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LR.py
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# =========================================================================
# Copyright (C) 2024. The FuxiCTR Library. All rights reserved.
# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =========================================================================
from fuxictr.pytorch.models import BaseModel
from fuxictr.pytorch.layers import LogisticRegression
class LR(BaseModel):
def __init__(self,
feature_map,
model_id="LR",
gpu=-1,
learning_rate=1e-3,
regularizer=None,
**kwargs):
super(LR, self).__init__(feature_map,
model_id=model_id,
gpu=gpu,
embedding_regularizer=regularizer,
net_regularizer=regularizer,
**kwargs)
self.lr_layer = LogisticRegression(feature_map, use_bias=True)
self.compile(kwargs["optimizer"], kwargs["loss"], learning_rate)
self.reset_parameters()
self.model_to_device()
def forward(self, inputs):
"""
Inputs: [X, y]
"""
X = self.get_inputs(inputs)
y_pred = self.lr_layer(X)
y_pred = self.output_activation(y_pred)
return_dict = {"y_pred": y_pred}
return return_dict