如何数字评估类似unet的CNN的结果？

我正在寻找一种方法来数字评估我类似unet的CNN的结果 .

CNN经过训练以从灰度图像中去除伪像 . 因此，CNN获得“9通道”灰度图像，其中包含每个通道中的伪像（9个灰度图像，其中部分冗余数据但不同的伪像被连接 - >尺寸[numTrainInputs，512,512,9]）作为输入，并应输出单个没有伪影的灰度图像[numTrainInputs，512,512,1] . CNN使用MSE作为损失函数训练，Adam作为Optimizer和Keras训练 . 到现在为止还挺好 .

与无伪影的“地面实况”图像 - >尺寸[numTrainInputs，512,512,1]相比，CNN在视觉上提供了良好的结果，但是训练期间的准确度保持在0％ . 我认为这是因为没有一个结果图像完全适合基本事实，对吧！？

但是，我如何在数值上评估结果呢？我在自动编码器领域搜索了一些数值评估但是找不到合适的方法 . 有人能给我一个暗示吗？

CNN看起来像这样：

input_1 = Input((X_train.shape[1],X_train.shape[2], X_train.shape[3]))

conv1 = Conv2D(16, (3,3), strides=(2,2), activation='elu',  use_bias=True, padding='same')(input_1)
conv2 = Conv2D(32, (3,3), strides=(2,2), activation='elu',  use_bias=True, padding='same')(conv1)
conv3 = Conv2D(64, (3,3), strides=(2,2), activation='elu',  use_bias=True, padding='same')(conv2)
conv4 = Conv2D(128, (3,3), strides=(2,2), activation='elu',  use_bias=True, padding='same')(conv3)
conv5 = Conv2D(256, (3,3), strides=(2,2), activation='elu',  use_bias=True, padding='same')(conv4)
conv6 = Conv2D(512, (3,3), strides=(2,2), activation='elu',  use_bias=True, padding='same')(conv5)

upconv1 = Conv2DTranspose(256, (3,3), strides=(1,1), activation='elu',  use_bias=True, padding='same')(conv6)
upconv2 = Conv2DTranspose(128, (3,3), strides=(2,2), activation='elu',  use_bias=True, padding='same')(upconv1)
upconv3 = Conv2DTranspose(64, (3,3), strides=(2,2), activation='elu',  use_bias=True, padding='same')(upconv2)
upconv3_1 = concatenate([upconv3, conv4], axis=3)

upconv4 = Conv2DTranspose(32, (3,3), strides=(2,2), activation='elu',  use_bias=True, padding='same')(upconv3_1)
upconv4_1 = concatenate([upconv4, conv3], axis=3)

upconv5 = Conv2DTranspose(16, (3,3), strides=(2,2), activation='elu',  use_bias=True, padding='same')(upconv4_1)
upconv5_1 = concatenate([upconv5,conv2], axis=3)

upconv6 = Conv2DTranspose(8, (3,3), strides=(2,2), activation='elu',  use_bias=True, padding='same')(upconv5_1)
upconv6_1 = concatenate([upconv6,conv1], axis=3)

upconv7 = Conv2DTranspose(1, (3,3), strides=(2,2), activation='linear',  use_bias=True, padding='same')(upconv6_1)

model = Model(outputs=upconv7, inputs=input_1)



model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, Y_train, batch_size=1, epochs=100, shuffle=True, validation_split=0.01, callbacks=[tbCallback])

非常感谢您的帮助！