load_deepmodels.py 文件源码

def load_model(self, frm_modelweights='', frmdiff_modelweights=''):
        frm_model = Sequential()
        frm_model.add(GRU(4096,
                          return_sequences=True,
                          input_dim=self.feature_size,
                          input_length=MAX_FRAMES,
                          activation='relu',
                          name='fc1'))
        frm_model.add(Dropout(0.3))
        frm_model.add(GRU(4096,
                          return_sequences=False,
                          activation='relu',
                          name='fc2'))
        frm_model.add(Dropout(0.3))
        frm_model.add(Dense(self.numclasses, activation='softmax', name='frm_prediction'))
        if frm_modelweights:
            frm_model.load_weights(frm_modelweights, by_name=True)
            print("Frame model loaded with weights from %s." % frm_modelweights)
        else:
            print "Empty frame model loaded."

        '''
        frmdiff_model = Sequential()
        frmdiff_model.add(GRU(4096, input_dim=self.feature_size, activation='relu', name='fc1'))
        frmdiff_model.add(Dropout(0.3))
        frmdiff_model.add(GRU(4096, activation='relu', name='fc2'))
        frmdiff_model.add(Dropout(0.3))
        frmdiff_model.add(Dense(self.numclasses, activation='softmax', name='frmdiff_feature'))

        if frmdiff_modelweights:
            frmdiff_model.load_weights(frmdiff_modelweights, by_name=True)
            print('Frame model loaded with weights from %s.' % frmdiff_modelweights)
        else:
            print "Empty frame model loaded."

        model = Sequential()
        model.add(Merge([frm_model, frmdiff_model], mode='concat'))
        model.add(Dense(self.numclasses, activation='softmax', name='predictions'))
        '''

        return frm_model