我正在运行CNN来分类左右鞋印。我有190,000个训练图像，使用其中10%进行验证。我的模型设置如下。我获取所有图像的路径，读取并调整它们的大小。我对图像进行归一化处理，然后将其适配到模型中。问题在于，我的训练准确率停留在62.5％左右，损失约为0.6615-0.6619。我是否有做错的地方？我该如何避免这种情况发生？
以下是一些有趣的要点：
我最初在10张图片上测试了这个方法，但是同样遇到了同样的问题，将优化器更改为Adam，批量大小改为4就能解决。
然后我测试了更多的图片，但每次都需要改变批量大小才能提高准确性和损失。在使用10,000张图像时，我需要使用批量大小为500和优化器RMSProp。然而，准确度和损失在第10轮之后才真正开始变化。
现在我正在使用190,000张图像进行训练，由于GPU已经达到最大值，我无法增加批量大小。
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');