[Python] Tensorflow gives error when predicting: expected axis -1 of input shape to have value...

I have been trying to create a neural network that can identify images, but When I go and try to fit my model, I get the following error:

ValueError: Exception encountered when calling Sequential.call().
Input 0 of layer "dense_82" is incompatible with the layer: expected axis -1
of input shape to have value 2304, but received input with shape (None, 118336)


I am building it on Collab and the code I am using is the following:

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from tensorflow.keras.utils import to_categorical

from google.colab import drive
from google.colab.patches import cv2_imshow
drive.mount('/content/drive')


img_height = 180
img_width = 180

normalization_layer = tf.keras.layers.Rescaling(1./255)
normalized_ds = training.map(lambda x, y: (normalization_layer(x), y))

AUTOTUNE = tf.data.AUTOTUNE

training = training.cache().prefetch(buffer_size=AUTOTUNE)
testing = testing.cache().prefetch(buffer_size=AUTOTUNE)

num_classes = 9

model = Sequential([
Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
MaxPooling2D(2, 2),
Conv2D(64, (3, 3), activation='relu'),
MaxPooling2D(2, 2),
Flatten(),
Dense(128, activation='relu'),
Dense(num_classes, activation='softmax')
])

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

model.summary()

history = model.fit(training, batch_size=64, epochs=20,validation_data=testing)


The Training and Testing variables are prefetchdirectories created using

tf.keras.utils.image_dataset_from_directory


How to fix this error.

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