
class TanHParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TANHPARAMETER
|
class WindowDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _WINDOWDATAPARAMETER
|
class V0LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _V0LAYERPARAMETER
|
class BlobShape(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBSHAPE
|
class BlobProto(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTO
|
class BlobProtoVector(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTOVECTOR
|
class Datum(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATUM
|
class FillerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FILLERPARAMETER
|
class NetParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETPARAMETER
|
class SolverParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERPARAMETER
|
class SolverState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERSTATE
|
class NetState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATE
|
class NetStateRule(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATERULE
|
class ParamSpec(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PARAMSPEC
|
class LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERPARAMETER
|
class TransformationParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TRANSFORMATIONPARAMETER
|
class LossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LOSSPARAMETER
|
class AccuracyParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ACCURACYPARAMETER
|
class ArgMaxParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ARGMAXPARAMETER
|
class ConcatParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONCATPARAMETER
|
class ContrastiveLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONTRASTIVELOSSPARAMETER
|
class ConvolutionParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONVOLUTIONPARAMETER
|
class DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATAPARAMETER
|
class DropoutParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DROPOUTPARAMETER
|
class DummyDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DUMMYDATAPARAMETER
|
class EltwiseParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ELTWISEPARAMETER
|
class EmbedParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _EMBEDPARAMETER
|
class ExpParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _EXPPARAMETER
|
class FlattenParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FLATTENPARAMETER
|
class HDF5DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5DATAPARAMETER
|
class HDF5OutputParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5OUTPUTPARAMETER
|
class HingeLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HINGELOSSPARAMETER
|
class ImageDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _IMAGEDATAPARAMETER
|
class InfogainLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INFOGAINLOSSPARAMETER
|
class InnerProductParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INNERPRODUCTPARAMETER
|
class LogParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LOGPARAMETER
|
class LRNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LRNPARAMETER
|
class MemoryDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MEMORYDATAPARAMETER
|
class MVNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MVNPARAMETER
|
class PoolingParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POOLINGPARAMETER
|
class PowerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POWERPARAMETER
|
class PythonParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PYTHONPARAMETER
|
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