Why does my Ml CNN Kaggle Cats / Dogs prection only spit out an error?

I´m pretty new to ML and Computer Vision.I am trying to do a categotrial prediction for Cats/ Dogs 0 as the Cats and 1 as the Dogs. But my model.fit() function spits out this error.

…

    ValueError: Input 0 of layer sequential_5 is incompatible with the layer: : expected min_ndim=4, found ndim=2. Full shape received: [None, 10000]

This is my ML model:

import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import cv2

#the images are stored in the Folders 'Cat/' and 'Dog/'
animal = ['Cat/','Dog/']
images_cat= []
images_dog=[]

# reads in the images 
for x in animal:
    for i in range(1,12500): # the are images from '1.jpg' till '12499.jpg' for each Cats and Dogs
        try:
            image_path = x+ str(i) +'.jpg'# this gets the path of the images for example 'Cat/1.jpg'
            #print(image_path)
            img = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2GRAY)
            img_resized = cv2.resize(img,dsize=(100,100))
            if x == 'Cat/':
                images_cat.append(img_resized)
            elif x == 'Dog/':
                images_dog.append(img_resized)
                
        except cv2.error as e:
            #some images spit out an errer and the apprently can't be read so therefore I just give them the first image to add to the list
            if x == 'Cat/':
                images_cat.append(images_cat[1])
            elif x == 'Dog/':
                images_dog.append(images_dog[1])

# assign targets to values

y_cat = np.zeros(len(images_cat)) # Cat == 0
y_dog = np.ones(len(images_dog)) # Dog == 1


# trainig_images = 80%   test_images= 20%
training_sample_count = round(0.8* len(y_cat))

#list slicing the images to get 80% of the images as calculated above
X_cat_train = images_cat [:training_sample_count]
y_cat_train_fin = y_cat[:training_sample_count]

X_dog_train = images_dog [:training_sample_count]
y_dog_train_fin = y_dog[:training_sample_count]

# create the final training list
X_train = X_cat_train + X_dog_train
y_train=[]

y_train.append(y_cat_train_fin.data)
y_train.append(y_dog_train_fin.data)

y_train = np.reshape(y_train,(19998,))
np.shape(y_train)# output: (19998,)

#normalizing the data
X_train = [x / 255.0 for x in X_train]
X_train = np.reshape(X_train,(19998,10000))
np.shape(X_train) #output: (19998, 10000)

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, Dropout, MaxPooling2D, Conv2D, Flatten 

model = Sequential()
model.add(Conv2D(32,kernel_size=(5,5),padding='same', activation ='relu'))
model.add(MaxPooling2D((3,3)))

model.add(Conv2D(32,kernel_size=(5,5),padding='same', activation ='relu'))
model.add(MaxPooling2D((3,3)))

model.add(Dropout(0.25))
model.add(Flatten())

model.add(Dense(1, activation='softmax'))
model.compile(optimizer='adam', loss="sparse_categorical_crossentropy", metrics=["accuracy"])

model.fit(
    X_train,
    y_train,
    epochs=10,
    batch_size=10000)

I haven’t gotten to the test Images yet, but I basicly try to train this model for future Data (like own Images of Cats or Dogs to be then predicted).
I would be happy if anyone could help me with my problem as I am stuck atm. Cheers 🙂