ddpg.py 文件源码

def build_network(self, conf, model=None, input_shape=None, is_conv=True):
        """Build network"""
        _model = model
        model = Sequential()
        if _model is None:
            model.add(Lambda(lambda x: x,  input_shape=input_shape))
        else:
            model.add(_model)

        for x in conf:
            if x['is_drop']:
                model.add(Dropout(x['drop_rate']))
            if x['type'] is 'full':
                if is_conv:
                    model.add(Flatten())
                    is_conv = False
                model.add(Dense(x['n_feature']))
            elif x['type'] is 'conv':
                model.add(Convolution2D(nb_filter=x['n_feature'], 
                                        nb_row=x['kw'], 
                                        nb_col=1, 
                                        border_mode='same'))  
                is_conv=True
            if x['is_batch']:
                if x['type'] is 'full':
                    model.add(BatchNormalization(mode=1, axis=-1))
                if x['type'] is 'conv':
                    model.add(BatchNormalization(mode=2, axis=-1))
            model.add(x['activation'])
        return model