incerrrr

import keras
from keras.datasets import fashion_mnist
import matplotlib.pyplot as plt
from keras.models import Sequential,Model
from keras.layers import Input, Dense, Activation,Conv2D,MaxPooling2D,Dropout,Flatten,Reshape,UpSampling2D,Deconvolution2D,Conv2DTranspose
import numpy as np
from keras.callbacks import TensorBoard
%matplotlib inline
#pip install jupyter-tensorboard
#harus install tensorbor buat notebook
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()

input_img = Input(shape=(28,28,1))
#encoder
l1 = Conv2D(16, (3,3),strides = 1 , activation = 'relu',padding='same')(input_img)
l1 = MaxPooling2D((2,2))(l1)
l1 = Conv2D(8,(3,3),strides = 1, activation='relu',padding='same')(l1)
l1 = MaxPooling2D((2,2),padding = 'same')(l1)
#
l1 = Conv2D(8,(3,3),strides = 1,activation = 'relu',padding='same')(l1)
#decoder network
l1 = Conv2DTranspose(8,(3,3),strides = 1,activation='relu',padding='same')(l1)
l1 = UpSampling2D((2,2))(l1)
l1 = Conv2DTranspose(16,(3,3),strides = 1,activation='relu',padding = 'same')(l1)
l1 = UpSampling2D((2,2))(l1)
l2 = Conv2DTranspose(1,(3,3),strides = 1, activation='sigmoid',padding  ='same')(l1)

model2 = Model(input_img, l2)
model2.compile(optimizer='Adam', loss='binary_crossentropy')
model2.summary()

x_train_1=x_train/255.0
from time import time
tensorboard = TensorBoard(log_dir="logs/{}".format(time()))

x_train_ = x_train_1 + 0.3 * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape)
x_train_ = np.clip(x_train_, 0., 1.)
x_ = x_train_[:,:,:,np.newaxis]
x_.shape

np.max(x_[0])
plt.imshow(np.reshape(x_train_[500],(28,28)),cmap='Greys')
x_train.shape
model2.fit(x_, x_train_1[:,:,:,np.newaxis], epochs = 10, batch_size = 256, callbacks = [tensorboard])

pp = model2.predict(np.reshape(x_[0],(1,28,28,1)))
pp = np.reshape(pp,(28,28))
plt.imshow(pp,cmap='Greys')
plt.imshow(np.reshape(x_[0],(28,28)),cmap='Greys')
plt.imshow(x_train[0],cmap='Greys')