Question

我必须确定CNN引起的视网膜疾病。我有1400张图像，每节课700张。我的课程是（0-无PDR）和（1-PDR）。我正在尝试建立一个模型来识别输入视网膜是否患有4级疾病。

我正在对图像进行跟踪操作，然后将所有图像调整为256x256：

ImageCV[index] = cv2.addWeighted(ImageCV[index],4, cv2.GaussianBlur(ImageCV[index],(0,0), 256/30), -4, 128)

它用我的img进行了跟踪： https://imgur.com/X1p9G1c

然后，当我训练模型时，我得到了非常高的准确性（例如99 ....），但是当我尝试预测一些测试图像时，它就失败了。例如，我在测试中放置了10个PDR示例文件夹并尝试预测它们（都必须为1）。这是结果：

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这是我的模特

visible = Input(shape=(256,256,3))
conv1 = Conv2D(16, kernel_size=(3,3), activation='relu', strides=(1, 1))(visible)
conv2 = Conv2D(16, kernel_size=(3,3), activation='relu', strides=(1, 1))(conv1)
bat1 = BatchNormalization()(conv2)
conv3 = ZeroPadding2D(padding=(1, 1))(bat1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv3)

conv4 = Conv2D(32, kernel_size=(3,3), activation='relu', padding='valid', kernel_regularizer=regularizers.l2(0.01))(pool1)
conv5 = Conv2D(32, kernel_size=(3,3), activation='relu', padding='valid', kernel_regularizer=regularizers.l2(0.01))(conv4)
bat2 = BatchNormalization()(conv5)
pool2 = MaxPooling2D(pool_size=(1, 1))(bat2)

conv6 = Conv2D(64, kernel_size=(3,3), activation='relu',strides=(1, 1), padding='valid')(pool2)
conv7 = Conv2D(64, kernel_size=(3,3), activation='relu',strides=(1, 1), padding='valid')(conv6)
bat3 = BatchNormalization()(conv7)
conv7 = ZeroPadding2D(padding=(1, 1))(bat3)
pool3 = MaxPooling2D(pool_size=(1, 1))(conv7)

conv8 = Conv2D(128, kernel_size=(3,3), activation='relu', padding='valid', kernel_regularizer=regularizers.l2(0.01))(pool3)
conv9 = Conv2D(128, kernel_size=(2,2), activation='relu', strides=(1, 1), padding='valid')(conv8)
bat4 = BatchNormalization()(conv9)
pool4 = MaxPooling2D(pool_size=(1, 1))(bat4)

flat = Flatten()(pool4)

output = Dense(1, activation='sigmoid')(flat)
model = Model(inputs=visible, outputs=output)

opt = optimizers.adam(lr=0.001, decay=0.0)

model.compile(optimizer= opt, loss='binary_crossentropy', metrics=['accuracy'])


data, labels = ReadImages(TRAIN_DIR)

test, lt = ReadImages(TEST_DIR)

data = np.array(data)
labels = np.array(labels)
test = np.array(test)
lt = np.array(lt)

np.random.permutation(len(data))
np.random.permutation(len(labels))
np.random.permutation(len(test))
np.random.permutation(len(lt))

model.fit(data, labels, epochs=7, validation_data = (test,lt))

model.save('model.h5')

这是预测。py

model = load_model('model.h5')

for filename in os.listdir(r'v/'):
    if filename.endswith(".jpg") or filename.endswith(".ppm") or filename.endswith(".jpeg"):
        ImageCV = cv2.resize(cv2.imread(os.path.join(TEST_DIR) + filename), (256,256))
        ImageCV = cv2.addWeighted(ImageCV,4, cv2.GaussianBlur(ImageCV,(0,0), 256/30), -4, 128)
        cv2.imshow('image', ImageCV)
        cv2.waitKey(0)
        cv2.destroyAllWindows()
        ImageCV = ImageCV.reshape(-1,256,256,3)
        print(model.predict(ImageCV))

我该怎么做才能改善我的预测？非常感谢您的帮助

更新好吧，我尝试着回答所有问题，但仍然无法正常工作... 这是我的代码：

visible = Input(shape=(256,256,3))
conv1 = Conv2D(16, kernel_size=(3,3), activation='relu', strides=(1, 1))(visible)
conv2 = Conv2D(32, kernel_size=(3,3), activation='relu', strides=(1, 1))(conv1)
bat1 = BatchNormalization()(conv2)
conv3 = ZeroPadding2D(padding=(1, 1))(bat1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv3)
drop1 = Dropout(0.30)(pool1)

conv4 = Conv2D(32, kernel_size=(3,3), activation='relu', padding='valid', kernel_regularizer=regularizers.l2(0.01))(drop1)
conv5 = Conv2D(64, kernel_size=(3,3), activation='relu', padding='valid', kernel_regularizer=regularizers.l2(0.01))(conv4)
bat2 = BatchNormalization()(conv5)
pool2 = MaxPooling2D(pool_size=(1, 1))(bat2)
drop1 = Dropout(0.30)(pool2)

conv6 = Conv2D(128, kernel_size=(3,3), activation='relu', padding='valid', kernel_regularizer=regularizers.l2(0.01))(pool2)
conv7 = Conv2D(128, kernel_size=(2,2), activation='relu', strides=(1, 1), padding='valid')(conv6)
bat3 = BatchNormalization()(conv7)
pool3 = MaxPooling2D(pool_size=(1, 1))(bat3)
drop1 = Dropout(0.30)(pool3)

flat = Flatten()(pool3)
drop4 = Dropout(0.50)(flat)

output = Dense(1, activation='sigmoid')(drop4)
model = Model(inputs=visible, outputs=output)

opt = optimizers.adam(lr=0.001, decay=0.0)

model.compile(optimizer= opt, loss='binary_crossentropy', metrics=['accuracy'])

data, labels = ReadImages(TRAIN_DIR)
test, lt = ReadImages(TEST_DIR)

data = np.array(data)
labels = np.array(labels)

perm = np.random.permutation(len(data))
data = data[perm]
labels = labels[perm]
#model.fit(data, labels, epochs=8, validation_data = (np.array(test), np.array(lt)))

aug = ImageDataGenerator(rotation_range=20, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.15,
    horizontal_flip=True)

# train the network
model.fit_generator(aug.flow(data, labels, batch_size=32),
    validation_data=(np.array(test), np.array(lt)), steps_per_epoch=len(data) // 32,
    epochs=7)

这是回报：

Epoch 1/7
43/43 [==============================] - 1004s 23s/step - loss: 1.8090 - acc: 0.9724 - val_loss: 1.7871 - val_acc: 0.9861
Epoch 2/7
43/43 [==============================] - 1003s 23s/step - loss: 1.8449 - acc: 0.9801 - val_loss: 1.4828 - val_acc: 1.0000
Epoch 3/7
43/43 [==============================] - 1092s 25s/step - loss: 1.5704 - acc: 0.9920 - val_loss: 1.3985 - val_acc: 1.0000
Epoch 4/7
43/43 [==============================] - 1062s 25s/step - loss: 1.5219 - acc: 0.9898 - val_loss: 1.3167 - val_acc: 1.0000
Epoch 5/7
43/43 [==============================] - 990s 23s/step - loss: 2.5744 - acc: 0.9222 - val_loss: 2.9347 - val_acc: 0.9028
Epoch 6/7
43/43 [==============================] - 983s 23s/step - loss: 1.6053 - acc: 0.9840 - val_loss: 1.3299 - val_acc: 1.0000
Epoch 7/7
43/43 [==============================] - 974s 23s/step - loss: 1.6180 - acc: 0.9801 - val_loss: 1.5181 - val_acc: 0.9861

我会添加辍学对象，减少模型层，增加数据扩充，并且根本不起作用（所有预测都返回0）...

请任何人都可以提供帮助。

Answer 1

似乎您在过拟合方面遇到问题。无论是否是题外话，我在这里都会遇到个人困境，因为这种方法可以基于某种观点，但是我在这里：首先，如果需要调整过度拟合的网络，则要使用dropout开始的0.25并检查是否可以改善模型。在处理过度拟合时，Data augmentation与batch normalization（正在应用）一起是必须的。如果这仍然不能解决您的过拟合问题，那么您应该尝试对网络体系结构进行调整以使其更好地推广。您是否检查了用于培训和测试的简单输入？

TLDR：尝试进行数据丢失和数据扩充，如果它不起作用并且您的数据正确，那么您可能必须进行改进体系结构以建立更好的泛化模型。

编辑：采用这种模型的共识是，首先以适当的准确性使其过拟合，然后在不损失准确性的情况下对其进行概括。

Answer 2

您的模型非常适合您的训练集大小，从而导致过拟合。减少图层数量

Answer 3

您拟合过度，即存在高方差问题。尝试在卷积块的末尾添加一些软衰减（0.2-0.3）。您还可以在输出层之前添加几个单位数量递减的密集层，并在这些层之间（0.5+）放置更多的退出层。

您还应该实现更多的数据增强功能，例如旋转，翻转，随机噪声，随机亮度等。请查阅Keras的ImageDataGenerator类文档。

Keras CNN的准确性很高，但预测错误。如何改善

3 个答案: