这是来自Coursera的一个问题。除了训练部分之外，所有输出都如预期。我尝试了不同的层，但结果都是一样的。可能是我的数据集处理上有些错误？

我找不到问题所在，有人能帮忙吗？谢谢

import csvimport numpy as npimport tensorflow as tffrom tensorflow.keras.preprocessing.image import ImageDataGeneratorfrom os import getcwddef get_data(filename):  # You will need to write code that will read the file passed  # into this function. The first line contains the column headers  # so you should ignore it  # Each successive line contians 785 comma separated values between 0 and 255  # The first value is the label  # The rest are the pixel values for that picture  # The function will return 2 np.array types. One with all the labels  # One with all the images  #  # Tips:   # If you read a full line (as 'row') then row[0] has the label  # and row[1:785] has the 784 pixel values  # Take a look at np.array_split to turn the 784 pixels into 28x28  # You are reading in strings, but need the values to be floats  # Check out np.array().astype for a conversion    with open(filename) as training_file:              # Your code starts here      reader = csv.reader(training_file)      next(reader,None)      images = []      labels = []      for i in reader:                        labels.append(i[0])            imageData = i[1:785]            images.append(np.array_split(imageData,28))                  # Your code ends here      labels = np.array(labels).astype('float')      images = np.array(images).astype('float')    return images, labelspath_sign_mnist_train = f"{getcwd()}/../tmp2/sign_mnist_train.csv"path_sign_mnist_test = f"{getcwd()}/../tmp2/sign_mnist_test.csv"training_images, training_labels = get_data(path_sign_mnist_train)testing_images, testing_labels = get_data(path_sign_mnist_test)# Keep theseprint(training_images.shape)print(training_labels.shape)print(testing_images.shape)print(testing_labels.shape)# In this section you will have to add another dimension to the data# So, for example, if your array is (10000, 28, 28)# You will need to make it (10000, 28, 28, 1)training_images = np.expand_dims(training_images,axis=-1)# Your Code Heretesting_images = np.expand_dims(testing_images,axis=-1)# Your Code Here# Create an ImageDataGenerator and do Image Augmentationtrain_datagen = ImageDataGenerator(rescale = 1./255.,                                   rotation_range = 40,                                   width_shift_range = 0.2,                                   height_shift_range = 0.2,                                   shear_range = 0.2,                                   zoom_range = 0.2,                                   horizontal_flip = True,                                   fill_mode = 'nearest'    )validation_datagen = ImageDataGenerator(rescale = 1./255.)    # Keep Theseprint(training_images.shape)print(testing_images.shape)    # Their output should be:# (27455, 28, 28, 1)# (7172, 28, 28, 1)# Define the model# Use no more than 2 Conv2D and 2 MaxPooling2Dfrom tensorflow.keras.optimizers import RMSpropmodel = tf.keras.models.Sequential([    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28, 28, 1)),    tf.keras.layers.MaxPooling2D(2,2),    tf.keras.layers.Conv2D(32, (3,3), activation='relu'),    tf.keras.layers.MaxPooling2D(2,2),    tf.keras.layers.Flatten(),    tf.keras.layers.Dense(512, activation='relu'),    tf.keras.layers.Dense(26, activation='softmax')])# Compile Model. model.compile(loss = 'sparse_categorical_crossentropy',              optimizer = RMSprop(lr=0.01),              metrics = ['accuracy'])# Train the Modeltrain_generator = train_datagen.flow(training_images,training_labels,                                                    batch_size = 10                                                                                                       )  validation_generator =  validation_datagen.flow( testing_images,                                                                                                testing_labels,                                                batch_size  = 10                                                           )history = model.fit_generator(train_generator,                              epochs=5,                              steps_per_epoch=len(training_images) / 32,                              validation_data=validation_generator                                                           )model.evaluate(testing_images, testing_labels,verbose=0)

模型的输出如下所示：

Epoch 1/5858/857 [==============================] - 78s 91ms/step - loss: 15.4250 - accuracy: 0.0422 - val_loss: 15.5210 - val_accuracy: 0.0371Epoch 2/5858/857 [==============================] - 75s 88ms/step - loss: 15.4719 - accuracy: 0.0401 - val_loss: 15.5210 - val_accuracy: 0.0371Epoch 3/5858/857 [==============================] - 77s 89ms/step - loss: 15.4230 - accuracy: 0.0431 - val_loss: 15.5210 - val_accuracy: 0.0371Epoch 4/5858/857 [==============================] - 76s 89ms/step - loss: 15.4268 - accuracy: 0.0429 - val_loss: 15.5120 - val_accuracy: 0.0371Epoch 5/5858/857 [==============================] - 75s 88ms/step - loss: 15.4287 - accuracy: 0.0428 - val_loss: 15.5120 - val_accuracy: 0.0371

批量大小设置得很低，因为Coursera的Jupyter笔记本将其限制为10。

回答：

你的代码是正确的。我怀疑这可能与优化器有关。尝试使用Adam代替RMSProp，并将Adam的学习率设置为0.001，这是默认的学习率。除此之外，你的笔记本正确地提取了标签和数据，制定了数据生成器，网络看起来也是正确的。