Denoising autoencoder

i have this code

import sys
import tensorflow as tf
import keras, keras.layers as L, keras.backend as K
import numpy as np
from sklearn.model_selection import train_test_split
from numpy import asarray
from tensorflow import keras
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras.layers import (Input, Conv2D, MaxPooling2D, UpSampling2D, LeakyReLU)
import cv2

import matplotlib.pyplot as plt





from keras.preprocessing import image



# load images

img = cv2.imread('image1.png')
img = cv2.resize(img, (500, 500))
print(img.shape)
plt.imshow(img)
plt.show()


#plt.imshow(img)
#img = np.asarray(img, dtype="float32")




# convert list to numpy array
X_train = img
Y_train = img
X_train = X_train.astype('float32') / 255

Y_train = Y_train.astype('float32') / 255
X_train = X_train.reshape(-1, 500, 500, 1)

Y_train = Y_train.reshape(-1, 500, 500, 1)





print(X_train.shape)




#X_train, Y_train= train_test_split(X_train, Y_train, test_size=0.15)
X_train_size = len(X_train) 
Y_train_size = len(Y_train) 
 
print(X_train_size) 
print(Y_train_size) 

#x_train, x_test = train_test_split(X)



#X_train[0].shape

#print(X_train, X_val)



noise_factor = 0.9
x_train_noisy = X_train + noise_factor * np.random.normal(loc=0.0, scale=1.0,
size=X_train.shape)
x_test_noisy = Y_train + noise_factor * np.random.normal(loc=0.0, scale=1.0,
size=Y_train.shape)
x_train_noisy = np.clip(x_train_noisy, 0., 1.)
x_test_noisy = np.clip(x_test_noisy, 0., 1.)
input_img = (500,500, 1)  

def DAE_CNN(features_shape, act='relu'):



    # Input

    x = Input(name='inputs', shape=features_shape, dtype='float32')

    o = x

    

    # Encoder

    o = Conv2D(32, (3, 3), activation=act, padding='same', strides=(1,1), name='en_conv1')(o)

    o = MaxPooling2D((2, 2), strides=(2,2), padding='same', name='en_pool1')(o)

    o = Conv2D(32, (3, 3), activation=act, padding='same', strides=(1,1), name='en_conv2')(o)

    enc = MaxPooling2D((2, 2), strides=(2,2), padding='same', name='en_pool2')(o)

    

    # Decoder

    o = Conv2D(32, (3, 3), activation=act, padding='same', strides=(1,1), name='de_conv1')(enc)

    o = UpSampling2D((2, 2), name='upsampling1')(o)

    o = Conv2D(32, (3, 3), activation=act, padding='same', strides=(1,1), name='de_conv2')(o)

    o = UpSampling2D((2, 2), name='upsampling2')(o)

    dec = Conv2D(1, (3, 3), activation='sigmoid', padding='same', strides=(1,1), name='de_conv3')(o)

    

    # Print network summary

    Model(inputs=x, outputs=dec).summary()

    

    return Model(inputs=x, outputs=dec)



batch_size = 128

epochs = 100




autoenc = DAE_CNN(input_img, act=LeakyReLU(alpha=0.1))

autoenc.compile(optimizer='adadelta', loss='binary_crossentropy')



autoenc.fit(x_train_noisy, X_train, epochs=epochs, batch_size=batch_size )



decoded_imgs = autoenc.predict(x_train_noisy)

plt.figure()

ax = plt.subplot()
plt.imshow(x_test_noisy[1].reshape(500,500))


plt.gray()
ax.get_xaxis().set_visible(False)

ax.get_yaxis().set_visible(False)

plt.show()






ax = plt.subplot()
plt.imshow(decoded_imgs[1].reshape(500,500))
plt.gray()



ax.get_xaxis().set_visible(False)

ax.get_yaxis().set_visible(False)

plt.show()

i try to enter a noisy image and the autoencoder give a denoising image but the image dont appear can someone help me

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