0

我对Keras完全陌生，我才工作了几天，所以我很缺乏经验。

我能够训练一个与类一起工作的 U-Net 网络，然后输入一个 RGB 图像和一个灰度掩码进行训练，代码如下：

def train_generator():

    while True:
        for start in range(0, len(ids_train_split), batch_size):
            x_batch = []
            y_batch = []
            end = min(start + batch_size, len(ids_train_split))
            ids_train_batch = ids_train_split[start:end]
            for id in ids_train_batch.values:

                img_name = 'IMG_'+str(id).split('_')[2]
                image_path = os.path.join("input", "train", "{}.JPG".format(str(img_name)))
                mca_mask_path = os.path.join("input", "train_mask", "{}.png".format(id))

                img = cv2.imread(image_path)
                img = cv2.resize(img, (input_size, input_size))

                mask_mca = cv2.imread(mca_mask_path, cv2.IMREAD_GRAYSCALE)
                mask_mca = cv2.resize(mask_mca, (input_size, input_size))

                img = randomHueSaturationValue(img,
                                               hue_shift_limit=(-50, 50),
                                               sat_shift_limit=(-5, 5),
                                               val_shift_limit=(-15, 15))
                img, mask = randomShiftScaleRotate(img, mask,
                                                   shift_limit=(-0.0625, 0.0625),
                                                   scale_limit=(-0.1, 0.1),
                                                   rotate_limit=(-0, 0))
                img, mask = randomHorizontalFlip(img, mask)
                mask = np.expand_dims(mask, axis=2)
                x_batch.append(img)
                y_batch.append(mask)
            x_batch = np.array(x_batch, np.float32) / 255
            y_batch = np.array(y_batch, np.float32) / 255
            yield x_batch, y_batch

这是我的 U-Net 模型：

def get_unet_1(pretrained_weights=None, input_shape=(1024, 1024, 3), num_classes=1, learning_rate=0.0001):
    inputs = Input(shape=input_shape)
    # 1024

    down0b = Conv2D(8, (3, 3), padding='same')(inputs)
    down0b = BatchNormalization()(down0b)
    down0b = Activation('relu')(down0b)
    down0b = Conv2D(8, (3, 3), padding='same')(down0b)
    down0b = BatchNormalization()(down0b)
    down0b = Activation('relu')(down0b)
    down0b_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0b)
    # 512

    down0a = Conv2D(16, (3, 3), padding='same')(down0b_pool)
    down0a = BatchNormalization()(down0a)
    down0a = Activation('relu')(down0a)
    down0a = Conv2D(16, (3, 3), padding='same')(down0a)
    down0a = BatchNormalization()(down0a)
    down0a = Activation('relu')(down0a)
    down0a_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0a)
    # 256

    down0 = Conv2D(32, (3, 3), padding='same')(down0a_pool)
    down0 = BatchNormalization()(down0)
    down0 = Activation('relu')(down0)
    down0 = Conv2D(32, (3, 3), padding='same')(down0)
    down0 = BatchNormalization()(down0)
    down0 = Activation('relu')(down0)
    down0_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0)
    # 128

    down1 = Conv2D(64, (3, 3), padding='same')(down0_pool)
    down1 = BatchNormalization()(down1)
    down1 = Activation('relu')(down1)
    down1 = Conv2D(64, (3, 3), padding='same')(down1)
    down1 = BatchNormalization()(down1)
    down1 = Activation('relu')(down1)
    down1_pool = MaxPooling2D((2, 2), strides=(2, 2))(down1)
    # 64

    down2 = Conv2D(128, (3, 3), padding='same')(down1_pool)
    down2 = BatchNormalization()(down2)
    down2 = Activation('relu')(down2)
    down2 = Conv2D(128, (3, 3), padding='same')(down2)
    down2 = BatchNormalization()(down2)
    down2 = Activation('relu')(down2)
    down2_pool = MaxPooling2D((2, 2), strides=(2, 2))(down2)
    # 32

    down3 = Conv2D(256, (3, 3), padding='same')(down2_pool)
    down3 = BatchNormalization()(down3)
    down3 = Activation('relu')(down3)
    down3 = Conv2D(256, (3, 3), padding='same')(down3)
    down3 = BatchNormalization()(down3)
    down3 = Activation('relu')(down3)
    down3_pool = MaxPooling2D((2, 2), strides=(2, 2))(down3)
    # 16

    down4 = Conv2D(512, (3, 3), padding='same')(down3_pool)
    down4 = BatchNormalization()(down4)
    down4 = Activation('relu')(down4)
    down4 = Conv2D(512, (3, 3), padding='same')(down4)
    down4 = BatchNormalization()(down4)
    down4 = Activation('relu')(down4)
    down4_pool = MaxPooling2D((2, 2), strides=(2, 2))(down4)
    # 8

    center = Conv2D(1024, (3, 3), padding='same')(down4_pool)
    center = BatchNormalization()(center)
    center = Activation('relu')(center)
    center = Conv2D(1024, (3, 3), padding='same')(center)
    center = BatchNormalization()(center)
    center = Activation('relu')(center)
    # center

    up4 = UpSampling2D((2, 2))(center)
    up4 = concatenate([down4, up4], axis=3)
    up4 = Conv2D(512, (3, 3), padding='same')(up4)
    up4 = BatchNormalization()(up4)
    up4 = Activation('relu')(up4)
    up4 = Conv2D(512, (3, 3), padding='same')(up4)
    up4 = BatchNormalization()(up4)
    up4 = Activation('relu')(up4)
    up4 = Conv2D(512, (3, 3), padding='same')(up4)
    up4 = BatchNormalization()(up4)
    up4 = Activation('relu')(up4)
    # 16

    up3 = UpSampling2D((2, 2))(up4)
    up3 = concatenate([down3, up3], axis=3)
    up3 = Conv2D(256, (3, 3), padding='same')(up3)
    up3 = BatchNormalization()(up3)
    up3 = Activation('relu')(up3)
    up3 = Conv2D(256, (3, 3), padding='same')(up3)
    up3 = BatchNormalization()(up3)
    up3 = Activation('relu')(up3)
    up3 = Conv2D(256, (3, 3), padding='same')(up3)
    up3 = BatchNormalization()(up3)
    up3 = Activation('relu')(up3)
    # 32

    up2 = UpSampling2D((2, 2))(up3)
    up2 = concatenate([down2, up2], axis=3)
    up2 = Conv2D(128, (3, 3), padding='same')(up2)
    up2 = BatchNormalization()(up2)
    up2 = Activation('relu')(up2)
    up2 = Conv2D(128, (3, 3), padding='same')(up2)
    up2 = BatchNormalization()(up2)
    up2 = Activation('relu')(up2)
    up2 = Conv2D(128, (3, 3), padding='same')(up2)
    up2 = BatchNormalization()(up2)
    up2 = Activation('relu')(up2)
    # 64

    up1 = UpSampling2D((2, 2))(up2)
    up1 = concatenate([down1, up1], axis=3)
    up1 = Conv2D(64, (3, 3), padding='same')(up1)
    up1 = BatchNormalization()(up1)
    up1 = Activation('relu')(up1)
    up1 = Conv2D(64, (3, 3), padding='same')(up1)
    up1 = BatchNormalization()(up1)
    up1 = Activation('relu')(up1)
    up1 = Conv2D(64, (3, 3), padding='same')(up1)
    up1 = BatchNormalization()(up1)
    up1 = Activation('relu')(up1)
    # 128

    up0 = UpSampling2D((2, 2))(up1)
    up0 = concatenate([down0, up0], axis=3)
    up0 = Conv2D(32, (3, 3), padding='same')(up0)
    up0 = BatchNormalization()(up0)
    up0 = Activation('relu')(up0)
    up0 = Conv2D(32, (3, 3), padding='same')(up0)
    up0 = BatchNormalization()(up0)
    up0 = Activation('relu')(up0)
    up0 = Conv2D(32, (3, 3), padding='same')(up0)
    up0 = BatchNormalization()(up0)
    up0 = Activation('relu')(up0)
    # 256

    up0a = UpSampling2D((2, 2))(up0)
    up0a = concatenate([down0a, up0a], axis=3)
    up0a = Conv2D(16, (3, 3), padding='same')(up0a)
    up0a = BatchNormalization()(up0a)
    up0a = Activation('relu')(up0a)
    up0a = Conv2D(16, (3, 3), padding='same')(up0a)
    up0a = BatchNormalization()(up0a)
    up0a = Activation('relu')(up0a)
    up0a = Conv2D(16, (3, 3), padding='same')(up0a)
    up0a = BatchNormalization()(up0a)
    up0a = Activation('relu')(up0a)
    # 512

    up0b = UpSampling2D((2, 2))(up0a)
    up0b = concatenate([down0b, up0b], axis=3)
    up0b = Conv2D(8, (3, 3), padding='same')(up0b)
    up0b = BatchNormalization()(up0b)
    up0b = Activation('relu')(up0b)
    up0b = Conv2D(8, (3, 3), padding='same')(up0b)
    up0b = BatchNormalization()(up0b)
    up0b = Activation('relu')(up0b)
    up0b = Conv2D(8, (3, 3), padding='same')(up0b)
    up0b = BatchNormalization()(up0b)
    up0b = Activation('relu')(up0b)
    # 1024

    classify = Conv2D(num_classes, (1, 1), activation='sigmoid')(up0b)

    model = Model(inputs=inputs, outputs=classify)

    model.compile(optimizer=RMSprop(lr=learning_rate), loss=make_loss('bce_dice'), metrics=[dice_coef, 'accuracy'])

    if pretrained_weights:
        model.load_weights(pretrained_weights)

    return model

现在我必须修改问题并使其成为一个多类分类器，所以我不再使用掩码，而是使用两个掩码。所以我有两种类型的 grasycale 面具（Mca_mask和NotMca_mask同一个火车 img），在这种情况下，标准做法是什么？将两个面具合二为一？

4

1 回答 1

0

在这一行中，我们可以看到您的输出层正在应用 sigmoid：

classify = Conv2D(num_classes, (1, 1), activation='sigmoid')(up0b)

这意味着您的所有输出都转换为介于两者之间[0,1]而没有任何依赖关系。这就是你想要的多类分类。作为旁注，将输出层转换为[0,1]范围的另一种常见方法是应用 softmax - 这对多类不利，因为随着一个类的信心增长，其他类必然减少。

您的损失函数在此行定义为二元交叉熵：

model.compile(optimizer=RMSprop(lr=learning_rate), loss=make_loss('bce_dice'), metrics=[dice_coef, 'accuracy'])

这适用于所有类型的分类（单类或多类），并且需要[0,1]范围内的输出。

所以基本上你已经准备好按照现在的配置进行多类分类了。您需要做的就是创建多类标签。例如，如果您的类是狗、猫、鸟、马、山羊，并且图像中有狗和猫，那么您的标签将是[1, 1, 0, 0, 0]，您可以按原样训练网络。

于 2018-10-18T17:01:02.150 回答