(x,y),(x_test,y_test) = tf.keras.datasets.cifar10.load_data()
x = x/255.
x = tf.cast(x,tf.float32)
x_test = x_test/255.
x_test = tf.cast(x_test,tf.float32)

y = tf.one_hot(y,depth=10)
y=tf.squeeze(y)
y_test = tf.one_hot(y_test,depth=10)
y_test=tf.squeeze(y_test)

datagen = tf.keras.preprocessing.image.ImageDataGenerator(
        
 
# set input mean to 0 over the dataset
        featurewise_center=False,
        # set each sample mean to 0
        samplewise_center=False,
        # divide inputs by std of dataset
        featurewise_std_normalization=False,
        # divide each input by its std
        samplewise_std_normalization=False,
        
 
# apply ZCA whitening
        zca_whitening=False,
        # epsilon for ZCA whitening
        zca_epsilon=1e-06,
        # randomly rotate images in the range (deg 0 to 180)
        rotation_range=0,
        # randomly shift images horizontally
        width_shift_range=0.1,
        # randomly shift images vertically
 

        height_shift_range=0.1,
        # set range for random shear
        shear_range=0.,
        # set range for random zoom
        zoom_range=0.,
        # set range for random channel shifts
        channel_shift_range=0.,
        # set mode for filling points outside the input boundaries
        fill_mode='nearest',
        # value used for fill_mode = "constant"
        cval=0.,
        # randomly flip images
        horizontal_flip=True,
        # randomly flip images
        vertical_flip=False,
        # set rescaling factor (applied before any other transformation)
        rescale=None,
        # set function that will be applied on each input
        preprocessing_function=None,
        # image data format, either "channels_first" or "channels_last"
        data_format=None,
        # fraction of images reserved for validation (strictly between 0 and 1)
        validation_split=0.0)
datagen.fit(x)

res_model = resnet18()
res_model.compile(optimizer=opt,loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),metrics=['accuracy'])
# res_model.fit(x,y,validation_data=(x_test,y_test),epochs=epochs,batch_size=bat, validation_freq=1)
res_model.fit_generator(datagen.flow(x,y,batch_size=bat),validation_data=(x_test,y_test),epochs=epochs, validation_freq=1)