我目前正在使用Cifar-10数据集制作一个小型的CNN。我只是稍微更新了一些代码，现在它不工作了。我找不到错误所在。预测告诉我“不是数字”。我找不到解决问题的答案。所以我无法在不添加更多文字的情况下发布问题，所以我不知道该写什么。吃一顿好的早餐会很不错。咖啡和煎饼之类的。我希望现在能发布这个问题了。
from keras.datasets import cifar10import numpy as np(x_training, y_training), (x_test,y_test) = cifar10.load_data()x_training = x_training / 255.0x_test = x_test / 255.0%matplotlib inlineimport matplotlib.pyplot as pltplt.imshow(x_training[3])plt.showfrom keras.models import Sequentialfrom keras.layers import Dense, Flatten,Conv2D , MaxPooling2D, Dropoutimport tensorflow as tfmodel = Sequential()model.add(Conv2D(32, kernel_size=(3, 3), input_shape=(32, 32, 3), activation="relu", padding="same"))model.add(Conv2D(32, kernel_size=(3, 3), activation="relu", padding="same"))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))model.add(Conv2D(64, kernel_size=(3, 3), activation="relu", padding="same"))model.add(Conv2D(64, kernel_size=(3, 3), activation="relu", padding="same"))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))model.add(Conv2D(128, kernel_size=(3, 3), activation="relu", padding="same"))model.add(Conv2D(128, kernel_size=(3, 3), activation="relu", padding="same"))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))model.add(Flatten())model.add(Dense(256, activation="relu"))model.add(Dense(128, activation="relu"))model.add(Dense(1, activation="sigmoid"))model.compile(optimizer='RMSProp', loss="binary_crossentropy", metrics=['accuracy'])model.summary()model.fit(x_training, y_training,batch_size=128, epochs=10, shuffle = True )model.evaluate(x_training, y_training)results = model.predict(x_training[1].reshape(-1, 32, 32, 3))resultsclass_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']max = np.max(results)max_position = np.argmax(results)class_name_predict = class_names[max_position]plt.imshow(x_training[1])plt.showtest = class_name_predicttestplt.imshow(x_training[1])plt.showx = class_names[y_training[1][0]]x回答：
你的模型中有一些错误：
对于多类问题，输出层必须具有与类别数量相同的维度，并且使用softmax激活函数。
多类问题的标准损失函数是categorical_crossentropy和sparse_categorical_crossentropy。当你的目标是独热编码时，可以使用categorical_crossentropy，当你有整数编码的标签时（这是你的情况），使用sparse_categorical_crossentropy。
model = Sequential()model.add(Conv2D(32, kernel_size=(3, 3), input_shape=(32, 32, 3), activation="relu", padding="same"))model.add(Conv2D(32, kernel_size=(3, 3), activation="relu", padding="same"))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))model.add(Conv2D(64, kernel_size=(3, 3), activation="relu", padding="same"))model.add(Conv2D(64, kernel_size=(3, 3), activation="relu", padding="same"))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))model.add(Conv2D(128, kernel_size=(3, 3), activation="relu", padding="same"))model.add(Conv2D(128, kernel_size=(3, 3), activation="relu", padding="same"))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))model.add(Flatten())model.add(Dense(256, activation="relu"))model.add(Dense(128, activation="relu"))model.add(Dense(len(class_names), activation="softmax"))model.compile(optimizer='RMSProp', loss="sparse_categorical_crossentropy", metrics=['accuracy'])model.summary() 