我的代码中出现了一个错误，我已经查阅了文档，但还是报错，这里的“维度必须相等”是什么意思？实际上，我已经在代码中添加了一些层 model.fit()

这是我的代码：

# USAGE# python train_simple_nn.py --dataset animals --model output/simple_nn.model --label-bin output/simple_nn_lb.pickle --plot output/simple_nn_plot.png# set the matplotlib backend so figures can be saved in the background# import the necessary packagesfrom sklearn.preprocessing import LabelBinarizerfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import classification_reportfrom keras.models import Sequentialfrom keras.layers.core import Densefrom keras.optimizers import SGDfrom imutils import pathsimport matplotlib.pyplot as pltimport numpy as npimport argparseimport randomimport pickleimport cv2import osfrom keras import layersimport tensorflow as tf# construct the argument parser and parse the argumentsap = argparse.ArgumentParser()ap.add_argument("-d", "--dataset", required=True,    help="path to input dataset of images")ap.add_argument("-m", "--model", required=True,    help="path to output trained model")ap.add_argument("-l", "--label-bin", required=True,    help="path to output label binarizer")ap.add_argument("-p", "--plot", required=True,    help="path to output accuracy/loss plot")args = vars(ap.parse_args())# initialize the data and labelsprint("[INFO] loading images...")data = []labels = []# grab the image paths and randomly shuffle themimagePaths = sorted(list(paths.list_images(args["dataset"])))random.seed(42)random.shuffle(imagePaths)# loop over the input imagesfor imagePath in imagePaths:    # load the image, resize the image to be 32x32 pixels (ignoring    # aspect ratio), flatten the image into 32x32x3=3072 pixel image    # into a list, and store the image in the data list    image = cv2.imread(imagePath)    image = cv2.resize(image, (32, 32)).flatten()    data.append(image)    # extract the class label from the image path and update the    # labels list    label = imagePath.split(os.path.sep)[-2]    labels.append(label)# scale the raw pixel intensities to the range [0, 1]data = np.array(data, dtype="float") / 255.0labels = np.array(labels)# partition the data into training and testing splits using 75% of# the data for training and the remaining 25% for testing(trainX, testX, trainY, testY) = train_test_split(data,    labels, test_size=0.25, random_state=42)# convert the labels from integers to vectors (for 2-class, binary# classification you should use Keras' to_categorical function# instead as the scikit-learn's LabelBinarizer will not return a# vector)lb = LabelBinarizer()trainY = lb.fit_transform(trainY)testY = lb.transform(testY)# define the 3072-1024-512-3 architecture using Kerasmodel = tf.keras.Sequential()tf.keras.layers.Dense(1024, input_shape=(3072,), activation="sigmoid")tf.keras.layers.Dense(512, activation="sigmoid")tf.keras.layers.Dense(len(lb.classes_), activation="softmax")# initialize our initial learning rate and # of epochs to train forINIT_LR = 0.01EPOCHS = 75# compile the model using SGD as our optimizer and categorical# cross-entropy loss (you'll want to use binary_crossentropy# for 2-class classification)print("[INFO] training network...")opt = tf.keras.optimizers.SGD(lr=INIT_LR)model.compile(loss="categorical_crossentropy", optimizer=opt,    metrics=["accuracy"])# train the neural networkH = model.fit(trainX, trainY, validation_data=(testX, testY),    epochs=EPOCHS, batch_size=32)# evaluate the networkprint("[INFO] evaluating network...")predictions = model.predict(testX, batch_size=32)print(classification_report(testY.argmax(axis=1),    predictions.argmax(axis=1), target_names=lb.classes_))# plot the training loss and accuracyN = np.arange(0, EPOCHS)plt.style.use("ggplot")plt.figure()plt.plot(N, H.history["loss"], label="train_loss")plt.plot(N, H.history["val_loss"], label="val_loss")plt.plot(N, H.history["acc"], label="train_acc")plt.plot(N, H.history["val_acc"], label="val_acc")plt.title("Training Loss and Accuracy (Simple NN)")plt.xlabel("Epoch #")plt.ylabel("Loss/Accuracy")plt.legend()plt.savefig(args["plot"])# save the model and label binarizer to diskprint("[INFO] serializing network and label binarizer...")model.save(args["model"])f = open(args["label_bin"], "wb")f.write(pickle.dumps(lb))f.close()

以及错误信息：

ValueError: 维度必须相等，但对于 ‘loss/output_1_loss/mul’ (操作: ‘Mul’) 来说，维度是3和3072，输入形状分别为：[?,3], [?,3072]. in model.fit(),

如何解决这个问题？

回答：

你的代码中的问题在这里：

model = tf.keras.Sequential()tf.keras.layers.Dense(1024, input_shape=(3072,), activation="sigmoid")tf.keras.layers.Dense(512, activation="sigmoid")tf.keras.layers.Dense(len(lb.classes_), activation="softmax")

你定义了这些层，但你从未将它们添加到你的模型中。

在使用 sequential 模型时，你需要通过 .add() 方法将这些层添加到你的模型中。

将这些行更改为：

model.add(tf.keras.layers.Dense(1024, input_shape=(3072,), activation="sigmoid"))model.add(tf.keras.layers.Dense(512, activation="sigmoid"))model.add(tf.keras.layers.Dense(len(lb.classes_), activation="softmax"))