How ca I reshape y_train , y_validation from train_generator?

Question

I retrained ResNet-50 for iris flower classification in tensorflow using the following code:

import tensorflow as tf
import cv2, random
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from random import shuffle 
from IPython.display import SVG
import numpy as np # linear algebra
import pandas as pd 
import shutil
import matplotlib.pyplot as plt
%matplotlib inline 
from IPython.display import Image, display
from sklearn.model_selection import train_test_split
import os
print(os.listdir("./iris recognition/flowers"))

labels = os.listdir("./iris recognition/flowers")
num_classes = len(set(labels))
IMAGE_SIZE= 224


# Create model
model = tf.keras.Sequential()
model.add(tf.keras.applications.ResNet50(include_top=False, weights='imagenet'))
model.add(tf.keras.layers.GlobalAveragePooling2D())
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.Dense(num_classes, activation='softmax'))

# Do not train first layer (ResNet) as it is already pre-trained
model.layers[0].trainable = False

# Compile model
from tensorflow.python.keras import optimizers

sgd = optimizers.SGD(lr = 0.01, decay = 1e-6, momentum = 0.9, nesterov = True)
model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
train_folder = './iris recognition/flowers'

image_size = 224
data_generator =  tf.keras.preprocessing.image.ImageDataGenerator(preprocessing_function=tf.keras.applications.resnet50.preprocess_input,
                                    horizontal_flip=True,
                                    width_shift_range=0.2,
                                    height_shift_range=0.2,
                                    validation_split=0.2)# set validation split

train_generator = data_generator.flow_from_directory(
    train_folder,
    target_size=(image_size, image_size),
    batch_size=10,
    class_mode='categorical',
    subset='training'
    )
validation_generator = data_generator.flow_from_directory(
    train_folder,
    target_size=(image_size, image_size),
    batch_size=10,
    class_mode='categorical',
    subset='validation'
    )

NUM_EPOCHS = 70
EARLY_STOP_PATIENCE = 5
from tensorflow.python.keras.callbacks import EarlyStopping, ModelCheckpoint

cb_early_stopper = EarlyStopping(monitor = 'val_loss', patience = EARLY_STOP_PATIENCE)
cb_checkpointer = ModelCheckpoint(filepath = './working/best.hdf5',
                                  monitor = 'val_loss',
                                  save_best_only = True,
                                  mode = 'auto')
import math

fit_history = model.fit(
    train_generator,
    steps_per_epoch=10,
    validation_data=validation_generator,
#     validation_steps=10,
    epochs=NUM_EPOCHS,
    callbacks=[cb_checkpointer, cb_early_stopper])
model.load_weights("./working/best.hdf5")

After that I saved all the training data and validation data extracted from train generator as follow:

    from platform import python_version_tuple
    
    if python_version_tuple()[0] == '3':
        xrange = range
        izip = zip
        imap = map
    else:
        from itertools import izip, imap
    
    import numpy as np
    
    x, y = izip(*(train_generator [i] for i in xrange(len(train_generator))))
    x_train, y_train = np.vstack(x), np.vstack(y)
    
    x1, y1 = izip(*(validation_generator [i] for i in xrange(len(validation_generator))))
    x_val, y_val = np.vstack(x1), np.vstack(y1)
    
    import pickle as pkl
    #to save it
    with open("validation.pkl", "wb") as f:
    pkl.dump([x_val, y_val], f)
    #to load it
    with open("validation.pkl", "rb") as f:
    x_val, y_val = pkl.load(f)

The shape of the obtained **y_val ** is (83, 3) I wanted to reshape it to get a shape of (83,) by trying this:

y_test = y_val.reshape(y_val.shape[0],)

However I got this error : valueError: cannot reshape array of size 249 into shape (83,)

Any suggestions please?!