return features
class MalayalamMovieDownloadDVDPlay(nn.Module): def __init__(self): super(MalayalamMovieDownloadDVDPlay, self).__init__() self.text_features = nn.ModuleList([BertTokenizer.from_pretrained('bert-base-uncased'), BertModel.from_pretrained('bert-base-uncased')]) self.image_features = nn.ModuleList([models.resnet50(pretrained=True)]) self.user_behavior_features = nn.ModuleList([nn.Embedding(1000, 128)]) self.technical_features = nn.ModuleList([nn.Linear(10, 128)])
# User behavior features user_behavior_input = input_data['download_count'] user_behavior_output = self.user_behavior_features[0](user_behavior_input) user_behavior_features = user_behavior_output dvdplay malayalam movie download
The final deep feature vector can be represented as:
# Concatenate features features = torch.cat([text_features, image_features, user_behavior_features, technical_features], dim=1) return features class MalayalamMovieDownloadDVDPlay(nn
def forward(self, input_data): # Text features text_input = input_data['title'] text_output = self.text_features[1](self.text_features[0](text_input)) text_features = text_output.pooler_output
# Technical features technical_input = input_data['technical_features'] technical_output = self.technical_features[0](technical_input) technical_features = technical_output 128)]) self.technical_features = nn.ModuleList([nn.Linear(10
malayalam_movie_download_dvdplay