我正在尝试使用Keras训练一个模型来识别良性和恶性图像，但我没有达到预期的结果。数据集分类得很好，来自ISIC – Archive (https://www.isic-archive.com/)。我多次尝试更改学习率，但都没有效果…训练间隔之一的结果
以下是我使用Adam优化器训练模型的代码：
# In[1]:from keras.callbacks import ModelCheckpointfrom keras.models import Sequentialfrom keras.layers import Dropout, Flatten, Densefrom keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2Dfrom PIL import ImageFilefrom tqdm import tqdmfrom keras.preprocessing import imagefrom sklearn.datasets import load_filesfrom keras.utils import np_utilsimport numpy as npfrom glob import globimport keras# define function to load train, test, and validation datasetsdef load_dataset(path): data = load_files(path) condition_files = np.array(data['filenames']) condition_targets = np_utils.to_categorical(np.array(data['target']), 2) print(condition_targets) return condition_files, condition_targets# load train, test, and validation datasetstrain_files, train_targets = load_dataset( '/Users/Grampun/Desktop/ISIC-Archive-Downloader-master/data_set/training_data')valid_files, valid_targets = load_dataset( '/Users/Grampun/Desktop/ISIC-Archive-Downloader-master/data_set/valid_data')test_files, test_targets = load_dataset( '/Users/Grampun/Desktop/ISIC-Archive-Downloader-master/data_set/test_data')# load list of labelscondition_names = [item[58:-1] for item in sorted( glob("/Users/Grampun/Desktop/ISIC-Archive-Downloader-master/data_set/training_data/*/"))]print(condition_names)# print statistics about the datasetprint('There are %d total categories.' % len(condition_names))print('There are %s total images.\n' % len(np.hstack([train_files, valid_files, test_files])))print('There are %d training images.' % len(train_files))print('There are %d validation images.' % len(valid_files))print('There are %d test images.' % len(test_files))# In[2]:def path_to_tensor(img_path): # loads RGB image as PIL.Image.Image type img = image.load_img(img_path, target_size=(224, 224)) # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3) x = image.img_to_array(img) # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor return np.expand_dims(x, axis=0)def paths_to_tensor(img_paths): list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)] return np.vstack(list_of_tensors)ImageFile.LOAD_TRUNCATED_IMAGES = True# pre-process the data for Kerastrain_tensors = paths_to_tensor(train_files).astype('float32')/255valid_tensors = paths_to_tensor(valid_files).astype('float32')/255test_tensors = paths_to_tensor(test_files).astype('float32')/255# (IMPLEMENTATION) Model Architecture## In[4]:model = Sequential()model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(224, 224, 3)))model.add(Conv2D(64, (3, 3), activation='relu'))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))model.add(Flatten())model.add(Dense(128, activation='relu'))model.add(Dropout(0.5))model.add(Dense(2, activation='sigmoid'))model.summary()# Compile the Model# In[ ]:# opt = keras.optimizers.Adadelta()opt = keras.optimizers.Adam(lr=0.00003, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])# ### Train the Model## In[ ]:# TODO: specify the number of epochs that you would like to use to train the model.epochs = 20checkpointer = ModelCheckpoint(filepath='weights.best.from_scratch.6.hdf5', verbose=1, save_best_only=True)model.fit(train_tensors, train_targets, validation_data=(valid_tensors, valid_targets), epochs=epochs, batch_size=10, callbacks=[checkpointer], verbose=1)# ### Load the Model with the Best Validation Loss# In[5]:model.load_weights('weights.best.from_scratch.6.hdf5')# ### Test the Model## In[6]:# get index of predicted label for each image in test setcondition_predictions = [np.argmax(model.predict( np.expand_dims(tensor, axis=0))) for tensor in test_tensors]# report test accuracytest_accuracy = 100*np.sum(np.array(condition_predictions) == np.argmax(test_targets, axis=1))/len(condition_predictions)print('Test accuracy: %.4f%%' % test_accuracy) # confusion matrix任何帮助都将不胜感激（这是我的第一个机器学习项目，我还在学习中）。谢谢！
回答：
这行代码是您问题的根源： model.add(Dense(2, activation='sigmoid'))。
请使用以下之一：
model.add(Dense(2, activation='softmax'))model.add(Dense(1, activation='sigmoid'))
请注意，在情况(1)中，您需要使用’categorical_crossentropy’而不是’binary_crossentropy’。因此，您还需要更改
model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy']) 为
model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
