108 lines
4 KiB
Python
108 lines
4 KiB
Python
# Copyright (c) 2018, deepakn94, robieta. 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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# -----------------------------------------------------------------------
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#
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# Copyright (c) 2018, NVIDIA CORPORATION. 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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import numpy as np
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import torch
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import torch.nn as nn
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import sys
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from os.path import abspath, join, dirname
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# enabling modules discovery from the global entrypoint
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sys.path.append(abspath(dirname(__file__) + '/'))
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from logger.logger import LOGGER
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from logger import tags
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LOGGER.model = 'ncf'
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class NeuMF(nn.Module):
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def __init__(self, nb_users, nb_items,
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mf_dim, mlp_layer_sizes, dropout=0):
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if mlp_layer_sizes[0] % 2 != 0:
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raise RuntimeError('u dummy, mlp_layer_sizes[0] % 2 != 0')
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super(NeuMF, self).__init__()
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nb_mlp_layers = len(mlp_layer_sizes)
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LOGGER.log(key=tags.MODEL_HP_MF_DIM, value=mf_dim)
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self.mf_user_embed = nn.Embedding(nb_users, mf_dim)
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self.mf_item_embed = nn.Embedding(nb_items, mf_dim)
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self.mlp_user_embed = nn.Embedding(nb_users, mlp_layer_sizes[0] // 2)
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self.mlp_item_embed = nn.Embedding(nb_items, mlp_layer_sizes[0] // 2)
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self.dropout = dropout
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LOGGER.log(key=tags.MODEL_HP_MLP_LAYER_SIZES, value=mlp_layer_sizes)
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self.mlp = nn.ModuleList()
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for i in range(1, nb_mlp_layers):
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self.mlp.extend([nn.Linear(mlp_layer_sizes[i - 1], mlp_layer_sizes[i])]) # noqa: E501
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self.final = nn.Linear(mlp_layer_sizes[-1] + mf_dim, 1)
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self.mf_user_embed.weight.data.normal_(0., 0.01)
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self.mf_item_embed.weight.data.normal_(0., 0.01)
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self.mlp_user_embed.weight.data.normal_(0., 0.01)
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self.mlp_item_embed.weight.data.normal_(0., 0.01)
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def glorot_uniform(layer):
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fan_in, fan_out = layer.in_features, layer.out_features
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limit = np.sqrt(6. / (fan_in + fan_out))
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layer.weight.data.uniform_(-limit, limit)
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def lecunn_uniform(layer):
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fan_in, fan_out = layer.in_features, layer.out_features # noqa: F841, E501
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limit = np.sqrt(3. / fan_in)
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layer.weight.data.uniform_(-limit, limit)
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for layer in self.mlp:
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if type(layer) != nn.Linear:
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continue
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glorot_uniform(layer)
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lecunn_uniform(self.final)
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def forward(self, user, item, sigmoid=False):
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xmfu = self.mf_user_embed(user)
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xmfi = self.mf_item_embed(item)
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xmf = xmfu * xmfi
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xmlpu = self.mlp_user_embed(user)
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xmlpi = self.mlp_item_embed(item)
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xmlp = torch.cat((xmlpu, xmlpi), dim=1)
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for i, layer in enumerate(self.mlp):
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xmlp = layer(xmlp)
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xmlp = nn.functional.relu(xmlp)
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if self.dropout != 0:
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xmlp = nn.functional.dropout(xmlp, p=self.dropout, training=self.training)
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x = torch.cat((xmf, xmlp), dim=1)
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x = self.final(x)
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if sigmoid:
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x = torch.sigmoid(x)
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return x
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