Failed to find data adapter that can handle input: <class 'NoneType'>, <class 'NoneType'> validation_generator - conv-neural-network

Question

I have been trying to create model to classify a set of images into 10 classes. I am using CNN to do the same. I am stuck with the above error and I am not able to proceed further. Please help me out. I think the problem is with the validation_generator. But I tried everything possible and I am not able to fix it.
The code that I have written:
import tensorflow as tf
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw, ImageEnhance
import tensorflow.keras as keras
train_path = "/Users/user/Desktop/train"
test_path = "/Users/user/Desktop/val"
model = tf.keras.models.Sequential([
# Note the input shape is the desired size of the image 256x256 with 3 bytes color
# This is the first convolution
tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(256, 256, 3)),
tf.keras.layers.MaxPooling2D(2, 2),
# The second convolution
tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
# The third convolution
tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
# The fourth convolution
tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
# Flatten the results to feed into a DNN
tf.keras.layers.Flatten(),
tf.keras.layers.Dropout(0.5),
# 512 neuron hidden layer
tf.keras.layers.Dense(512, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
# Print the model summary
model.summary()
# Set the training parameters
model.compile(loss = 'categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
from keras_preprocessing.image import ImageDataGenerator
TRAINING_DIR = train_path
training_datagen = ImageDataGenerator(
rescale = 1./255,
rotation_range=10,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0,
zoom_range=0.2,
horizontal_flip=False,
fill_mode='nearest')
VALIDATION_DIR = test_path
validation_datagen = ImageDataGenerator(rescale = 1./255)
train_generator = training_datagen.flow_from_directory(
TRAINING_DIR,
target_size=(256,256),
class_mode='categorical',
batch_size=64
)
validation_generator = validation_datagen.flow_from_directory(
VALIDATION_DIR,
target_size=(256,256),
class_mode='categorical',
batch_size=64
)
# the error raises here
history = model.fit(epochs=25, validation_data = validation_generator, verbose = 1, validation_steps=3)