这是我为CNN创建的代码，但我注意到损失/轮次图上有这些尖峰，我无法解释。我尝试了Adam优化器，但结果仍然相同。我试图分类恶性或良性的乳腺肿瘤，但我的数据集相当小，只有3390张图片。

# -*- coding: utf-8 -*-    """    Created on Wed Dec 18 16:05:12 2019    @author: Panagiotis Gkanos    """    import numpy as np     import tensorflow as tf    from numpy.random import seed    seed(1)    tf.compat.v1.set_random_seed(2)    from tensorflow.python.client import device_lib    print(device_lib.list_local_devices())    import tensorflow as tf    sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(log_device_placement=True))    import os    os.environ['KERAS_BACKEND']='tensorflow'    import keras    from tensorflow.keras.models import Sequential    from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten    from tensorflow.keras.layers import Conv2D,MaxPooling2D    from keras.utils import np_utils    from tensorflow.keras.optimizers import SGD    from tensorflow.keras.metrics import categorical_crossentropy    from keras.preprocessing.image import ImageDataGenerator    from tensorflow.keras.layers import BatchNormalization    import matplotlib as plt    from matplotlib import pyplot as plt    from sklearn.metrics import confusion_matrix    import itertools    keras.initializers.glorot_normal(seed=42)    train_path='C:/Users/Panagiotis Gkanos/Desktop/dataset/40X/train'    train_batches=ImageDataGenerator(rescale=1./255,     samplewise_center=True,rotation_range=180).flow_from_directory(train_path,                                                      target_size=[224,224],                                                      classes=['malignant','benign'],                                                      class_mode='categorical',batch_size=80)    test_path='C:/Users/Panagiotis Gkanos/Desktop/dataset/40X/test'    test_batches=ImageDataGenerator(rescale=1./255,     samplewise_center=True,rotation_range=180).flow_from_directory(test_path,                                                      target_size=[224,224],                                                      classes=['malignant','benign'],                                                      class_mode='categorical',batch_size=80)    model=Sequential()    model.add(Conv2D(16,(3,3),padding='same',input_shape=(224,224,3)))    model.add(Activation('relu'))    model.add(Conv2D(16,(3,3),padding='same'))    model.add(Activation('relu'))    model.add(MaxPooling2D(pool_size=(2, 2),strides=2))    model.add(Dropout(0.3))    model.add(Conv2D(32,(3,3),padding='same'))    model.add(Activation('relu'))    model.add(MaxPooling2D(pool_size=(2, 2),strides=2))    model.add(Dropout(0.3))    model.add(Flatten())    model.add(Dense(512,activation='relu'))    model.add(Dense(2,activation='softmax'))    sgd = SGD(lr=0.01)    model.compile(optimizer=sgd,loss='categorical_crossentropy',metrics=['accuracy'])    history=model.fit_generator(train_batches,steps_per_epoch=20 ,validation_data=test_batches,                    validation_steps=8 ,epochs=50)    def plot_loss(history):        train_loss=history.history['loss']        val_loss=history.history['val_loss']        x=list(range(1,len(val_loss)+1))        plt.plot(x,val_loss,color='red',label='validation loss')        plt.plot(x,train_loss,label='training loss')        plt.xlabel('Epoch')        plt.ylabel('Loss')        plt.title('Loss vs. Epoch')        plt.legend()        plot_loss(history)

损失与轮次图:

回答：

SGD和Adam都是随机优化器，因此损失值不一定在每个步骤都减少，图中出现尖峰是可以接受的，只要总体上损失在减少。尽管如此，我认为你的模型可能在训练数据上过拟合了。尝试使用正则化器，或者在模型的最后一个全连接层后添加 dropout。在CNN的中间层使用 dropout 并不常规。