layers.py 文件源码

def __init__(
        self,
        input_size,
        nb_channels=3,
        conditional=False,
        latent_dim=10,
        nb_pixelcnn_layers=13,
        nb_filters=128,
        filter_size_1st=(7,7),
        filter_size=(3,3),
        optimizer='adadelta',
        es_patience=100,
        save_root='/tmp/pixelcnn',
        save_best_only=False,
        **kwargs):
        '''
        Args:
            input_size ((int,int))      : (height, width) pixels of input images
            nb_channels (int)           : Number of channels for input images. (1 for grayscale images, 3 for color images)
            conditional (bool)          : if True, use latent vector to model the conditional distribution p(x|h) (default:False)
            latent_dim (int)            : (if conditional==True,) Dimensions for latent vector.
            nb_pixelcnn_layers (int)    : Number of layers (except last two ReLu layers). (default:13)
            nb_filters (int)            : Number of filters (feature maps) for each layer. (default:128)
            filter_size_1st ((int, int)): Kernel size for the first layer. (default: (7,7))
            filter_size ((int, int))    : Kernel size for the subsequent layers. (default: (3,3))
            optimizer (str)             : SGD optimizer (default: 'adadelta')
            es_patience (int)           : Number of epochs with no improvement after which training will be stopped (EarlyStopping)
            save_root (str)             : Root directory to which {trained model file, parameter.txt, tensorboard log file} are saved
            save_best_only (bool)       : if True, the latest best model will not be overwritten (default: False)
        '''
        K.set_image_dim_ordering('tf')

        self.input_size = input_size
        self.conditional = conditional
        self.latent_dim = latent_dim
        self.nb_pixelcnn_layers = nb_pixelcnn_layers
        self.nb_filters = nb_filters
        self.filter_size_1st = filter_size_1st
        self.filter_size = filter_size
        self.nb_channels = nb_channels
        if self.nb_channels == 1:
            self.loss = 'binary_crossentropy'
        elif self.nb_channels == 3:
            self.loss = 'categorical_crossentropy'
        self.optimizer = optimizer
        self.es_patience = es_patience
        self.save_best_only = save_best_only

        tensorboard_dir = os.path.join(save_root, 'pixelcnn-tensorboard')
        checkpoint_path = os.path.join(save_root, 'pixelcnn-weights.{epoch:02d}-{val_loss:.4f}.hdf5')
        self.tensorboard = TensorBoard(log_dir=tensorboard_dir)
        ### "save_weights_only=False" causes error when exporting model architecture. (json or yaml)
        self.checkpointer = ModelCheckpoint(filepath=checkpoint_path, verbose=1, save_weights_only=True, save_best_only=save_best_only)
        self.earlystopping = EarlyStopping(monitor='val_loss', patience=es_patience, verbose=0, mode='auto')