如何正确设置用于为灰度图像着色的神经网络？

Question

我正在尝试为CIFAR-10数据集中的鸟类图像着色。 问题设置： X: (5000,32,32,1)，其中每个条目都是鸟类图像的灰度版本 Y: (5000,4096)是一个热编码数组。例如，第一个像素将具有[0,0,1,0]，其中1表示要使用哪种颜色。 Y只是每个图像所有一键编码的折叠版本。

我关注了许多文章，这些文章实现了灰度图像的着色，但是我的损失/准确性仍然很高/很低。

model = Sequential()
model.add(Convolution2D(32, (5, 5), strides=(1,1), input_shape=(32,32,1),padding='same', activation='relu'))
model.add(Dropout(0.2))

model.add(Convolution2D(32, (5, 5),activation='relu', padding='same' ))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Convolution2D(64, (5, 5), activation='relu', padding='same' ))
model.add(Flatten())

model.add(Dense(128))

model.add(Dense(4096, activation='softmax'))



# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

model.fit(Xtrain, Ytrain, validation_data=(val_data,Ytest),epochs=5, batch_size=32)

我希望随着时间的推移，精度会有所提高，但还会越来越差。

Answer 1

You'll have to put some work into architecture (which it sounds like you've been thinking about), but to simply black-box it, you can pump in the gray images and draw out the color images. Why not?

Use model.summary() to make sure your shapes are to your liking. (See below)

I haven't tested this code, but it should be pretty close...

model = Sequential()

model.add(InputLayer(input_shape=(32,32,1)))

model.add(Conv2D(32,(5,5),strides=(1,1), activation='relu', padding='same'))
model.add(SpatialDropout2D(rate=0.2)) # holla at this layer
model.add(Conv2D(32,(5,5), activation='relu', padding='same'))
model.add(MaxPool2D((2,2)))
model.add(Conv2D(64,(5,5),activation='relu',padding='same'))
model.add(Dense(128))

# have to upsample to get your height/width back from max pooling!
model.add(UpSampling2D((2,2)))

model.add(Conv2D(3,(2,2),activation='relu',padding='same'))
model.add(Activation('softmax'))


model.compile(optimizer='adam',loss='mse')
model.summary()

Here's the output of model.summary(). The output layer is (32,32,3); 32 height, 32 width, channels. [1]

Now just train it with grayscales as X, and the color originals as Y. And post results, for the curious!