我是tensorflow的初学者，我正在使用着色灰度图像的模型，但是当我运行optmizer时，它会给出相同的错误（ MSE ）每个时代，我都无法弄清楚错误是什么，所以我的代码有什么问题，我缺少什么？
逻辑：我从图像中获取低级和全局和中级特征，并将全局特征传递给多层函数，并将其输出与全局部分融合在一起融合层并将融合特征向量发送到着色网络，我有 Get_images_chrominance 函数，它从标签图像中获取 a，b 值并存储它们以供给跟他们说话。
守则
Ab_values = None
Batch_size = 3
Batch_indx = 1
Batch_GreyImages = []
Batch_ColorImages = []
EpochsNum = 11
ExamplesNum = 9
Imgsize = 224, 224
Channels = 1
Input_images = tf.placeholder(dtype=tf.float32,shape=[None,224,224,1])
Ab_Labels_tensor = tf.placeholder(dtype=tf.float32,shape=[None,224,224,2])
def ReadNextBatch():
global Batch_GreyImages
global Batch_ColorImages
global Batch_indx
global Batch_size
global Ab_values
Batch_GreyImages = []
Batch_ColorImages = []
for ind in range(Batch_size):
Colored_img = Image.open(r'Path' + str(Batch_indx) + '.jpg')
Batch_ColorImages.append(Colored_img)
Grey_img = Image.open(r'Path' + str(Batch_indx) + '.jpg')
Grey_img = np.asanyarray(Grey_img)
img_shape = Grey_img.shape
img_reshaped = Grey_img.reshape(img_shape[0],img_shape[1], Channels)#[224,224,1]
Batch_GreyImages.append(img_reshaped)#[#imgs,224,224,1]
Batch_indx = Batch_indx + 1
Get_Images_Chrominance()
return Batch_GreyImages
#-------------------------------------------------------------------------------
def Get_Images_Chrominance():
global Ab_values
global Batch_ColorImages
Ab_values = np.empty((Batch_size,224,224,2),"float32")
for indx in range(Batch_size):
lab = color.rgb2lab(Batch_ColorImages[indx])
for i in range(len(lab[:,1,1])):
for j in range(len(lab[1,:,1])):
Ab_values[indx][i][j][0] = lab[i,j,1]
Ab_values[indx][i][j][1] = lab[i,j,2]
min_value = np.amin(Ab_values[indx])
max_value = np.amax(Ab_values[indx])
for i in range(len(lab[:,1,1])):
for j in range(len(lab[1,:,1])):
Ab_values[indx][i][j][0] = Normalize(lab[i,j,1],min_value,max_value)
Ab_values[indx][i][j][1] = Normalize(lab[i,j,1],min_value,max_value)
#-------------------------------------------------------------------------------
def Normalize(value,min_value,max_value):
min_norm_value = 0
max_norm_value = 1
value = min_norm_value + (((max_norm_value - min_norm_value) * (value - min_value)) / (max_value - min_value))
return value
def Frobenius_Norm(M):
return tf.reduce_sum(M ** 2) ** 0.5
def Model(Input_images):
low_layer1 = ConstructLayer(Input_images,1,3,3,64,2,'Relu')
low_layer2 = ConstructLayer(low_layer1,64,3,3,128,1,'Relu')
low_layer3 = ConstructLayer(low_layer2,128,3,3,128,2,'Relu')
low_layer4 = ConstructLayer(low_layer3,128,3,3,256,1,'Relu')
low_layer5 = ConstructLayer(low_layer4,256,3,3,256,2,'Relu')
low_layer6 = ConstructLayer(low_layer5,256,3,3,512,1,'Relu')
mid_layer1 = ConstructLayer(low_layer6,512,3,3,512,1,'Relu')
mid_layer2 = ConstructLayer(mid_layer1,512,3,3,256,1,'Relu')
global_layer1 = ConstructLayer(low_layer6,512,3,3,512,2,'Relu')
global_layer2 = ConstructLayer(global_layer1,512,3,3,512,1,'Relu')
global_layer3 = ConstructLayer(global_layer2,512,3,3,512,2,'Relu')
global_layer4 = ConstructLayer(global_layer3,512,3,3,512,1,'Relu')
ML_Net = ConstructML(global_layer4,3,[1024,512,256])
Fuse = Fusion_layer(mid_layer2, ML_OUTPUT)
Color_layer1 = ConstructLayer(Fuse,256,3,3,128,1,'Relu')
Color_layer1 = UpSampling(56,56,Color_layer1)
Color_layer2 = ConstructLayer(Color_layer1,128,3,3,64,1,'Relu')
Color_layer3 = ConstructLayer(Color_layer2,64,3,3,64,1,'Relu')
Color_layer3 = UpSampling(112,112,Color_layer3)
Color_layer4 = ConstructLayer(Color_layer3,64,3,3,32,1,'Relu')
Output = ConstructLayer(Color_layer4,32,3,3,2,1,'Sigmoid')
Output = UpSampling(224,224,Output)
return Output
#----------------------------------------------------Training-------------------
def RunModel(Input_images):
global Ab_values
global Batch_indx
Prediction = Model(Input_images)
Colorization_MSE = tf.reduce_mean((Frobenius_Norm(tf.sub(Prediction,Ab_Labels_tensor))))
Optmizer = tf.train.AdadeltaOptimizer().minimize(Colorization_MSE)
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
for epoch in range(EpochsNum):
epoch_loss = 0
Batch_indx = 1
for i in range(int(ExamplesNum / Batch_size)):#over batches
print("Batch Num ",i+1)
ReadNextBatch()
_, c = sess.run([Optmizer,Colorization_MSE],feed_dict={Input_images:Batch_GreyImages,Ab_Labels_tensor:Ab_values})
epoch_loss += c
print("epoch: ",epoch+1, ",Los: ",epoch_loss)
#---- ---------------------------------------------------------------------------
RunModel(Input_images)
编辑：这是full code，如果有人想要帮助我