Tensorflow 带警告消息的Unet运行时执行停止

我正在尝试运行一个u-net，用它的标签屏蔽图像数据。代码中的数据管理部分运行良好。在这里，当我运行unet.py时，我正在努力处理这段代码。代码执行并抛出警告，并在不进入结果的情况下进一步停止进程。想知道它的库版本是否存在问题？我没有发现类似的东西。在后面的一行中，我已经调试过了，仍然没有给出任何线索。代码执行将在“使用TensorFlow后端”停止。在不给出任何错误的情况下，继续训练模型并保存模型阶段。所有目录结构都已检查并按正确顺序排列

from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from tensorflow.keras.optimizers import *
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.preprocessing.image import array_to_img
import cv2
from data import *


class myUnet(object):
def __init__(self, img_rows=512, img_cols=512):
    self.img_rows = img_rows
    self.img_cols = img_cols

def load_data(self):
    mydata = dataProcess(self.img_rows, self.img_cols)
    imgs_train, imgs_mask_train = mydata.load_train_data()
    imgs_test = mydata.load_test_data()
    return imgs_train, imgs_mask_train, imgs_test

    def get_unet(self):
    inputs = Input((self.img_rows, self.img_cols, 3))

    conv1 = Conv2D(64, 3, activation='relu', padding='same',                                                                      kernel_initializer='he_normal')(inputs)
    # print(conv1)
    conv1 = BatchNormalization()(conv1)
    print ("conv1 shape:", conv1.shape)
    conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
    conv1 = BatchNormalization()(conv1)
    print ("conv1 shape:", conv1.shape)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    print ("pool1 shape:", pool1.shape)

    conv2 = Conv2D(128, 3, activation='relu', padding='same',   kernel_initializer='he_normal')(pool1)
    print ("conv2 shape:", conv2.shape)
    conv2 = BatchNormalization()(conv2)
    conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
    print ("conv2 shape:", conv2.shape)
    conv2 = BatchNormalization()(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    print ("pool2 shape:", pool2.shape)

    conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
    print ("conv3 shape:", conv3.shape)
    conv3 = BatchNormalization()(conv3)
    conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
    print ("conv3 shape:", conv3.shape)
    conv3 = BatchNormalization()(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
    print ("pool3 shape:", pool3.shape)

    conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
    conv4 = BatchNormalization()(conv4)
    conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
    conv4 = BatchNormalization()(conv4)
    drop4 = Dropout(0.5)(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)

    conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
    conv5 = BatchNormalization()(conv5)
    conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
    conv5 = BatchNormalization()(conv5)
    drop5 = Dropout(0.5)(conv5)

    up6 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(drop5))
    up6 = BatchNormalization()(up6)
    merge6 = concatenate([drop4, up6], axis=3)
    print(up6)
    print(merge6)
    conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
    print(conv6)
    conv6 = BatchNormalization()(conv6)
    conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
    print(conv6)
    conv6 = BatchNormalization()(conv6)
    up7 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
        UpSampling2D(size=(2, 2))(conv6))
    up7 = BatchNormalization()(up7)
    merge7 = concatenate([conv3, up7], axis=3)
    print(up7)
    print(merge7)
    conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
    conv7 = BatchNormalization()(conv7)
    print(conv7)
    conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
    print(conv7)
    conv7 = BatchNormalization()(conv7)
    up8 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
        UpSampling2D(size=(2, 2))(conv7))
    up8 = BatchNormalization()(up8)
    merge8 = concatenate([conv2, up8], axis=3)
    conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
    conv8 = BatchNormalization()(conv8)
    conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
    conv8 = BatchNormalization()(conv8)
    up9 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
        UpSampling2D(size=(2, 2))(conv8))
    up9 = BatchNormalization()(up9)
    merge9 = concatenate([conv1, up9], axis=3)
    print(up9)
    print(merge9)
    conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
    conv9 = BatchNormalization()(conv9)
    print(conv9)
    conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
    conv9 = BatchNormalization()(conv9)
    print(conv9)
    conv9 = Conv2D(2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
    conv9 = BatchNormalization()(conv9)
    print ("conv9 shape:", conv9.shape)

    conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)
    print(conv10)
    model = Model(inputs=inputs, outputs=conv10)

    model.compile(optimizer=Adam(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy'])

    return model

def train(self):
    print("loading data")
    imgs_train, imgs_mask_train, imgs_test = self.load_data()
    print("loading data done")
    model = self.get_unet()
    print("got unet")
    model_checkpoint = ModelCheckpoint('unet.hdf5', monitor='loss', verbose=1, save_best_only=True)
    print('Fitting model...')
    model.fit(imgs_train, imgs_mask_train, batch_size=4, epochs=100, verbose=1,
              validation_split=0.2, shuffle=True, callbacks=[model_checkpoint])

    print('predict test data')
    imgs_mask_test = model.predict(imgs_test, batch_size=1, verbose=1)
    np.save('./data/results/imgs_mask_test.npy', imgs_mask_test)

def save_img(self):
    print("array to image")
    imgs = np.load('./data/results/imgs_mask_test.npy')
    piclist = []
    for line in open("./data/results/pic.txt"):
        line = line.strip()
        picname = line.split('/')[-1]
        piclist.append(picname)
    print(len(piclist))
    for i in range(imgs.shape[0]):
        path = "./data/results/" + piclist[i]
        img = imgs[i]
        img = array_to_img(img)
        img.save(path)
        cv_pic = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
        cv_pic = cv2.resize(cv_pic,(1918,1280),interpolation=cv2.INTER_CUBIC)
        binary, cv_save = cv2.threshold(cv_pic, 127, 255, cv2.THRESH_BINARY)
        cv2.imwrite(path, cv_save)

def load_model_weights(self, model):
    model.load_weights('./data/unet.hdf5')


if __name__ == '__main__':
    print("going to create model")
    myunet = myUnet()
    print("model created.. going to retreive model")
    model = myunet.get_unet()
    # model.summary()
    # plot_model(model, to_file='model.png')
    # myunet.load_model_weights(model)
    print("train model")
    myunet.train()
    print("save model")
    myunet.save_img()

WARNING:tensorflow:From unet.py:4: The name tf.keras.layers.CuDNNGRU   is deprecated. Please use tf.compat.v1.keras.layers.CuDNNGRU instead.

WARNING:tensorflow:From unet.py:4: The name tf.keras.layers.CuDNNLSTM is deprecated. Please use tf.compat.v1.keras.layers.CuDNNLSTM instead.

Using TensorFlow backend.

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是什么让你认为它停止了？我已经调试了所有的代码。一切都好。但它仍然没有开始训练并生成权重.h5文件它应该将文件保存在哪里？可能是训练，但速度很慢吗？有时加载数据可能需要一段时间。这里有很多未知数。在这种情况下，文件保存在结果文件夹中。如果您只能看到下面的内容，如果\uuuu name\uuuuu==“main”，则打印（“将创建模型”）甚至不打印。它在“使用TensorFlow后端”之后进行处理。整个过程在这里完全停止了。所以你没有通过导入语句？停止导入所有内容。