我正在运行一个CNN用于左右鞋印分类。我有190，000个训练图像，我使用其中的10%进行验证。我的模型设置如下所示。我获取所有图像的路径，读取它们并调整它们的大小。我将图像标准化，然后将其拟合到模型中。我的问题是，我坚持了62.5%的训练准确率，损失约为0.6615-0.6619。我做错了什么吗？我怎么才能阻止这一切的发生？
只是一些有趣的点要注意：
1.我首先在10张图片上测试了这个，我有同样的问题，但将优化器改为adam，批量大小改为4工作。
1.然后，我在越来越多的图像上进行测试，但每次我都需要改变批量大小以提高精度和损失。对于10，000张图像，我不得不使用500的批量大小和优化器rmsprop。然而，精度和损失只有在第10个纪元后才真正开始改变。
1.我现在正在训练190，000张图像，我不能增加批量大小，因为我的GPU是最大的。

imageWidth = 50
    imageHeight = 150
    
    def get_filepaths(directory):
        file_paths = []
        for filename in files:
            filepath = os.path.join(root, filename)
            file_paths.append(filepath) # Add it to the list.
        return file_paths
    
    def cleanUpPaths(fullFilePaths):
        cleanPaths = []
        for f in fullFilePaths:
            if f.endswith(".png"):
                cleanPaths.append(f)
        return cleanPaths
    
    def getTrainData(paths):
        trainData = []
        for i in xrange(1,190000,2):
            im = image.imread(paths[i])
            im = image.imresize(im, (150,50))
            im = (im-255)/float(255)
            trainData.append(im)
        trainData = np.asarray(trainData)
        right = np.zeros(47500)
        left = np.ones(47500)
        trainLabels = np.concatenate((left, right))
        trainLabels = np_utils.to_categorical(trainLabels)
        return (trainData, trainLabels)

    #create the convnet
    model = Sequential()
    
    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(imageWidth,imageHeight,1),strides=1))#32
    model.add(Conv2D(32, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    
    model.add(Conv2D(64, (3, 3), activation='relu',strides=1))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(1, 3)))
    model.add(Dropout(0.25))
    
    model.add(Conv2D(64, (1, 2), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 1)))
    model.add(Dropout(0.25))
    
    model.add(Flatten())
    model.add(Dense(256, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(2, activation='softmax'))
    
    sgd = SGD(lr=0.01)
    model.compile(loss='categorical_crossentropy', optimizer='rmsprop',metrics=['accuracy'])
    
    #prepare the training data*/
    
    trainPaths = get_filepaths("better1/train")
    trainPaths = cleanUpPaths(trainPaths)
    (trainData, trainLabels) = getTrainData(trainPaths)
    trainData = np.reshape(trainData,(95000,imageWidth,imageHeight,1)).astype('float32')
    trainData = (trainData-255)/float(255)
    
    #train the convnet***
    model.fit(trainData, trainLabels, batch_size=500, epochs=50, validation_split=0.2)
    
    #/save the model and weights*/
    model.save('myConvnet_model5.h5');
    model.save_weights('myConvnet_weights5.h5');

字符串