如何用CNN python代码解决以下错误？

图像数据说明：尺寸为200x200的二维二进制图像 有123个标签类，每个类标签包含10个图像帧，其中我认为作为测试用例剩余的前4个图像将是训练数据集

据我所知，我更改了CNN代码以对图像数据进行分类，但我得到以下错误：

警告：tensorflow:From C:\Users\hp\PycharmProjects\FirstProject3\venv\lib\site packages\tensorflow\python\framework\op_def_library.py:263:colocate_with From tensorflow.python.framework.ops已弃用，将在未来版本中删除

更新说明：

由placer自动处理Colocations

警告：tensorflow:From C:\Users\hp\PycharmProjects\FirstProject3\venv\lib\site packages\keras\backend\tensorflow\u backend.py:3445:使用keep\u prob从tensorflow.python.ops.nn\u ops调用退出已不推荐，并将在未来版本中删除

更新说明：

请使用费率而不是保留问题。速率应设置为速率=1-保持概率

回溯最近一次呼叫上次：

文件C:/Users/hp/PycharmProjects/FirstProject3/test.py，第79行，在 model.fitx\u train，y\u train，批大小=批大小，历代=历代，详细=1，验证数据=x\u测试，y\u测试

文件C:\Users\hp\PycharmProjects\FirstProject3\venv\lib\site packages\keras\engine\training.py，第952行 批次大小=批次大小

文件C:\Users\hp\PycharmProjects\FirstProject3\venv\lib\site packages\keras\engine\training.py，第789行，在用户数据中 例外情况\u前缀='target'

标准化输入数据中的文件C:\Users\hp\PycharmProjects\FirstProject3\venv\lib\site packages\keras\engine\training\u utils.py，第138行 标准形状

ValueError：检查目标时出错：预期稠密_2具有形状123，但获得了形状124的数组

如何解决错误

我的代码：

    import keras
    from keras.models import Sequential
    from keras.layers import Dense, Dropout, Flatten
    from keras.layers import Conv2D, MaxPooling2D
    import numpy as np
    import cv2
    import os

    path1='C:\\Data\\For new Paper3\Old\\GaitDatasetB-silh_PerfectlyAlingedImages_EnergyImage\\';
    all_images = []
    all_labels = []
    subjects = os.listdir(path1)
    numberOfSubject = len(subjects)
    print('Number of Subjects: ', numberOfSubject)
    for number1 in range(0, numberOfSubject):  # numberOfSubject
        path2 = (path1 + subjects[number1] + '/')
        sequences = os.listdir(path2);
        numberOfsequences = len(sequences)
        for number2 in range(4, numberOfsequences):
            path3 = path2 + sequences[number2]
            img = cv2.imread(path3 , 0)
            img = img.reshape(200, 200, 1)
            all_images.append(img)
            all_labels.append(number1+1)
    x_train = np.array(all_images)
    y_train = np.array(all_labels)
    y_train = keras.utils.to_categorical(y_train)
    print(y_train)

    print(x_train)


    all_images = []
    all_labels = []

    for number1 in range(0, numberOfSubject):  # numberOfSubject
        path2 = (path1 + subjects[number1] + '/')
        sequences = os.listdir(path2);
        numberOfsequences = len(sequences)
        for number2 in range(0, 4):
            path3 = path2 + sequences[number2]
            img = cv2.imread(path3 , 0)
            img = img.reshape(200, 200, 1)
            all_images.append(img)
            all_labels.append(number1+1)
    x_test = np.array(all_images)
    y_test = np.array(all_labels)
    y_test = keras.utils.to_categorical(y_test)
    print(y_test)

    print(x_test)

    batch_size = 738
    num_classes = 123
    epochs = 12

    model = Sequential()
    model.add(Conv2D(32, kernel_size=(5, 5), activation='relu', input_shape=(200,200,1)))
    model.add(Conv2D(64, (5, 5), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(738, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes, activation='softmax'))

    model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adadelta(), metrics=['accuracy'])

    model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test))

    score = model.evaluate(x_test, y_test, verbose=0)
    print('Test loss:', score[0])
    print('Test accuracy:', score[1])

代码参考：

在分配num_class=123时，您的数据有124个类

---

警告是由于您拥有最新的tensorflow版本，而keras尚未更新以完全支持它

您使用tf的tensorflow/keras的版本是什么？keras.\uuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuu。电子邮件ID？tf版本为1.13.1，keras版本为2.2.4，请将其上传到某个地方并在此处发布链接。我如何查找批次大小和历代编号？你是什么意思？如果你想知道如何选择批量大小和历代次数，我建议你在谷歌上快速搜索并阅读相关内容。大多数情况下，它是根据经验选择的，但根据经验，您可以开发一些启发式方法。