如何用CNN python代码解决以下错误？

Question

图像数据描述：存在具有200x200大小123标签（类）的2D二进制图像，每个类（标签）包含10个图像帧，其中作为剩余测试用例的前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，但是我收到以下错误：

警告：tensorflow：来自C：\ Users \ hp \ PycharmProjects \ FirstProject3 \ venv \ lib \ site-packages \ tensorflow \ python \ framework \ op_def_library.py:263：colocate_with（来自tensorflow.python.framework.ops）已弃用，将在以后的版本中删除。

更新说明：

由占位符自动处理的位置。

警告：tensorflow：从C：\ Users \ hp \ PycharmProjects \ FirstProject3 \ venv \ lib \ site-packages \ keras \ backend \ tensorflow_backend.py:3445：不推荐使用keep_prob调用dropout（来自tensorflow.python.ops.nn_ops）并将在以后的版本中删除。

更新说明：

请使用rate而不是keep_prob。费率应设为rate = 1 - keep_prob。

Traceback（最近一次调用最后一次）：

文件“C：/Users/hp/PycharmProjects/FirstProject3/test.py”，第79行，在model.fit中（x_train，y_train，batch_size = batch_size，epochs = epochs，verbose = 1，validation_data =（x_test，y_test））

文件“C：\ Users \ hp \ PycharmProjects \ FirstProject3 \ venv \ lib \ site-packages \ keras \ engine \ training.py”，第952行，适合batch_size = batch_size）

文件“C：\ Users \ hp \ PycharmProjects \ FirstProject3 \ venv \ lib \ site-packages \ keras \ engine \ training.py”，第789行，_standardize_user_data exception_prefix ='target'）

在standardize_input_data str（data_shape）中的文件“C：\ Users \ hp \ PycharmProjects \ FirstProject3 \ venv \ lib \ site-packages \ keras \ engine \ training_utils.py”，第138行

ValueError：检查目标时出错：期望dense_2有形状（123，）但是有形状的数组（124，）

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：https://towardsdatascience.com/build-your-own-convolution-neural-network-in-5-mins-4217c2cf964f

Answer 1

当你分配124时，你的数据有num_classes=123类。

警告是由于你有最新的tensorflow版本和keras尚未更新，以完全支持它。