Tensorflow optmizer Error don't change - machine-learning

Question

I'm beginner in tensorflow and i'm working on a model which Colorize Greyscale images but when i run the optmizer it give the same Error (MSE) every epoch and i can't figure out what is the error, so what is wrong with my code , what am i missing?
The logic: I get the low level and global and mid level features from the image and pass the global Features to multilayer function and fuse it's output with the global part in a one fusion layer and send the fused features vector to the colorization network ,, and i have Get_images_chrominance function which get the a,b values from the labels images and store them to feed the lables with them.
The Code
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)
EDIT: this is the full code if any anyone want to help me in