84 lines
2.8 KiB
Python
84 lines
2.8 KiB
Python
# Copyright 2019 Mycroft AI Inc.
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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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import attr
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from os.path import isfile
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from typing import *
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from precise_lite.functions import load_keras, false_pos, false_neg, \
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weighted_log_loss, set_loss_bias
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from precise_lite.params import inject_params, pr
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if TYPE_CHECKING:
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from tensorflow.keras.models import Sequential
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@attr.s()
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class ModelParams:
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"""
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Attributes:
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recurrent_units:
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dropout:
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extra_metrics: Whether to include false positive and false negative metrics
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skip_acc: Whether to skip accuracy calculation while training
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"""
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recurrent_units = attr.ib(20) # type: int
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dropout = attr.ib(0.2) # type: float
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extra_metrics = attr.ib(False) # type: bool
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skip_acc = attr.ib(False) # type: bool
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loss_bias = attr.ib(0.7) # type: float
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freeze_till = attr.ib(0) # type: bool
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def load_precise_model(model_name: str) -> Any:
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"""Loads a Keras model from file, handling custom loss function"""
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if not model_name.endswith('.net'):
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print('Warning: Unknown model type, ', model_name)
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inject_params(model_name)
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from tensorflow.keras.models import load_model
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return load_model(model_name, custom_objects=globals())
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def create_model(model_name: Optional[str], params: ModelParams) -> 'Sequential':
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"""
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Load or create a precise_lite model
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Args:
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model_name: Name of model
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params: Parameters used to create the model
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Returns:
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model: Loaded Keras model
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"""
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if model_name and isfile(model_name):
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print('Loading from ' + model_name + '...')
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model = load_precise_model(model_name)
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else:
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from tensorflow.keras.layers import Dense, GRU
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from tensorflow.keras.models import Sequential
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model = Sequential()
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model.add(GRU(
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params.recurrent_units, activation='linear',
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input_shape=(pr.n_features, pr.feature_size), dropout=params.dropout, name='net'
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))
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model.add(Dense(1, activation='sigmoid'))
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metrics = ['accuracy'] + params.extra_metrics * [false_pos, false_neg]
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set_loss_bias(params.loss_bias)
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for i in model.layers[:params.freeze_till]:
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i.trainable = False
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model.compile('rmsprop', weighted_log_loss, metrics=(not params.skip_acc) * metrics)
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return model
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