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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Hyperparameter values."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import json
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import numbers
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import re
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import six
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## from tensorflow.contrib.training.python.training import hparam_pb2
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## from tensorflow.python.framework import ops
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## from tensorflow.python.util import compat
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## from tensorflow.python.util import deprecation
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# Define the regular expression for parsing a single clause of the input
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# (delimited by commas). A legal clause looks like:
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# <variable name>[<index>]? = <rhs>
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# where <rhs> is either a single token or [] enclosed list of tokens.
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# For example: "var[1] = a" or "x = [1,2,3]"
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PARAM_RE = re.compile(r"""
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(?P<name>[a-zA-Z][\w\.]*) # variable name: "var" or "x"
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(\[\s*(?P<index>\d+)\s*\])? # (optional) index: "1" or None
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\s*=\s*
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((?P<val>[^,\[]*) # single value: "a" or None
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|
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\[(?P<vals>[^\]]*)\]) # list of values: None or "1,2,3"
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($|,\s*)""", re.VERBOSE)
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def _parse_fail(name, var_type, value, values):
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"""Helper function for raising a value error for bad assignment."""
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raise ValueError(
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'Could not parse hparam \'%s\' of type \'%s\' with value \'%s\' in %s' %
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(name, var_type.__name__, value, values))
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def _reuse_fail(name, values):
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"""Helper function for raising a value error for reuse of name."""
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raise ValueError('Multiple assignments to variable \'%s\' in %s' %
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(name, values))
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def _process_scalar_value(name, parse_fn, var_type, m_dict, values,
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results_dictionary):
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"""Update results_dictionary with a scalar value.
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Used to update the results_dictionary to be returned by parse_values when
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encountering a clause with a scalar RHS (e.g. "s=5" or "arr[0]=5".)
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Mutates results_dictionary.
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Args:
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name: Name of variable in assignment ("s" or "arr").
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parse_fn: Function for parsing the actual value.
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var_type: Type of named variable.
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m_dict: Dictionary constructed from regex parsing.
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m_dict['val']: RHS value (scalar)
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m_dict['index']: List index value (or None)
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values: Full expression being parsed
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results_dictionary: The dictionary being updated for return by the parsing
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function.
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Raises:
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ValueError: If the name has already been used.
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"""
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try:
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parsed_value = parse_fn(m_dict['val'])
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except ValueError:
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_parse_fail(name, var_type, m_dict['val'], values)
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# If no index is provided
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if not m_dict['index']:
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if name in results_dictionary:
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_reuse_fail(name, values)
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results_dictionary[name] = parsed_value
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else:
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if name in results_dictionary:
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# The name has already been used as a scalar, then it
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# will be in this dictionary and map to a non-dictionary.
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if not isinstance(results_dictionary.get(name), dict):
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_reuse_fail(name, values)
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else:
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results_dictionary[name] = {}
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index = int(m_dict['index'])
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# Make sure the index position hasn't already been assigned a value.
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if index in results_dictionary[name]:
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_reuse_fail('{}[{}]'.format(name, index), values)
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results_dictionary[name][index] = parsed_value
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def _process_list_value(name, parse_fn, var_type, m_dict, values,
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results_dictionary):
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"""Update results_dictionary from a list of values.
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Used to update results_dictionary to be returned by parse_values when
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encountering a clause with a list RHS (e.g. "arr=[1,2,3]".)
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Mutates results_dictionary.
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Args:
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name: Name of variable in assignment ("arr").
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parse_fn: Function for parsing individual values.
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var_type: Type of named variable.
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m_dict: Dictionary constructed from regex parsing.
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m_dict['val']: RHS value (scalar)
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values: Full expression being parsed
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results_dictionary: The dictionary being updated for return by the parsing
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function.
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Raises:
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ValueError: If the name has an index or the values cannot be parsed.
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"""
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if m_dict['index'] is not None:
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raise ValueError('Assignment of a list to a list index.')
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elements = filter(None, re.split('[ ,]', m_dict['vals']))
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# Make sure the name hasn't already been assigned a value
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if name in results_dictionary:
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raise _reuse_fail(name, values)
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try:
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results_dictionary[name] = [parse_fn(e) for e in elements]
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except ValueError:
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_parse_fail(name, var_type, m_dict['vals'], values)
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def _cast_to_type_if_compatible(name, param_type, value):
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"""Cast hparam to the provided type, if compatible.
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Args:
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name: Name of the hparam to be cast.
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param_type: The type of the hparam.
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value: The value to be cast, if compatible.
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Returns:
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The result of casting `value` to `param_type`.
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Raises:
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ValueError: If the type of `value` is not compatible with param_type.
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* If `param_type` is a string type, but `value` is not.
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* If `param_type` is a boolean, but `value` is not, or vice versa.
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* If `param_type` is an integer type, but `value` is not.
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* If `param_type` is a float type, but `value` is not a numeric type.
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"""
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fail_msg = ("Could not cast hparam '%s' of type '%s' from value %r" %
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(name, param_type, value))
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# Some callers use None, for which we can't do any casting/checking. :(
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if issubclass(param_type, type(None)):
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return value
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# Avoid converting a non-string type to a string.
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if (issubclass(param_type, (six.string_types, six.binary_type)) and
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not isinstance(value, (six.string_types, six.binary_type))):
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raise ValueError(fail_msg)
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# Avoid converting a number or string type to a boolean or vice versa.
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if issubclass(param_type, bool) != isinstance(value, bool):
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raise ValueError(fail_msg)
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# Avoid converting float to an integer (the reverse is fine).
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if (issubclass(param_type, numbers.Integral) and
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not isinstance(value, numbers.Integral)):
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raise ValueError(fail_msg)
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# Avoid converting a non-numeric type to a numeric type.
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if (issubclass(param_type, numbers.Number) and
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not isinstance(value, numbers.Number)):
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raise ValueError(fail_msg)
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return param_type(value)
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def parse_values(values, type_map):
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"""Parses hyperparameter values from a string into a python map.
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`values` is a string containing comma-separated `name=value` pairs.
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For each pair, the value of the hyperparameter named `name` is set to
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`value`.
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If a hyperparameter name appears multiple times in `values`, a ValueError
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is raised (e.g. 'a=1,a=2', 'a[1]=1,a[1]=2').
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If a hyperparameter name in both an index assignment and scalar assignment,
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a ValueError is raised. (e.g. 'a=[1,2,3],a[0] = 1').
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The hyperparameter name may contain '.' symbols, which will result in an
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attribute name that is only accessible through the getattr and setattr
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functions. (And must be first explicit added through add_hparam.)
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WARNING: Use of '.' in your variable names is allowed, but is not well
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supported and not recommended.
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The `value` in `name=value` must follows the syntax according to the
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type of the parameter:
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* Scalar integer: A Python-parsable integer point value. E.g.: 1,
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100, -12.
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* Scalar float: A Python-parsable floating point value. E.g.: 1.0,
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-.54e89.
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* Boolean: Either true or false.
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* Scalar string: A non-empty sequence of characters, excluding comma,
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spaces, and square brackets. E.g.: foo, bar_1.
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* List: A comma separated list of scalar values of the parameter type
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enclosed in square brackets. E.g.: [1,2,3], [1.0,1e-12], [high,low].
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When index assignment is used, the corresponding type_map key should be the
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list name. E.g. for "arr[1]=0" the type_map must have the key "arr" (not
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"arr[1]").
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Args:
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values: String. Comma separated list of `name=value` pairs where
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'value' must follow the syntax described above.
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type_map: A dictionary mapping hyperparameter names to types. Note every
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parameter name in values must be a key in type_map. The values must
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conform to the types indicated, where a value V is said to conform to a
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type T if either V has type T, or V is a list of elements of type T.
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Hence, for a multidimensional parameter 'x' taking float values,
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'x=[0.1,0.2]' will parse successfully if type_map['x'] = float.
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Returns:
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A python map mapping each name to either:
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* A scalar value.
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* A list of scalar values.
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* A dictionary mapping index numbers to scalar values.
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(e.g. "x=5,L=[1,2],arr[1]=3" results in {'x':5,'L':[1,2],'arr':{1:3}}")
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Raises:
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ValueError: If there is a problem with input.
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* If `values` cannot be parsed.
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* If a list is assigned to a list index (e.g. 'a[1] = [1,2,3]').
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* If the same rvalue is assigned two different values (e.g. 'a=1,a=2',
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'a[1]=1,a[1]=2', or 'a=1,a=[1]')
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"""
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results_dictionary = {}
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pos = 0
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while pos < len(values):
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m = PARAM_RE.match(values, pos)
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if not m:
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raise ValueError('Malformed hyperparameter value: %s' %
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values[pos:])
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# Check that there is a comma between parameters and move past it.
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pos = m.end()
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# Parse the values.
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m_dict = m.groupdict()
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name = m_dict['name']
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if name not in type_map:
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raise ValueError('Unknown hyperparameter type for %s' % name)
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type_ = type_map[name]
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# Set up correct parsing function (depending on whether type_ is a bool)
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if type_ == bool:
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def parse_bool(value):
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if value in ['true', 'True']:
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return True
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elif value in ['false', 'False']:
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return False
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else:
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try:
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return bool(int(value))
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except ValueError:
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_parse_fail(name, type_, value, values)
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parse = parse_bool
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else:
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parse = type_
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# If a singe value is provided
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if m_dict['val'] is not None:
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_process_scalar_value(name, parse, type_, m_dict, values,
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results_dictionary)
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# If the assigned value is a list:
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elif m_dict['vals'] is not None:
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_process_list_value(name, parse, type_, m_dict, values,
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results_dictionary)
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else: # Not assigned a list or value
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_parse_fail(name, type_, '', values)
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return results_dictionary
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class HParams(object):
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"""Class to hold a set of hyperparameters as name-value pairs.
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A `HParams` object holds hyperparameters used to build and train a model,
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such as the number of hidden units in a neural net layer or the learning rate
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to use when training.
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You first create a `HParams` object by specifying the names and values of the
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hyperparameters.
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To make them easily accessible the parameter names are added as direct
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attributes of the class. A typical usage is as follows:
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```python
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# Create a HParams object specifying names and values of the model
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# hyperparameters:
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hparams = HParams(learning_rate=0.1, num_hidden_units=100)
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# The hyperparameter are available as attributes of the HParams object:
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hparams.learning_rate ==> 0.1
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hparams.num_hidden_units ==> 100
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```
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Hyperparameters have type, which is inferred from the type of their value
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passed at construction type. The currently supported types are: integer,
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float, boolean, string, and list of integer, float, boolean, or string.
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You can override hyperparameter values by calling the
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[`parse()`](#HParams.parse) method, passing a string of comma separated
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`name=value` pairs. This is intended to make it possible to override
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any hyperparameter values from a single command-line flag to which
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the user passes 'hyper-param=value' pairs. It avoids having to define
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one flag for each hyperparameter.
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The syntax expected for each value depends on the type of the parameter.
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See `parse()` for a description of the syntax.
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Example:
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```python
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# Define a command line flag to pass name=value pairs.
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# For example using argparse:
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import argparse
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parser = argparse.ArgumentParser(description='Train my model.')
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parser.add_argument('--hparams', type=str,
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help='Comma separated list of "name=value" pairs.')
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args = parser.parse_args()
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...
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def my_program():
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# Create a HParams object specifying the names and values of the
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# model hyperparameters:
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hparams = tf.HParams(learning_rate=0.1, num_hidden_units=100,
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activations=['relu', 'tanh'])
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# Override hyperparameters values by parsing the command line
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hparams.parse(args.hparams)
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# If the user passed `--hparams=learning_rate=0.3` on the command line
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# then 'hparams' has the following attributes:
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hparams.learning_rate ==> 0.3
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hparams.num_hidden_units ==> 100
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hparams.activations ==> ['relu', 'tanh']
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# If the hyperparameters are in json format use parse_json:
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hparams.parse_json('{"learning_rate": 0.3, "activations": "relu"}')
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```
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"""
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_HAS_DYNAMIC_ATTRIBUTES = True # Required for pytype checks.
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def __init__(self, hparam_def=None, model_structure=None, **kwargs):
|
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"""Create an instance of `HParams` from keyword arguments.
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The keyword arguments specify name-values pairs for the hyperparameters.
|
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The parameter types are inferred from the type of the values passed.
|
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|
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The parameter names are added as attributes of `HParams` object, so they
|
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can be accessed directly with the dot notation `hparams._name_`.
|
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|
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Example:
|
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|
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```python
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# Define 3 hyperparameters: 'learning_rate' is a float parameter,
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# 'num_hidden_units' an integer parameter, and 'activation' a string
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# parameter.
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hparams = tf.HParams(
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learning_rate=0.1, num_hidden_units=100, activation='relu')
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|
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hparams.activation ==> 'relu'
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```
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|
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Note that a few names are reserved and cannot be used as hyperparameter
|
||||
names. If you use one of the reserved name the constructor raises a
|
||||
`ValueError`.
|
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|
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Args:
|
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hparam_def: Serialized hyperparameters, encoded as a hparam_pb2.HParamDef
|
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protocol buffer. If provided, this object is initialized by
|
||||
deserializing hparam_def. Otherwise **kwargs is used.
|
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model_structure: An instance of ModelStructure, defining the feature
|
||||
crosses to be used in the Trial.
|
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**kwargs: Key-value pairs where the key is the hyperparameter name and
|
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the value is the value for the parameter.
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|
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Raises:
|
||||
ValueError: If both `hparam_def` and initialization values are provided,
|
||||
or if one of the arguments is invalid.
|
||||
|
||||
"""
|
||||
# Register the hyperparameters and their type in _hparam_types.
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||||
# This simplifies the implementation of parse().
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# _hparam_types maps the parameter name to a tuple (type, bool).
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||||
# The type value is the type of the parameter for scalar hyperparameters,
|
||||
# or the type of the list elements for multidimensional hyperparameters.
|
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# The bool value is True if the value is a list, False otherwise.
|
||||
self._hparam_types = {}
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self._model_structure = model_structure
|
||||
if hparam_def:
|
||||
## self._init_from_proto(hparam_def)
|
||||
## if kwargs:
|
||||
## raise ValueError('hparam_def and initialization values are '
|
||||
## 'mutually exclusive')
|
||||
raise ValueError('hparam_def has been disabled in this version')
|
||||
else:
|
||||
for name, value in six.iteritems(kwargs):
|
||||
self.add_hparam(name, value)
|
||||
|
||||
## def _init_from_proto(self, hparam_def):
|
||||
## """Creates a new HParams from `HParamDef` protocol buffer.
|
||||
##
|
||||
## Args:
|
||||
## hparam_def: `HParamDef` protocol buffer.
|
||||
## """
|
||||
## assert isinstance(hparam_def, hparam_pb2.HParamDef)
|
||||
## for name, value in hparam_def.hparam.items():
|
||||
## kind = value.WhichOneof('kind')
|
||||
## if kind.endswith('_value'):
|
||||
## # Single value.
|
||||
## if kind.startswith('int64'):
|
||||
## # Setting attribute value to be 'int' to ensure the type is compatible
|
||||
## # with both Python2 and Python3.
|
||||
## self.add_hparam(name, int(getattr(value, kind)))
|
||||
## elif kind.startswith('bytes'):
|
||||
## # Setting attribute value to be 'str' to ensure the type is compatible
|
||||
## # with both Python2 and Python3. UTF-8 encoding is assumed.
|
||||
## self.add_hparam(name, compat.as_str(getattr(value, kind)))
|
||||
## else:
|
||||
## self.add_hparam(name, getattr(value, kind))
|
||||
## else:
|
||||
## # List of values.
|
||||
## if kind.startswith('int64'):
|
||||
## # Setting attribute value to be 'int' to ensure the type is compatible
|
||||
## # with both Python2 and Python3.
|
||||
## self.add_hparam(name, [int(v) for v in getattr(value, kind).value])
|
||||
## elif kind.startswith('bytes'):
|
||||
## # Setting attribute value to be 'str' to ensure the type is compatible
|
||||
## # with both Python2 and Python3. UTF-8 encoding is assumed.
|
||||
## self.add_hparam(
|
||||
## name, [compat.as_str(v) for v in getattr(value, kind).value])
|
||||
## else:
|
||||
## self.add_hparam(name, [v for v in getattr(value, kind).value])
|
||||
|
||||
def add_hparam(self, name, value):
|
||||
"""Adds {name, value} pair to hyperparameters.
|
||||
|
||||
Args:
|
||||
name: Name of the hyperparameter.
|
||||
value: Value of the hyperparameter. Can be one of the following types:
|
||||
int, float, string, int list, float list, or string list.
|
||||
|
||||
Raises:
|
||||
ValueError: if one of the arguments is invalid.
|
||||
"""
|
||||
# Keys in kwargs are unique, but 'name' could the name of a pre-existing
|
||||
# attribute of this object. In that case we refuse to use it as a
|
||||
# hyperparameter name.
|
||||
if getattr(self, name, None) is not None:
|
||||
raise ValueError('Hyperparameter name is reserved: %s' % name)
|
||||
if isinstance(value, (list, tuple)):
|
||||
if not value:
|
||||
raise ValueError(
|
||||
'Multi-valued hyperparameters cannot be empty: %s' % name)
|
||||
self._hparam_types[name] = (type(value[0]), True)
|
||||
else:
|
||||
self._hparam_types[name] = (type(value), False)
|
||||
setattr(self, name, value)
|
||||
|
||||
def set_hparam(self, name, value):
|
||||
"""Set the value of an existing hyperparameter.
|
||||
|
||||
This function verifies that the type of the value matches the type of the
|
||||
existing hyperparameter.
|
||||
|
||||
Args:
|
||||
name: Name of the hyperparameter.
|
||||
value: New value of the hyperparameter.
|
||||
|
||||
Raises:
|
||||
ValueError: If there is a type mismatch.
|
||||
"""
|
||||
param_type, is_list = self._hparam_types[name]
|
||||
if isinstance(value, list):
|
||||
if not is_list:
|
||||
raise ValueError(
|
||||
'Must not pass a list for single-valued parameter: %s' %
|
||||
name)
|
||||
setattr(self, name, [
|
||||
_cast_to_type_if_compatible(name, param_type, v) for v in value
|
||||
])
|
||||
else:
|
||||
if is_list:
|
||||
raise ValueError(
|
||||
'Must pass a list for multi-valued parameter: %s.' % name)
|
||||
setattr(self, name,
|
||||
_cast_to_type_if_compatible(name, param_type, value))
|
||||
|
||||
def del_hparam(self, name):
|
||||
"""Removes the hyperparameter with key 'name'.
|
||||
|
||||
Args:
|
||||
name: Name of the hyperparameter.
|
||||
"""
|
||||
if hasattr(self, name):
|
||||
delattr(self, name)
|
||||
del self._hparam_types[name]
|
||||
|
||||
def parse(self, values):
|
||||
"""Override hyperparameter values, parsing new values from a string.
|
||||
|
||||
See parse_values for more detail on the allowed format for values.
|
||||
|
||||
Args:
|
||||
values: String. Comma separated list of `name=value` pairs where
|
||||
'value' must follow the syntax described above.
|
||||
|
||||
Returns:
|
||||
The `HParams` instance.
|
||||
|
||||
Raises:
|
||||
ValueError: If `values` cannot be parsed.
|
||||
"""
|
||||
type_map = dict()
|
||||
for name, t in self._hparam_types.items():
|
||||
param_type, _ = t
|
||||
type_map[name] = param_type
|
||||
|
||||
values_map = parse_values(values, type_map)
|
||||
return self.override_from_dict(values_map)
|
||||
|
||||
def override_from_dict(self, values_dict):
|
||||
"""Override hyperparameter values, parsing new values from a dictionary.
|
||||
|
||||
Args:
|
||||
values_dict: Dictionary of name:value pairs.
|
||||
|
||||
Returns:
|
||||
The `HParams` instance.
|
||||
|
||||
Raises:
|
||||
ValueError: If `values_dict` cannot be parsed.
|
||||
"""
|
||||
for name, value in values_dict.items():
|
||||
self.set_hparam(name, value)
|
||||
return self
|
||||
|
||||
## @deprecation.deprecated(None, 'Use `override_from_dict`.')
|
||||
|
||||
def set_from_map(self, values_map):
|
||||
"""DEPRECATED. Use override_from_dict."""
|
||||
return self.override_from_dict(values_dict=values_map)
|
||||
|
||||
def set_model_structure(self, model_structure):
|
||||
self._model_structure = model_structure
|
||||
|
||||
def get_model_structure(self):
|
||||
return self._model_structure
|
||||
|
||||
def to_json(self, indent=None, separators=None, sort_keys=False):
|
||||
"""Serializes the hyperparameters into JSON.
|
||||
|
||||
Args:
|
||||
indent: If a non-negative integer, JSON array elements and object members
|
||||
will be pretty-printed with that indent level. An indent level of 0, or
|
||||
negative, will only insert newlines. `None` (the default) selects the
|
||||
most compact representation.
|
||||
separators: Optional `(item_separator, key_separator)` tuple. Default is
|
||||
`(', ', ': ')`.
|
||||
sort_keys: If `True`, the output dictionaries will be sorted by key.
|
||||
|
||||
Returns:
|
||||
A JSON string.
|
||||
"""
|
||||
return json.dumps(
|
||||
self.values(),
|
||||
indent=indent,
|
||||
separators=separators,
|
||||
sort_keys=sort_keys)
|
||||
|
||||
def parse_json(self, values_json):
|
||||
"""Override hyperparameter values, parsing new values from a json object.
|
||||
|
||||
Args:
|
||||
values_json: String containing a json object of name:value pairs.
|
||||
|
||||
Returns:
|
||||
The `HParams` instance.
|
||||
|
||||
Raises:
|
||||
ValueError: If `values_json` cannot be parsed.
|
||||
"""
|
||||
values_map = json.loads(values_json)
|
||||
return self.override_from_dict(values_map)
|
||||
|
||||
def values(self):
|
||||
"""Return the hyperparameter values as a Python dictionary.
|
||||
|
||||
Returns:
|
||||
A dictionary with hyperparameter names as keys. The values are the
|
||||
hyperparameter values.
|
||||
"""
|
||||
return {n: getattr(self, n) for n in self._hparam_types.keys()}
|
||||
|
||||
def get(self, key, default=None):
|
||||
"""Returns the value of `key` if it exists, else `default`."""
|
||||
if key in self._hparam_types:
|
||||
# Ensure that default is compatible with the parameter type.
|
||||
if default is not None:
|
||||
param_type, is_param_list = self._hparam_types[key]
|
||||
type_str = 'list<%s>' % param_type if is_param_list else str(
|
||||
param_type)
|
||||
fail_msg = ("Hparam '%s' of type '%s' is incompatible with "
|
||||
'default=%s' % (key, type_str, default))
|
||||
|
||||
is_default_list = isinstance(default, list)
|
||||
if is_param_list != is_default_list:
|
||||
raise ValueError(fail_msg)
|
||||
|
||||
try:
|
||||
if is_default_list:
|
||||
for value in default:
|
||||
_cast_to_type_if_compatible(key, param_type, value)
|
||||
else:
|
||||
_cast_to_type_if_compatible(key, param_type, default)
|
||||
except ValueError as e:
|
||||
raise ValueError('%s. %s' % (fail_msg, e))
|
||||
|
||||
return getattr(self, key)
|
||||
|
||||
return default
|
||||
|
||||
def __contains__(self, key):
|
||||
return key in self._hparam_types
|
||||
|
||||
def __str__(self):
|
||||
return str(sorted(self.values().items()))
|
||||
|
||||
def __repr__(self):
|
||||
return '%s(%s)' % (type(self).__name__, self.__str__())
|
||||
|
||||
@staticmethod
|
||||
def _get_kind_name(param_type, is_list):
|
||||
"""Returns the field name given parameter type and is_list.
|
||||
|
||||
Args:
|
||||
param_type: Data type of the hparam.
|
||||
is_list: Whether this is a list.
|
||||
|
||||
Returns:
|
||||
A string representation of the field name.
|
||||
|
||||
Raises:
|
||||
ValueError: If parameter type is not recognized.
|
||||
"""
|
||||
if issubclass(param_type, bool):
|
||||
# This check must happen before issubclass(param_type, six.integer_types),
|
||||
# since Python considers bool to be a subclass of int.
|
||||
typename = 'bool'
|
||||
elif issubclass(param_type, six.integer_types):
|
||||
# Setting 'int' and 'long' types to be 'int64' to ensure the type is
|
||||
# compatible with both Python2 and Python3.
|
||||
typename = 'int64'
|
||||
elif issubclass(param_type, (six.string_types, six.binary_type)):
|
||||
# Setting 'string' and 'bytes' types to be 'bytes' to ensure the type is
|
||||
# compatible with both Python2 and Python3.
|
||||
typename = 'bytes'
|
||||
elif issubclass(param_type, float):
|
||||
typename = 'float'
|
||||
else:
|
||||
raise ValueError('Unsupported parameter type: %s' % str(param_type))
|
||||
|
||||
suffix = 'list' if is_list else 'value'
|
||||
return '_'.join([typename, suffix])
|
||||
|
||||
|
||||
## def to_proto(self, export_scope=None): # pylint: disable=unused-argument
|
||||
## """Converts a `HParams` object to a `HParamDef` protocol buffer.
|
||||
##
|
||||
## Args:
|
||||
## export_scope: Optional `string`. Name scope to remove.
|
||||
##
|
||||
## Returns:
|
||||
## A `HParamDef` protocol buffer.
|
||||
## """
|
||||
## hparam_proto = hparam_pb2.HParamDef()
|
||||
## for name in self._hparam_types:
|
||||
## # Parse the values.
|
||||
## param_type, is_list = self._hparam_types.get(name, (None, None))
|
||||
## kind = HParams._get_kind_name(param_type, is_list)
|
||||
##
|
||||
## if is_list:
|
||||
## if kind.startswith('bytes'):
|
||||
## v_list = [compat.as_bytes(v) for v in getattr(self, name)]
|
||||
## else:
|
||||
## v_list = [v for v in getattr(self, name)]
|
||||
## getattr(hparam_proto.hparam[name], kind).value.extend(v_list)
|
||||
## else:
|
||||
## v = getattr(self, name)
|
||||
## if kind.startswith('bytes'):
|
||||
## v = compat.as_bytes(getattr(self, name))
|
||||
## setattr(hparam_proto.hparam[name], kind, v)
|
||||
##
|
||||
## return hparam_proto
|
||||
|
||||
## @staticmethod
|
||||
## def from_proto(hparam_def, import_scope=None): # pylint: disable=unused-argument
|
||||
## return HParams(hparam_def=hparam_def)
|
||||
|
||||
## ops.register_proto_function(
|
||||
## 'hparams',
|
||||
## proto_type=hparam_pb2.HParamDef,
|
||||
## to_proto=HParams.to_proto,
|
||||
## from_proto=HParams.from_proto)
|
||||
|
|
@ -1,8 +0,0 @@
|
|||
Source: hparam.py copied from tensorflow v1.12.0.
|
||||
|
||||
https://github.com/tensorflow/tensorflow/blob/v1.12.0/tensorflow/contrib/training/python/training/hparam.py
|
||||
|
||||
with the following:
|
||||
wget https://github.com/tensorflow/tensorflow/raw/v1.12.0/tensorflow/contrib/training/python/training/hparam.py
|
||||
|
||||
Once all other tensorflow dependencies of these file are removed, the class keeps its goal. Functions not available due to this process are not used in this project.
|
||||
|
|
@ -19,7 +19,7 @@ import paddle
|
|||
from paddle import fluid
|
||||
import paddle.fluid.dygraph as dg
|
||||
|
||||
from weight_norm import Conv2D, Conv2DTranspose
|
||||
from .weight_norm import Conv2D, Conv2DTranspose
|
||||
|
||||
|
||||
class Conv1D(dg.Layer):
|
||||
|
|
|
|||
|
|
@ -18,8 +18,8 @@ import paddle.fluid.dygraph as dg
|
|||
|
||||
import numpy as np
|
||||
|
||||
import conv
|
||||
import weight_norm as weight_norm
|
||||
from .import conv
|
||||
from . import weight_norm
|
||||
|
||||
|
||||
def FC(name_scope,
|
||||
|
|
|
|||
Loading…
Reference in New Issue